GRAMO:geometric resampling augmentation for monocular 3D object detection  

作　　者：He GUAN Chunfeng SONG Zhaoxiang ZHANG 

机构地区：[1]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China [2]Center for Research on Intelligent Perception and Computing,State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Frontiers of Computer Science》2024年第5期161-169,共9页计算机科学前沿（英文版）

基　　金：This work was supported in part by the National Key R&D Program of China(No.2022ZD0160102);the National Natural Science Foundation of China(Grant Nos.61836014,U21B2042,62072457,62006231).

摘　　要：Data augmentation is widely recognized as an effective means of bolstering model robustness.However,when applied to monocular 3D object detection,non-geometric image augmentation neglects the critical link between the image and physical space,resulting in the semantic collapse of the extended scene.To address this issue,we propose two geometric-level data augmentation operators named Geometric-Copy-Paste(Geo-CP)and Geometric-Crop-Shrink(Geo-CS).Both operators introduce geometric consistency based on the principle of perspective projection,complementing the options available for data augmentation in monocular 3D.Specifically,Geo-CP replicates local patches by reordering object depths to mitigate perspective occlusion conflicts,and Geo-CS re-crops local patches for simultaneous scaling of distance and scale to unify appearance and annotation.These operations ameliorate the problem of class imbalance in the monocular paradigm by increasing the quantity and distribution of geometrically consistent samples.Experiments demonstrate that our geometric-level augmentation operators effectively improve robustness and performance in the KITTI and Waymo monocular 3D detection benchmarks.

关 键 词：3D detection MONOCULAR augmentation GEOMETRY 

分 类 号：O17[理学—数学]