ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918038752722944 |
|---|---|
| author | Ito, Shintaro Takama, Natsuki Ito, Koichi Chen, Hwann-Tzong Aoki, Takafumi |
| author_facet | Ito, Shintaro Takama, Natsuki Ito, Koichi Chen, Hwann-Tzong Aoki, Takafumi |
| contents | The use of multi-view images acquired by a 360-degree camera can reconstruct a 3D space with a wide area. There are 3D reconstruction methods from equirectangular images based on NeRF and 3DGS, as well as Novel View Synthesis (NVS) methods. On the other hand, it is necessary to overcome the large distortion caused by the projection model of a 360-degree camera when equirectangular images are used. In 3DGS-based methods, the large distortion of the 360-degree camera model generates extremely large 3D Gaussians, resulting in poor rendering accuracy. We propose ErpGS, which is Omnidirectional GS based on 3DGS to realize NVS addressing the problems. ErpGS introduce some rendering accuracy improvement techniques: geometric regularization, scale regularization, and distortion-aware weights and a mask to suppress the effects of obstacles in equirectangular images. Through experiments on public datasets, we demonstrate that ErpGS can render novel view images more accurately than conventional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization Ito, Shintaro Takama, Natsuki Ito, Koichi Chen, Hwann-Tzong Aoki, Takafumi Computer Vision and Pattern Recognition The use of multi-view images acquired by a 360-degree camera can reconstruct a 3D space with a wide area. There are 3D reconstruction methods from equirectangular images based on NeRF and 3DGS, as well as Novel View Synthesis (NVS) methods. On the other hand, it is necessary to overcome the large distortion caused by the projection model of a 360-degree camera when equirectangular images are used. In 3DGS-based methods, the large distortion of the 360-degree camera model generates extremely large 3D Gaussians, resulting in poor rendering accuracy. We propose ErpGS, which is Omnidirectional GS based on 3DGS to realize NVS addressing the problems. ErpGS introduce some rendering accuracy improvement techniques: geometric regularization, scale regularization, and distortion-aware weights and a mask to suppress the effects of obstacles in equirectangular images. Through experiments on public datasets, we demonstrate that ErpGS can render novel view images more accurately than conventional methods. |
| title | ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.19883 |