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.

翻译：近期针对激光雷达点云中目标检测的研究主要聚焦于围绕目标预测边界框。这一预测通常借助基于锚点或无锚点的检测器实现，但其正常工作需依赖大量关于目标的显式先验知识。为解决上述局限性，我们提出MaskBEV——一种基于鸟瞰视图（BEV）掩码的目标检测神经网络架构。MaskBEV可预测一组表征检测目标足迹的BEV实例掩码，且能够在单次处理中同步实现目标检测与足迹补全。该架构将检测问题纯粹重构为分类任务，彻底摒弃了边界框预测中常用的回归方法。我们在SemanticKITTI与KITTI数据集上评估了MaskBEV的性能，并分析了该架构的优势与局限性。