  Recent works in object detection in LiDAR point clouds mostly focus on
predicting bounding boxes around objects. This prediction is commonly achieved
using anchor-based or anchor-free detectors that predict bounding boxes,
requiring significant explicit prior knowledge about the objects to work
properly. To remedy these limitations, we propose MaskBEV, a bird's-eye view
(BEV) mask-based object detector neural architecture. MaskBEV predicts a set of
BEV 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 of
classification, doing away with regression usually done to predict bounding
boxes. We evaluate the performance of MaskBEV on both SemanticKITTI and KITTI
datasets while analyzing the architecture advantages and limitations.
