DRSM: efficient neural 4d decomposition for dynamic reconstruction in stationary monocular cameras
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_ | 1866910314656694272 |
|---|---|
| author | Xie, Weixing Dong, Xiao Yang, Yong Lin, Qiqin Chen, Jingze Yao, Junfeng Guo, Xiaohu |
| author_facet | Xie, Weixing Dong, Xiao Yang, Yong Lin, Qiqin Chen, Jingze Yao, Junfeng Guo, Xiaohu |
| contents | With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DRSM: efficient neural 4d decomposition for dynamic reconstruction in stationary monocular cameras Xie, Weixing Dong, Xiao Yang, Yong Lin, Qiqin Chen, Jingze Yao, Junfeng Guo, Xiaohu Computer Vision and Pattern Recognition With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation. |
| title | DRSM: efficient neural 4d decomposition for dynamic reconstruction in stationary monocular cameras |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.00740 |