NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields
University at Buffalo, State University of New York · TH Bingen University of Applied Sciences
Abstract
Visually exploring in a real-world 4D spatiotemporal space freely in VR has been a long-term quest. The task is especially appealing when only a few or even single RGB cameras are used for capturing the dynamic scene. To this end, we present an efficient framework capable of fast reconstruction, compact modeling, and streamable rendering. First, we propose to decompose the 4D spatiotemporal space according to temporal characteristics. Points in the 4D space are associated with probabilities of belonging to three categories: static, deforming, and new areas. Each area is represented and regularized by a separate neural field. Second, we propose a hybrid representations based feature streaming scheme for…
Citation impact
- FWCI
- 24.45
- Percentile
- 100%
- References
- 95
Authors
8Topics & keywords
- Computer science
- Rendering (computer graphics)
- Artificial intelligence
- Computer vision
- RGB color model
- Computer graphics (images)
- Radiance
- Frame rate
- Sustainable cities and communities