Surface Reconstruction from Silhouette and Laser Scanners as a Positive-Unlabeled Learning Problem

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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Typical 3D reconstruction pipelines employ a combination of line-laser scanners and robotic actuators to produce a point cloud and then proceed with surface reconstruction. In this work we propose a new technique to learn an Implicit Neural Representation (INR) of a 3D shape S without directly observing points on its surface. We just assume being able to determine whether a 3D point is exterior to S (e.g. observing if the projection falls outside the silhouette or detecting on which side of the laser line the point is). In this setting, we cast the reconstruction process as a Positive-Unlabelled learning problem where sparse 3D points, sampled according to a distribution depending on the INR's local gradient, have to be classified as being interior or exterior to S. These points, are used to train the INR in an iterative way so that its zero-crossing converges to the boundary of the shape. Preliminary experiments performed on a synthetic dataset demonstrates the advantages of the approach.
Description

CCS Concepts: Computing methodologies → Shape modeling; Shape representations; Reconstruction

        
@inproceedings{
10.2312:stag.20241348
, booktitle = {
Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
}, editor = {
Caputo, Ariel
and
Garro, Valeria
and
Giachetti, Andrea
and
Castellani, Umberto
and
Dulecha, Tinsae Gebrechristos
}, title = {{
Surface Reconstruction from Silhouette and Laser Scanners as a Positive-Unlabeled Learning Problem
}}, author = {
Gottardo, Mario
and
Pistellato, Mara
and
Bergamasco, Filippo
}, year = {
2024
}, publisher = {
The Eurographics Association
}, ISSN = {
2617-4855
}, ISBN = {
978-3-03868-265-3
}, DOI = {
10.2312/stag.20241348
} }
Citation