Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Shape Modeling and Analysis · 3D Surveying and Cultural Heritage · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Jiangsu Key Laboratory of Spectral Imaging & Intelligence Sense (SIIS), Nanjing University of Science and Technology, Nanjing 210094, China
Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Funding
National Natural Science Foundation of China
References (35)
Show source-provided references
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