Efficient and Accurate Template-based Reconstruction of Deformable Surfaces
Year: 2024
Type: conference-paper
Abstract: 3D surface reconstruction in deformable environments presents significant challenges. Template-based methods have proven robust for achieving accurate reconstructions by utilising images and textured triangulated meshes as reference data. These methods rely on feature detection in both the reference and current images to establish corresponding points, leveraging reprojection and deformation constraints for precise reconstruction. However, challenges arise when features are not uniformly distributed across mesh triangles, potentially resulting in sparse or coarse reconstructions. Moreover, the combined computational cost of reprojection and deformable constraints often leads to prolonged optimisation times. This study aims to enhance efficiency in reconstructing deformations within the field of view. Our approach involves back-projecting vertices from a reference mesh onto the reference image plane and subsequently tracking them directly in the subsequent image. This method assumes the resulting observations are sufficiently accurate, encoding the deformation within this information. By eliminating the re-projection constraint and focusing solely on a deformation constraint based on Euclidean distances between vertices, we significantly reduce computational and memory costs. The results of our proposed algorithm demonstrate a notable reduction in computational cost and memory cost, while maintaining reconstruction accuracy comparable to related methods. The code of our algorithm is publicly available at https://github.com/DominikSlomma/Efficient-and-Accurate-Template-based-Reconstruction-of-Deformable-Surfaces
Authors: Dominik Slomma, Shoudong Huang, Liang Zhao
Institutions: University of Technology Sydney, University of Edinburgh
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406208929