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An Optimized Point Cloud Classification and Object Extraction Method Using Graph Cuts
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DOI：10.1109/ACCESS.2020.3030717.png)
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Abstract
En 中文
The requirement for 3D scene classification and understanding has dramatically increased with the widespread use of airborne Light Detection And Ranging (LiDAR). This paper focuses on precise classification and object extraction based on point cloud data in complex scenes. There are usually gross errors in the initial classification based on locally independent classifiers, due to the over- and under-clustering in the feature extraction. We introduce a graph-cut based method to improve the classification precision and eliminate errors by using contextual information. The intuition behind our algorithm is based on the fact that nearby points have a high probability of belonging to the same object, while the distances of points belonging to different objects will be large. Based on this assumption, objects of interest can be precisely extracted, and we can thus optimize the classification results. The experiments undertaken in this study proved that the classification method we propose can be effectively used for point cloud classification in complex scenes.
Keywords:
Three-dimensional displays
Optimization
Feature extraction
Data mining
Classification algorithms
Deep learning
Laser radar
LiDAR
point cloud classification
object extraction
graph cuts
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