NeuralFusion
This is the official implementation of NeuralFusion: Online Depth Map Fusion in Latent Space. We provide code to train the proposed pipeline on ShapeNet, ModelNet, as well as Tanks and Temples.
If you plan to use NeuralFusion for commercial purposes, please contact the author first. For more information, please also see the license.
Installation
Install the code using the following steps
conda env create -f environment.yml
conda activate neural-fusion
Data Preparation
In order to prepare the data, please follow the instructions explained in this repo.
Training
In order to train the pipeline, run the following
python train.py --experiment_path /path/where/you/want/to/save/the/experiment \
--data_path /path/to/your/data \
--config configs/train/your/config.yaml
Testing
In order to test the pipeline, run the following
python test.py --test /path/to/your/test/config \
--root_path /path/where/you/saved/your/experiments \
--data_path /path/to/your/data \
--experiment $experiment_name \
--version $experiment_version \
--checkpoint $experiment_checkpoint
For example, if you would like to test the pretrained on ShapeNet, you need to run the following command
export DATA_PATH=/path/to/your/preprocessed/shapenet/data
python test.py --test configs/test/shapenet/shapenet.noise.005.yaml \
--root_path pretrained_models \
--data_path $DATA_PATH \
--experiment shapenet_noise_005 \
--version 0 \
--checkpoint best.ckpt