Fully automatic registration of multiple 3D data sets

By Daniel F. Huber, Martial Hebert. Image and Vision Computing, 2003

421 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/b014c21af785a7444251dc26c1d8d7a4e4bcdf93

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In Aerospace Engineering

In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (2016, methodology): “Given multiple partial scans of an object, it can be applied to merge them into a complete 3D model [1], [2].”
Fast Global Registration (2016, methodology): “Joint global registration of multiple partially overlapping surfaces has also been considered [7, 20, 39].”
Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data (2011, methodology): “In our experiments, this registration was performed manually, but automated methods for solving this problem exist (Huber and Hebert, 2003).”
Vines and vineyards by updating persistence in linear time (2006, background): “Can we exploit this ability to gain a better understanding of the stability or instability of critical points? In particular, can this ability be developed into a global alignment algorithm for shapes that is more general and more reliable than what is currently available [11, 12]?”
A formalism for utilization of sensor systems and integrated project models for active construction quality control (2006, methodology): “Finally, automated methods based on computer-vision algorithms can be used to automatically register views without any knowledge of the scanner position or orientation [39].”
Continuous 3D scan-matching with a spinning 2D laser (2009, methodology): “Each error distribution is computed by running a sliding window of a specified length along the trajectories, aligning the trajectories at the beginning of the window and measuring the error at the end of the window.”
Data-driven grasping with partial sensor data (2009, background): “Many researchers have proposed alignment methods that find correspondences between local features [14], [16].”
Fully Automatic Registration of 3D Point Clouds (2006, background): “Many approaches dealing with scans with low overlap forgo global characteristics in favor of the extraction of lo-cal invariant features [27, 14, 12].”
Robust reconstruction of indoor scenes (2015, methodology): “Huber and Hebert [32] described an algorithm that rejects outliers by searching for a maximally consistent spanning tree in the pairwise relation graph; a similar technique has been used for reassembling fractured objects [30] .”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “Popular algorithms include Iterative Closest Point (ICP) [164], where each reference scan is matched with a query scan by aligning them according to a distance metric [165], and feature histogram based scan matching algorithms, such as Normal Distribution Transform (NDT) [166] and Spin Images [167].”
Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST (2010, methodology): “Although automated registration methods have been developed [43], in current practice, registration is still a semi-automated process.”
Recognizing Objects in Range Data Using Regional Point Descriptors (2004, background): “Spin-images are well-known 3D shape descriptors that have proven useful for object recognition [13], classification [20], and modeling [10].”
Homological illusions of persistence and stability (2008, background): “Can we exploit this ability to gain a better understanding of the stability or instability of critical points? In particular, can this ability be developed into a global alignment algorithm for shapes that is more general and more reliable than what is currently available [46, 55]?”