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Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation

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Abstract

Event cameras or neuromorphic cameras mimic the human perception system as they measure the per-pixel intensity change rather than the actual intensity level. In contrast to traditional cameras, such cameras capture new information about the scene at MHz frequency in the form of sparse events. The high temporal resolution comes at the cost of losing the familiar per-pixel intensity information. In this work we propose a variational model that accurately models the behaviour of event cameras, enabling reconstruction of intensity images with arbitrary frame rate in real-time. Our method is formulated on a per-event-basis, where we explicitly incorporate information about the asynchronous nature of events via an event manifold induced by the relative timestamps of events. In our experiments we verify that solving the variational model on the manifold produces high-quality images without explicitly estimating optical flow. This paper is an extended version of our previous work (Reinbacher et al. in British machine vision conference (BMVC), 2016) and contains additional details of the variational model, an investigation of different data terms and a quantitative evaluation of our method against competing methods as well as synthetic ground-truth data.

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Notes

  1. https://github.com/VLOGroup/dvs-reconstruction.

  2. https://www.youtube.com/watch?v=rvB2URrGT94.

  3. We note that the small image size of \(128 \times 128\) is not enough to fully load the GPU such that we measured almost the same wall clock time on a NVidia 780 GTX Ti.

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Acknowledgements

This work was supported by the research initiative Mobile Vision with funding from the AIT and the Austrian Federal Ministry of Science, Research and Economy HRSM Programme (BGBl. II Nr. 292/2012).

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Authors and Affiliations

  1. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Gottfried Munda, Christian Reinbacher & Thomas Pock

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  1. Gottfried Munda
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  2. Christian Reinbacher
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  3. Thomas Pock
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Correspondence to Gottfried Munda.

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Communicated by Xiaoou Tang.

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Munda, G., Reinbacher, C. & Pock, T. Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation. Int J Comput Vis 126, 1381–1393 (2018). https://doi.org/10.1007/s11263-018-1106-2

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