Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.20979 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916811193188352 |
|---|---|
| author | Dai, Weichen Ma, Kangcheng Wang, Jiaxin Pan, Kecen Ming, Yuhang Zhang, Hua Kong, Wanzeng |
| author_facet | Dai, Weichen Ma, Kangcheng Wang, Jiaxin Pan, Kecen Ming, Yuhang Zhang, Hua Kong, Wanzeng |
| contents | Representing scenes from multi-view images is a crucial task in computer vision with extensive applications. However, inherent photometric distortions in the camera imaging can significantly degrade image quality. Without accounting for these distortions, the 3D scene representation may inadvertently incorporate erroneous information unrelated to the scene, diminishing the quality of the representation. In this paper, we propose a novel 3D scene-camera representation with joint camera photometric optimization. By introducing internal and external photometric model, we propose a full photometric model and corresponding camera representation. Based on simultaneously optimizing the parameters of the camera representation, the proposed method effectively separates scene-unrelated information from the 3D scene representation. Additionally, during the optimization of the photometric parameters, we introduce a depth regularization to prevent the 3D scene representation from fitting scene-unrelated information. By incorporating the camera model as part of the mapping process, the proposed method constructs a complete map that includes both the scene radiance field and the camera photometric model. Experimental results demonstrate that the proposed method can achieve high-quality 3D scene representations, even under conditions of imaging degradation, such as vignetting and dirt. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Scene-Camera Representation with Joint Camera Photometric Optimization Dai, Weichen Ma, Kangcheng Wang, Jiaxin Pan, Kecen Ming, Yuhang Zhang, Hua Kong, Wanzeng Computer Vision and Pattern Recognition Representing scenes from multi-view images is a crucial task in computer vision with extensive applications. However, inherent photometric distortions in the camera imaging can significantly degrade image quality. Without accounting for these distortions, the 3D scene representation may inadvertently incorporate erroneous information unrelated to the scene, diminishing the quality of the representation. In this paper, we propose a novel 3D scene-camera representation with joint camera photometric optimization. By introducing internal and external photometric model, we propose a full photometric model and corresponding camera representation. Based on simultaneously optimizing the parameters of the camera representation, the proposed method effectively separates scene-unrelated information from the 3D scene representation. Additionally, during the optimization of the photometric parameters, we introduce a depth regularization to prevent the 3D scene representation from fitting scene-unrelated information. By incorporating the camera model as part of the mapping process, the proposed method constructs a complete map that includes both the scene radiance field and the camera photometric model. Experimental results demonstrate that the proposed method can achieve high-quality 3D scene representations, even under conditions of imaging degradation, such as vignetting and dirt. |
| title | 3D Scene-Camera Representation with Joint Camera Photometric Optimization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.20979 |