Delving into the Pre-training Paradigm of Monocular 3D Object Detection

Understand

The labels of monocular 3D object detection (M3OD) are expensive to obtain.

• Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is an efficient way of exploiting the knowledge in unlabeled data.
• However, the pre-training paradigm for M3OD is hardly studied.
• We aim to bridge this gap in this work.