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arXiv Computer Vision By Elton Cao, Hod Lipson

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

Read the original on arXiv Computer Vision →

The paper presents a method for reconstructing 3D wireframes from single 2D line drawings by treating the task as conditional depth estimation. It uses a Latent Diffusion Model with spatial conditioning on the sketch and optional partial-depth cues to predict depth for each drawn pixel, producing a 3D wireframe that faithfully matches the visible strokes. Trained on over three million synthetic image‑depth pairs from CAD wireframes, the approach achieves a best‑of‑five normalized depth error of 3.9% and demonstrates robust performance across varying shape complexities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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