[arXiv]
[Colab]
[Project Page]
[Bibtex]
We recommend using a conda environment to manage dependencies. Install a version of Pytorch compatible with your CUDA version from the Pytorch website.
git clone --depth 1 https://github.com/amyxlase/relpose-plus-plus.git
conda create -n relposepp python=3.8
conda activate relposepp
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia -y
pip install -r requirements.txt
Then, follow the directions to install Pytorch3D here.
A Colab notebook is available here
To run locally, first download pre-trained weights:
mkdir -p weights
gdown https://drive.google.com/uc?id=1FGwMqgLXv4R0xMzEKVv3n3Aghn0MQXKY&export=download
unzip relposepp_weights.zip -d weights
The demo can be run on any image directory with 2-8 images. Each image must be associated with a bounding box. The colab notebook has an interactive interface for selecting bounding boxes.
The bounding boxes can either be extracted automatically from masks or specified in a json file.
Run demo by extracting bounding boxes from masks:
python relpose/demo.py --image_dir examples/robot/images \
--mask_dir examples/robot/masks --output_path robot.html
Run demo using the masked model (ignores background):
python relpose/demo.py --image_dir examples/robot/images --model_dir weights/relposepp_masked \
--mask_dir examples/robot/masks --output_path robot.html
Run demo with specified bounding boxes:
python relpose/demo.py --image_dir examples/robot/images \
--bbox_path examples/robot/bboxes.json --output_path robot.html
The demo will output an html file that can be opened in a browser. The html file will display the input images and predicted cameras. An example is shown here.
Download the CO3Dv2 dataset from here.
Then, pre-process the annotations:
python -m preprocess.preprocess_co3d --category all --precompute_bbox \
--co3d_v2_dir /path/to/co3d_v2
python -m preprocess.preprocess_co3d --category all \
--co3d_v2_dir /path/to/co3d_v2
Trainer should be run via:
torchrun --rdzv_backend=c10d --rdzv_endpoint=localhost:0 --nnodes=1 --nproc_per_node=8 \
relpose/trainer_ddp.py --batch_size=48 --num_images=8 --random_num_images=true \
--gpu_ids=0,1,2,3,4,5,6,7 --lr=1e-5 --normalize_cameras --use_amp
Our released model was trained to 800,000 iterations using 8 GPUS (A6000).
Please refer to eval.md for instructions on running evaluations.
If you use find this code helpful, please cite:
@article{lin2023relposepp,
title={RelPose++: Recovering 6D Poses from Sparse-view Observations},
author={Lin, Amy and Zhang, Jason Y and Ramanan, Deva and Tulsiani, Shubham},
journal={arXiv preprint arXiv:2305.04926},
year={2023}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent