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deep Learning-Based 3d Instance and Semantic segmentation: A Review

Journal on Artificial Intelligence 2022年第2期4卷 99-114页

作者： Siddiqui Muhammad Yasir Hyunsik Ahn department of Robot System Engineering Tongmyong UniversityBusan48520Korea School of Artificial Intelligence Tongmyong UniversityBusan48520Korea

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3d *** is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent *** research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and *** learning has been successfully used to a spectrum of 2d vision domains as a prevailing ***,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial *** study examines many strategies that have been presented to 3d instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3d *** these approaches’benefits,draw backs,and design mechanisms are studied and *** study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.

关键词： Artificial intelligence computer vision robot vision 3d instance segmentation 3d semantic segmentation 3d data deep learning point cloud mesh voxel rgb-d segmentation

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