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snap-research/R2L

0
View on GitHub↗
189 stars·24 forks·Python·1 viewsnap-research.github.io/R2L↗

R2L

[ECCV 2022] R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis

Features

  • Efficient Rendering - Distills radiance fields into light fields for efficient synthesis.

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Frequently asked questions

What does snap-research/r2l do?

[ECCV 2022] R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis

What are the main features of snap-research/r2l?

The main features of snap-research/r2l are: Efficient Rendering.

What are some open-source alternatives to snap-research/r2l?

Open-source alternatives to snap-research/r2l include: computational-imaging/automatic-integration — Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021. creiser/kilonerf — Code for KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs. facebookresearch/donerf — Code for "DONeRF Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks". facebookresearch/nsvf — Open source code for the paper of Neural Sparse Voxel Fields. heng14/dylin — Source code for CVPR 2023 DyLiN paper. snap-research/mobiler2l — [CVPR 2023] Real-Time Neural Light Field on Mobile Devices.