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Recent works in object detection in LiDAR point clouds mostly focus onpredicting bounding boxes around objects. This prediction is commonly achievedusing anchor-based or anchor-free detectors that predict bounding boxes,requiring significant explicit prior knowledge about the objects to workproperly. To remedy these limitations, we propose MaskBEV, a bird's-eye view(BEV) mask-based object detector neural architecture. MaskBEV predicts a set ofBEV instance masks that represent the footprints of detected objects. Moreover,our approach allows object detection and footprint completion in a single pass.MaskBEV also reformulates the detection problem purely in terms ofclassification, doing away with regression usually done to predict boundingboxes. We evaluate the performance of MaskBEV on both SemanticKITTI and KITTIdatasets while analyzing the architecture advantages and limitations.

MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Clouds