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Morphing and Sampling Network for Dense Point Cloud Completion [article]

Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, Shi-Min Hu
2019 arXiv   pre-print
For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point  ...  3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.  ...  Figure 1 : Our network predicts realistic structures from partial views and completes the point clouds evenly.  ... 
arXiv:1912.00280v1 fatcat:crhd5h2qnjderc66gz2owzknei

Morphing and Sampling Network for Dense Point Cloud Completion

Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, Shi-Min Hu
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point  ...  3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.  ...  Figure 1 : Our network predicts realistic structures from partial views and completes the point clouds evenly.  ... 
doi:10.1609/aaai.v34i07.6827 fatcat:tj7ffakx4jesrjuet4v4vc6gla

3D Face Morphing Attacks: Generation, Vulnerability and Detection [article]

Jag Mohan Singh, Raghavendra Ramachandra
2022 arXiv   pre-print
We then back-project the 2D morphing color-map and the depth-map to the point cloud using the canonical (fixed) view.  ...  The proposed method will generate the 3D face morphing by projecting the input 3D face point clouds to depth-maps \& 2D color images followed by the image blending and wrapping operations performed independently  ...  However, the 3D face morphing generation using point clouds introduces numerous challenges (a) Establishing a dense 3D correspondence between two different bona fide 3D point clouds that are to be morphed  ... 
arXiv:2201.03454v1 fatcat:523aqu4pg5eqldiguz7y4x3w3u

Data-driven Upsampling of Point Clouds [article]

Wentai Zhang, Haoliang Jiang, Zhangsihao Yang, Soji Yamakawa, Kenji Shimada, Levent Burak Kara
2018 arXiv   pre-print
We also explore the desirable characteristics of input point clouds as a function of the distribution of the point samples.  ...  High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.  ...  Figure 7 : 7 Sample results for shape morphing between the point clouds of a car and a boat. Figure B. 9 : 9 Ten randomly selected benches in the dataset.  ... 
arXiv:1807.02740v2 fatcat:ubkq3lzylje7lj2o35rpueb7je

3DMotion-Net: Learning Continuous Flow Function for 3D Motion Prediction [article]

Shuaihang Yuan, Xiang Li, Anthony Tzes, Yi Fang
2020 arXiv   pre-print
While in this paper we focus on predicting dense 3D motions in the from of 3D point clouds.  ...  future motions and naturally bring out the correspondences among consecutive point clouds at the same time.  ...  There are two main challenges for the task of dense 3D motions prediction from point clouds: 1) first, it's still a difficult task to learn robust and representative feature embeddings from 3D point clouds  ... 
arXiv:2006.13906v1 fatcat:mrguq5nbebhanl3dh25clgma6y

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis [article]

Ben Fei, Weidong Yang, Wenming Chen, Zhijun Li, Yikang Li, Tao Ma, Xing Hu, Lipeng Ma
2022 arXiv   pre-print
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision.  ...  The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion.  ...  [92] proposed an end-to-end generative adversarial network-based dense point cloud completion architecture (DPCG-Net).  ... 
arXiv:2203.03311v2 fatcat:e2kvryolufearetp4ujlw2gwwy

GASCN: Graph Attention Shape Completion Network [article]

Haojie Huang, Ziyi Yang, Robert Platt
2022 arXiv   pre-print
For each completed point, our model infers the normal and extent of the local surface patch which is used to produce dense yet precise shape completions.  ...  Shape completion, the problem of inferring the complete geometry of an object given a partial point cloud, is an important problem in robotics and computer vision.  ...  Morphing and sampling network for dense point pattern recognition, pages 1912–1920, 2015. [32] Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun.  ... 
arXiv:2201.07937v1 fatcat:pgtt4b6a4fcwliznobb46g5vpi

High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling [article]

Ren-Wu Li, Bo Wang, Chun-Peng Li, Ling-Xiao Zhang, Lin Gao
2021 arXiv   pre-print
After obtaining a sparse and complete point cloud, we propose a patch-wise upsampling strategy.  ...  Completing an unordered partial point cloud is a challenging task.  ...  ing and sampling network for dense point cloud completion.  ... 
arXiv:2112.11271v2 fatcat:rewnufojqraulkevufwz56nr7a

Accurate Point Cloud Registration with Robust Optimal Transport [article]

Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel Hernán Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer
2021 arXiv   pre-print
We also release PVT1010, a new public dataset of 1,010 pairs of lung vascular trees with densely sampled points.  ...  This dataset provides a challenging use case for point cloud registration algorithms with highly complex shapes and deformations.  ...  The authors would also like to thank the anonymous reviewers for their most valuable advice.  ... 
arXiv:2111.00648v1 fatcat:5mt5njhpzzhaji4uqseoafyla4

PointCutMix: Regularization Strategy for Point Cloud Classification [article]

Jinlai Zhang, Lyujie Chen, Bo Ouyang, Binbin Liu, Jihong Zhu, Yujing Chen, Yanmei Meng, Danfeng Wu
2021 arXiv   pre-print
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent.  ...  It finds the optimal assignment between two point clouds and generates new training data by replacing the points in one sample with their optimal assigned pairs.  ...  ., and Hu, S.-M. Morphing and sampling network for dense point cloud completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 11596-11603, 2020a.  ... 
arXiv:2101.01461v2 fatcat:pgpyx37jwbetxpb24k3l2aipne

Skeleton-guided 3D shape distance field metamorphosis

Bo Wu, Kai Xu, Yang Zhou, Yueshan Xiong, Hui Huang
2016 Graphical Models  
We introduce an automatic 3D shape morphing method without the need of manually placed anchor correspondence points.  ...  Given a source and a target shape, our approach extracts their skeletons and computes the meaningful anchor points based on their skeleton node correspondences.  ...  Acknowledgement We thank all the reviewers for their comments and feedback. We would also like to acknowl-  ... 
doi:10.1016/j.gmod.2016.03.003 fatcat:nocsfx656zg4taofenwtppgn6q

P2P-NET

Kangxue Yin, Hui Huang, Daniel Cohen-Or, Hao Zhang
2018 ACM Transactions on Graphics  
and complete scans, etc.  ...  The architecture of the P2P-NET is that of a bi-directional point displacement network, which transforms a source point set to a target point set with the same cardinality, and vice versa, by applying  ...  The completed point cloud is shown on the right.  ... 
doi:10.1145/3197517.3201288 fatcat:74pnbauqubdhvjywgbqa5xuljm

Social Snapshot: A System for Temporally Coupled Social Photography

R Patro, Cheuk Yiu Ip, S Bista, A Varshney
2011 IEEE Computer Graphics and Applications  
We construct locally optimized texture-mapped 2.5D dense point clouds and meshes for each image.  ...  For each spring particle, we find the closest projection of the visible 3D points from the dense point cloud that's within a radius k.  ... 
doi:10.1109/mcg.2010.107 pmid:24807972 fatcat:j7zsruejazhg3af4msellvlooe

Morfit

Kangxue Yin, Hui Huang, Hao Zhang, Minglun Gong, Daniel Cohen-Or, Baoquan Chen
2014 ACM Transactions on Graphics  
For surface completion, we introduce a novel skeleton-driven morph-to-fit, or morfit, scheme which reconstructs the shape as an ensemble of generalized cylinders.  ...  With significant data missing in a point scan, reconstructing a complete surface with sufficient geometric and topological fidelity is highly challenging.  ...  Acknowledgments The authors would like to thank all the reviewers for their valuable comments and feedback. This work is supported in part by grants from NSFC (61379090,  ... 
doi:10.1145/2661229.2661241 fatcat:obw7enc6m5erpidvplfcmss7te

Learning to Drop Points for LiDAR Scan Synthesis [article]

Kazuto Nakashima, Ryo Kurazume
2021 arXiv   pre-print
In this paper, we propose a novel generative model for learning LiDAR data based on generative adversarial networks.  ...  To simulate the lossy measurement, we adopt a differentiable sampling framework to drop points based on the learned uncertainty.  ...  Several studies [5, 6] proposed generative models that produce point clouds as a set of unordered points and demonstrated on data uniformly sampled from CAD models [7] In contrast, the point clouds  ... 
arXiv:2102.11952v2 fatcat:eh7dhydctvdrpbxctz3pilzngu
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