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基于分割一切模型SAM的实景三维场景语义分割

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基于深度学习和计算机视觉技术的场景语义分割是当前研究的热点.本文提出了包含"场景输入—预处理—模型推断—语义分割"的实景三维场景语义分割框架,通过将实景三维场景作为输入,按照正交投影的方式转为多视图二维图像,开展分割推理,获得分割掩码并进行处理,实现了实景三维对象拣选、单体化、语义化处理.试验结果表明,本文方法具有较好的语义分割效果和运行效率.
Semantic segmentation of 3D real scene based on segment anything model
Scene semantic segmentation based on deep learning and computer vision technology is currently a hot research topic.This paper proposes a 3D real scene semantic segmentation framework that includes"scene input-preprocessing-model inference-semantic segmentation".By transforming the 3D real scene as input into multi-view 2D images through orthogonal projection,segmentation inference is carried out,and segmentation masks are generated and further processed,achieving the object selection,singulation,and semantic processing of 3D real scene.The experiments show that the method has good semantic segmentation performance and efficiency.

segment anything model3D real scenesemantic segmentation

李锋、薛梅、詹勇、杨元

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重庆市测绘科学技术研究院,重庆 401120

自然资源部智能城市时空信息与装备工程技术创新中心,重庆 401120

重庆市勘测院智能城市空间技术创新中心,重庆 401120

分割一切模型 实景三维 语义分割

2024

测绘通报
测绘出版社

测绘通报

CSTPCD北大核心
影响因子:1.027
ISSN:0494-0911
年,卷(期):2024.(12)