Efforts to connect LiDAR data with text, such as LidarCLIP, have primarily focused on embedding 3D point clouds into CLIP text-image space. However, these approaches rely on 3D point clouds, which present challenges in encoding efficiency and neural network processing. With the advent of advanced LiDAR sensors like Ouster OS1, which, in addition to 3D point clouds, produce fixed resolution depth, signal, and ambient panoramic 2D images, new opportunities emerge for LiDAR based tasks. In this work, we propose an alternative approach to connect LiDAR data with text by leveraging 2D imagery generated by the OS1 sensor instead of 3D point clouds. Using the Florence 2 large model in a zero-shot setting, we perform image captioning and object detection. Our experiments demonstrate that Florence 2 generates more informative captions and achieves superior performance in object detection tasks compared to existing methods like CLIP. By combining advanced LiDAR sensor data with a large pre-trained model, our approach provides a robust and accurate solution for challenging detection scenarios, including real-time applications requiring high accuracy and robustness.

It was clear when Ouster started developing the OS-1 three years ago that deep learning research for cameras was outpacing lidar research. Lidar data has incredible benefits -- rich spatial information and lighting agnostic sensing to name a couple -- but it lacks the raw resolution and efficient array structure of camera images, and 3D point clouds are still more difficult to encode in a neural net or process with hardware acceleration. With the tradeoffs between both sensing modalities in mind, we set out to bring the best aspects of lidars and cameras together in a single device from the very beginning. Today we're releasing a firmware upgrade and update to our open source driver that deliver on that goal. Our OS-1 lidar now outputs fixed resolution depth images, signal images, and ambient images in real time, all without a camera.