Papers › Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of individual objects and learn their 3D motion vector jointly with depth and ego-motion. We obtain more accurate results, especially for challenging dynamic scenes not addressed by previous approaches. This is an extended version of Casser et al. [AAAI'19]. Code and models have been open sourced at https://sites.google.com/corp/view/struct2depth.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular Depth Estimation | KITTI Eigen split unsupervised | Struct2Depth M | absolute relative error | 0.1412 | #55 of 55 | Archive leaderboard | report |
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