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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
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 5 Pith papers

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  3. PREF: Phasorial Embedding Fields for Compact Neural Representations

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  4. 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.

  5. NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)

    cs.CV 2022-10 unverdicted novelty 2.0 of 10

    A literature survey of NeRF and neural field methods from 2020-2025, organized by architecture and application taxonomies with benchmarks and dataset overviews, covering both pre- and post-Gaussian Splatting periods.

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