Plenoxels: Radiance Fields without Neural Networks
University of California, Berkeley · Berkeley College
Abstract
We introduce Plenoxels (plenoptic voxels), a systemfor photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regularization without any neural components. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality. For video and code, please see https://alexyu.net/plenoxels.
Citation impact
- FWCI
- 69.86
- Percentile
- 100%
- References
- 58
Authors
6- SFSara Fridovich-KeilCorresponding
University of California, Berkeley, Berkeley College
- AYAlex Yu
University of California, Berkeley, Berkeley College
- MTMatthew Tancik
University of California, Berkeley, Berkeley College
- QCQinhong Chen
University of California, Berkeley, Berkeley College
- BRBenjamin Recht
University of California, Berkeley, Berkeley College
Topics & keywords
- Radiance
- Computer science
- Voxel
- Artificial neural network
- Spherical harmonics
- Artificial intelligence
- Grid
- Benchmark (surveying)