Frame 3D Object Detection

Frame-based 3D object detection aims to accurately locate and classify three-dimensional objects within a scene using multiple image or point cloud frames. Current research heavily utilizes transformer-based architectures, often incorporating graph neural networks to model inter-object relationships and spatial-temporal dependencies across frames, and exploring techniques like self-supervised learning to reduce reliance on large annotated datasets. These advancements are crucial for improving the robustness and efficiency of 3D perception systems in applications such as autonomous driving and robotics, particularly in handling challenging scenarios with occlusions and motion blur.