论文标题
边界：通过本地邻里统计数据在3D点云中的神经边界和边缘检测
BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics
论文作者
论文摘要
从3D点云中提取高级结构信息是具有挑战性的，但对于诸如城市规划或自动驾驶的任务，需要对现场的现场进行深入了解至关重要。现有方法仍然无法始终如一地产生高质量的结果，同时又足够快地部署在需要交互性的情况下。我们建议利用一组新型的功能，通过第一阶和二阶统计信息以每点描述本地邻域，作为简单而紧凑的分类网络的输入，以区分给定数据中的非边缘，尖端和边界点。利用此功能嵌入，使我们的算法在质量和处理时间方面胜过最先进的技术。
Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios requiring interactivity. We propose to utilize a novel set of features describing the local neighborhood on a per-point basis via first and second order statistics as input for a simple and compact classification network to distinguish between non-edge, sharp-edge, and boundary points in the given data. Leveraging this feature embedding enables our algorithm to outperform the state-of-the-art techniques in terms of quality and processing time.
