Research map / 3D vision / Depth and stereo
3D Packing for Self-Supervised Monocular Depth Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add velocity supervision (ablation) | PackNet-SfM · KITTI Odometry Seqs. 09–10 | ATE (m): Seq. 09: 0.011 ± 0.006 -> 0.010 ± 0.005; Seq. 10: 0.009 ± 0.007 -> 0.009 ± 0.008 | Mixed |
| Remove ground-truth translation scaling at test time (ablation) | PackNet-SfM with velocity supervision · KITTI Odometry Seqs. 09–10 | ATE (m): Seq. 09: 0.010 ± 0.005 -> 0.014 ± 0.007; Seq. 10: 0.009 ± 0.008 -> 0.012 ± 0.008 | Hurt |
| Use PackNet instead of related methods | Dense depth estimation on DDAD · DDAD | depth estimation performance | Helped |
Closest papers by meaning
- The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth (CVPR 2021 · similarity 0.90)
- Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume (CVPR 2020 · similarity 0.89)
- PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation (AAAI 2024 · similarity 0.88)
- SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation (AAAI 2024 · similarity 0.88)
- From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior (CVPR 2024 · similarity 0.87)
- Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation (CVPR 2024 · similarity 0.87)
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