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Plenoxels: Radiance Fields without Neural Networks

📅 June 1, 2022 👤 Sara Fridovich-Keil, Alex Yu, Matthew Tancik et al. 📖 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 📊 1,252 citations

🤖 Plain-English Summary

We introduce Plenoxels (plenoptic voxels), a systemfor photorealistic view synthesis. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality.

🔑 Key Findings

  • 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.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jun 01, 2022
Journal 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Authors Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht
DOI 10.1109/cvpr52688.2022.00542
Citations 1,252
Source OpenAlex

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