Research map / 3D vision / Depth and stereo
Deep Stereo Matching With Explicit Cost Aggregation Sub-Architecture
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add explicit two-stream learning-based cost aggregation | Deep stereo matching pipeline · Scene Flow | MAE(px): 2.21 -> 1.75 | Helped |
| Add explicit two-stream learning-based cost aggregation | Deep stereo matching pipeline · KITTI2015 | D1-all, All pixels: 2.38 -> 2.79 | Hurt |
| Add explicit two-stream learning-based cost aggregation | Deep stereo matching pipeline · KITTI2012 | Mean Error, All: 0.6 -> 0.7 | Hurt |
| Add the guidance stream (ablation) | Cost aggregation on Scene Flow · Scene Flow | MAE(px): 2.15 -> 1.75 | Helped |
| Add the proposal stream (ablation) | Cost aggregation on Scene Flow · Scene Flow | MAE(px): 1.83 -> 1.75 | Helped |
| Add cost aggregation (ablation) | Deep stereo matching pipeline on Scene Flow · Scene Flow | MAE(px): 2.71 -> 1.75 | Helped |
Closest papers by meaning
- GA-Net: Guided Aggregation Net for End-To-End Stereo Matching (CVPR 2019 · similarity 0.93)
- AANet: Adaptive Aggregation Network for Efficient Stereo Matching (CVPR 2020 · similarity 0.93)
- Multi-Level Context Ultra-Aggregation for Stereo Matching (CVPR 2019 · similarity 0.91)
- Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence (ICLR 2024 · similarity 0.88)
- Stereo Risk: A Continuous Modeling Approach to Stereo Matching (ICML 2024 · similarity 0.87)
- Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching (NeurIPS 2024 · similarity 0.86)
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