• Neural Sparse Voxel Fields (NSVF)
• • • Project Page | Video | Paper | Data
  • Table of contents
  • Requirements and Installation
  • Dataset
  • • Prepare your own dataset
  • Train a new model
  • Evaluation
  • Free Viewpoint Rendering
  • Extract the Geometry
  • License
  • Citation

Neural Sparse Voxel Fields (NSVF)

Project Page | Video | Paper | Data

Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is a challenging problem because it requires the difficult step of capturing detailed appearance and geometry models.
Neural rendering is an emerging field that employs deep neural networks to implicitly learn scene representations encapsulating both geometry and appearance from 2D observations with or without a coarse geometry.
However, existing approaches in this field often show blurry renderings or suffer from slow rendering process. We propose Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering.

Here is the official repo for the paper:

• Neural Sparse Voxel Fields (Liu et al., 2020, NeurIPS 2020 Spotlight).

We also provide our unofficial implementation for:

• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (Mildenhall et al., 2020).

Table of contents

---

• Installation
• Dataset
• Usage
  • Training
  • Evaluation
  • Free-view Rendering
  • Extracting Geometry
• License
• Citation

---

Requirements and Installation

This code is implemented in PyTorch using fairseq framework.

The code has been tested on the following system:

• Python 3.7
• PyTorch 1.4.0
• Nvidia apex library (optional)
• Nvidia GPU (Tesla V100 32GB) CUDA 10.1

Only learning and rendering on GPUs are supported.

To install, first clone this repo and install all dependencies:

pip install -r requirements.txt

Then, run

pip install --editable ./

Or if you want to install the code locally, run:

python setup.py build_ext --inplace

Dataset

You can download the pre-processed synthetic and real datasets used in our paper.
Please also cite the original papers if you use any of them in your work.

Dataset	Download Link	Notes on Dataset Split
Synthetic-NSVF	download (.zip)	0_* (training) 1_* (validation) 2_* (testing)
Synthetic-NeRF	download (.zip)	0_* (training) 1_* (validation) 2_* (testing)
BlendedMVS	download (.zip)	0_* (training) 1_* (testing)
Tanks&Temples	download (.zip)	0_* (training) 1_* (testing)

Prepare your own dataset

To prepare a new dataset of a single scene for training and testing, please follow the data structure:

<dataset_name>
|-- bbox.txt         # bounding-box file
|-- intrinsics.txt   # 4x4 camera intrinsics
|-- rgb
    |-- 0.png        # target image for each view
    |-- 1.png
    ...
|-- pose
    |-- 0.txt        # camera pose for each view (4x4 matrices)
    |-- 1.txt
    ...
[optional]
|-- test_traj.txt    # camera pose for free-view rendering demonstration (4N x 4)

where the bbox.txt file contains a line describing the initial bounding box and voxel size:

x_min y_min z_min x_max y_max z_max initial_voxel_size

Note that the file names of target images and those of the corresponding camera pose files are not required to be exactly the same. However, the orders of these two kinds of files (sorted by string) must match. The datasets are split with view indices.
For example, "train (0..100), valid (100..200) and test (200..400)" mean the first 100 views for training, 100-199th views for validation, and 200-399th views for testing.

Train a new model

Given the dataset of a single scene ({DATASET}), we use the following command for training an NSVF model to synthesize novel views at 800x800 pixels, with a batch size of 4 images per GPU and 2048 rays per image. By default, the code will automatically detect all available GPUs.

In the following example, we use a pre-defined architecture nsvf_base with specific arguments:

• By setting --no-sampling-at-reader, the model only samples pixels in the projected image region of sparse voxels for training.
• By default, we set the ray-marching step size to be the ratio 1/8 (0.125) of the voxel size which is typically described in the bbox.txt file.
• It is optional to turn on --use-octree. It will build a sparse voxel octree to speed-up the ray-voxel intersection especially when the number of voxels is larger than 10000.
• By setting --pruning-every-steps as 2500, the model performs self-pruning at every 2500 steps.
• By setting --half-voxel-size-at and --reduce-step-size-at as 5000,25000,75000, the voxel size and step size are halved at 5k, 25k and 75k, respectively.

Note that, although above parameter settings are used for most of the experiments in the paper, it is possible to tune these parameters to achieve better quality. Besides the above parameters, other parameters can also use default settings.

Besides the architecture nsvf_base, you may check other architectures or define your own architectures in the file fairnr/models/nsvf.py.

python -u train.py ${DATASET} \
    --user-dir fairnr \
    --task single_object_rendering \
    --train-views "0..100" --view-resolution "800x800" \
    --max-sentences 1 --view-per-batch 4 --pixel-per-view 2048 \
    --no-preload \
    --sampling-on-mask 1.0 --no-sampling-at-reader \
    --valid-views "100..200" --valid-view-resolution "400x400" \
    --valid-view-per-batch 1 \
    --transparent-background "1.0,1.0,1.0" --background-stop-gradient \
    --arch nsvf_base \
    --initial-boundingbox ${DATASET}/bbox.txt \
    --use-octree \
    --raymarching-stepsize-ratio 0.125 \
    --discrete-regularization \
    --color-weight 128.0 --alpha-weight 1.0 \
    --optimizer "adam" --adam-betas "(0.9, 0.999)" \
    --lr 0.001 --lr-scheduler "polynomial_decay" --total-num-update 150000 \
    --criterion "srn_loss" --clip-norm 0.0 \
    --num-workers 0 \
    --seed 2 \
    --save-interval-updates 500 --max-update 150000 \
    --virtual-epoch-steps 5000 --save-interval 1 \
    --half-voxel-size-at  "5000,25000,75000" \
    --reduce-step-size-at "5000,25000,75000" \
    --pruning-every-steps 2500 \
    --keep-interval-updates 5 --keep-last-epochs 5 \
    --log-format simple --log-interval 1 \
    --save-dir ${SAVE} \
    --tensorboard-logdir ${SAVE}/tensorboard \
    | tee -a $SAVE/train.log

The checkpoints are saved in {SAVE}. You can launch tensorboard to check training progress:

tensorboard --logdir=${SAVE}/tensorboard --port=10000

There are more examples of training scripts to reproduce the results of our paper under examples.

Evaluation

Once the model is trained, the following command is used to evaluate rendering quality on the test views given the {MODEL_PATH}.

python validate.py ${DATASET} \
    --user-dir fairnr \
    --valid-views "200..400" \
    --valid-view-resolution "800x800" \
    --no-preload \
    --task single_object_rendering \
    --max-sentences 1 \
    --valid-view-per-batch 1 \
    --path ${MODEL_PATH} \
    --model-overrides '{"chunk_size":512,"raymarching_tolerance":0.01,"tensorboard_logdir":"","eval_lpips":True}' \

Note that we override the raymarching_tolerance to 0.01 to enable early termination for rendering speed-up.

Free Viewpoint Rendering

Free-viewpoint rendering can be achieved once a model is trained and a rendering trajectory is specified. For example, the following command is for rendering with a circle trajectory (angular speed 3 degree/frame, 15 frames per GPU). This outputs per-view rendered images and merge the images into a .mp4 video in ${SAVE}/output as follows:

By default, the code can detect all available GPUs.

python render.py ${DATASET} \
    --user-dir fairnr \
    --task single_object_rendering \
    --path ${MODEL_PATH} \
    --model-overrides '{"chunk_size":512,"raymarching_tolerance":0.01}' \
    --render-beam 1 --render-angular-speed 3 --render-num-frames 15 \
    --render-save-fps 24 \
    --render-resolution "800x800" \
    --render-path-style "circle" \
    --render-path-args "{'radius': 3, 'h': 2, 'axis': 'z', 't0': -2, 'r':-1}" \
    --render-output ${SAVE}/output \
    --render-output-types "color" "depth" "voxel" "normal" --render-combine-output \
    --log-format "simple"

Our code also supports rendering for given camera poses.
For instance, the following command is for rendering with the camera poses defined in the 200-399th files under folder ${DATASET}/pose:

python render.py ${DATASET} \
    --user-dir fairnr \
    --task single_object_rendering \
    --path ${MODEL_PATH} \
    --model-overrides '{"chunk_size":512,"raymarching_tolerance":0.01}' \
    --render-save-fps 24 \
    --render-resolution "800x800" \
    --render-camera-poses ${DATASET}/pose \
    --render-views "200..400" \
    --render-output ${SAVE}/output \
    --render-output-types "color" "depth" "voxel" "normal" --render-combine-output \
    --log-format "simple"

The code also supports rendering with camera poses defined in a .txt file. Please refer to this example.

Extract the Geometry

We also support running marching cubes to extract the iso-surfaces as triangle meshes from a trained NSVF model and saved as {SAVE}/{NAME}.ply.

python extract.py \
    --user-dir fairnr \
    --path ${MODEL_PATH} \
    --output ${SAVE} \
    --name ${NAME} \
    --format 'mc_mesh' \
    --mc-threshold 0.5 \
    --mc-num-samples-per-halfvoxel 5

It is also possible to export the learned sparse voxels by setting --format 'voxel_mesh'.
The output .ply file can be opened with any 3D viewers such as MeshLab.

License

NSVF is MIT-licensed.
The license applies to the pre-trained models as well.

Citation

Please cite as

@article{liu2020neural,
  title={Neural Sparse Voxel Fields},
  author={Liu, Lingjie and Gu, Jiatao and Lin, Kyaw Zaw and Chua, Tat-Seng and Theobalt, Christian},
  journal={NeurIPS},
  year={2020}
}