论文标题
PV-RCNN：用于3D检测 / 3D跟踪 / Waymo打开数据集挑战的3D检测 / 3D跟踪 /域改编的最佳刺激性解决方案
PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges
论文作者
论文摘要
在这份技术报告中，我们介绍了3D检测，3D跟踪和域适应的最佳性激光雷达解决方案，Waymo Open DataSet挑战2020年的三个轨道。我们的比赛解决方案是基于我们最近提出的PV-RCNN 3D对象检测框架构建的。探索了我们的PV-RCNN的几种变体，包括时间信息融合，动态素素化，自适应训练样品选择，带有ROI功能的分类等。采用了非最大程度的抑制和盒子投票的简单模型集成策略，以生成最终结果。通过仅使用激光点云数据，我们的模型最终在所有仅激光雷达方法中获得了第一名，而在所有多模式方法中，在3D检测，3D跟踪和域适应Waymo Open DataSet挑战的三个轨道上，第2位。我们的解决方案将在https://github.com/open-mmlab/openpcdet上找到
In this technical report, we present the top-performing LiDAR-only solutions for 3D detection, 3D tracking and domain adaptation three tracks in Waymo Open Dataset Challenges 2020. Our solutions for the competition are built upon our recent proposed PV-RCNN 3D object detection framework. Several variants of our PV-RCNN are explored, including temporal information incorporation, dynamic voxelization, adaptive training sample selection, classification with RoI features, etc. A simple model ensemble strategy with non-maximum-suppression and box voting is adopted to generate the final results. By using only LiDAR point cloud data, our models finally achieve the 1st place among all LiDAR-only methods, and the 2nd place among all multi-modal methods, on the 3D Detection, 3D Tracking and Domain Adaptation three tracks of Waymo Open Dataset Challenges. Our solutions will be available at https://github.com/open-mmlab/OpenPCDet
