Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature

Abstract

With widely used LiDAR sensors included in consumer electronic devices, it is increasingly convenient to acquire point cloud data, but it is also difficult to segment the point cloud data obtained from these unprofessional LiDAR devices, due to their low accuracy and high noise. To address the issue, a point cloud segmentation method using the tensor feature is proposed. The normal vectors of the point cloud are computed based on initial tensor encoding, which are further encoded into the tensor of each point. Using the tensor from a nearby point, the tensor of the center point is aggregated in all dimensions from its neighborhood. Then, the tensor feature in the point is decomposed and different dimensional shape features are detected, and the point cloud dataset is segmented based on the clustering of the tensor feature. Using the point cloud dataset acquired from the iPhone-based LiDAR sensor, experiments were conducted, and results show that both normal vectors and tensors are computed, then the dataset is successfully segmented.