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RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild

[arXiv] [Project Page] [Bibtex]

Installation

Follow directions for setting up CO3D (v1 or v2) from here

Setup

We recommend using conda to manage dependencies. Make sure to install a cudatoolkit compatible with your GPU.

git clone --depth 1 https://github.com/jasonyzhang/relpose.git
conda create -n relpose python=3.8
conda activate relpose
conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Model Weights

You can download the pre-trained model weights on both CO3Dv1 and CO3Dv2 from Google Drive. Alternatively, you can use gdown:

gdown --output data/pretrained_relpose.zip https://drive.google.com/uc?id=1XwRjxOzqj6DXGg_bzYFy83iDlZx8mkQ-
unzip data/pretrained_relpose.zip -d data

Installing Pytorch3d

Here, we list the recommended steps for installing Pytorch3d. Refer to the official installation directions for troubleshooting and additional details.

mkdir -p external
git clone --depth 1 --branch v0.7.0 https://github.com/facebookresearch/pytorch3d.git external/pytorch3d
cd external/pytorch3d
conda activate relpose
conda install -c conda-forge -c fvcore -c iopath -c bottler fvcore iopath nvidiacub
python setup.py install

If you need to compile for multiple architectures (e.g. Turing for 2080TI and Maxwell for 1080TI), you can pass the architectures as an environment variable, i.e. TORCH_CUDA_ARCH_LIST="Maxwell;Pascal;Turing;Volta" python setup.py install.

If you get a warning about the default C/C++ compiler on your machine, you should compile Pytorch3D using the same compiler that your pytorch installation uses, likely gcc/g++. Try: CC=gcc CXX=g++ python setup.py install.

Dataset Preparation

Please see docs/dataset.md for instructions on preparing the CO3Dv1 dataset or your own dataset.

Training

Once the datasets are setup, run the following command to train on 4 GPUs on CO3Dv2:

python -m relpose.trainer --batch_size 64 --num_gpus 4 --output_dir output --dataset co3d

With 4 2080TI GPUs, we expect training to take a little less than 2 days.

Inference

Please see notebooks/demo.ipynb for a demo of visualizing pairwise relative pose distributions given 2 images as well as recovering camera rotations using the pairwise predictor. Currently, the demo supports using a Maximum Spanning Tree and Coordinate Ascent for joint camera pose inference.

Evaluation

Please see docs/eval.md for instructions on evaluating on sequential, MST, and coordinate ascent inference.

Citing RelPose

If you use find this code helpful, please cite:

@InProceedings{zhang2022relpose,
    title = {{RelPose}: Predicting Probabilistic Relative Rotation for Single Objects in the Wild},
    author = {Zhang, Jason Y. and Ramanan, Deva and Tulsiani, Shubham},
    booktitle = {European Conference on Computer Vision},
    year = {2022},
}