Object detection and semantic segmentation with the 3D lidar point cloud data require expensive annotation. We propose a data augmentation method that takes advantage of already annotated data multiple times. We propose an augmentation framework that reuses real data, automatically finds suitable placements in the scene to be augmented, and handles occlusions explicitly. Due to the usage of the real data, the scan points of newly inserted objects in augmentation sustain the physical characteristics of the lidar, such as intensity and raydrop. The pipeline proves competitive in training top-performing models for 3D object detection and semantic segmentation. The new augmentation provides a significant performance gain in rare and essential classes, notably 6.65% average precision gain for "Hard" pedestrian class in KITTI object detection or 2.14 mean IoU gain in the SemanticKITTI segmentation challenge over the state of the art.
翻译:3D Lidar 点云数据中的天体探测和语义分割法需要昂贵的注释。 我们提议了一种数据增强方法,该方法可多次利用已经附加说明的数据。 我们提议了一个增强框架,用于再利用真实数据,自动发现适合的场景位置,并明确处理隔离。 由于使用真实数据, 增强中新插入的物体的扫描点保持了利达尔的物理特性, 如强度和射线滴。 管道证明在培训3D 对象探测和语义分割最佳模型方面具有竞争力。 新的增强提供了稀有和基本类别的显著性能增益, 特别是KITTI 对象探测中“ Hard” 行人类的平均精度增6.65%, 或者说2.14 表示在艺术状态的Smantic KITTI 分割挑战中IoU 增益。