Hallucinating dense optical flow from sparse lidar for autonomous vehicles
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
European Commisision's projectEC-H2020-687534-LOGIMATIC
In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacement of any image-based optical flow system when images are not reliable due to e.g. adverse weather conditions or at night. In order to infer high resolution 2D flows from discrete range data we devise a three-block architecture of multiscale filters that combines multiple intermediate objectives, both in the lidar and image domain. To train this network we introduce a dataset with approximately 20K lidar samples of the Kitti dataset which we have augmented with a pseudo ground-truth image-based optical flow computed using FlowNet2. We demonstrate the effectiveness of our approach on Kitti, and show that despite using the low-resolution and sparse measurements of the lidar, we can regress dense optical flow maps which are at par with those estimated with image-based methods.
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CitationVaquero, V.; Sanfeliu, A.; Moreno-Noguer, F. Hallucinating dense optical flow from sparse lidar for autonomous vehicles. A: International Conference on Pattern Recognition. "2018 24th International Conference on Pattern Recognition (ICPR)". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1959-1964.
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos 
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos 
- ROBiri - Grup de Robòtica de l'IRI - Ponències/Comunicacions de congressos 
- VIS - Visió Artificial i Sistemes Intel.ligents - Ponències/Comunicacions de congressos