Three-Dimensional Point Cloud Segmentation Algorithm Based on Depth Camera for Large Size Model Point Cloud Unsupervised Class Segmentation.

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.

Method for measuring the center of mass and moment of inertia of a model using 3D point clouds.

We propose a method for measuring the center of mass and moment of inertia of a model using 3D point clouds. First, we use point cloud registration and segmentation to obtain point clouds of model parts with different material densities. Then, we correct the point cloud coordinates by principal component analysis, and we perform 2.