Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913506153988096 |
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| author | Cheng, Lei Hu, Junpeng Yan, Haodong Gladkova, Mariia Huang, Tianyu Liu, Yun-Hui Cremers, Daniel Li, Haoang |
| author_facet | Cheng, Lei Hu, Junpeng Yan, Haodong Gladkova, Mariia Huang, Tianyu Liu, Yun-Hui Cremers, Daniel Li, Haoang |
| contents | Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world. However, the assumption of photometric consistency is often violated since the non-diffuse reflection is common in real-world environments. The photometric inconsistency significantly affects the reliability of existing PBA methods. To solve this problem, we propose a novel physically-based PBA method. Specifically, we introduce the physically-based weights regarding material, illumination, and light path. These weights distinguish the pixel pairs with different levels of photometric inconsistency. We also design corresponding models for material estimation based on sequential images and illumination estimation based on point clouds. In addition, we establish the first SLAM-related dataset of non-Lambertian scenes with complete ground truth of illumination and material. Extensive experiments demonstrated that our PBA method outperforms existing approaches in accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments Cheng, Lei Hu, Junpeng Yan, Haodong Gladkova, Mariia Huang, Tianyu Liu, Yun-Hui Cremers, Daniel Li, Haoang Robotics Computer Vision and Pattern Recognition Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world. However, the assumption of photometric consistency is often violated since the non-diffuse reflection is common in real-world environments. The photometric inconsistency significantly affects the reliability of existing PBA methods. To solve this problem, we propose a novel physically-based PBA method. Specifically, we introduce the physically-based weights regarding material, illumination, and light path. These weights distinguish the pixel pairs with different levels of photometric inconsistency. We also design corresponding models for material estimation based on sequential images and illumination estimation based on point clouds. In addition, we establish the first SLAM-related dataset of non-Lambertian scenes with complete ground truth of illumination and material. Extensive experiments demonstrated that our PBA method outperforms existing approaches in accuracy. |
| title | Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.11854 |