Monocular 3D object detection remains a challenging task due to the inherent limitations of single-view depth perception. While conventional methods treat all parts of the Region of Interest (RoI) uniformly, we argue that different RoI regions hold varying importance for accurate detection. This study introduces the Multi-Scale Grid Attention (MSGA) mechanism to investigate the significance of RoI regions at multiple scales. Furthermore, we propose a novel probabilistic post-processing method to enhance detection robustness by effectively utilizing the probabilistic properties of depth estimation during inference. Our approach achieves state-of-the-art performance on the KITTI and Waymo datasets, demonstrating significant improvements in detection accuracy and robustness.
Multi-Scale Grid Attention and Probabilistic Refinement for Accurate RoI-Based Monocular 3D Object Detection
IEEE Transactions on Intelligent Transportation Systems ; 26 , 6 ; 9109-9122
2025-06-01
2917902 byte
Article (Journal)
Electronic Resource
English
Change detection by probabilistic segmentation from monocular view
British Library Online Contents | 2014
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