A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916290516484096 |
|---|---|
| author | Kerbl, Bernhard Meuleman, Andréas Kopanas, Georgios Wimmer, Michael Lanvin, Alexandre Drettakis, George |
| author_facet | Kerbl, Bernhard Meuleman, Andréas Kopanas, Georgios Wimmer, Michael Lanvin, Alexandre Drettakis, George |
| contents | Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels.We introduce a divide-and-conquer approach that allows us to train very large scenes in independent chunks. We consolidate the chunks into a hierarchy that can be optimized to further improve visual quality of Gaussians merged into intermediate nodes. Very large captures typically have sparse coverage of the scene, presenting many challenges to the original 3D Gaussian splatting training method; we adapt and regularize training to account for these issues. We present a complete solution, that enables real-time rendering of very large scenes and can adapt to available resources thanks to our LOD method. We show results for captured scenes with up to tens of thousands of images with a simple and affordable rig, covering trajectories of up to several kilometers and lasting up to one hour. Project Page: https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12080 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets Kerbl, Bernhard Meuleman, Andréas Kopanas, Georgios Wimmer, Michael Lanvin, Alexandre Drettakis, George Computer Vision and Pattern Recognition Graphics Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels.We introduce a divide-and-conquer approach that allows us to train very large scenes in independent chunks. We consolidate the chunks into a hierarchy that can be optimized to further improve visual quality of Gaussians merged into intermediate nodes. Very large captures typically have sparse coverage of the scene, presenting many challenges to the original 3D Gaussian splatting training method; we adapt and regularize training to account for these issues. We present a complete solution, that enables real-time rendering of very large scenes and can adapt to available resources thanks to our LOD method. We show results for captured scenes with up to tens of thousands of images with a simple and affordable rig, covering trajectories of up to several kilometers and lasting up to one hour. Project Page: https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/ |
| title | A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2406.12080 |