Research map / 3D vision / Depth and stereo
Deep Depth Estimation From Thermal Image
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the unified model in stereo rather than monocular mode | Ours · MS2 evaluation set (Avg, monocular depth) | AbsRel: 0.123 -> 0.120 | Helped |
| Use the unified model in stereo rather than monocular mode | Ours · MS2 evaluation set (Avg, monocular depth) | SqRel: 1.103 -> 1.057 | Helped |
| Use the unified model in stereo rather than monocular mode | Ours · MS2 evaluation set (Avg, disparity) | EPE-all(px): 1.066 -> 0.976 | Helped |
| Use the unified model in stereo rather than monocular mode | Ours · MS2 evaluation set (Avg, disparity) | > 1px(%): 23.1 -> 22.7 | Hurt |
| Use a 3D correlation cost volume instead of the 4D cost-volume aggregation used by ACVNet | Ours (Stereo) · MS2 evaluation set (Avg, disparity) | EPE-all(px): 0.962 -> 0.976 | Hurt |
Closest papers by meaning
- CATS: A Color and Thermal Stereo Benchmark (CVPR 2017 · similarity 0.87)
- Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priors (CVPR 2025 · similarity 0.85)
- Cross-spectral Gated-RGB Stereo Depth Estimation (CVPR 2024 · similarity 0.85)
- Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving (CVPR 2024 · similarity 0.84)
- STViT: Improving Self-Supervised Multi-Camera Depth Estimation with Spatial-Temporal Context and Adversarial Geometry Regularization (Student Abstract) (AAAI 2024 · similarity 0.83)
- HELVIPAD: A Real-World Dataset for Omnidirectional Stereo Depth Estimation (CVPR 2025 · similarity 0.83)
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