A Method for Registration of 3-D Shapes

By P. Besl, Neil D. McKay. 1992

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Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Several approaches for whole-skeleton matching are based on the Iterative Closest Point (ICP) method [88], which can iteratively decide a rigid transformation such that the input query points fit to the points in the given model under this transformation.”
A Papier-Mache Approach to Learning 3D Surface Generation (2018, positive): “We randomly selected 260 shapes, 20 from each category, and ran the iterative closest point (ICP) algorithm [3] to optimize a similarity transform between PointSetGen and the target point cloud.”
Data-Driven Grasp Synthesis—A Survey (2013, methodology): “Ciocarlie et al. [58] propose a robust grasping pipeline in which known object models are ﬁtted to point cloud clusters using standard ICP [59].”
An Efficient Algorithm for Calculating the Exact Hausdorff Distance (2015, background): “, to evaluate the performance of registration [4], [5] and segmentation algorithms [19], [3], [15].”
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot (2015, methodology): “, using an Iterative Closest Point algorithm [9].”
Registration of point cloud data from a geometric optimization perspective (2004, methodology): “A popular method for aligning two point clouds is the Iterated Closest Point (ICP) algorithm [ BM92 , CM91].”
Real-time visual tracking of 3D objects with dynamic handling of occlusion (1997, methodology): “As p̂ usually cannot be uniquely determined, we use the approximation suggested in [1].”
6-DoF object pose from semantic keypoints (2017, methodology): “ca with the image, our method can provide a robust way to initialize the iterative closest point (ICP) algorithm [2].”
Boresight alignment method for mobile laser scanning systems (2010, methodology): “The consecutive scan data adjustment applies a modified ICP (Iterative Closest Point) algorithm (Besl and McKay 1992), varying roll, pitch and yaw angles iteratively reducing the mean square distance error s of all N pairs of corresponding planar surfaces, shown in equation (1), based on the project”
F-LOAM : Fast LiDAR Odometry and Mapping (2021, methodology): “For raw point cloud matching, the Iterative Closest Points (ICP) [7] method is the most classic method.”
KinectFaceDB: A Kinect Database for Face Recognition (2014, methodology): “, using the iterative closest point (ICP) method [27], [28]), which is considered as a more difficult problem in 2-D face recognition.”
Dense visual SLAM for RGB-D cameras (2013, background): “It can be shown that the depth error is equivalent to the formulation of point-to-plane ICP with projective lookup.”
A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching (2010, methodology): “8 In both cases, the canonical forms in R 3 were compared using ICP (while in the infinite dimensional case this is not needed for the diffusion embedding, since the embedding is unique and invariant, it helps in the finite and discrete scenario which provides only an approximation of the embedding)”
MNE software for processing MEG and EEG data (2013, background): “This initial approximation is thenautomaticallyre ﬁ nedbyusingtheiterativeclosest-pointalgorithm (Besl and McKay, 1992) which optimizes the transformation given the requirement that the digitized scalp surface points must be as close as possible to the MRI-de ﬁ ned scalp.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “In comparison, LiDAR-based localization heavily uses the iterative closest point algorithm, which is hard to parallelize [133], and thus may be difficult to offload.”