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This paper presents Multi-view Labelling Object Detector (MLOD). The detectortakes an RGB image and a LIDAR point cloud as input and follows the two-stageobject detection framework. A Region Proposal Network (RPN) generates 3Dproposals in a Bird's Eye View (BEV) projection of the point cloud. The secondstage projects the 3D proposal bounding boxes to the image and BEV feature mapsand sends the corresponding map crops to a detection header for classificationand bounding-box regression. Unlike other multi-view based methods, the croppedimage features are not directly fed to the detection header, but masked by thedepth information to filter out parts outside 3D bounding boxes. The fusion ofimage and BEV features is challenging, as they are derived from differentperspectives. We introduce a novel detection header, which provides detectionresults not just from fusion layer, but also from each sensor channel. Hencethe object detector can be trained on data labelled in different views to avoidthe degeneration of feature extractors. MLOD achieves state-of-the-artperformance on the KITTI 3D object detection benchmark. Most importantly, theevaluation shows that the new header architecture is effective in preventingimage feature extractor degeneration.

MLOD: A multi-view 3D object detection based on robust feature fusion method