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Active Stereo in the Wild through Virtual Pattern Projection

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Abstract

This paper presents a novel general-purpose guided stereo paradigm that mimics the active stereo principle by replacing the unreliable physical pattern projector with a depth sensor. It works by projecting virtual patterns consistent with the scene geometry onto the left and right images acquired by a conventional stereo camera, using the sparse hints obtained from a depth sensor, to facilitate the visual correspondence. Purposely, any depth sensing device can be seamlessly plugged into our framework, enabling the deployment of a virtual active stereo setup in any possible environment and overcoming the severe limitations of physical pattern projection, such as the limited working range and environmental conditions. Exhaustive experiments on indoor and outdoor datasets featuring both long and close range, including those providing raw, unfiltered depth hints from off-the-shelf depth sensors, highlight the effectiveness of our approach in notably boosting the robustness and accuracy of algorithms and deep stereo without any code modification and even without re-training. Additionally, we assess the performance of our strategy on active stereo evaluation datasets with conventional pattern projection. Indeed, in all these scenarios, our virtual pattern projection paradigm achieves state-of-the-art performance. The source code is available at: https://github.com/bartn8/vppstereo.

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Data Availability

The datasets and benchmark results supporting the findings of this study are publicly available. The KITTI 2012, 2015 and derived datasets can be accessed at https://www.cvlibs.net/datasets/kitti/eval_stereo.php. The Middlebury 2014 and 2021 datasets are available at https://vision.middlebury.edu/stereo/data/. The ETH3D dataset can be downloaded at https://www.eth3d.net/datasets. The DSEC dataset can be accessed at https://dsec.ifi.uzh.ch/. The M3ED dataset is available at https://m3ed.io/. The SIMSTEREO dataset can be accessed at https://ieee-dataport.org/open-access/active-passive-simstereo. All the pre-processed data used in our experiments are also available at https://github.com/bartn8/vppstereo/?tab=readme-ov-file#floppy_disk-datasets.

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Acknowledgements

This study was carried out within the MOST – Sustainable Mobility National Research Center and received funding from the European Union Next-GenerationEU – PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1033 17/06/2022, CN00000023. This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them. We acknowledge the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support.

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  1. Advanced Research Center on Electronic System (ARCES), Viale Carlo Pepoli 3/2, Bologna, 40123, Italy

    Luca Bartolomei, Matteo Poggi & Stefano Mattoccia

  2. Department of Computer Science and Engineering (DISI), University of Bologna, Viale del Risorgimento 2, Bologna, 40136, Italy

    Luca Bartolomei, Matteo Poggi, Fabio Tosi, Andrea Conti & Stefano Mattoccia

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  1. Luca Bartolomei
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  2. Matteo Poggi
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Bartolomei, L., Poggi, M., Tosi, F. et al. Active Stereo in the Wild through Virtual Pattern Projection. Int J Comput Vis 133, 7242–7269 (2025). https://doi.org/10.1007/s11263-025-02511-6

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