Point cloud completion aims to infer complete point clouds based on partial 3D point cloud inputs.Various previous methods apply coarseto-fine strategy networks for generating complete point clouds.However,such method...Point cloud completion aims to infer complete point clouds based on partial 3D point cloud inputs.Various previous methods apply coarseto-fine strategy networks for generating complete point clouds.However,such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial inputs.In this paper,a novel feature alignment fast point cloud completion network(FACNet)is proposed to directly and efficiently generate the detailed shapes of objects.FACNet aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete shape.During its decoding process,the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point cloud.Experimental results show that FACNet outperforms the state-of-theart on PCN,Completion3D,and MVP datasets,and achieves competitive performance on ShapeNet-55 and KITTI datasets.Moreover,FACNet and a simplified version,FACNet-slight,achieve a significant speedup of 3–10 times over other state-of-the-art methods.展开更多
我国煤层渗透率低且地质条件复杂,采用常规油气储层改造的开发方式难度大、技术适应性差。近年来,基于应力释放的煤层气改造新方法“煤层气水平井水力喷射造穴”很好地解决了这一技术瓶颈问题,但是造穴卸压—增渗的作用机制及其主控地...我国煤层渗透率低且地质条件复杂,采用常规油气储层改造的开发方式难度大、技术适应性差。近年来,基于应力释放的煤层气改造新方法“煤层气水平井水力喷射造穴”很好地解决了这一技术瓶颈问题,但是造穴卸压—增渗的作用机制及其主控地质因素尚不明晰。为此,考虑了煤岩层理和天然裂隙的影响,采用有限元—离散元耦合方法(Finite-Discrete Element Method,FDEM)建立了煤层气水平井扇形洞穴完井数值模型,探究了造穴后岩体的应力演化历程和储层的卸压—增渗机制,并对比分析了不同储层参数(孔隙压缩系数、储层强度、弱面强度和地应力场)对应力释放的影响规律。研究结果表明:(1)围岩演化过程为造穴后岩体收缩,储层发生应力重构,围岩强度逐渐降低,岩体内部发生新生裂隙萌生和原生裂隙扩展,形成开挖损伤区和应力释放区;(2)参数敏感性分析表明孔隙压缩系数是决定造穴完井储层适应性的关键,弱面强度、储层强度和地应力场分布决定了围岩的应力演化模式和裂缝扩展形态;(3)造穴卸压后储层增渗机制为穴周裂缝提升导流能力,储层应力释放提升基质渗透率。结论认为,模型首次综合考虑了地层特点、造穴过程和煤岩裂隙的影响,研究结果揭示了煤层造穴后的应力演化过程及其卸压、增渗作用机制,深化了对煤层气水平井洞穴完井增产机理的认识,对我国煤层储层改造具有重要的工程参考价值。展开更多
We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and no...We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper: (i) A generic prior model that prefers minimal global shape transformation (including non-rigid deformation and affine transformation with respect to a reference object shape) of the recovered complete shape; and (ii) a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.展开更多
基金supported by the Zhuhai Industry-University-Research Project(No.2220004002411)National Key R&D Program of China(No.2021YFE0205700)+3 种基金Science and Technology Development Fund of Macao(Nos.0070/2020/AMJ,00123/2022/A3,and 0096/2023/RIA2)Zhuhai City Polytechnic Research Project(No.2024KYBS02)Shenzhen Science and Technology Innovation Committee(No.SGDX20220530111001006)the University of Macao under Grants MYRG(Nos.GRG2023-00061-FST UMDF and 2022-00084-FST)。
文摘Point cloud completion aims to infer complete point clouds based on partial 3D point cloud inputs.Various previous methods apply coarseto-fine strategy networks for generating complete point clouds.However,such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial inputs.In this paper,a novel feature alignment fast point cloud completion network(FACNet)is proposed to directly and efficiently generate the detailed shapes of objects.FACNet aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete shape.During its decoding process,the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point cloud.Experimental results show that FACNet outperforms the state-of-theart on PCN,Completion3D,and MVP datasets,and achieves competitive performance on ShapeNet-55 and KITTI datasets.Moreover,FACNet and a simplified version,FACNet-slight,achieve a significant speedup of 3–10 times over other state-of-the-art methods.
文摘我国煤层渗透率低且地质条件复杂,采用常规油气储层改造的开发方式难度大、技术适应性差。近年来,基于应力释放的煤层气改造新方法“煤层气水平井水力喷射造穴”很好地解决了这一技术瓶颈问题,但是造穴卸压—增渗的作用机制及其主控地质因素尚不明晰。为此,考虑了煤岩层理和天然裂隙的影响,采用有限元—离散元耦合方法(Finite-Discrete Element Method,FDEM)建立了煤层气水平井扇形洞穴完井数值模型,探究了造穴后岩体的应力演化历程和储层的卸压—增渗机制,并对比分析了不同储层参数(孔隙压缩系数、储层强度、弱面强度和地应力场)对应力释放的影响规律。研究结果表明:(1)围岩演化过程为造穴后岩体收缩,储层发生应力重构,围岩强度逐渐降低,岩体内部发生新生裂隙萌生和原生裂隙扩展,形成开挖损伤区和应力释放区;(2)参数敏感性分析表明孔隙压缩系数是决定造穴完井储层适应性的关键,弱面强度、储层强度和地应力场分布决定了围岩的应力演化模式和裂缝扩展形态;(3)造穴卸压后储层增渗机制为穴周裂缝提升导流能力,储层应力释放提升基质渗透率。结论认为,模型首次综合考虑了地层特点、造穴过程和煤岩裂隙的影响,研究结果揭示了煤层造穴后的应力演化过程及其卸压、增渗作用机制,深化了对煤层气水平井洞穴完井增产机理的认识,对我国煤层储层改造具有重要的工程参考价值。
基金supported by the National Basic Research Program of China (2009CB320904)the National Natural Science Foundation of China (61103087,61121002 and 61272027)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry
文摘We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper: (i) A generic prior model that prefers minimal global shape transformation (including non-rigid deformation and affine transformation with respect to a reference object shape) of the recovered complete shape; and (ii) a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.
