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Plenoxels: Radiance Fields without Neural Networks

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arxiv 2112.05131 v1 pith:GDNINIPX submitted 2021-12-09 cs.CV cs.GR

Alex Yu , Sara Fridovich-Keil , Matthew Tancik , Qinhong Chen , Benjamin Recht , Angjoo Kanazawa This is my paper · ORCID
classification cs.CVcs.GR
keywords plenoxelsneuralfieldsoptimizedradiancewithoutbenchmarkcalibrated
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quo Vadis, World Modeling?

    cs.CV 2026-08 conditional novelty 5.0 of 10

    An agent-centric reframing of world modeling, replacing physical state prediction with 'information transitions' organized into six proxy functions and three empowerment levels.

  2. DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management

    cs.RO 2025-11 conditional novelty 5.0 of 10

    Storing inactive spatial chunks of a 3D Gaussian map on disk and loading only camera-visible chunks into GPU memory lets DiskChunGS map all 11 KITTI sequences on a 24 GB GPU without memory failures.

  3. VistaFlow: Photorealistic Volumetric Reconstruction with Dynamic Resolution Management via Q-Learning

    cs.CV 2025-02 reject novelty 4.0 of 10

    VistaFlow claims fast, framerate-stable radiance field rendering on consumer hardware via a Q-learning controller, but the paper's own equations and tables do not support the headline claims.

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