GPU Acceleration of Robust Point Matching

Mourning, Chad; Nykl, Scott; Xu, Huihui; Chelberg, David; Liu, Jundong

doi:10.1007/978-3-642-17277-9_43

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each type can be further classified with different standards, such as optimization technique [27], displacement regularizer [13], probabilistic model [28], acceleration technique [29], and type of physical law [30]. Most researches, as far as we know, are either nearest-neighbor search based [9,14,31] or probabilistic based [32,33].…”

Section: Non-rigid Registrationmentioning

confidence: 99%

Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

Zhu¹,

Tian²,

Ye³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Non-rigid registration of point clouds is still far from stable, especially for the largely deformed one. Sparse initial correspondences are often adopted to facilitate the process. However, there are few studies on how to build them automatically. Therefore, in this paper, we propose a robust method to compute such priors automatically, where a global and local combined strategy is adopted. These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences. To further utilize the matches, this paper also proposes a novel registration method based on the Coherent Point Drift framework. This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations. Qualitative and quantitative experiments demonstrate the advantages of the proposed method.

show abstract

Section: Non-rigid Registrationmentioning

confidence: 99%

Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

Zhu¹,

Tian²,

Ye³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Since aircraft are rigid bodies, the point-to-point iterative closest point (ICP) [9] was chosen for this work. However, alternate methods such as parallel ICP [10], fast global registration [11], or a deep learning approach [12] may provide different performance and precision tradeoffs.…”

Section: B Six-degree-of-freedom Pose Estimationmentioning

confidence: 99%

Long-Range Pose Estimation for Aerial Refueling Approaches Using Deep Neural Networks

Lee

Dallmann

Nykl

et al. 2020

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

Automated aerial refueling (AAR) provides unique challenges for computer vision systems. Aerial refueling maneuvers require high-precision low-variance pose estimates. The performance of two stereoscopic (stereo) vision systems is quantified in ground tests specially designed to mimic AAR. In this experiment, three-dimensional (3-D) pose-estimation errors of 6 cm on a target 30 m from the current vision system are achieved. Next, a novel computer vision pipeline to efficiently generate a 3-D point cloud of the target object using stereo vision that leverages a convolutional neural network (CNN) is proposed. Using the proposed approach, a high-fidelity 3-D point cloud with ultra-high-resolution imagery 11.3 times faster than previous approaches can be generated.

show abstract