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Visual data in autonomous driving perception, such as camera image and LiDARpoint cloud, can be interpreted as a mixture of two aspects: semantic featureand geometric structure. Semantics come from the appearance and context ofobjects to the sensor, while geometric structure is the actual 3D shape ofpoint clouds. Most detectors on LiDAR point clouds focus only on analyzing thegeometric structure of objects in real 3D space. Unlike previous works, wepropose to learn both semantic feature and geometric structure via a unifiedmulti-view framework. Our method exploits the nature of LiDAR scans -- 2D rangeimages, and applies well-studied 2D convolutions to extract semantic features.By fusing semantic and geometric features, our method outperformsstate-of-the-art approaches in all categories by a large margin. Themethodology of combining semantic and geometric features provides a uniqueperspective of looking at the problems in real-world 3D point cloud detection.

PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection