Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection

Understand

State-of-the-art 3D object detectors are often trained on massive labeled datasets.

• However, annotating 3D bounding boxes remains prohibitively expensive and time-consuming, particularly for LiDAR.
• Instead, recent works demonstrate that self-supervised pre-training with unlabeled data can improve detection accuracy with limited labels.
• Contemporary methods adapt best-practices for self-supervised learning from the image domain to point clouds (such as contrastive learning).