Pseudo LiDAR

Pseudo-LiDAR generates dense, synthetic 3D point clouds from other sensor data, primarily images, to augment or replace sparse LiDAR data for applications in autonomous systems. Current research focuses on improving the accuracy and efficiency of pseudo-LiDAR generation, exploring various depth estimation techniques and neural network architectures, including those leveraging stereo vision and semantic segmentation. This approach aims to improve the performance of 3D object detection, scene flow estimation, visual odometry, and camera pose regression, offering a cost-effective alternative to relying solely on expensive and sometimes limited LiDAR sensors. The resulting advancements have significant implications for robotics and autonomous driving, enabling more robust and reliable perception capabilities.