论文标题
Panonet3D：结合Lidarpoint云检测的语义和几何理解
PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection
论文作者
论文摘要
自主驾驶感知中的视觉数据，例如摄像机图像和激光点云，可以解释为两个方面的混合：语义特征和几何结构。语义来自传感器对象的外观和上下文，而几何结构是点云的实际3D形状。 LIDAR点云上的大多数检测器仅着重于分析实际3D空间中对象的几何结构。与以前的作品不同，我们建议通过统一的多视框架同时学习语义功能和几何结构。我们的方法利用了LiDAR扫描的性质-2D范围图像，并应用了良好的2D卷积来提取语义特征。通过融合语义和几何特征，我们的方法在所有类别中都超过了最先进的方法。结合语义和几何特征的方法提供了一个独特的观点，即研究现实世界3D点云检测中的问题。
Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure. Semantics come from the appearance and context of objects to the sensor, while geometric structure is the actual 3D shape of point clouds. Most detectors on LiDAR point clouds focus only on analyzing the geometric structure of objects in real 3D space. Unlike previous works, we propose to learn both semantic feature and geometric structure via a unified multi-view framework. Our method exploits the nature of LiDAR scans -- 2D range images, and applies well-studied 2D convolutions to extract semantic features. By fusing semantic and geometric features, our method outperforms state-of-the-art approaches in all categories by a large margin. The methodology of combining semantic and geometric features provides a unique perspective of looking at the problems in real-world 3D point cloud detection.
