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1.华南理工大学 自动化科学与工程学院,广东 广州 510640
2.华南理工大学 软件学院,广东 广州 510006
3.中国科学院 力学研究所 国家微重力实验室,北京 100190
Received:10 September 2022,
Revised:14 October 2022,
Published:10 March 2023
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肖海鸿,吴秋遐,李玉琼等.三维补全关键技术研究综述[J].光学精密工程,2023,31(05):667-696.
XIAO Haihong,WU Qiuxia,LI Yuqiong,et al.Key techniques for three-dimensional completion: a review[J].Optics and Precision Engineering,2023,31(05):667-696.
肖海鸿,吴秋遐,李玉琼等.三维补全关键技术研究综述[J].光学精密工程,2023,31(05):667-696. DOI: 10.37188/OPE.20233105.0667.
XIAO Haihong,WU Qiuxia,LI Yuqiong,et al.Key techniques for three-dimensional completion: a review[J].Optics and Precision Engineering,2023,31(05):667-696. DOI: 10.37188/OPE.20233105.0667.
从部分观测信息中推断出完整三维形状与语义场景信息对自动驾驶、机器人视觉、元宇宙生态体系构建等而言是至关重要的,因此,主要围绕三维形状补全、三维场景补全和三维语义场景补全任务而展开的三维补全技术被广泛研究。本文围绕上述三维补全任务,对近年来的相关研究工作进行了系统性的分析和总结。首先,针对三维形状补全任务,对基于传统方法的形状补全和基于深度学习的形状补全这两个方面的研究进展进行了综述。其次,针对三维场景补全任务,对基于模型拟合的场景补全和基于生成式的场景补全方法这两个方面的研究进展进行了综述。再次,针对三维语义场景补全任务,深入分析了场景补全和语义分割两大任务之间的耦合特性,并根据输入数据的不同类型,对基于深度图的语义场景补全方法、基于深度图联合彩色图像的语义场景补全方法、以及基于点云的语义场景补全方法这三个方面的研究进展进行了综述。最后,对三维补全任务目前面临的主要问题及未来发展趋势进行了分析和展望,旨在为三维视觉中这一新兴领域的相关研究者提供一些有益的参考。
The inference of complete three-dimensional (3D) shape and semantic scene information from partial observations is crucial for various applications, such as autonomous driving, robotic vision, and metaverse ecosystem construction. Research on 3D completion has primarily focused on 3D-shape, 3D-scene, and 3D-semantic scene completion. In this paper, we systematically summarize and analyze recent relevant studies concerning these 3D completion tasks. First, for 3D-shape completion, the research progress is reviewed from two aspects: traditional shape completion and deep learning-based shape completion. Second, for 3D-scene completion, the research progress is reviewed from two aspects: the scene completion method based on model fitting and the scene completion method based on a generative approach. For 3D-semantic scene completion, the coupling characteristics between the two tasks of scene completion and semantic segmentation are analyzed, and the research progress is reviewed from three aspects: the depth map-based semantic scene completion method, the depth map-based semantic scene completion method with color images, and the point cloud-based semantic scene completion method, according to the different forms of input data. Finally, we analyze the current problems and future development trends of 3D completion tasks, aiming to provide a reference for related studies in this emerging field in 3D vision.
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