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This paper addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2.5D representation approaches. We revisit the amodal 3D detectiondoi:10.1109/cvpr.2017.50 dblp:conf/cvpr/DengL17 fatcat:b7gxdyda4bauze2esq7fa5be5q