Summary of Depthmamba with Adaptive Fusion, by Zelin Meng and Zhichen Wang
DepthMamba with Adaptive Fusion
by Zelin Meng, Zhichen Wang
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to multi-view depth estimation, addressing the limitation of current systems relying on ideal camera poses. A novel robustness benchmark is introduced to evaluate the system under noisy pose settings, revealing that current methods fail in such scenarios. The authors develop a two-branch network architecture that fuses single-view and multi-view depth estimation results, leveraging mamba as a feature extraction backbone and attention-based fusion to select the most robust estimates. This approach demonstrates competitive performance on challenging scenes and benchmarks like KITTI and DDAD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to improve how we get accurate measurements of distance from cameras in different directions. Right now, many systems assume they know where the cameras are looking, but this isn’t always true. The authors create a test to see how well these systems do when their assumptions are wrong. They also develop a new way for computers to combine information from multiple cameras to get better results. This approach works well even in tricky situations with things moving or without clear textures. |
Keywords
» Artificial intelligence » Attention » Depth estimation » Feature extraction
