  Research connecting text and images has recently seen several breakthroughs,
with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection
between text and other visual modalities, such as lidar data, has received less
attention, prohibited by the lack of text-lidar datasets. In this work, we
propose LidarCLIP, a mapping from automotive point clouds to a pre-existing
CLIP embedding space. Using image-lidar pairs, we supervise a point cloud
encoder with the image CLIP embeddings, effectively relating text and lidar
data with the image domain as an intermediary. We show the effectiveness of
LidarCLIP by demonstrating that lidar-based retrieval is generally on par with
image-based retrieval, but with complementary strengths and weaknesses. By
combining image and lidar features, we improve upon both single-modality
methods and enable a targeted search for challenging detection scenarios under
adverse sensor conditions. We also explore zero-shot classification and show
that LidarCLIP outperforms existing attempts to use CLIP for point clouds by a
large margin. Finally, we leverage our compatibility with CLIP to explore a
range of applications, such as point cloud captioning and lidar-to-image
generation, without any additional training. Code and pre-trained models are
available at https://github.com/atonderski/lidarclip.
