Official implementation of `Splatter Image: Ultra-Fast Single-View 3D Reconstruction'

1. Create a conda environment and install requirements:

conda create --name splatter-image
conda activate splatter-image
pip install -r requirements.txt

2. Install Gaussian Splatting renderer, i.e. the library for rendering a Gaussian Point cloud to an image. To do so, pull the Gaussian Splatting repository and, with your conda environment activated, run pip install submodules/diff-gaussian-rasterization. You will need to meet the hardware and software requirements. We did all our experimentation on an NVIDIA A6000 GPU and speed measurements on an NVIDIA V100 GPU.

3. If you want to train on CO3D data you will need to install Pytorch3D. See instructions here.

• For training / evaluating on ShapeNet-SRN follow instructions from PixelNeRF and change SHAPENET_DATASET_ROOT in scene/srn.py to your download directory. No additional preprocessing is needed.

• For training / evaluating on CO3D download the hydrant and teddybear classes from the CO3D release. Next, set CO3D_RAW_ROOT to your download directory in data_preprocessing/preoprocess_co3d.py. Set CO3D_OUT_ROOT to where you want to store preprocessed data. Run python -m data_preprocessing.preprocess_co3d and set CO3D_DATASET_ROOT:=CO3D_OUT_ROOT.

Pretrained models will be released in early 2024!

Single-view models can be trained with the following command:

python train_network.py +dataset=[cars,chairs,hydrants,teddybears]

To train a 2-view model run:

python train_network.py +dataset=cars cam_embd=pose_pos data.input_images=2 opt.imgs_per_obj=5

Once a model is trained evaluation can be run with

python eval.py [model directory path]

To save renders modify variable save_vis and out_folder in eval.py.

Training loop is implemented in train_network.py and evaluation code is in eval.py. Datasets are implemented in scene/srn.py and scene/co3d.py. Model is implemented in scene/gaussian_predictor.py. The call to renderer can be found in gaussian_renderer/__init__.py.

Gaussian rasterizer assumes row-major order of rigid body transform matrices, i.e. that position vectors are row vectors. It also requires cameras in the COLMAP / OpenCV convention, i.e., that x points right, y down, and z away from the camera (forward).

@inproceedings{szymanowicz23splatter,
      title={Splatter Image: Ultra-Fast Single-View 3D Reconstruction},
      author={Stanislaw Szymanowicz and Christian Rupprecht and Andrea Vedaldi},
      year={2023},
      booktitle={arXiv},
}

S. Szymanowicz is supported by an EPSRC Doctoral Training Partnerships Scholarship (DTP) EP/R513295/1 and the Oxford-Ashton Scholarship. A. Vedaldi is supported by ERC-CoG UNION 101001212. We thank Eldar Insafutdinov for his help with installation requirements.