Name
Abstract | ||
|---|---|---|
We describe an approach for upgrading 2D optical flow to 3D scene flow. Our key insight is that dense optical expansion - which can be reliably inferred from monocular frame pairs - reveals changes in depth of scene elements, e.g., things moving closer will get bigger. When integrated with camera intrinsics, optical expansion can be converted into a normalized 3D scene flow vectors that provide meaningful directions of 3D movement, but not their magnitude (due to an underlying scale ambiguity). Normalized scene flow can be further "upgraded" to the true 3D scene flow knowing depth in one frame. We show that dense optical expansion between two views can be learned from annotated optical flow maps or unlabeled video sequences, and applied to a variety of dynamic 3D perception tasks including optical scene flow, LiDAR scene flow, time-to-collision estimation and depth estimation, often demonstrating significant improvement over the prior art. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.00141 | 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 1 |
PageRank | References | Authors |
0.35 | 39 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gengshan Yang | 1 | 15 | 3.34 |
| deva ramanan | 2 | 10678 | 566.72 |