Mr. Tuo Yao Profile

Tuo Yao

at Shanghai Univ.

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Publications (2)

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SPIE Journal Paper | 17 May 2024

Multiscale region fusion algorithm for 3D plane segmentation

Qinghua Yang, Tuo Yao, Changfa Wang

JARS, Vol. 18, Issue 02, 026503, (May 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.026503

KEYWORDS: Point clouds, Image segmentation, Data modeling, Voxels, Data processing, 3D modeling, Roentgenium, Detection and tracking algorithms, Tunable filters, Feature fusion

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To address the problem of large point cloud data and low segmentation accuracy in indoor and outdoor scenes, we propose a point cloud plane segmentation algorithm based on multiscale region fusion. When using traditional region growing methods for point cloud plane segmentation, the segmentation results are often unstable, and adjacent planes are difficult to separate. In our method, we convert the point cloud data in space into a three-dimensional grid model, and then use the proposed tensor voting method for accurate normal vector estimation. By introducing a multiscale weighted similarity measure to optimize the seed point growing rules, we enhance the accuracy of plane segmentation. Specifically, the algorithm first performs custom grid downsampling and filtering on the original point cloud data. Then different region growing parameters are applied to segment the super-voxel point cloud data at each scale, resulting in multiple segmentation results. The tensor voting is used for estimating the normal vectors of the point cloud, which achieves good estimation performance even at sharp edges. Finally, weights are assigned to similar feature regions, and the similarity measure optimizes the seed point growing rules. With accurate normal vector estimation and appropriate seed point selection, the plane segmentation is more complete. The final segmentation result is obtained by edge preservation and refinement of multiple segmentation results. Experimental results on public and self-collected datasets demonstrate that our proposed method can effectively improve segmentation accuracy while ensuring the real-time performance.

Proceedings Article | 9 October 2022 Paper

Fast 3D object segmentation using DBSCAN clustering based on supervoxel

Tuo Yao, Qinghua Yang, Rui Zhang

Proceedings Volume 12246, 122461E (2022) https://doi.org/10.1117/12.2643697

KEYWORDS: Clouds, Image segmentation, Detection and tracking algorithms, Voltage controlled current source, Visualization, Machine learning, Data processing, Data modeling, 3D vision, 3D acquisition

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For visual perception of indoor mobile robots, real-time segmentation of 3D objects is a very challenging problem. Due to the complexity and disorder of indoor point cloud data, many methods have been proposed to improve the segmentation accuracy, which most of them cannot meet the requirement of rapidity. Therefore, this paper proposes a method for fast segmentation of 3D point clouds based on supervoxel density clustering. First, the ground under the indoor scene is removed by using plane fitting algorithm and the point cloud data after ground removal is denoised at the same time. Then, the target object is over-segmented to obtain the supervoxels with local geometric features. The core points of the nearest neighbors at the center of the supervoxels are searched out using the kdtree indexing acceleration. Finally, the density clustering of the supervoxels is performed based on the core point density up to. Experimental results on publicly available datasets show that our proposed method can effectively improve the speed of the algorithm while ensuring accuracy.

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