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Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation

arxiv.org/abs/2604.13549

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Updated 2 h ago · first seen 11 Sept 2026

paper_01M294H396MP5TT1N9GZ31RNSC

Published
11 Sept 2026
T1 · 2 h ago
arXiv
2604.13549
T1 · 2 h ago
Category
cs.CV
T1 · 2 h ago

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Abstractabstract1

Claim history for Abstract
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Reconstructing 3D geometry from 2D engineering line drawings is an inherently ambiguous problem: while visible strokes determine the object's projected structure, they do not specify the depth of each stroke. Rather than treating this problem as sketch-based asset generation, where models often infer unobserved structure, we study projection-faithful wireframe reconstruction: lifting a user-provided drawing into 3D according to its visible strokes. We formulate this task as conditional depth estimation over line drawings, predicting a depth value for each drawn pixel to produce a 3D wireframe. To model the ambiguities of orthographic projection, we implement a Latent Diffusion Model with spatial conditioning on the input sketch and optional partial-depth conditioning for iterative reconstruction. We train and evaluate our models on over three million synthetic image-depth pairs derived from CAD wireframes, including a newly curated corpus of roughly 90,000 shapes. Across varying shape complexities, our framework achieves robust reconstruction performance; scaling from 256 to 512 resolution with a retrained latent space roughly halves reconstruction error, reaching a 3.9% best-of-five (7.0% average) normalized depth error. These results demonstrate the potential of projection-faithful depth estimation as a user-controlled approach for iterative 3D wireframe creation in engineering design.currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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