LidarCLIP or: How I Learned to Talk to Point Clouds (2212.06858v3)

Abstract: 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.

• The paper presents LidarCLIP, a novel approach that aligns automotive lidar point clouds with CLIP image embeddings using a dedicated point cloud encoder.
• It employs efficient training with mean squared error and cosine similarity losses on the ONCE dataset, achieving competitive performance and improved cyclist identification.
• The method enables joint lidar and image queries, opening avenues for multi-modal applications like scene captioning and enhanced AI-driven autonomous driving.

Exploring LidarCLIP: Bridging Text and Lidar Point Clouds

The paper "LidarCLIP or: How I Learned to Talk to Point Clouds" introduces a novel method named LidarCLIP, which aims to connect lidar point clouds with the CLIP (Contrastive Language–Image Pre-training) embedding space. This research addresses the gap between text and less-explored visual modalities such as lidar data, leveraging existing methodologies like CLIP that merge language and images.

Methodology

LidarCLIP is designed to map automotive lidar point cloud data into the CLIP embedding space, traditionally a domain for language and images. The authors achieve this by training a point cloud encoder using image-lidar pairings, supervising it specifically through CLIP image embeddings to bridge text and lidar data via the intermediary of images. They employ the ONCE dataset, a large-scale automotive dataset with simultaneous image and point cloud capture, demonstrating the viability of this approach in autonomous driving contexts.

The training methodology is straightforward yet effective; it involves aligning the point cloud data with the camera's perspective and directly mimicking the CLIP image representations using either mean squared error or cosine similarity as loss functions. This strategy eschews the more resource-intensive contrastive learning but maintains competitive performance, thus underscoring the model's efficiency.

Evaluation and Results

The effectiveness of LidarCLIP is validated through extensive experiments demonstrating its prowess in retrieval tasks and zero-shot classification. Notably, LidarCLIP outperforms existing methods in zero-shot classification within the point cloud domain, validating its ability to generalize CLIP's powerful semantic understanding to a new modality. In retrieval tasks, LidarCLIP shows comparable performance to image-only methods and, in some cases, like cyclist identification, even surpasses them, suggesting complementary strengths between the lidar and image modalities.

The research also explores joint retrieval capabilities, allowing queries to leverage both lidar and image data, leading to improved identification of scenarios that challenge single-modality systems. These capabilities are particularly advantageous in automotive settings, where safety-critical scenarios under adverse conditions are paramount.

Implications and Future Directions

LidarCLIP's implications are substantial, offering new avenues for research in the field of point cloud understanding and its applications in AI and autonomous systems. The model's compatibility with CLIP opens potential applications beyond retrieval and classification, such as scene captioning and lidar-to-image generation without additional training, effectively broadening the scope of generative AI applications.

The paper not only demonstrates LidarCLIP's current capabilities but also suggests future research directions, such as further exploration of multi-modal reasoning and extending CLIP embeddings to other underrepresented domains. This research holds the possibility of enriching machine understanding of complex, multi-modal data, prompting a reevaluation of CLIP's utility in non-image visual domains.

Conclusion

The authors of this paper present LidarCLIP as a substantive contribution to bridging the textual and visual data modalities with lidar point clouds, laying groundwork for future exploratory research at this intersection. They propose a practical solution to a longstanding challenge in computer vision—extending robust language-vision models to encompass 3D data—thereby enhancing the semantic integration between language, images, and point clouds. As this field continues to evolve, LidarCLIP stands as a stepping stone towards more sophisticated, multi-modal AI systems capable of operating seamlessly in diverse and complex environments.