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WO2018107679A1 - Procédé et dispositif d'acquisition d'image tridimensionnelle dynamique - Google Patents

Procédé et dispositif d'acquisition d'image tridimensionnelle dynamique Download PDF

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Publication number
WO2018107679A1
WO2018107679A1 PCT/CN2017/088162 CN2017088162W WO2018107679A1 WO 2018107679 A1 WO2018107679 A1 WO 2018107679A1 CN 2017088162 W CN2017088162 W CN 2017088162W WO 2018107679 A1 WO2018107679 A1 WO 2018107679A1
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WIPO (PCT)
Prior art keywords
terminal device
image
depth
matching
dynamic
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Ceased
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PCT/CN2017/088162
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English (en)
Chinese (zh)
Inventor
邵明明
钟小飞
王金波
王林
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Priority to CN201780076051.4A priority Critical patent/CN110169056B/zh
Publication of WO2018107679A1 publication Critical patent/WO2018107679A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/204—Image signal generators using stereoscopic image cameras
    • H04N13/207—Image signal generators using stereoscopic image cameras using a single 2D image sensor
    • H04N13/221—Image signal generators using stereoscopic image cameras using a single 2D image sensor using the relative movement between cameras and objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/204—Image signal generators using stereoscopic image cameras
    • H04N13/254—Image signal generators using stereoscopic image cameras in combination with electromagnetic radiation sources for illuminating objects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/271—Image signal generators wherein the generated image signals comprise depth maps or disparity maps
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/296—Synchronisation thereof; Control thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/698—Control of cameras or camera modules for achieving an enlarged field of view, e.g. panoramic image capture
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/32—Indexing scheme for image data processing or generation, in general involving image mosaicing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20028—Bilateral filtering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N2013/0074—Stereoscopic image analysis
    • H04N2013/0092—Image segmentation from stereoscopic image signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N2213/00—Details of stereoscopic systems
    • H04N2213/003—Aspects relating to the "2D+depth" image format

Definitions

  • the present application relates to the field of image recognition, and more particularly to a method and apparatus for dynamic three-dimensional image acquisition.
  • images are captured by input devices such as cameras to describe the real world.
  • camera devices have been able to provide increasingly finer image quality and larger and larger image resolution.
  • image algorithms are generated to assist the camera to produce more diverse images, such as panoramic photos, panoramic selfies, skin photos, audio photos, face recognition and smile recognition. It makes the photo more interesting and enriches the form of the real world.
  • the existing two-dimensional camera device can acquire a scene inside and outside a fixed-size area at a certain moment, and generate a two-dimensional, static description of the real world, and the acquired data is a two-dimensional matrix in units of pixels, which is compressed. After the algorithm is processed and saved, the compressed image is extracted and decompressed and pushed to the display device cache. Since the real world is three-dimensional and dynamic, designing a camera system capable of acquiring dynamic three-dimensional images and a series of storage display methods will open a new camera revolution.
  • the panoramic image acquisition mode is horizontally moved or horizontally rotated by the user's handheld terminal device, and the newly acquired image is stitched into the existing image by the internal stitching algorithm in real time, and can be viewed by sliding, zooming, etc. after completion.
  • the method is simple in operation, and can obtain a static image in a wider horizontal direction, which broadens the imaging range of the conventional two-dimensional image.
  • the surrounding image acquisition mode is moved or rotated by the user handheld terminal device in one of four directions of up, down, left, and right, and the internal terminal device records the current terminal device posture and the acquired scene image and performs inter-frame feature region matching. With compression, you can view it by sliding or rotating the device.
  • the method is simple in operation, and can obtain a dynamic image of a wider area in a certain direction, and realizes acquisition of a local dynamic three-dimensional image in one direction.
  • the panoramic image acquisition method limits the user to move or rotate a fixed distance in one direction.
  • the shooting process is prone to jitter and affect the shooting effect.
  • the final stitched image is curved and deformed, and it is difficult to restore the real scene.
  • the surround image acquisition method can only shoot around in a single direction after shooting starts. When shooting close-up shots, it is not possible to handle changes in the distance between the device and the subject. When a captured image is displayed, it cannot be freely enlarged and reduced. Storage and display do not form an industry standard, and the acquired images can only be viewed within the shooting software.
  • the present application provides a method and a terminal device for dynamic three-dimensional image acquisition, which can improve user experience.
  • a method for dynamic three-dimensional image acquisition which can be applied to a terminal device, and the method includes:
  • the overlapping region is fused by the fusion algorithm to generate a dynamic three-dimensional image.
  • the three-dimensional gesture unlocking method of the embodiment of the present application the three-dimensional gesture image presented by the user in the three-dimensional space in front of the camera is acquired in real time, the gesture of the user in the gesture image is extracted, and the unlocking gesture is matched with the previously set unlocking gesture of the user to achieve the unlocking.
  • the purpose of the terminal device the user is provided with a new, interesting, accurate and fast unlocking method.
  • the motion posture of the terminal device is acquired by the accelerometer, the gyroscope and the electronic compass of the terminal device.
  • performing fast segmentation matching with the depth image according to a motion posture of the terminal device including:
  • the feature region of the depth map of the end time point is calculated based on the completed segmented feature region of the depth map of the start time point and the pose change of the terminal device.
  • the result of the fast segmentation matching is accurately matched according to the color image, including:
  • the overlapping region is fused by the fusion algorithm to generate a dynamic three-dimensional image, including:
  • the fusion processing is performed to update the overlapping region.
  • a terminal device for performing the method of the first aspect or any possible implementation of the first aspect.
  • the terminal device may comprise means for performing the method of the first aspect or any of the possible implementations of the first aspect.
  • a third aspect provides a terminal device including a memory, a processor, and a display, the memory being used to store a computer program, the processor is configured to call and run a computer program from the memory, and when the program is executed, the processor executes the above The method of any of the first aspect or any of the possible implementations of the first aspect.
  • a computer readable medium for storing a computer program comprising instructions for performing the method of the first aspect or any of the possible implementations of the first aspect.
  • FIG. 1 is a schematic diagram of a minimum hardware system for implementing a terminal device of an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a dynamic three-dimensional image acquisition method according to an embodiment of the present application.
  • FIG. 3 is a block diagram showing the design of motion pose recognition and trajectory acquisition in accordance with one embodiment of the present application.
  • FIG. 4 is a schematic diagram of data fusion of a gyroscope and an accelerometer according to an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a method for data fusion of a gyroscope and an electronic compass according to an embodiment of the present application.
  • FIG. 6 is a schematic flowchart of performing fast segmentation matching according to a motion posture and a depth image of a terminal device according to an embodiment of the present application.
  • FIG. 7 is an accurate match of a result of fast segmentation matching according to the color image according to an embodiment of the present application.
  • FIG. 8 is a schematic flowchart of a method for image overlap region fusion according to an embodiment of the present application.
  • FIG. 9 is a schematic flowchart of a user capturing a dynamic three-dimensional image according to an embodiment of the present application.
  • FIG. 10 is a schematic flowchart of a user viewing a dynamic three-dimensional image according to an embodiment of the present application.
  • FIG. 11 is a schematic block diagram of an example of a terminal device according to an embodiment of the present application.
  • the terminal device in this embodiment may be an access terminal, a user equipment (UE), a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, and a wireless communication device. , user agent or user device.
  • the terminal device may be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), and a wireless communication function.
  • FIG. 1 is a schematic diagram of a minimum hardware system 100 of a terminal device implementing the three-dimensional gesture unlocking method of the present application.
  • the system 100 shown in FIG. 1 includes a light source transmitter 110, a depth camera 120, a spectrum analysis module 130, a color camera 140, a processor 150, a display unit 160, a nonvolatile memory 170, a memory 180, and a sensing unit 190.
  • the color camera 140, the light source emitter 110 and the depth camera 120 constitute a spectral input module, and the spectral analysis module 130 constitutes an image generation module.
  • the light source emitter 110, color camera 140, and depth camera 120 can be mounted side by side over the device (eg, directly above the device).
  • the light source emitter 110 can be an infrared emitter
  • the depth camera 120 can be an infrared camera
  • the spectrum analysis module 130 can be an infrared spectrum analysis module. In this case, the light source emitter 110 cooperates with the depth camera 120 to project the scene through the infrared light encoded image.
  • the light source emitter 110 outputs a common laser light source, which is filtered by a frosted glass and an infrared filter to form near-infrared light. Wherein, the light source emitter 110 can continuously output infrared light having a wavelength of 840 nanometers (nm).
  • the depth camera 120 is a Complementary Metal Oxide Semiconductor (CMOS) image sensor for receiving an excitation light source reflected from the outside, such as infrared light, and digitally encoding the excitation light source to form a digital image for transmission to the spectrum analysis module. 130.
  • CMOS Complementary Metal Oxide Semiconductor
  • the spectral analysis module 130 analyzes the speckles, calculates the distance between the corresponding pixel points of the image and the depth camera 120, and forms a depth data matrix for the driver to read.
  • the sensing unit 190 is connected to the processor 150, detects location information of the terminal device or a change in the surrounding environment, and transmits the sensed information to the processor 150.
  • the sensing unit 190 includes at least one of: a gyro sensor for detecting rotation, rotational movement, angular displacement, tilt, or any other non-linear motion, for A triaxial acceleration sensor that senses acceleration in one or more directions, an electronic compass that senses the earth's magnetic field to determine the north-south direction.
  • the sensing unit 190 operates under the control of the processor 150.
  • the terminal device may receive motion sensor data generated by a motion sensor (eg, a gyro sensor or an acceleration sensor) in the sensing unit 190, and process the generated motion sensor data using the motion sensing application.
  • a motion sensor eg, a gyro sensor or an acceleration sensor
  • a processor running a motion sensing application can analyze motion sensor data to identify specific types of motion events.
  • Display unit 160 is configured to display graphics, images or data to a user.
  • the display unit 160 is configured to provide various screens associated with the operation of the terminal device.
  • the display unit 160 provides a home screen, a message composing screen, a phone screen, a game screen, a music playing screen, and a video playing screen.
  • the display unit 160 can be implemented using a flat display panel such as a liquid crystal display (LCD), an organic light emitting diode (OLED), and an active matrix OLED (AMOLED).
  • LCD liquid crystal display
  • OLED organic light emitting diode
  • AMOLED active matrix OLED
  • the display unit 160 can operate as an input device.
  • the display unit 160 includes a touch panel for detecting a touch gesture.
  • the touch panel is configured to convert a pressure applied to a specific position of the display unit 160 or a capacitance change at a specific area of the display unit 160 into an electric input signal.
  • the touch panel can be implemented in one of add-on or on-cell (or in-cell).
  • the touch panel can be implemented in one of the following panels: a resistive touch panel, a capacitive touch panel, an electromagnetic induction touch panel, and a pressure touch panel.
  • the touch panel is configured to detect the pressure of the touch as well as the location and area being touched. If a touch gesture is made on the touch panel, a corresponding input signal is generated to the processor 150. The processor 150 then checks the user's touch input information to perform the corresponding function.
  • the processor 150 can be responsible for executing various software programs (e.g., applications and operating systems) to provide computing and processing operations for the terminal devices.
  • the non-volatile memory 170 is used to store program files, system files, and data.
  • Memory 180 is used for system and program running caches.
  • FIG. 2 is a schematic flow chart of a method for dynamic three-dimensional image acquisition according to an embodiment of the present application. The method shown in FIG. 2 can be performed by the terminal device shown in FIG. 1.
  • the following describes the acquisition method of the motion posture of the device.
  • a “pose” or “motion pose” as referred to herein is a set of motions of a device, which may be a set of motions included, such as a swing or a circular motion, or may be a simple movement of the device, eg, the device is specific The tilt of the axis or angle.
  • Figure 3 shows a block diagram of a design for motion pose recognition and acquisition of trajectories.
  • the sampling unit 310 can receive motion data from the gyroscope, the accelerometer, and the electronic compass and sample.
  • the attitude solving unit 320 reads the data of the gyroscope, the accelerometer and the electronic compass, calculates the triaxial angular velocity of the device, calculates the angular increment matrix, solves the attitude differential equation, and finally updates the attitude quaternion.
  • the data fusion unit 330 filters the noise in the correlation output based on the Kalman filter algorithm and finally outputs the device pose and trajectory.
  • the error model used in the gyroscope or accelerometer error calibration process can be expressed by equation (1).
  • [D x D y D z ] T is the true value of the physical quantity measured by the gyroscope or accelerometer
  • [M x M y M z ] T is the actual measured value of the gyroscope or accelerometer
  • [B x B y B z ] T is the sensor bias.
  • D x , D y , and D z are all 0, and for the accelerometers D x and D y in the horizontal stationary state, both are 0, and D z is a gravitational acceleration value.
  • x 1 and y 1 are the outputs of the calibrated electronic compass, and x and y are the outputs when the electronic compass is deviated.
  • This application can obtain x 0 , y 0 , ⁇ , a, b by least square fitting. Eliminate errors.
  • the quaternion is used to describe the attitude of the device.
  • the gyro data is read, the three-axis angular velocity of the device is calculated, the angular increment matrix is calculated, the attitude differential equation is solved, and the attitude quaternion is finally updated.
  • the rotation quaternion from the inertial coordinate system a to the device coordinate system b is:
  • the Pika method can be used to solve the quaternion differential equation.
  • the process is to first calculate the corresponding quaternion Q(t) when the carrier moves, and then according to the quaternion and the attitude matrix.
  • the rollover angle calculated by the accelerometer and the rollover angular velocity of the gyroscope test the pitch angle data calculated by the accelerometer and the gyroscope test pitch rate data are respectively filtered, and the accelerometer and the gyroscope can be made.
  • the data compensates each other, reduces the measurement noise, and the pitch angle and roll angle test values are more accurate, which makes the magnetic sensor tilt angle compensation effect better, can perform static calibration, and can also perform dynamic calibration.
  • the noise variance matrix of these two sensors is set as a variable, and the external disturbance is monitored in real time, and the noise variance matrix of the accelerometer and the electronic compass is dynamically changed, and then the gain in the Kalman filter is corrected.
  • the values of the accelerometer and the electronic compass are read to obtain the observation, and the a priori quaternion is used as the initial value of the state quantity, and the formula of the Kalman filter is brought. Get the final pose quaternion.
  • the gyroscope is integrated with the accelerometer, and the pitch angle ⁇ and the roll angle ⁇ are estimated, and the gyroscope is integrated with the electronic compass to estimate the heading angle.
  • the data fusion process of gyroscope and accelerometer is shown in Figure 4.
  • the data fusion process of gyroscope and electronic compass is shown in Figure 5.
  • S220 Collect a depth image and a color image respectively by using a depth camera and a color camera.
  • the depth image is also referred to as a range image, and refers to an image from a image collector (for example, the depth camera 120 in the present application) to a point (depth) of each point in the scene as a pixel value. It directly reflects the geometry of the visible surface of the scene.
  • the depth camera 120 digitally encodes the excitation light source by receiving an excitation light source reflected by the outside, such as infrared light, to form a digital image and transmit it to the spectrum analysis module 130.
  • the spectral analysis module 130 analyzes the speckles and calculates a distance z between the corresponding pixel point (x, y) in the current image and the depth camera 120, so that the current depth image can be acquired.
  • S230 Perform fast segmentation matching with the depth image according to the motion posture of the terminal device.
  • FIG. 6 shows a fast matching method of a central region three-dimensional object in which a motion figure and a depth image of a device are fused, the method tracking a device state change in real time, and extracting a start and end time point corresponding to each fixed time period when the device smoothly moves.
  • Depth map frame Based on the start time point, the depth map has completed the segmentation of the feature region and the device pose change in the close-up mode, and the approximate range of each feature region of the depth map at the end of the time is inferred, thereby performing fast segmentation matching.
  • the depth image Since the value of each pixel of the depth image is the linear distance between the object and the camera, there is a similarity between the distance from the same object to the camera. Therefore, the coarse segmentation based on the depth map adopts the region growing method. However, since the depth image has noise and is easy to lose information, the depth image is first filtered to achieve image smoothing and loss depth filling.
  • the specific implementation manner is as follows:
  • the image is filtered using a bilateral filter defined as:
  • I is the original image
  • I' is the filtered image
  • ⁇ is the neighborhood of (x, y)
  • w(i, j) is the weight of the filter at the corresponding coordinates.
  • Pixels with similar depths in the image are combined to form a similar feature region.
  • the specific implementation is:
  • the selection of the starting point is very important for the efficiency of depth image segmentation. If properly selected, the segmentation can be speeded up.
  • This application is based on the relative posture and trajectory of the device to roughly estimate the position of the feature region in the depth map at the end time point to accelerate. . Since the distance of the captured object from the camera is usually close, the minimum value region in the depth map is selected, and a multi-fork tree of the image minimum value region is established to realize the selection of the starting point.
  • the similarity criterion is used to distinguish the object from the background. Select the average depth and difference mean of the pixel points in the previous comparison, and select the average depth and difference mean of the pixel points. The difference between the mean value of the pixel depth and the difference between the two is It is determined to be the same area within 5%.
  • the present application accurately matches the result of the fast matching for the close-up mode and the image segmentation method of the color image, and directly draws the frame for the device to smoothly move or rotate for the perspective mode, and color
  • the images are accurately matched, and the precise matching is mainly optimized at the edge of the feature region obtained by the fast matching.
  • the matching process is shown in FIG. 7 .
  • the depth image and the color image are separately filtered, and then the feature regions are quickly matched according to the posture information and the depth information, and a series of feature regions are obtained, and the representative pixel points of each feature region are provided to the color image for segmentation.
  • the image segmentation adopts the watershed algorithm, and after filtering, the grayscale image after the coloration is generated, and the water injection operation is directly performed according to the provided characteristic pixel points, and finally the boundary of each feature region is obtained. Based on the segmentation of the boundary region of the color image, the boundary points of the feature regions obtained by the fast matching are compared with each other. If there is no deviation, the matching results are both normal.
  • the color image segmentation result is the final result. If there is a deviation, and the neighborhood depth data is missing or the drop is not clear, the color image segmentation result is the final result. If there is a deviation, and the neighborhood depth data is perfect and the drop is not clear, the depth image segmentation result is the final result. If there is a deviation, and the neighborhood depth data is perfect and the difference is clear, the color image segmentation result is the final result.
  • Accurate matching results can provide feedback for device attitude and trajectory information, making gesture recognition more accurate.
  • the process of obtaining attitude information based on the exact matching result is as follows:
  • the present application allows the device to acquire an image in all directions, and the overlapping regions need to be merged when the acquired image overlaps with the captured image.
  • the overlap region fusion is based on the historical feature matrix and the current device pose information.
  • the present application associates each feature matrix with the device pose, so that the previous device pose information of the historical feature matrix can be obtained.
  • the specific integration process is shown in Figure 8.
  • the posture information of the device during the motion shooting will be continuously recorded and saved, and the attitude information may be extracted at regular intervals as the iconic data for comparison with the device posture in the future. Since different device poses may also have overlapping regions, the method records the field of view that can be captured by each gesture experienced by the device, and stores the feature region and posture information in combination. The current relative pose and trajectory are calculated in real time during the movement of the device, and the comparable iconic data is extracted from the historical feature matrix for matching. If the matching result indicates that the current image frame overlaps with the historical image frame, the fusion processing is performed. , updated to the overlap area, while recording the current device pose.
  • the method provided by the present application can dynamically identify whether the current scene is a distant view or a close-up view.
  • the determining method is to scan the depth image matrix, calculate the number of pixel points whose depth value is less than the threshold value, and determine the foreground when the number is less than the threshold value.
  • the depth camera is automatically turned off and periodically activated to detect if it is in close-up mode, which reduces power consumption.
  • the method tracks the change of the depth state of the feature area.
  • the distance is stored in the feature matrix, so that the display can recognize whether there is a close to the moving action when shooting, thereby prompting the user to Zoom out and zoom in.
  • the method does not activate the depth camera, so there is no need to perform fast matching of the central region of the depth map, and only the color image and the attitude information are accurately matched to obtain the feature matrix.
  • the method prompts the user to move or rotate the mobile phone in any direction to shoot the target object.
  • the simultaneous starting attitude sensor, depth camera and color camera work are triggered.
  • the device attitude and trajectory identification module reads the data of the gyroscope, accelerometer and electronic compass in real time, performs attitude calculation on it, and then fuses the multi-sensor data to obtain the posture and trajectory of the device.
  • the depth camera collects the depth data in real time, and after filtering, recognizes the near and far modes.
  • the depth image is roughly segmented, and the device region and the trajectory data are quickly matched to the central region to speed up the matching.
  • the color camera collects the color data in real time, and after filtering, performs the water injection operation according to the feature area provided by the fast matching result, and finally obtains the boundary of the scene, compares and determines the boundary of the feature area obtained by the fast matching, and adjusts the feature area.
  • the boundary eventually produces a subtle matching feature area.
  • the method is based on the device posture and the feature area in the device.
  • the position in the coordinate system is calculated, and the coincident area is fused and updated, and the posture of the device taken twice is recorded, so that it can be cyclically viewed when supplied to the display.
  • the coincidence region is merged, the final feature region set of the image is generated.
  • the posture sensor When the user clicks on the picture to view, the posture sensor is activated to acquire the device posture, and the image feature region set is read to obtain the final image frame.
  • the method supports the user to rotate the mobile phone to view the picture, and the gesture of the mobile phone is corresponding to the posture of the mobile phone when the picture is taken.
  • the device attitude and trajectory identification module reads the data of the gyroscope, accelerometer and electronic compass in real time, performs attitude calculation on it, and then fuses the multi-sensor data to obtain the posture of the device.
  • the image frames After reading the image feature region set, the image frames are synthesized according to the coordinates in the image frame in which each feature region is located, and finally the image frames are buffered one by one to be read.
  • the current pose of the device After the current pose of the device is generated, it needs to correspond to the initial device pose of the captured photo. After that, the change of the gesture of the mobile phone will trigger the display of the corresponding state picture frame. The change of the device pose will trigger the selection of the image frame of the corresponding pose and submit the display. After the image is implemented, it is judged whether the current frame is scalable. If possible, the screen prompts that the current zoom is possible, and then the posture sensor data is acquired to start a new cycle. If you can't zoom, you also get the attitude sensor data to start a new loop.
  • the application is equipped with a gyroscope, an accelerometer and an electronic compass sensor on the terminal device for providing device attitude information; an infrared transmitter and an infrared camera for providing depth image data; and a color camera for use
  • a gyroscope, an accelerometer and an electronic compass sensor on the terminal device for providing device attitude information
  • an infrared transmitter and an infrared camera for providing depth image data
  • a color camera for use
  • the combination of the three provides raw data support for the acquisition of dynamic three-dimensional images.
  • the method for gesture recognition of a three-dimensional space terminal device can obtain an initial posture of a device by sampling, posture settlement, and data fusion of three attitude sensors.
  • the attitude generation algorithm is compensated according to the change of the depth image and the color image, and the closed-loop tracking of the attitude detection is completed.
  • the fast matching method of the three-dimensional object in the central region of the device posture and depth image fusion in the present application can provide a strategy of speeding up matching in the form of frame drawing when the device posture changes at a constant speed, and realize the same three-dimensional object between multiple frames of images. Quick match.
  • the fine matching method based on the color image and the fast matching result of the same three-dimensional object between the multi-frame images according to the present application can compensate and optimize the fast matching result according to the data information of the corresponding position of the color image for each feature region. , get the most detailed image feature description.
  • the present application can realize 360-degree omnidirectional imaging, and supports shooting of an already photographed object, and can dynamically recognize the overlapped area that has been photographed according to the current posture information of the device and the historical feature matrix information. Data fusion is performed on the overlapping regions, and the overlapping data information is added, so that the display can be smoothly switched according to the posture.
  • This application can dynamically recognize the distant and near mode of the subject, and achieve a full range of camera for the near view.
  • Scenery the integration of the panorama.
  • the Vision automatically turns off the depth camera to reduce power consumption.
  • FIG. 11 is another schematic block diagram of a terminal device according to an embodiment of the present application.
  • the terminal device 1100 shown in FIG. 11 includes: a radio frequency (RF) circuit 1110, a memory 1120, other input devices 1130, a display screen 1140, a sensor 1150, an audio circuit 1160, an I/O subsystem 1170, and a processor 1180. And power supply 1190 and other components.
  • RF radio frequency
  • the terminal device structure shown in FIG. 11 does not constitute a limitation of the terminal device, and may include more or less components than those illustrated, or combine some components or split some components. , or different parts layout.
  • the display screen 1140 belongs to a User Interface (UI), and the terminal device 1100 may include a user interface that is smaller than illustrated or less.
  • UI User Interface
  • terminal device 1100 The specific components of the terminal device 1100 are specifically described below with reference to FIG. 11:
  • the RF circuit 1110 can be used for receiving and transmitting signals during and after receiving or transmitting information, in particular, after receiving the downlink information of the base station, and processing it to the processor 1180; in addition, transmitting the designed uplink data to the base station.
  • RF circuits include, but are not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like.
  • LNA Low Noise Amplifier
  • RF circuitry 1110 can also communicate with the network and other devices via wireless communication.
  • the wireless communication may use any communication standard or protocol, including but not limited to Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (Code). Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), E-mail, Short Messaging Service (SMS), etc.
  • GSM Global System of Mobile communication
  • GPRS General Packet Radio Service
  • the memory 1120 can be used to store software programs and modules, and the processor 1180 executes various functional applications and data processing of the terminal device 1100 by running software programs and modules stored in the memory 1120.
  • the memory 1120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data (such as audio data, phone book, etc.) created by the use of the terminal device 1100.
  • memory 1120 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
  • Other input devices 1130 can be used to receive input numeric or character information, as well as to generate key signal inputs related to user settings and function control of terminal device 1100.
  • other input devices 1130 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, joysticks, and light mice (the light mouse is not sensitive to display visual output).
  • function keys such as volume control buttons, switch buttons, etc.
  • trackballs mice, joysticks, and light mice (the light mouse is not sensitive to display visual output).
  • Other input devices 1130 are coupled to other input device controllers 1171 of I/O subsystem 1170 for signal interaction with processor 1180 under the control of other device input controllers 1171.
  • the display 1140 can be used to display information entered by the user or information provided to the user as well as various menus of the terminal device 1100, and can also accept user input.
  • the specific display screen 1140 can include a display panel 1141 and a touch panel 1142.
  • the display panel 1141 can be configured by using a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.
  • the touch panel 1142 also referred to as a touch screen, a touch sensitive screen, etc., can collect contact or non-contact operations on or near the user (eg, the user uses any suitable object or accessory such as a finger, a stylus, etc. on the touch panel 1142.
  • the operation in the vicinity of the touch panel 1142 may also include a somatosensory operation; the operation includes a single-point control operation, a multi-point control operation, and the like, and the corresponding connection device is driven according to a preset program.
  • the touch panel 1142 may include two parts: a touch detection device and a touch controller. Wherein, the touch detection device detects a user's touch orientation and posture, and Detecting a signal brought by the touch operation, transmitting the signal to the touch controller; the touch controller receives the touch information from the touch detection device, and converts it into information that the processor can process, and sends the information to the processor 1180, and can receive the processing The command sent by the device 1180 is executed.
  • the touch panel 1142 can be implemented by using various types such as resistive, capacitive, infrared, and surface acoustic waves, and the touch panel 1142 can be implemented by any technology developed in the future.
  • the touch panel 1142 can cover the display panel 1141, and the user can display the content according to the display panel 1141 (the display content includes, but is not limited to, a soft keyboard, a virtual mouse, a virtual button, an icon, etc.) on the display panel 1141. Operation is performed on or near the covered touch panel 1142. After detecting the operation on or near the touch panel 1142, the touch panel 1142 transmits to the processor 1180 through the I/O subsystem 1170 to determine user input, and then the processor 1180 is based on the user.
  • the input provides a corresponding visual output on display panel 1141 via I/O subsystem 1170.
  • the touch panel 1142 and the display panel 1141 are two independent components to implement the input and input functions of the terminal device 1100 , in some embodiments, the touch panel 1142 and the display panel 1141 may be The input and output functions of the terminal device 1100 are implemented integrated.
  • the terminal device 1100 may also include at least one type of sensor 1150, such as a light sensor, a motion sensor, and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1141 according to the brightness of the ambient light, and the proximity sensor may close the display panel 1141 when the terminal device 1100 moves to the ear. And / or backlight.
  • the accelerometer sensor can detect the magnitude of acceleration in all directions (usually three axes). When it is stationary, it can detect the magnitude and direction of gravity.
  • the terminal device 1100 can also be configured with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors, here No longer.
  • An audio circuit 1160, a speaker 1161, and a microphone 1162 can provide an audio interface between the user and the terminal device 1100.
  • the audio circuit 1160 can transmit the converted audio data to the speaker 1161, and convert it into a sound signal output by the speaker 1161; on the other hand, the microphone 1162 converts the collected sound signal into a signal, which is received by the audio circuit 1160.
  • the audio data is converted, and the audio data is output to the RF circuit 1110 for transmission to, for example, another mobile phone, or the audio data is output to the memory 1120 for further processing.
  • the I/O subsystem 1170 is used to control external devices for input and output, and may include other device input controllers 1171, sensor controllers 1172, and display controllers 1173.
  • one or more other input control device controllers 1171 receive signals from other input devices 1130 and/or send signals to other input devices 1130, and other input devices 1130 may include physical buttons (press buttons, rocker buttons, etc.) , dial, slide switch, joystick, click wheel, light mouse (light mouse is a touch-sensitive surface that does not display visual output, or an extension of a touch-sensitive surface formed by a touch screen). It is worth noting that other input control device controllers 1171 can be connected to any one or more of the above devices.
  • Display controller 1173 in I/O subsystem 1170 receives signals from display 1140 and/or transmits signals to display 1140. After the display 1140 detects the user input, the display controller 1173 converts the detected user input into an interaction with the user interface object displayed on the display 1140, ie, implements human-computer interaction. Sensor controller 1172 can receive signals from one or more sensors 1150 and/or send signals to one or more sensors 1150.
  • the processor 1180 is a control center of the terminal device 1100 that connects various portions of the entire terminal device using various interfaces and lines, by running or executing software programs and/or modules stored in the memory 1120, and recalling stored in the memory 1120.
  • the data performs various functions and processing data of the terminal device 1100, thereby performing overall monitoring of the terminal device.
  • the processor 1180 can include one or more processing units; optionally, the processor 1180
  • the application processor and the modem processor can be integrated, wherein the application processor mainly processes an operating system, a user interface, an application, etc., and the modem processor mainly processes wireless communication. It will be appreciated that the above described modem processor may also not be integrated into the processor 1180.
  • the processor 1180 is configured to: acquire a motion posture of the terminal device; separately acquire a depth image and a color image by using the depth camera and the color camera; perform fast segmentation matching with the depth image according to the motion posture of the terminal device; The result of the fast segmentation matching is accurately matched; if the acquired current image overlaps with the captured image, the overlapping region is fused by the fusion algorithm to generate a dynamic three-dimensional image. .
  • the terminal device 1100 further includes a power source 1190 (such as a battery) for supplying power to the various components.
  • a power source 1190 such as a battery
  • the power source can be logically connected to the processor 1180 through the power management system to manage functions such as charging, discharging, and power consumption through the power management system. .
  • the terminal device 1100 may further include a camera (a depth camera and a color camera), a Bluetooth module, and the like, and details are not described herein again.
  • a camera a depth camera and a color camera
  • a Bluetooth module a Bluetooth module
  • the terminal device 1100 may correspond to a terminal device in a dynamic three-dimensional image acquisition method according to an embodiment of the present application, and the terminal device 1100 may include a physical unit for performing a method performed by a terminal device or an electronic device in the above method. .
  • the physical units in the terminal device 1100 and the other operations and/or functions described above are respectively used for the corresponding processes of the foregoing methods, and are not described herein for brevity.
  • the terminal device 1100 can include a physical unit in a method for performing the above-described dynamic three-dimensional image acquisition.
  • the physical units in the terminal device 1100 and the other operations and/or functions described above are respectively used for the corresponding processes of the foregoing methods, and are not described herein for brevity.
  • the processor in the embodiment of the present application may be an integrated circuit chip with signal processing capability.
  • each step of the foregoing method embodiment may be completed by an integrated logic circuit of hardware in a processor or an instruction in a form of software.
  • the processor may be a central processing unit (CPU), the processor may be another general-purpose processor, a digital signal processor (DSP), or an application specific integrated circuit (ASIC). ), Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • the methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the steps of the method disclosed in the embodiments of the present application may be directly implemented by the hardware decoding processor, or may be performed by a combination of hardware and software in the decoding processor.
  • the software can be located in a random storage medium, such as a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register, and the like.
  • the storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method.
  • the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read only memory (PROM), an erasable programmable read only memory (Erasable PROM, EPROM), or an electric Erase programmable read only memory (EEPROM) or flash memory.
  • the volatile memory can be a Random Access Memory (RAM) that acts as an external cache.
  • RAM Random Access Memory
  • many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (Synchronous DRAM).
  • SDRAM Double Data Rate Synchronous Dynamic Random Access Memory
  • ESDRAM Enhanced Synchronous Dynamic Random Access Memory
  • SDRAM Synchronous Connection Dynamic Random Access Memory
  • DR RAM Direct Memory Bus Random Access Memory
  • bus system may include a power bus, a control bus, a status signal bus, and the like in addition to the data bus.
  • bus systems may include a power bus, a control bus, a status signal bus, and the like in addition to the data bus.
  • various buses are labeled as bus systems in the figure.
  • B corresponding to A means that B is associated with A, and B can be determined according to A.
  • determining B from A does not mean that B is only determined based on A, and that B can also be determined based on A and/or other information.
  • the term "and/or” herein is merely an association relationship describing an associated object, indicating that there may be three relationships, for example, A and/or B, which may indicate that A exists separately while 10 is stored in A. And B, there are three cases of B alone.
  • the character "/" in this article generally indicates that the contextual object is an "or" relationship.
  • each step of the above method may be completed by an integrated logic circuit of hardware in a processor or an instruction in a form of software.
  • the steps of the method for transmitting an uplink signal disclosed in the embodiments of the present application may be directly implemented as hardware processor execution completion, or performed by a combination of hardware and software in a processor.
  • the software can be located in a random storage medium, such as a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register, and the like.
  • the storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.
  • the embodiment of the present application further provides a computer readable storage medium storing one or more programs, the one or more programs including instructions, when the portable electronic device is included in a plurality of applications When executed, the portable electronic device can be caused to perform the method of the embodiment shown in Figures 2 and/or 3.
  • the disclosed systems, devices, and methods may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the embodiments of the present application may be integrated into one processing unit, It may be that each unit physically exists alone, or two or more units may be integrated into one unit.
  • the functions may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a standalone product. Based on such understanding, the technical solution of the embodiments of the present application, or the part contributing to the prior art or the part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • the instructions include a plurality of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the embodiments of the present application.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like, which can store program codes. .

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Abstract

L'invention concerne un procédé d'acquisition d'une image tridimensionnelle dynamique. Le procédé d'acquisition d'une image tridimensionnelle dynamique consiste à : acquérir une posture de mouvement d'un dispositif terminal ; collecter respectivement une image de profondeur et une image couleur au moyen d'un appareil photo de profondeur et d'un appareil photo couleur ; en fonction de la posture de mouvement du dispositif terminal et de l'image de profondeur, effectuer une segmentation et un appariement rapides ; en fonction de l'image couleur, effectuer un appariement exact sur un résultat de la segmentation et de l'appariement rapides ; et s'il existe un chevauchement entre l'image actuelle acquise et une image photographiée, réaliser une fusion sur la zone de chevauchement par l'intermédiaire d'un algorithme de fusion, de façon à générer une image tridimensionnelle dynamique. La présente invention permet de réaliser, par rapport aux défauts apparaissant actuellement dans une photographie panoramique et une photographie d'ambiance, et au moyen de l'ajout d'un appareil photo de profondeur à un dispositif et de la combinaison des données d'un capteur de posture de téléphone mobile et d'un capteur d'image couleur, un procédé capable d'enregistrer en une fois une apparence de scène à partir de diverses directions, de façon à acquérir une image tridimensionnelle dynamique et un affichage de stockage de support.
PCT/CN2017/088162 2016-12-12 2017-06-13 Procédé et dispositif d'acquisition d'image tridimensionnelle dynamique Ceased WO2018107679A1 (fr)

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