JAX implementation of Tensorial Radiance Fields, written as an exercise.

@misc{TensoRF,
      title={TensoRF: Tensorial Radiance Fields},
      author={Anpei Chen and Zexiang Xu and Andreas Geiger and and Jingyi Yu and Hao Su},
      year={2022},
      eprint={2203.09517},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

We don't attempt to reproduce the original paper exactly, but can achieve decent results after 5~10 minutes of training:

As proposed, TensoRF only supports scenes that fit in a fixed-size bounding box. We've also added basic support for unbounded "real" scenes via mip-NeRF 360-inspired scene contraction¹. From nerfstudio's "dozer" dataset:

1. Download nerf_synthetic dataset: Google Drive. With the default training script arguments, we expect this to be extracted to ./data, eg ./data/nerf_synthetic/lego.

2. Install dependencies. Probably you want the GPU version of JAX; see the official instructions. Then:

   pip install -r requirements.txt

3. To print training options:

   python ./train_lego.py --help

4. To monitor training, we use Tensorboard:

   tensorboard --logdir=./runs/

5. To render:

   python ./render_360.py --help

Things aren't totally matched to the official implementation:

• The official implementation relies heavily on masking operations to improve runtime (for example, by using a weight threshold for sampled points). These require dynamic shapes and are currently difficult to implement in JAX, so we replace them with workarounds like weighted sampling.
• Several training details that would likely improve performance are not yet implemented: bounding box refinement, ray filtering, regularization, etc.
• We include mixed-precision training, which can speed training throughput up by a significant factor. (is this actually faster in terms of wall-clock time? unclear)

Implementation details are based loosely on the original PyTorch implementation apchsenstu/TensoRF.

unixpickle/learn-nerf and google-research/jaxnerf were also really helpful for understanding core NeRF concepts + connecting them to JAX!

• Main implementation
  • Point sampling
  • Feature MLP
  • Rendering
  • VM decomposition
    • Basic implementation
    • Vectorized
  • Dataloading
    • Blender
    • nerfstudio
      • Basics
      • Fisheye support
      • Compute samples without undistorting images (throws away a lot of pixels)
  • Tricks for real data
    • Scene contraction (~mip-NeRF 360)
    • Camera embeddings
• Training
  • Learning rate scheduler
    • ADAM + grouped LR
    • Exponential decay
    • Reset decay after upsampling
  • Running
  • Checkpointing
  • Logging
    • Loss
    • PSNR
    • Test metrics
    • Test images
    • Render previews
  • Ray filtering
  • Bounding box refinement
  • Incremental upsampling
  • Regularization terms
• Performance
  • Weight thresholding for computing appearance features
    • per ray top-k
    • global top-k (bad & deleted)
  • Mixed-precision
    • implemented
    • stable
  • Multi-GPU (should be quick)
• Rendering
  • RGB
  • Depth (median)
  • Depth (mean)
  • Batching
  • Generate some GIFs
• Misc engineering
  • Actions
  • Understand vmap performance differences (details)