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Most Influential CVPR 2022 Paper · 2026-03 edition

Plenoxels: Radiance Fields Without Neural Networks

Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa

Venue
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Recognition
Most Influential CVPR 2022 Paper (Rank No. 8)
Edition
2026-03
Impact factor
8
Certificate ID
932cfe3fa76f68fa

Abstract

We introduce Plenoxels (plenoptic voxels), a system for 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.

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