Human perception faithful curve reconstruction based on persistent homology and principal curve
Year: 2025
Type: article
Abstract: Reconstructing curves that align with human visual perception from a noisy point cloud presents a significant challenge in the field of curve reconstruction. A specific problem involves reconstructing curves from a noisy point cloud sampled from multiple intersecting curves, ensuring that the reconstructed results align with the Gestalt principles and thus produce curves faithful to human perception. This task involves identifying all potential curves from a point cloud and reconstructing approximating curves, which is critical in applications such as trajectory reconstruction, path planning, and computer vision. In this study, we propose an automatic method that utilizes the topological understanding provided by persistent homology and the local principal curve method to separate and approximate the intersecting closed curves from point clouds, ultimately achieving successful human perception faithful curve reconstruction results using B-spline curves. This technique effectively addresses noisy data clouds and intersections, as demonstrated by experimental results.
Source: Graphical Models
Authors: Yu Chen, Hongwei Lin, Yifan Xing
Institution: Zhejiang University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,180
Awards: G5225885529, G5650446935
OpenAlex ID: W4410709339