Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.04714 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917830331465728 |
|---|---|
| author | Kurita, Teppei Kondo, Yuhi Sun, Legong Sasaki, Takayuki Nitta, Sho Hashimoto, Yasuhiro Muramatsu, Yoshinori Moriuchi, Yusuke |
| author_facet | Kurita, Teppei Kondo, Yuhi Sun, Legong Sasaki, Takayuki Nitta, Sho Hashimoto, Yasuhiro Muramatsu, Yoshinori Moriuchi, Yusuke |
| contents | In this study, we propose a high-performance disparity (depth) estimation method using dual-pixel (DP) images with few parameters. Conventional end-to-end deep-learning methods have many parameters but do not fully exploit disparity constraints, which limits their performance. Therefore, we propose a lightweight disparity estimation method based on a completion-based network that explicitly constrains disparity and learns the physical and systemic disparity properties of DP. By modeling the DP-specific disparity error parametrically and using it for sampling during training, the network acquires the unique properties of DP and enhances robustness. This learning also allows us to use a common RGB-D dataset for training without a DP dataset, which is labor-intensive to acquire. Furthermore, we propose a non-learning-based refinement framework that efficiently handles inherent disparity expansion errors by appropriately refining the confidence map of the network output. As a result, the proposed method achieved state-of-the-art results while reducing the overall system size to 1/5 of that of the conventional method, even without using the DP dataset for training, thereby demonstrating its effectiveness. The code and dataset are available on our project site. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting Disparity from Dual-Pixel Images: Physics-Informed Lightweight Depth Estimation Kurita, Teppei Kondo, Yuhi Sun, Legong Sasaki, Takayuki Nitta, Sho Hashimoto, Yasuhiro Muramatsu, Yoshinori Moriuchi, Yusuke Computer Vision and Pattern Recognition In this study, we propose a high-performance disparity (depth) estimation method using dual-pixel (DP) images with few parameters. Conventional end-to-end deep-learning methods have many parameters but do not fully exploit disparity constraints, which limits their performance. Therefore, we propose a lightweight disparity estimation method based on a completion-based network that explicitly constrains disparity and learns the physical and systemic disparity properties of DP. By modeling the DP-specific disparity error parametrically and using it for sampling during training, the network acquires the unique properties of DP and enhances robustness. This learning also allows us to use a common RGB-D dataset for training without a DP dataset, which is labor-intensive to acquire. Furthermore, we propose a non-learning-based refinement framework that efficiently handles inherent disparity expansion errors by appropriately refining the confidence map of the network output. As a result, the proposed method achieved state-of-the-art results while reducing the overall system size to 1/5 of that of the conventional method, even without using the DP dataset for training, thereby demonstrating its effectiveness. The code and dataset are available on our project site. |
| title | Revisiting Disparity from Dual-Pixel Images: Physics-Informed Lightweight Depth Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.04714 |