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LiDAR-Event Stereo Fusion with Hallucinations

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Event stereo matching is an emerging technique to estimate depth from neuromorphic cameras; however, events are unlikely to trigger in the absence of motion or the presence of large, untextured regions, making the correspondence problem extremely challenging. Purposely, we propose integrating a stereo event camera with a fixed-frequency active sensor – e.g., a LiDAR – collecting sparse depth measurements, overcoming the aforementioned limitations. Such depth hints are used by hallucinating – i.e., inserting fictitious events – the stacks or raw input streams, compensating for the lack of information in the absence of brightness changes. Our techniques are general, can be adapted to any structured representation to stack events and outperform state-of-the-art fusion methods applied to event-based stereo.

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Acknowledgement

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. University of Bologna, Bologna, Italy

    Luca Bartolomei, Matteo Poggi, Andrea Conti & Stefano Mattoccia

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  1. Luca Bartolomei
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  2. Matteo Poggi
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  3. Andrea Conti
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  4. Stefano Mattoccia
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Correspondence to Luca Bartolomei .

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  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Bartolomei, L., Poggi, M., Conti, A., Mattoccia, S. (2025). LiDAR-Event Stereo Fusion with Hallucinations. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15064. Springer, Cham. https://doi.org/10.1007/978-3-031-72658-3_8

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