Automatic Point Cloud Registration for Large Outdoor Scenes Using a Priori Semantic Information

As an important and fundamental step in 3D reconstruction, point cloud registration aims to find rigid transformations that register two point sets. The major challenge in point cloud registration techniques is finding correct correspondences in the scenes that may contain many repetitive structures...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 13; no. 17; p. 3474
Main Authors Li, Jian, Huang, Shuowen, Cui, Hao, Ma, Yurong, Chen, Xiaolong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
Subjects
Algorithms
Artificial neural networks
Correspondence
Deep learning
Evaluation
feature extraction
Image reconstruction
Methods
Neighborhoods
Neural networks
Noise
Pipelines
point cloud registration
Registration
Repetitive structures
Segments
Semantic segmentation
Semantics
terrestrial laser scanning
Three dimensional models
Transformations
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Summary:As an important and fundamental step in 3D reconstruction, point cloud registration aims to find rigid transformations that register two point sets. The major challenge in point cloud registration techniques is finding correct correspondences in the scenes that may contain many repetitive structures and noise. This paper is primarily concerned with improving registration using a priori semantic information in the search for correspondences. In particular, we present a new point cloud registration pipeline for large, outdoor scenes that takes advantage of semantic segmentation. Our method consisted of extracting semantic segments from point clouds using an efficient deep neural network, then detecting the key points of the point cloud and using a feature descriptor to get the initial correspondence set, and, finally, applying a Random Sample Consensus (RANSAC) strategy to estimate the transformations that align segments with the same labels. Instead of using all points to estimate a global alignment, our method aligned two point clouds using transformations calculated by each segment with the highest inlier ratio. We evaluated our method on the publicly available Whu-TLS registration data set. These experiments demonstrate how a priori semantic information improves registration in terms of precision and speed.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13173474