Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

instant-nerf-pytorch

This is WORK IN PROGRESS, please feel free to contribute via pull request.

We are trying to make NeRF train super fast in pytorch by using pytorch bindings for Instant-NGP.

Current Progress:

  • Code is implemented and runs, but cannot achieve super good results.
  • Per iteration it is ~3.5x faster than the nerf-pytorch code it is built upon.
  • VERY quickly (1 min) gets up to ~20 PSNR (this is MUCH faster, even in iteration count than the normal NeRF).
  • But doesn't really get above ~25 PSNR even when training for a long time.
  • There is a bug where running the 'fine' network doesn't work, results above are only for coarse network (e.g. N_importance = 0), and speed comparisons also to only course network, this might be the reason the PSNR doesn't get super high.

How to get running:

  1. Install tiny-cuda-nn (https://github.com/NVlabs/tiny-cuda-nn).
  2. Download, install dependencies and run this code.

Links to sources:

Authors:

  • Jonathon Luiten
  • Kangle Deng

Notes:

Specify --backbone ngp to enable Instant-NGP (already done in configs/fern_ngp.txt).

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Comments
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    Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling backward the first time.

    when I use the config, fern_ngp, it raise error, I add retain_graph, but still _module_function_backwardBackward.forward: expected Tensor or tuple of Tensor (got NoneType) for return value 0 may you tell me how to fix it? torch 1.7.1 cu11

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