Depth-aware Volume Attention for Texture-less Stereo Matching
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917598813224960 |
|---|---|
| author | Zhao, Tong Ding, Mingyu Zhan, Wei Tomizuka, Masayoshi Wei, Yintao |
| author_facet | Zhao, Tong Ding, Mingyu Zhan, Wei Tomizuka, Masayoshi Wei, Yintao |
| contents | Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of rich geometric and semantic information. In this paper, we propose a lightweight volume refinement scheme to tackle the texture deterioration in practical outdoor scenarios. Specifically, we introduce a depth volume supervised by the ground-truth depth map, capturing the relative hierarchy of image texture. Subsequently, the disparity discrepancy volume undergoes hierarchical filtering through the incorporation of depth-aware hierarchy attention and target-aware disparity attention modules. Local fine structure and context are emphasized to mitigate ambiguity and redundancy during volume aggregation. Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models. We extensively validate the superiority of our proposed methods on public datasets. Results demonstrate that our model achieves state-of-the-art performance, particularly excelling in scenarios with texture-less images. The code is available at https://github.com/ztsrxh/DVANet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Depth-aware Volume Attention for Texture-less Stereo Matching Zhao, Tong Ding, Mingyu Zhan, Wei Tomizuka, Masayoshi Wei, Yintao Computer Vision and Pattern Recognition Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of rich geometric and semantic information. In this paper, we propose a lightweight volume refinement scheme to tackle the texture deterioration in practical outdoor scenarios. Specifically, we introduce a depth volume supervised by the ground-truth depth map, capturing the relative hierarchy of image texture. Subsequently, the disparity discrepancy volume undergoes hierarchical filtering through the incorporation of depth-aware hierarchy attention and target-aware disparity attention modules. Local fine structure and context are emphasized to mitigate ambiguity and redundancy during volume aggregation. Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models. We extensively validate the superiority of our proposed methods on public datasets. Results demonstrate that our model achieves state-of-the-art performance, particularly excelling in scenarios with texture-less images. The code is available at https://github.com/ztsrxh/DVANet. |
| title | Depth-aware Volume Attention for Texture-less Stereo Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.08931 |