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.
Highlights
The point cloud is a universal spatial information acquisition format and plays an important role in indoor and outdoor environment understanding [1]
The point cloud segmentation procedure is composed of the following steps: (1) normal vector computation based on the initial tensor encoding. (2) tensor aggregation from the tensor encoded with normal information. (3) tensor feature decomposition and shape classification by tensor analysis. (4) point cloud segmentation according to the tensor clustering
Point cloud segmentation is the fundamental procedure for advanced point cloud applications, such as indoor/outdoor scene understanding; work has become more difficult in noisy sampling situations, where point cloud data are acquired by consumer electronic devices
Summary
The point cloud is a universal spatial information acquisition format and plays an important role in indoor and outdoor environment understanding [1]. With different kinds of Light Detection And Ranging (LiDAR) sensors included in consumer electronic devices, such as the iPhone or Kinect, it has become increasingly convenient to acquire point cloud data. It raised many challenges for data processing, due to the low accuracy and high noise of the point cloud data collected by these unprofessional LiDAR devices. Many kinds of point cloud segmentation methods were proposed that extract the feature in different ways, including deep learning-based approaches To deal with these kinds of problems in a universal framework, this work proposes a tensor feature-based point cloud segmentation method, and the point cloud data in an actual scene is obtained from the iPhone LiDAR sensor.
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