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

Source: [Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation](<https://www.sota2.com/research/paper/69e06d257d0172140ec7cb80>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-07T05:00:04.653Z

Authors: Elton Cao, Hod Lipson

Published: 2026-04-15T06:52:04.000Z

## Abstract

The conversion of 2D freehand sketches into 3D models remains a pivotal challenge in computer vision, bridging the gap between fluent sketching and CAD. Traditional monocular depth reconstruction techniques are not suitable for line drawing interpretation. We propose a generative approach by framing reconstruction as a conditional dense depth estimation task. To achieve this, we implemented a Latent Diffusion Model (LDM) with a conditioning framework to resolve the inherent ambiguities of orthographic projections. We trained our model using a dataset of over one million image-depth pairs. Our framework demonstrated robust performance across varying shape complexities, with 5.3 percent average depth error.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Depth Estimation on Wireframe Projections 1.0 (test)](<https://www.sota2.com/research/sota/depth-estimation-on-wireframe-projections-1-0-test.md>): Depth Estimation; Wireframe Projections 1.0 (test); NMAE: 0.031.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.13549>)

[PDF](<https://arxiv.org/pdf/2604.13549.pdf>)