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EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. State of the art is based on traditional RGB frames that enable optimization of photo-consistency cross views. In this paper, we study the problem of 3D reconstruction from event-cameras, motivated by the advantages of event-based cameras in terms of low power and latency as well as by the biological evidence that eyes in nature capture the same data and still perceive well 3D shape. The foundation of our hypothesis that 3D-reconstruction is feasible using events lies in the information contained in the occluding contours and in the continuous scene acquisition with events. We propose Apparent Contour Events (ACE), a novel event-based representation that defines the geometry of the apparent contour of an object. We represent ACE by a spatially and temporally continuous implicit function defined in the event x-y-t space. Furthermore, we design a novel continuous Voxel Carving algorithm enabled by the high temporal resolution of the Apparent Contour Events. To evaluate the performance of the method, we collect MOEC-3D, a 3D event dataset of a set of common real-world objects. We demonstrate EvAC3D’s ability to reconstruct high-fidelity mesh surfaces from real event sequences while allowing the refinement of the 3D reconstruction for each individual event. The code, data and supplementary material for this work can be accessed through the project page: https://www.cis.upenn.edu/~ziyunw/evac3d/.

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Acknowledgement

We thank the support from the following grants: NSF TRIPODS 1934960, NSF CPS 2038873, ARL DCIST CRA W911NF-17-2-0181, ARO MURI W911NF-20-1-0080, ONR N00014-17-1-2093, DARPA-SRC C-BRIC, and IARPA ME4AI. We also thank William Sturgeon from the Fisher Fine Arts Materials Library for providing the Artec Spider scanner and assistance.

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Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, 19104, USA

    Ziyun Wang, Kenneth Chaney & Kostas Daniilidis

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  1. Ziyun Wang
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  2. Kenneth Chaney
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  3. Kostas Daniilidis
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Corresponding authors

Correspondence to Ziyun Wang or Kenneth Chaney .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Wang, Z., Chaney, K., Daniilidis, K. (2022). EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_17

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  • DOI: https://doi.org/10.1007/978-3-031-20071-7_17

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