3D Shape Reconstruction from Free-Hand Sketches
Jiayun Wang, Jierui Lin, Qian Yu, Runtao Liu, Yubei Chen, Stella X. Yu

TL;DR
This paper introduces a novel method for reconstructing 3D shapes from free-hand sketches, addressing challenges like sketch diversity and distortion through data synthesis and standardization, with strong experimental validation.
Contribution
It presents a new approach that uses synthesized sketches and a standardization module to improve 3D reconstruction from free-hand sketches, a less explored area.
Findings
Effective reconstruction from diverse free-hand sketches
Strong generalizability demonstrated through extensive experiments
Code implementation is publicly available
Abstract
Sketches are the most abstract 2D representations of real-world objects. Although a sketch usually has geometrical distortion and lacks visual cues, humans can effortlessly envision a 3D object from it. This suggests that sketches encode the information necessary for reconstructing 3D shapes. Despite great progress achieved in 3D reconstruction from distortion-free line drawings, such as CAD and edge maps, little effort has been made to reconstruct 3D shapes from free-hand sketches. We study this task and aim to enhance the power of sketches in 3D-related applications such as interactive design and VR/AR games. Unlike previous works, which mostly study distortion-free line drawings, our 3D shape reconstruction is based on free-hand sketches. A major challenge for free-hand sketch 3D reconstruction comes from the insufficient training data and free-hand sketch diversity, e.g.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topics3D Shape Modeling and Analysis · Advanced Vision and Imaging · Computer Graphics and Visualization Techniques
