MonoDAPE: Data Augmentation and Perception Enhancement for Robust Monocular 3D Object Detection

Monocular 3D object detection has attracted attention due to its cost-effectiveness and ease of deployment. To improve detection performance, current methods commonly adopt data augmentation strategies to increase sample diversity. However, these approaches often rely on additional data or external models, and may introduce noise interference. Meanwhile, most methods neglect the widespread occlusion problem in real world scenarios, which significantly restricts further progress in detection performance. To address these challenges, we propose MonoDAPE, a monocular 3D object detection framework that integrates a Data Augmentation strategy named Cut-Clone3D and an occlusion Perception Enhancement (OPE) module. Specifically, Cut-Clone3D enriches sample and object distributions by seamlessly transplanting annotated objects into novel 3D scenes while strictly adhering to geometric projection constraints. The OPE module simulates realistic occlusion by generating random masks according to occlusion priors and substituting features of non-occluded objects with those from occluding objects, ensuring the continuity and asymmetry of occlusion. Moreover, we introduce a depth consistency loss that adjusts the distance relationship between mutually occluded objects, further improving prediction robustness. We conducted extensive experiments on the KITTI dataset to validate the effectiveness of our approach. On the KITTI test set, MonoDAPE achieved AP3D (IoU = 0.7, Car) of 27.24%, 20.41%, and 17.52% at the easy, moderate, and hard levels, respectively, demonstrating state of-the-art performance.