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snap-research avatar

snap-research/R2L

0
View on GitHub↗
189 stars·24 forks·Python·8 viewssnap-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.

Star history

Star history chart for snap-research/r2lStar history chart for snap-research/r2l

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with R2L

These projects share indexed features with R2L. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • creiser/kilonerfcreiser avatar

    creiser/kilonerf

    492View on GitHub↗

    Code for KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs

    Cuda
    View on GitHub↗492
  • facebookresearch/donerffacebookresearch avatar

    facebookresearch/DONERF

    312View on GitHub↗

    Code for "DONeRF Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks"

    Python
    View on GitHub↗312
  • facebookresearch/nsvffacebookresearch avatar

    facebookresearch/NSVF

    818View on GitHub↗

    Open source code for the paper of Neural Sparse Voxel Fields.

    Python
    View on GitHub↗818
  • computational-imaging/automatic-integrationcomputational-imaging avatar

    computational-imaging/automatic-integration

    188View on GitHub↗

    Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

    Python
    View on GitHub↗188
Compare all 9 related projects→

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.

Which projects share features with snap-research/r2l?

Projects with overlapping indexed features 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.