Point cloud
Related papers: 20
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A point cloud is a collection of discrete data points in three-dimensional space, where each point represents a sampled location on a surface, typically described by its X, Y, Z coordinates and optionally additional attributes such as color or intensity. Point clouds are generated by sensors like LiDAR, depth cameras (RGB-D), and stereo vision systems, which measure the geometry of physical environments by emitting signals and recording returns. In robotics and AI, point clouds serve as a foundational data structure for tasks including 3D object detection and recognition, simultaneous localization and mapping (SLAM), robot navigation, grasp planning, pose estimation, and scene segmentation. Algorithms such as Iterative Closest Point (ICP) registration, voxel-based convolutional networks, and deep learning architectures like PointNet process these representations to extract meaningful geometric understanding from raw sensor data. Point clouds matter because they provide rich, metric 3D information about the world that 2D images cannot fully capture. As autonomous vehicles, industrial robots, and service robots increasingly operate in complex, unstructured environments, the ability to accurately perceive and interpret spatial geometry becomes essential for safe, reliable decision-making and interaction.
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Top Cited Papers
3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu, Steve Cousins
Citations: 4825 • 2011
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou, Oncel Tuzel
Citations: 4542 • 2018
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana, Sebastian Scherer
Citations: 3579 • 2015
SECOND: Sparsely Embedded Convolutional Detection
Yan Yan, Yuxing Mao, Bo Li
Citations: 3212 • 2018
Deep Learning for 3D Point Clouds: A Survey
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, Mohammed Bennamoun
Citations: 2225 • 2020
Argoverse: 3D Tracking and Forecasting With Rich Maps
Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Sławomir Bąk, Andrew T. Hartnett, Wang De, Peter Carr, Simon Lucey, Deva Ramanan, James Hays
Citations: 1420 • 2019
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Pablo Aparicio, Ken Goldberg
Citations: 1162 • 2017
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, Silvio Savarese
Citations: 1121 • 2019
StereoScan: Dense 3d reconstruction in real-time
Andreas Geiger, Julius Ziegler, Christoph Stiller
Citations: 1102 • 2011
Towards 3D Point cloud based object maps for household environments
Radu Bogdan Rusu, Zoltán-Csaba Márton, Nico Blodow, Mihai Dolha, Michael Beetz
Citations: 1078 • 2008
Deep Closest Point: Learning Representations for Point Cloud Registration
Yue Wang, Justin Solomon
Citations: 1008 • 2019
PCN: Point Completion Network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert
Citations: 955 • 2018
Fast 3D recognition and pose using the Viewpoint Feature Histogram
Radu Bogdan Rusu, Gary Bradski, R. Thibaux, JJ Hsu
Citations: 859 • 2010
Scan registration for autonomous mining vehicles using 3D‐NDT
Martin Magnusson, Achim J. Lilienthal, Tom Duckett
Citations: 767 • 2007
A Review of Point Cloud Registration Algorithms for Mobile Robotics
François Pomerleau, Francis Colas, Roland Siegwart
Citations: 686 • 2015
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Feng Lu, Evangelos Milios
Citations: 672 • 1997
Sparse Iterative Closest Point
Sofien Bouaziz, Andrea Tagliasacchi, Mark V. Pauly
Citations: 538 • 2013
Nerfstudio: A Modular Framework for Neural Radiance Field Development
Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, Justin Kerr, Angjoo Kanazawa
Citations: 528 • 2023
FlowNet3D: Learning Scene Flow in 3D Point Clouds
Xingyu Liu, Charles R. Qi, Leonidas Guibas
Citations: 517 • 2019
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
Xinshuo Weng, Jianren Wang, David Held, Kris Kitani
Citations: 486 • 2020