Stage 3D Object

Stage 3D object detection aims to accurately locate and classify three-dimensional objects within a scene, primarily using LiDAR point clouds or camera images, or a fusion of both. Current research emphasizes improving single-stage detectors through refined regression models, data augmentation techniques targeting low-quality objects, and innovative approaches like diffusion models for proposal refinement. These advancements focus on enhancing accuracy and efficiency, particularly addressing challenges such as sparsity in LiDAR data and the inherent ambiguity in monocular vision. The resulting improvements are crucial for applications like autonomous driving and robotics, where reliable 3D perception is paramount.