3D Shape Transfer Learning for Enhanced Monocular 3D Object Detection

Monocular 3D object detection (M3D) is challenging due to the lack of depth information in the RGB image. Existing works resort to various additional resources to enhance detection performance, including LiDAR data, depth information, CAD models, stereo images, and others, where strict correspondence or synchronization may limit their applicability and scalability. In this work, we propose a simple yet effective framework, 3D Shape Transfer Learning for Enhanced Monocular 3D Object Detection (STLM3D). It views M3D as 3D shape reconstruction and leverages transfer learning across datasets to enhance shape reconstruction capability, thereby enhancing M3D performance. In addition, we design a plug-and-play 3D detection branch for 3D attributes prediction. Experimental results on the KITTI benchmark demonstrate that the proposed method achieves state-of-the-art performance compared to existing approaches.