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3DVNet: Multi-View Depth Prediction and Volumetric Refinement

Dependencies/Installation

The following will install all dependencies for 3DVNet training and evaluation. This installation has only been tested on Ubuntu 20.04.

conda create -n 3dvnet python=3.8 -y
conda activate 3dvnet
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
pip install \
  pytorch-lightning==1.1.2 \
  wandb \
  tqdm \
  opencv-python \
  open3d==0.11.2 \
  scikit-image==0.17.2 \
  pyrender \
  trimesh \
  kornia==0.4.1 \
  path

# PyTorch Geometric installation
pip install torch-scatter==2.0.5 -f https://data.pyg.org/whl/torch-1.7.1+cu110.html
pip install torch-sparse==0.6.8 -f https://data.pyg.org/whl/torch-1.7.1+cu110.html
pip install torch-cluster==1.5.8 -f https://data.pyg.org/whl/torch-1.7.1+cu110.html
pip install torch-geometric==1.6.3

# Minkowski Engine installation (https://github.com/NVIDIA/MinkowskiEngine)
# note our code is only tested with Minkowski Engine 0.5.0
conda install openblas-devel -c anaconda -y
export CUDA_HOME=/usr/local/cuda-11.xx   # replace with local cuda version >=11.0
pip install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option="--blas_include_dirs=${CONDA_PREFIX}/include" --install-option="--blas=openblas"

# finally, install code as editable local package
python -m pip install --editable . --user

The following will install all additional dependencies for evaluating competing baselines. Should any dependency issues arise, we refer you to the author's original codebase for specific installation instructions.

# Atlas (note installation has been changed from original Atlas to match our cudatoolkit/pytorch version)
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu110/torch1.7/index.html

# NeuralRecon (torchsparse: https://github.com/mit-han-lab/torchsparse)
sudo apt-get install libsparsehash-dev
pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git@v1.2.0
pip install loguru transforms3d

Data

See data_preprocess directory for data preprocessing scripts for ScanNet, ICL-NUIM, and TUM-RGBD. We briefly outline the ScanNet preprocessing.

Given an existing ScanNet directory scannet_src extracted using the author provided extraction scripts, run

python data_preprocess/preprocess_scannet.py --src path/to/scannet_src --dst path/to/new/scannet_dst

NOTE: If --src and --dst are the same directory, you will overwrite your existing ScanNet dataset.

This script expects a ScanNet src directory with structure

scannet_src/
    scannetv2_*.txt
    scans*/
        scene_****_**/
            scene_****_**_vh_clean_2.ply
            color/
            depth/
            intrinsic/
            pose/

and creates a new ScanNet directory with structure

scannet_dst/
    scannetv2_*.txt
    scans*/
        scene_****_**/
            info.json
            scene_****_**_vh_clean_2.ply
            color/
            depth/

with depth and color images resized properly. The info.json file is used by the dataloader for frame selection. Once data has been preprocessed, modify mv3d/config.py such that SCANNET_DIR = path/to/scannet_dst. mv3d/dsets/dataset.py can be run as a script to visualize a random ScanNet scene. This can be used for convenient debugging of the data preprocessing.

A note on our data batch setup

We use PyTorch Geometric edge indexing to specify our reference and source images in each batch from our dataloader. Specifically, our batch of images is returned from the dataloader flattened, i.e. batch.images.shape = [num_images, 3, 256, 320]. The attribute batch.ref_src_idx of shape [2, num_source_images] then specifies the corresponding reference and source images, and batch.images_batch specifies the corresponding batch index of each image. We find the flexibility of this setup quite convenient. See the linked PyTorch Geometric documentation for more details.

Training

All training parameters can be changed in mv3d/config.py. For logging, we use wandb. It should be quite easy to modify the code to use TensorBoard. Initial training is done with the CNN backbone fixed. Simply run python train.py. No args are used, all parameters are specified in mv3d/config.py. Once a model has converged in the previous run (~120 epochs), specify model save location using the PATH variable in mv3d/config.py. Run mv3d/finetune.py until convergence (~100 epochs).

For each baseline used for comparison and not trained on ScanNet, we include a fine-tuning script finetune.py. We also provide our fine-tuned weights trained using the fine-tuning script.

Evaluation

To run 3DVNet evaluation, use mv3d/eval-3dvnet.py. To run evaluation on each baseline method, use the mv3d/baselines/${method}/eval-${method}.py. The evaluation scripts have no arguments. Instead, evaluation configuration can be changed in mv3d/eval/config.py. The common code used for evaluation of all methods can be found in mv3d/eval. We provide all weights for 3DVNet and all baselines (both author provided and ScanNet finetuned when applicable) here. To use, simply download and place each *_weights directory in the same directory as the corresponding eval_*.py script.

Point cloud fusion implementation

We use a depth-based multi-view consistency check in point cloud fusion. Code modified from the MVSNet point cloud fusion implementation can be found here. Once built, modify the FUSIBILE_EXE_PATH variable in mv3d/eval/config.py to point to the built binary. We also provide a very slow PyTorch implementation of point cloud fusion. If no fusibile binary is found, our evaluation pipeline defaults to this implementation.

Disparity-based point cloud fusion can also be used. To do this, use the original MVSNet implementation and modify the FUSIBILE_EXE_PATH variable in mv3d/eval/config.py to point to the built binary. Note that the Z_THRESH variable in mv3d/eval/config.py must be updated to a value appropriate for disparity thresholding. We recommend Z_THRESH=0.125 based on validation results.

Results folder structure

The parent folder for evaluation results for all methods is specified in the SAVE_DIR variable in eval/config.py. When running the evaluation script for a given method, the folder SAVE_DIR/methodname will be created with the following structure:

methodname/
    metrics_*.json
    scenes/
        scene1/
            preds.npz
            metrics_*.json
            *.ply
        scene2/
            preds.npz
            metrics_*.json
            *.ply
        ...

Depth predictions for each scene are stored in preds.npz file. The metric filenames are reflective of the metrics they calculate. For example, metrics_2d.json contains 2D depth metrics, while metrics_3d_0.010_3v_masked.json contains 3D metrics calculated using point cloud fusion with 3-view consistency, a depth consistency threshold of 0.01, and holes in observed regions masked out. The ply files in each scene folder are also correspondingly named. The top-level metrics files contain aggregated metrics.

Visualization

We include a convenient Open3D visualization script mv3d/eval/visualization.py for visualizing the reconstructions produced by all baseline methods. To change which methods are visualized, modify the 3 lists found at the start of the script. The key callbacks are defined at the end of the script.

Evaluating your own data

To evaluate all methods on your own data, you must prepare an info.json file for each scene. See the data preprocessing scripts for examples of how to do this. Then, modify mv3d/eval/config.py to specify a parent directory for the results. Finally, modify mv3d/eval/main.py to properly load the list of custom scenes you wish to evaluate. You can check if your info.json file is prepared properly by modifying and running the visualization script at the bottom of mv3d/dsets/dataset.py.

Evaluating your own method

All of the evaluation scripts follow a common boilerplate structure. Use one of the eval scripts as a starting template. For volumetric methods (or any method that directly predicts 3D scene geometry), mv3d/baselines/atlas/eval-atlas.py is the best starting point. For depth-based methods, mv3d/baselines/gpmvs/eval-gpmvs.py or mv3d/baselines/pointmvsnet/eval-pointmvsnet.py are the best starting points. If your method includes confidence maps for each depth map, use the latter.

Evaluating your own frame selection method

Frame selection classes are found in mv3d/dsets/frameselector.py. We provide several options. To write your own, subclass the FrameSelector class. To use this in evaluation, modify mv3d/eval/main.py to use your new frame selection class. To use this during training or finetuning, modify the corresponding train.py or finetune.py file where the frame selector is specified. If you intend to only use your frame selection for evaluation, the seed_idx argument can be ignored in your subclass.

Citation

@inproceedings{rich20213dvnet,
  title={{3DVNet}: Multi-View Depth Prediction and Volumetric Refinement},
  author={Alexander Rich and Noah Stier and Pradeep Sen and Tobias H\"ollerer},
  booktitle={Proceedings of the International Conference on {3D} Vision (3DV)},
  year={2021}
}

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Official repository for 3DV 2021 paper "3DVNet: Multi-View Depth Prediction and Volumetric Refinement"

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