Deformation Estimation of Textureless Objects from a Single Image
Sahand Eivazi Adli, Joshua K. Pickard, Ganyun Sun, Rickey Dubay
TL;DR
This paper introduces a method to estimate deformations in textureless plastic objects using a single image, improving 3D geometric accuracy for inspection.
Contribution
A novel method for deformation estimation using graph convolution and a unique dataset for textureless plastic objects.
Findings
The proposed method achieves sub-millimeter accuracy on synthetic images.
The method achieves approximately 2.0 mm accuracy on real images.
The sequential deformation method outperforms the chamfer distance algorithm for mesh label generation.
Abstract
Deformations introduced during the production of plastic components degrade the accuracy of their 3D geometric information, a critical aspect of object inspection processes. This phenomenon is prevalent among primary plastic products from manufacturers. This work proposes a solution for the deformation estimation of textureless plastic objects using only a single RGB image. This solution encompasses a unique image dataset of five deformed parts, a novel method for generating mesh labels, sequential deformation, and a training model based on graph convolution. The proposed sequential deformation method outperforms the prevalent chamfer distance algorithm in generating precise mesh labels. The training model projects object vertices into features extracted from the input image, and then, predicts vertex location offsets based on the projected features. The predicted meshes using these…
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Taxonomy
Topics3D Shape Modeling and Analysis · 3D Surveying and Cultural Heritage · Advanced Neural Network Applications