This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF).

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Deep Learning VaxNeRF
Overview

VaxNeRF

Paper | Google Colab Open In Colab

This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF).

This codebase is implemented using JAX, building on JaxNeRF.

VaxNeRF provides very fast training and slightly higher scores compared to original (Jax)NeRF!!

fast

Installation

Please see the README of JaxNeRF.

Quick start

Training

# make a bounding volume voxel using Visual Hull
python visualhull.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --voxel_dir data/voxel_dil7/lego \
    --dilation 7 \
    --thresh 1. \
    --alpha_bkgd True

# train VaxNeRF
python train.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --voxel_dir data/voxel_dil7/lego \
    --train_dir logs/lego_vax_c800 \
    --num_coarse_samples 800 \
    --render_every 2500

Evaluation

python eval.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --voxel_dir data/voxel_dil7/lego \
    --train_dir logs/lego_vax_c800 \
    --num_coarse_samples 800

Try other NeRFs

Original NeRF

python train.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --train_dir logs/lego_c64f128 \
    --num_coarse_samples 64 \
    --num_fine_samples 128 \
    --render_every 2500

VaxNeRF with hierarchical sampling

# hierarchical sampling needs more dilated voxel
python visualhull.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --voxel_dir data/voxel_dil47/lego \
    --dilation 47 \
    --thresh 1. \
    --alpha_bkgd True

# train VaxNeRF
python train.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --voxel_dir data/voxel_dil47/lego \
    --train_dir logs/lego_vax_c64f128 \
    --num_coarse_samples 64 \
    --num_fine_samples 128 \
    --render_every 2500

Option details

Visual Hull

  • Use --dilation 11 / --dilation 51 for NSVF-Synthetic dataset for training VaxNeRF without / with hierarchical sampling.
  • The following options were used for the Lifestyle, Spaceship, Steamtrain scenes (included in the NSVF dataset) because these datasets do not have alpha channel.
    • Lifestyle: --thresh 0.95, Spaceship: --thresh 0.9, Steamtrain: --thresh 0.95

NeRFs

  • We used --small_lr_at_first option for original NeRF training on the Robot and Spaceship scenes to avoid local minimum.

Code modification from JaxNeRF

  • You can see the main difference between (Jax)NeRF (jaxnerf branch) and VaxNeRF (vaxnerf branch) here
  • The main branch (derived from the vaxnerf branch) contains the following features.
    • Support for original NeRF
    • Support for VaxNeRF with hierarchical sampling
    • Support for the NSVF-Synthetic dataset
    • Visualization of number of sampling points evaluated by MLP (VaxNeRF)
    • Automatic choice of the number of sampling points to be evaluated (VaxNeRF)

Citation

Please use the following bibtex for citations:

@misc{kondo2021vaxnerf,
      title={VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field}, 
      author={Naruya Kondo and Yuya Ikeda and Andrea Tagliasacchi and Yutaka Matsuo and Yoichi Ochiai and Shixiang Shane Gu},
      year={2021},
      eprint={2111.13112},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

and also cite the original NeRF paper and JaxNeRF implementation:

@inproceedings{mildenhall2020nerf,
  title={NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis},
  author={Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},
  year={2020},
  booktitle={ECCV},
}

@software{jaxnerf2020github,
  author = {Boyang Deng and Jonathan T. Barron and Pratul P. Srinivasan},
  title = {{JaxNeRF}: an efficient {JAX} implementation of {NeRF}},
  url = {https://github.com/google-research/google-research/tree/master/jaxnerf},
  version = {0.0},
  year = {2020},
}

Acknowledgement

We'd like to express deep thanks to the inventors of NeRF and JaxNeRF.

Have a good VaxNeRF'ed life!

Comments
  • Does all sampling positions need be fed into the NeRF model after using visual hull?

    Does all sampling positions need be fed into the NeRF model after using visual hull?

    Thanks for your great work. I want to know whether you eliminate the possitions which are not in visual hull or not before you put them into NeRF model. If you eliminate them, there will be less positions for computing. But this will lead to different number of positions for each ray. So I want to know how you solve this problem?

    opened by DRosemei 5
  • Fails to train on GPU

    Fails to train on GPU

    Hi,

    Congrats on your work! I'm trying to Vax the lego dataset from NeRF on a GCP vm with A100. I have followed instructions from JaxNeRF repo and installed cudatoolkit 11.0.221 through Conda. The visuals hull was created successfully but when I run training I get the following message: E external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NOT_INITIALIZED: initialization error 140511409292096 xla_bridge.py:243] Unable to initialize backend 'gpu': FAILED_PRECONDITION: No visible GPU devices

    Here's the output of nvidia-smi:

    +-----------------------------------------------------------------------------+
| NVIDIA-SMI 495.29.05    Driver Version: 495.29.05    CUDA Version: 11.5     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  On   | 00000000:00:04.0 Off |                    0 |
| N/A   31C    P0    44W / 400W |    111MiB / 40536MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1178      G   /usr/lib/xorg/Xorg                 95MiB |
|    0   N/A  N/A      1218      G   /usr/bin/gnome-shell               14MiB |
+-----------------------------------------------------------------------------+

    This driver and CUDA is what I've manually installed but pasted the output here to show that nvidia-smi works. For VaxNeRF I've installed cudatoolkit and cud (8.2) through Conda.

    I want to train on my own data after I've trained it on lego. Will it be possible for you to add instructions to achieve the same?

    Thanks!

    opened by sixftninja 4
  • question of digitize function

    question of digitize function

    Hello, I cannot understand this function clearly. I know that digitize is used to convert p's location to voxel index. But why do you add rsize to p ?p+rsize.

    Rsize is half value of far-near, which will change p's location. Then, you limit voxelized p to [0, vsize-1], but in real world coordinator(e.g. llff), voxelized p in range [0, vsize-1] may not contain object.

    Or rsize here is just an empirical offset？

    @partial(jit, static_argnums=(1,2,))
def digitize(p, rsize, vsize):
    p = jnp.round((p+rsize) * (vsize/(rsize*2)))
    return jnp.clip(p.astype(jnp.uint16), 0, vsize-1)\
    opened by zhangjian94cn 2
  • Does not work on real scenes

    Does not work on real scenes

    I tried the visual hull code on a few real scenes (from LLFF and Tanks&Temple datasets), all scenes return wrong visual hulls (because of ground plane and background noise). Does this approach only work on synthetic data?

    opened by qiaosongwang 2
  • using the concept of voxel acceleration for Deblr-NeRF

    using the concept of voxel acceleration for Deblr-NeRF

    Hi @naruya,

    This is amazing work. I am working on deblurring NeRF using blurry input scenes. Is it possible to use the concept of VaxNeRF in thisnerf model?

    Thanks

    opened by addy1997 1
  • TypeError: broadcast_to requires ndarray or scalar arguments, got <class 'list'> at position 0.

    TypeError: broadcast_to requires ndarray or scalar arguments, got at position 0. 

    (jaxnerf) ➜  VaxNeRF git:(main) python train.py \
    --config configs/demo \
    --data_dir data/nerf_synthetic/lego \
    --train_dir logs/lego_c64f128 \
    --num_coarse_samples 64 \
    --num_fine_samples 128 \
    --render_every 2500
I0530 19:00:30.947573 140039295452352 xla_bridge.py:330] Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker:
I0530 19:00:31.048327 140039295452352 xla_bridge.py:330] Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.
/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/lib/xla_bridge.py:501: UserWarning: jax.host_id has been renamed to jax.process_index. This alias will eventually be removed; please update your code.
  warnings.warn(
/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/lib/xla_bridge.py:514: UserWarning: jax.host_count has been renamed to jax.process_count. This alias will eventually be removed; please update your code.
  warnings.warn(
Traceback (most recent call last):
  File "train.py", line 316, in <module>
    app.run(main)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "train.py", line 312, in main
    train(FLAGS.max_steps)
  File "train.py", line 146, in train
    model, variables = models.get_model(key, dataset.peek(), FLAGS)
  File "/root/workspace/VaxNeRF/nerf/models.py", line 33, in get_model
    return model_dict[args.model](key, example_batch, args)
  File "/root/workspace/VaxNeRF/nerf/models.py", line 322, in construct_nerf
    init_variables = model.init(
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 162, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/module.py", line 1224, in init
    _, v_out = self.init_with_output(
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 162, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/module.py", line 1191, in init_with_output
    return self.apply(
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 162, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/module.py", line 1156, in apply
    return apply(
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/core/scope.py", line 831, in wrapper
    y = fn(root, *args, **kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/module.py", line 1440, in scope_fn
    return fn(module.clone(parent=scope), *args, **kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/transforms.py", line 1239, in wrapped_fn
    return prewrapped_fn(self, *args, **kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/module.py", line 350, in wrapped_module_method
    return self._call_wrapped_method(fun, args, kwargs)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/flax/linen/module.py", line 648, in _call_wrapped_method
    y = fun(self, *args, **kwargs)
  File "/root/workspace/VaxNeRF/nerf/models.py", line 153, in __call__
    comp_rgb, disp, acc, weights = model_utils.volumetric_rendering(
  File "/root/workspace/VaxNeRF/nerf/model_utils.py", line 173, in volumetric_rendering
    jnp.broadcast_to([1e10], z_vals[Ellipsis, :1].shape)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/numpy/util.py", line 345, in _broadcast_to
    _check_arraylike("broadcast_to", arr)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/numpy/util.py", line 298, in _check_arraylike
    raise TypeError(msg.format(fun_name, type(arg), pos))
jax._src.traceback_util.UnfilteredStackTrace: TypeError: broadcast_to requires ndarray or scalar arguments, got <class 'list'> at position 0.

The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.

--------------------

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "train.py", line 316, in <module>
    app.run(main)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "train.py", line 312, in main
    train(FLAGS.max_steps)
  File "train.py", line 146, in train
    model, variables = models.get_model(key, dataset.peek(), FLAGS)
  File "/root/workspace/VaxNeRF/nerf/models.py", line 33, in get_model
    return model_dict[args.model](key, example_batch, args)
  File "/root/workspace/VaxNeRF/nerf/models.py", line 322, in construct_nerf
    init_variables = model.init(
  File "/root/workspace/VaxNeRF/nerf/models.py", line 153, in __call__
    comp_rgb, disp, acc, weights = model_utils.volumetric_rendering(
  File "/root/workspace/VaxNeRF/nerf/model_utils.py", line 173, in volumetric_rendering
    jnp.broadcast_to([1e10], z_vals[Ellipsis, :1].shape)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/numpy/util.py", line 345, in _broadcast_to
    _check_arraylike("broadcast_to", arr)
  File "/root/.pyenv/versions/jaxnerf/lib/python3.8/site-packages/jax/_src/numpy/util.py", line 298, in _check_arraylike
    raise TypeError(msg.format(fun_name, type(arg), pos))
TypeError: broadcast_to requires ndarray or scalar arguments, got <class 'list'> at position 0.
    opened by naruya 1
  • vaxnerf can not reach convergence

    vaxnerf can not reach convergence

    this can give a coarse result after 2500 step python train.py
    --config configs/demo
    --data_dir data/nerf_synthetic/lego
    --train_dir logs/lego_c64f128
    --num_coarse_samples 64
    --num_fine_samples 128
    --render_every 2500

    but using voxel to train can not reach convergence python train.py
    --config configs/demo
    --data_dir data/nerf_synthetic/lego
    --voxel_dir data/voxel_dil7/lego
    --train_dir logs/lego_vax_c800
    --num_coarse_samples 800
    --render_every 2500

    the result is totally a white background

    opened by YuiNsky 0
Owner
naruya
DNG
naruya
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