base on Code for "Cameras as Rays" # Cameras as Rays [[`arXiv`](https://arxiv.org/abs/2402.14817)] [[`Project Page`](https://jasonyzhang.com/RayDiffusion/)] [[`Bibtex`](#citing-cameras-as-rays)] [[`Colab`](https://colab.research.google.com/drive/1dqp9qnFyHA71y3motSoJpJFBHZVftXzb?usp=sharing)] This repository contains code for "Cameras as Rays: Pose Estimation via Ray Diffusion" (ICLR 2024). Clone the repository: ``` git clone --depth=1 --branch=main https://github.com/jasonyzhang/RayDiffusion.git ``` ## Setting up Environment We recommend using a conda environment to manage dependencies. Install a version of Pytorch compatible with your CUDA version from the [Pytorch website](https://pytorch.org/get-started/locally/). ``` conda create -n raydiffusion python=3.10 conda activate raydiffusion conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia conda install xformers -c xformers pip install -r requirements.txt ``` Then, follow the directions to install Pytorch3D [here](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md). We recommend installing Pytorch3D using the pre-built wheel with the corresponding Python/Pytorch/CUDA version: ``` pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu118_pyt211/download.html ``` If you are having trouble installing using the pre-built wheel, you can also try building from source, but this will take a lot longer. ## Run Demo Download the model weights from [Google Drive](https://drive.google.com/file/d/1anIKsm66zmDiFuo8Nmm1HupcitM6NY7e/view?usp=drive_link). ``` gdown https://drive.google.com/uc\?id\=1anIKsm66zmDiFuo8Nmm1HupcitM6NY7e unzip models.zip ``` Run ray diffusion with known bounding boxes (provided as a json): ``` python demo.py --model_dir models/co3d_diffusion --image_dir examples/robot/images \ --bbox_path examples/robot/bboxes.json --output_path robot.html ``` Run ray diffusion with bounding boxes extracted automatically from masks: ``` python demo.py --model_dir models/co3d_diffusion --image_dir examples/robot/images \ --mask_dir examples/robot/masks --output_path robot.html ``` Run ray regression: ``` python demo.py --model_dir models/co3d_regression --image_dir examples/robot/images \ --bbox_path examples/robot/bboxes.json --output_path robot.html ``` ## Training Training command for ray diffusion: ``` accelerate launch --multi_gpu --gpu_ids 0,1,2,3,4,5,6,7 --num_processes 8 train.py \ training.batch_size=8 training.max_iterations=450000 ``` See [docs/train.md](docs/train.md) for more detailed instructions on training. ## Evaluation See [docs/eval.md](docs/eval.md) for instructions on how to run evaluation code. ## Citing Cameras as Rays If you find this code helpful, please cite: ``` @InProceedings{zhang2024raydiffusion, title={Cameras as Rays: Pose Estimation via Ray Diffusion}, author={Zhang, Jason Y and Lin, Amy and Kumar, Moneish and Yang, Tzu-Hsuan and Ramanan, Deva and Tulsiani, Shubham}, booktitle={International Conference on Learning Representations (ICLR)}, year={2024} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"8120","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"