2,234 research outputs found
Modified mass-spring system for physically based deformation modeling
Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented
Modified mass-spring system for physically based deformation modeling
Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented
Inexact Bayesian point pattern matching for linear transformations
PublishedArticleWe introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-to-one correspondence between the point sets and the presence of noise. The algorithm is itself inexact; we use variational Bayesian approximation to estimate the posterior distributions in the face of a problematic evidence term. The method turns out to be similar in structure to the iterative closest point algorithm.This work was supported by the University of Exeter’s Bridging the Gaps initiative, which was funded by EPSRC award EP/I001433/1 and the collaboration was formed through the Exeter Imaging Network
Batch-based Model Registration for Fast 3D Sherd Reconstruction
3D reconstruction techniques have widely been used for digital documentation
of archaeological fragments. However, efficient digital capture of fragments
remains as a challenge. In this work, we aim to develop a portable,
high-throughput, and accurate reconstruction system for efficient digitization
of fragments excavated in archaeological sites. To realize high-throughput
digitization of large numbers of objects, an effective strategy is to perform
scanning and reconstruction in batches. However, effective batch-based scanning
and reconstruction face two key challenges: 1) how to correlate partial scans
of the same object from multiple batch scans, and 2) how to register and
reconstruct complete models from partial scans that exhibit only small
overlaps. To tackle these two challenges, we develop a new batch-based matching
algorithm that pairs the front and back sides of the fragments, and a new
Bilateral Boundary ICP algorithm that can register partial scans sharing very
narrow overlapping regions. Extensive validation in labs and testing in
excavation sites demonstrate that these designs enable efficient batch-based
scanning for fragments. We show that such a batch-based scanning and
reconstruction pipeline can have immediate applications on digitizing sherds in
archaeological excavations. Our project page:
https://jiepengwang.github.io/FIRES/.Comment: Project page: https://jiepengwang.github.io/FIRES
KSS-ICP: Point Cloud Registration based on Kendall Shape Space
Point cloud registration is a popular topic which has been widely used in 3D
model reconstruction, location, and retrieval. In this paper, we propose a new
registration method, KSS-ICP, to address the rigid registration task in Kendall
shape space (KSS) with Iterative Closest Point (ICP). The KSS is a quotient
space that removes influences of translations, scales, and rotations for shape
feature-based analysis. Such influences can be concluded as the similarity
transformations that do not change the shape feature. The point cloud
representation in KSS is invariant to similarity transformations. We utilize
such property to design the KSS-ICP for point cloud registration. To tackle the
difficulty to achieve the KSS representation in general, the proposed KSS-ICP
formulates a practical solution that does not require complex feature analysis,
data training, and optimization. With a simple implementation, KSS-ICP achieves
more accurate registration from point clouds. It is robust to similarity
transformation, non-uniform density, noise, and defective parts. Experiments
show that KSS-ICP has better performance than the state of the art.Comment: 13 pages, 20 figure
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