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.

The World Health Organization, with support from the Qatar Ministry of Health, today launched the AI-powered WHO Digital Health Worker, Florence version 2.0, offering an innovative and interactive platform to share a myriad of health topics in seven languages at the World Innovation Summit for Health (WISH) in Qatar. Florence can share advice on mental health, give tips to destress, provide guidance on how to eat right, be more active, and quit tobacco and e-cigarettes. She can also offer information on COVID-19 vaccines and more. Florence 2.0 is now available in English with Arabic, French, Spanish, Chinese, Hindi and Russian to follow. Florence has helped fight misinformation around COVID-19 since the beginning of the pandemic.