GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions
Jie Zhou, Zhongjin Luo, Qian Yu, Xiaoguang Han, and Hongbo Fu

TL;DR
GA-Sketching introduces a geometry-aligned deep implicit function approach for multi-view sketch-based 3D shape modeling, enabling accurate, iterative, and user-friendly shape creation and editing from sketches.
Contribution
The paper presents a novel geometry-aligned feature encoding technique for deep implicit functions, improving multi-view sketch-based 3D shape reconstruction and editing.
Findings
Outperforms existing methods in accuracy and efficiency
Enables iterative shape modeling from multiple sketches
Enhances user satisfaction in shape editing tasks
Abstract
Sketch-based shape modeling aims to bridge the gap between 2D drawing and 3D modeling by providing an intuitive and accessible approach to create 3D shapes from 2D sketches. However, existing methods still suffer from limitations in reconstruction quality and multi-view interaction friendliness, hindering their practical application. This paper proposes a faithful and user-friendly iterative solution to tackle these limitations by learning geometry-aligned deep implicit functions from one or multiple sketches. Our method lifts 2D sketches to volume-based feature tensors, which align strongly with the output 3D shape, enabling accurate reconstruction and faithful editing. Such a geometry-aligned feature encoding technique is well-suited to iterative modeling since features from different viewpoints can be easily memorized or aggregated. Based on these advantages, we design a unified…
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Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Human Motion and Animation
