论文标题
鹿角：非结构化，大小的点云数据的贝叶斯非线性张量学习和建模者
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data
论文作者
论文摘要
通过激光三角剖分或光检测和范围（LIDAR），在各种环境中越来越多地获取具有不同大小的非结构点云。根据非结构化点云预测标量响应是一个常见的问题，在各种应用中都会出现。当前的文献依赖于几个预处理步骤，例如结构化亚采样和特征提取来分析点云数据。这些技术导致量化伪像，并且在预处理过程中没有考虑回归响应与点云之间的关系。因此，我们提出了一般和整体的“贝叶斯非线性张量学习和建模者”（鹿角），以建模非结构化的，大小的点云数据与标量或多元响应的关系。所提出的鹿角同时优化了具有3D点云输入和标量或多元响应的非线性张量尺寸降低和非线性回归模型。鹿角有能力考虑复杂的数据表示，高维度和3D点云数据的不一致大小。
Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR). Predicting a scalar response based on unstructured point clouds is a common problem that arises in a wide variety of applications. The current literature relies on several pre-processing steps such as structured subsampling and feature extraction to analyze the point cloud data. Those techniques lead to quantization artifacts and do not consider the relationship between the regression response and the point cloud during pre-processing. Therefore, we propose a general and holistic "Bayesian Nonlinear Tensor Learning and Modeler" (ANTLER) to model the relationship of unstructured, varying-size point cloud data with a scalar or multivariate response. The proposed ANTLER simultaneously optimizes a nonlinear tensor dimensionality reduction and a nonlinear regression model with a 3D point cloud input and a scalar or multivariate response. ANTLER has the ability to consider the complex data representation, high-dimensionality,and inconsistent size of the 3D point cloud data.
