GaSLight: Gaussian Splats for Spatially-Varying Lighting in HDR
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914100269809664 |
|---|---|
| author | Bolduc, Christophe Hold-Geoffroy, Yannick Shu, Zhixin Lalonde, Jean-François |
| author_facet | Bolduc, Christophe Hold-Geoffroy, Yannick Shu, Zhixin Lalonde, Jean-François |
| contents | We present GaSLight, a method that generates spatially-varying lighting from regular images. Our method proposes using HDR Gaussian Splats as light source representation, marking the first time regular images can serve as light sources in a 3D renderer. Our two-stage process first enhances the dynamic range of images plausibly and accurately by leveraging the priors embedded in diffusion models. Next, we employ Gaussian Splats to model 3D lighting, achieving spatially variant lighting. Our approach yields state-of-the-art results on HDR estimations and their applications in illuminating virtual objects and scenes. To facilitate the benchmarking of images as light sources, we introduce a novel dataset of calibrated and unsaturated HDR to evaluate images as light sources. We assess our method using a combination of this novel dataset and an existing dataset from the literature. Project page: https://lvsn.github.io/gaslight/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GaSLight: Gaussian Splats for Spatially-Varying Lighting in HDR Bolduc, Christophe Hold-Geoffroy, Yannick Shu, Zhixin Lalonde, Jean-François Computer Vision and Pattern Recognition We present GaSLight, a method that generates spatially-varying lighting from regular images. Our method proposes using HDR Gaussian Splats as light source representation, marking the first time regular images can serve as light sources in a 3D renderer. Our two-stage process first enhances the dynamic range of images plausibly and accurately by leveraging the priors embedded in diffusion models. Next, we employ Gaussian Splats to model 3D lighting, achieving spatially variant lighting. Our approach yields state-of-the-art results on HDR estimations and their applications in illuminating virtual objects and scenes. To facilitate the benchmarking of images as light sources, we introduce a novel dataset of calibrated and unsaturated HDR to evaluate images as light sources. We assess our method using a combination of this novel dataset and an existing dataset from the literature. Project page: https://lvsn.github.io/gaslight/ |
| title | GaSLight: Gaussian Splats for Spatially-Varying Lighting in HDR |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.10809 |