LidarCLIP

This is the official implementation of the WACV 2024 paper LidarCLIP or: How I Learned to Talk to Point Clouds.

Checkpoints

CLIP Version	Training Dataset	Link
ViT-L/14	ONCE	google drive
ViT-B/32	ONCE	google drive

Instructions

• download the SST submodule git submodule update --init --recursive
• build the dockerfile docker build -t lidarclip -f docker/Dockerfile .
• spin up a container with access to the dataset and at least one gpu. (docker run -v <lidarclip-path>:/lidarclip <...> lidarclip)
• in the docker container, change directories to /lidarclip
• in the docker container, run python train.py --datadir=<dataset-path> --checkpoint-save-dir=<checkpoint-dir> --name=<experiment-name>. You can specify many additional flags, here is an example command: --name lidarclip-main --batch-size 128 --workers 4 --checkpoint-save-dir /proj/lidarclip/checkpoints/ --clip-model ViT-L/14.
• pre-compute LidarCLIP features: python.py scripts/cache_embeddings.py --checkpoint=<checkpoint-dir>/<checkpoint-file>. By default these are for the once validation set, but it is easily changed with the --dataset-name, --data-path, and --split arguments.
• now you can explore the capabilities of the model by running one of the notebooks, placed under notebooks/:
  • retrieval_and_zero_shot.ipynb allows for qualitative exploration of retrieval and zero-shot classification
  • retrieval_metric.ipynb is for computing quantitative retrieval metrics
  • diffusion/CLIP_Guided_Stable_Diffusion.ipynb is for lidar-to-image generation
  • generate_captions.ipynb is for lidar captioning. Note that you need to train LidarCLIP against ViT-B/32 (instead of the default which is ViT-L/14).

Note that some paths have to be modified in the notebooks to point your desired dataset and cached features.

Dataset preparation

We refer to the official download and preparation instructions for ONCE and NuScenes. For ONCE, you'll also need the ImageSets folder from the ONCE devkit. Once the dataset directories are set up according to the official instructions, our code works without any additional steps. Note that NuScenes is entirely optional and only used for evaluating domain shift capabilities.

Citation

If you find this work useful, please cite

@inproceedings{lidarclip2024,
  title={LidarCLIP or: How I Learned to Talk to Point Clouds},
  author={Hess, Georg and Tonderski, Adam and Petersson, Christoffer and {\AA}str{\"o}m, Kalle} and Svensson, Lennart,
  year = {2024},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
}