Selective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detection
Monocular 3D object detection is a promising yet ill-posed task for autonomous vehicles due to the lack of accurate depth information. Cross-modality knowledge distillation could effectively transfer depth information from LiDAR to image-based network. However, modality gap between image and LiDAR seriously limits its accuracy. In this paper, we systematically investigate the negative transfer problem induced by modality gap in cross-modality distillation for the first time, including not only the architecture inconsistency issue but more importantly the feature overfitting issue. We propose a selective learning approach named MonoSTL to overcome these issues, which encourages positive tr...
| Authors | Rui Ding, Meng Yang, Nanning Zheng |
|---|---|
| Primary category | cs.CV |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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