Point Cloud Analysis在逆向工程中通过测量仪器得到的产品外观表面的点数据集合称之为点云
Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao,Shimin Hu
AAAI, pp.11596-11603, (2020)
We have presented a novel approach for point cloud completion, which completes the partial point cloud in two stages
Cited by23BibtexViews140Links
0
0
CVPR, pp.6968-6979, (2020)
While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete
Cited by9BibtexViews33Links
0
0
Zhe Liu, Xin Zhao,Tengteng Huang, Hu Ruolan, Yu Zhou,Xiang Bai
AAAI, pp.11677-11684, (2020)
This paper proposes a novel Triple Attention Network for 3D object detection in point clouds, especially for noising point clouds
Cited by4BibtexViews30Links
0
0
CVPR, pp.4403-4412, (2020)
In this work we have explored how image data can assist a voting-based 3D detection pipeline
Cited by3BibtexViews103Links
0
0
Mingye Xu, Zhipeng Zhou,Yu Qiao
AAAI, pp.12500-12507, (2020)
Experiments have shown that Geometry Sharing Network achieves the state-of-the-art performance and has robustness to geometric transformations
Cited by2BibtexViews17Links
0
0
CVPR, pp.1756-1766, (2020)
Research on local descriptors for pairwise registration of 3D point clouds is centered on deep learning approaches that succeed in capturing and encoding evidence hidden to hand-engineered descriptors
Cited by2BibtexViews70Links
0
0
CVPR, pp.5660-5669, (2020)
We propose Grid-graph convolutional networks for fast and scalable point cloud learning
Cited by1BibtexViews21Links
0
0
CVPR, pp.6377-6386, (2020)
We presented PointAugment, the first auto-augmentation framework that we are aware of for 3D point clouds, considering both the capability of the classification network and the complexity of the training samples
Cited by1BibtexViews48Links
0
0
CVPR, pp.11363-11371, (2020)
We propose a feature-metric framework to solve the point cloud registration, and the framework can be trained using a semi-supervised or unsupervised manner
Cited by0BibtexViews37Links
0
0
Xie Qian,Lai Yu-Kun,Wu Jing, Wang Zhoutao, Zhang Yiming,Xu Kai,Wang Jun
CVPR, pp.10444-10453, (2020)
It is even difficult for humans to recognize the objects in the scene when merely a 3D point cloud is given without any color information
Cited by0BibtexViews74Links
0
0
Huang Zitian, Yu Yikuan, Xu Jiawen,Ni Feng,Le Xinyi
CVPR, pp.7659-7667, (2020)
Single-resolution Multi-Layer Perception consists of 5 layers Multi-Layer Perception and 2 linear layers, each layer followed by batch normalization and ReLU activation
Cited by0BibtexViews14Links
0
0
Wenkai Han,Chenglu Wen,Cheng Wang, Xin Li, Qing Li
AAAI, pp.10925-10932, (2020)
We present a novel framework, Point2Node, for correlation learning among 3D points
Cited by0BibtexViews15Links
0
0
CVPR, pp.13823-13832, (2020)
We present a convex mixed-integer programming formulation for non-rigid shape matching
Cited by0BibtexViews34Links
0
0
european conference on computer vision, pp.574-591, (2020)
We have demonstrated an extensive evaluation of the transferability of learned representations in 3D point clouds to high-level 3D understanding tasks
Cited by0BibtexViews69Links
0
0
Urbach Dahlia, Ben-Shabat Yizhak, Lindenbaum Michael
european conference on computer vision, pp.545-560, (2020)
The Deep Point Cloud Distance method is based on estimating the distances of points from one cloud to the underlying continuous surface corresponding to the other point cloud
Cited by0BibtexViews5Links
0
0
Ben-Shabat Yizhak,Gould Stephen
european conference on computer vision, pp.20-34, (2020)
In this paper we presented a novel method for deep surface fitting for unstructured 3D point clouds
Cited by0BibtexViews10Links
0
0
Haojie Liu, Kang Liao,Chunyu Lin,Yao Zhao,Meiqin Liu
Sensors, no. 6 (2020): 1573
The bidirectional optical flow explicitly guides consecutive sparse depth maps to generate an intermediate depth map, which is further improved by the warping layer
Cited by0BibtexViews13Links
0
0
CVPR 2020, (2019)
This is the first dataset collected from an AV approved for testing on public roads and that contains the full 360◦ sensor suite. nuScenes has the largest collection of 3D box annotations of any previously released dataset
Cited by192BibtexViews59Links
0
0
CVPR, (2019): 770-779
The stage-2 network refines the proposals in the canonical coordinate by combining semantic features and local spatial features
Cited by186BibtexViews69Links
0
0
CVPR, (2019): 12697-12705
We introduce PointPillars, a novel deep network and encoder that can be trained end-to-end on lidar point clouds
Cited by175BibtexViews26Links
0
0
Keywords
Point CloudUnsupervised LearningDeep LearningGenerative Adversarial Network3D Point Cloud3d Shape AnalysisAuto-encoderAutoencoderBackbone NetworkConvolutional Neural Networks
Authors
Leonidas J. Guibas
Paper 11
Birdal Tolga
Paper 7
Hao Su
Paper 7
Chi-Wing Fu
Paper 7
Raquel Urtasun
Paper 6
Niloy J. Mitra
Paper 4
Xianzhi Li
Paper 4
Xiaoyong Shen
Paper 4
Shu Liu
Paper 4
Pheng Ann Heng
Paper 4