EMVS: Event-based Multi-View Stereo

Henri Rebecq, Guillermo Gallego and Davide Scaramuzza

Abstract

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of Event-based Multi-View Stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (i) its ability to respond to scene edges„which naturally provide semidense geometric information without any pre-processing operation„and (ii) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semidense depth maps. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU.

Session

3D Computer Vision

Files

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DOI

10.5244/C.30.63
https://dx.doi.org/10.5244/C.30.63

Citation

Henri Rebecq, Guillermo Gallego and Davide Scaramuzza. EMVS: Event-based Multi-View Stereo. In Richard C. Wilson, Edwin R. Hancock and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 63.1-63.11. BMVA Press, September 2016.

Bibtex

        @inproceedings{BMVC2016_63,
        	title={EMVS: Event-based Multi-View Stereo},
        	author={Henri Rebecq, Guillermo Gallego and Davide Scaramuzza},
        	year={2016},
        	month={September},
        	pages={63.1-63.11},
        	articleno={63},
        	numpages={11},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.63},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.63}
        }