论文标题
3D级联RCNN：高质量的对象检测点云
3D Cascade RCNN: High Quality Object Detection in Point Clouds
论文作者
论文摘要
2D对象检测的最新进展以CASCADE RCNN为特征，该级联rcnn利用了一系列级联检测器，以逐步提高提议质量，以提高高质量对象检测。但是，没有证据支持建造3D对象检测的这种级联结构，这是一种充满稀疏的LiDAR点云的挑战性检测方案。在这项工作中，我们提出了一个简单而有效的级联体系结构，名为3D Cascade RCNN，该体系结构在级联范式中根据体素化点云分配了多个检测器，逐渐追求更高质量的3D对象检测器。此外，我们定量定义每个对象的3D边界框中的点的稀疏度作为点完整性得分，该分数被用作每个建议的任务权重，以指导每个阶段检测器的学习。背后的精神是为具有相对完整的点分布的高质量提案分配更高的权重，而在训练期间经常会引起噪音的非常稀疏点的提议下降。这种完整性意识重新加权的设计优雅地升级了级联范式，使其更适用于稀疏输入数据，而无需增加任何FLOP预算。通过对KITTI数据集和Waymo Open数据集进行的广泛实验，我们在与最新的3D对象检测技术相比时验证了我们提出的3D Cascade RCNN的优越性。源代码可在\ url {https://github.com/caiqi/cascasde-3d}上公开获得。
Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection. However, there has not been evidence in support of building such cascade structures for 3D object detection, a challenging detection scenario with highly sparse LiDAR point clouds. In this work, we present a simple yet effective cascade architecture, named 3D Cascade RCNN, that allocates multiple detectors based on the voxelized point clouds in a cascade paradigm, pursuing higher quality 3D object detector progressively. Furthermore, we quantitatively define the sparsity level of the points within 3D bounding box of each object as the point completeness score, which is exploited as the task weight for each proposal to guide the learning of each stage detector. The spirit behind is to assign higher weights for high-quality proposals with relatively complete point distribution, while down-weight the proposals with extremely sparse points that often incur noise during training. This design of completeness-aware re-weighting elegantly upgrades the cascade paradigm to be better applicable for the sparse input data, without increasing any FLOP budgets. Through extensive experiments on both the KITTI dataset and Waymo Open Dataset, we validate the superiority of our proposed 3D Cascade RCNN, when comparing to state-of-the-art 3D object detection techniques. The source code is publicly available at \url{https://github.com/caiqi/Cascasde-3D}.
