ArticleSensors (Basel, Switzerland)2023
Three-Dimensional Point Cloud Segmentation Algorithm Based on Depth Camera for Large Size Model Point Cloud Unsupervised Class Segmentation.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Pareto Optimal Weight Learning and Gradient Anisotropic Supervoxel Segmentation for Thermo-Geometric Point Clouds.Sensors (Basel, Switzerland) · 2026Article
- A Global-Local Attention Model for 3D Point Cloud Segmentation in Intraoral Scanning: A Novel Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Tomato seedling stem and leaf segmentation method based on an improved ResNet architecture.Frontiers in plant science · 2025Article
- Adherent Peanut Image Segmentation Based on Multi-Modal Fusion.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
This paper proposes a 3D point cloud segmentation algorithm based on a depth camera for large-scale model point cloud unsupervised class segmentation. The algorithm utilizes depth information obtained from a depth camera and a voxelization technique to reduce the size of the point cloud, and then uses clustering methods to segment the voxels based on their density and distance to the camera. Experimental results show that the proposed algorithm achieves high segmentation accuracy and fast segmentation speed on various large-scale model point clouds. Compared with recent similar works, the algorithm demonstrates superior performance in terms of accuracy metrics, with an average Intersection over Union (IoU) of 90.2% on our own benchmark dataset.
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