WeChat Mini Program
Old Version Features

3D Shape Reconstruction with a Multiple-Constraint Estimation Approach

Frontiers in Neuroscience(2023)

Anhui Agr Univ

Cited 0|Views1
Abstract
In this study, a multiple-constraint estimation algorithm is presented to estimate the 3D shape of a 2D image sequence. Given the training data, a sparse representation model with an elastic net, i.e., l(1)-norm and l(2)-norm constraints, is devised to extract the shape bases. In the sparse model, the l(1)-norm and l(2)-norm constraints are enforced to regulate the sparsity and scale of coefficients, respectively. After obtaining the shape bases, a penalized least-square model is formulated to estimate 3D shape and motion, by considering the orthogonal constraint of the transformation matrix, and the similarity constraint between the 2D observations and the shape bases. Moreover, an Augmented Lagrange Multipliers (ALM) iterative algorithm is adopted to solve the optimization of the proposed approach. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed model.
More
Translated text
Key words
non-rigid structure from motion,elastic net,similarity constraint,Augmented Lagrange multipliers,3D reconstruction
PDF
Bibtex
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined