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
In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature learning.Our key observation is that to recover the underlying structures as well as surface details,given part...In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature learning.Our key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric details.We accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions.We then integrate FSNet into a coarse-to-fine pipeline for point cloud completion.Specifically,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud.Next,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output.To efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud.We have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.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.
基金This work was supported by the National Natural Science Foundation of China(61872250,U2001206,U21B2023)the GD Natural Science Foundation(2021B1515020085)+2 种基金DEGP Innovation Team(2022KCXTD025)Shenzhen Science and Technology Innovation Program(JCYJ20210324120213036)Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ).
文摘In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature learning.Our key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric details.We accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions.We then integrate FSNet into a coarse-to-fine pipeline for point cloud completion.Specifically,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud.Next,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output.To efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud.We have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.
