Pseudoconvex Proximal Splitting for L-infinity Problems in Multiview Geometry

2014 ◽  
Author(s):  
Anders Eriksson ◽  
Mats Isaksson
Keyword(s):  
Multiview Geometry

scholarly journals Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes

2007 ◽  
Vol 78 (2-3) ◽  
pp. 237-260 ◽  
Author(s):  
Ioannis Stamos ◽  
Lingyun Liu ◽  
Chao Chen ◽  
George Wolberg ◽  
Gene Yu ◽  
...  
Keyword(s):  
Large Scale ◽  
Multiview Geometry

scholarly journals Smooth determinantal varieties and critical loci in multiview geometry

2021 ◽  
Author(s):  
Marina Bertolini ◽  
Roberto Notari ◽  
Cristina Turrini
Keyword(s):  
Computer Vision ◽  
Reconstruction Problem ◽  
Projective Varieties ◽  
Critical Locus ◽  
Multiview Geometry ◽  
Determinantal Varieties ◽  
Set Of Points

AbstractLinear projections from $$\mathbb {P}^k$$ P k to $$\mathbb {P}^h$$ P h appear in computer vision as models of images of dynamic or segmented scenes. Given multiple projections of the same scene, the identification of sufficiently many correspondences between the images allows, in principle, to reconstruct the position of the projected objects. A critical locus for the reconstruction problem is a variety in $$\mathbb {P}^k$$ P k containing the set of points for which the reconstruction fails. Critical loci turn out to be determinantal varieties. In this paper we determine and classify all the smooth critical loci, showing that they are classical projective varieties.

scholarly journals 3D hypothesis clustering for cross-view matching in multi-person motion capture

2020 ◽  
Vol 6 (2) ◽  
pp. 147-156 ◽  
Author(s):  
Miaopeng Li ◽  
Zimeng Zhou ◽  
Xinguo Liu
Keyword(s):  
Motion Capture ◽  
Estimation Errors ◽  
Clustering Problem ◽  
Markerless Motion Capture ◽  
Multiview Geometry ◽  
Main Challenge ◽  
Lower Estimation ◽  
Core Idea ◽  
View Matching ◽  
2D Space

Abstract We present a multiview method for markerless motion capture of multiple people. The main challenge in this problem is to determine cross-view correspondences for the 2D joints in the presence of noise. We propose a 3D hypothesis clustering technique to solve this problem. The core idea is to transform joint matching in 2D space into a clustering problem in a 3D hypothesis space. In this way, evidence from photometric appearance, multiview geometry, and bone length can be integrated to solve the clustering problem efficiently and robustly. Each cluster encodes a set of matched 2D joints for the same person across different views, from which the 3D joints can be effectively inferred. We then assemble the inferred 3D joints to form full-body skeletons for all persons in a bottom–up way. Our experiments demonstrate the robustness of our approach even in challenging cases with heavy occlusion, closely interacting people, and few cameras. We have evaluated our method on many datasets, and our results show that it has significantly lower estimation errors than many state-of-the-art methods.

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