Weakly Supervised 3D Object Detection

Weakly supervised 3D object detection aims to train accurate 3D object detectors using significantly less annotation effort than traditional fully supervised methods. Current research focuses on leveraging limited annotations, such as coarse clicks, 2D bounding boxes, or even just object center points, to generate pseudo-labels or guide the training process, often employing techniques like multi-view consistency, shape priors from large language models, or attention mechanisms to improve feature representation and address the inherent ambiguity of weak supervision. This research is significant because it drastically reduces the cost and time associated with creating large-scale 3D datasets, enabling the development of more robust and widely applicable 3D perception systems for applications such as autonomous driving and robotics.