SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910398976884736 |
|---|---|
| author | Dahmani, Hiba Bennehar, Moussab Piasco, Nathan Roldao, Luis Tsishkou, Dzmitry |
| author_facet | Dahmani, Hiba Bennehar, Moussab Piasco, Nathan Roldao, Luis Tsishkou, Dzmitry |
| contents | Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D Gaussian Splatting emerged as a much faster alternative with superior rendering quality and training efficiency, especially for small-scale and object-centric scenarios. Nevertheless, this technique suffers from poor performance on unstructured in-the-wild data. To tackle this, we extend over 3D Gaussian Splatting to handle unstructured image collections. We achieve this by modeling appearance to seize photometric variations in the rendered images. Additionally, we introduce a new mechanism to train transient Gaussians to handle the presence of scene occluders in an unsupervised manner. Experiments on diverse photo collection scenes and multi-pass acquisition of outdoor landmarks show the effectiveness of our method over prior works achieving state-of-the-art results with improved efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10427 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians Dahmani, Hiba Bennehar, Moussab Piasco, Nathan Roldao, Luis Tsishkou, Dzmitry Computer Vision and Pattern Recognition Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D Gaussian Splatting emerged as a much faster alternative with superior rendering quality and training efficiency, especially for small-scale and object-centric scenarios. Nevertheless, this technique suffers from poor performance on unstructured in-the-wild data. To tackle this, we extend over 3D Gaussian Splatting to handle unstructured image collections. We achieve this by modeling appearance to seize photometric variations in the rendered images. Additionally, we introduce a new mechanism to train transient Gaussians to handle the presence of scene occluders in an unsupervised manner. Experiments on diverse photo collection scenes and multi-pass acquisition of outdoor landmarks show the effectiveness of our method over prior works achieving state-of-the-art results with improved efficiency. |
| title | SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10427 |