Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911645618405376 |
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| author | Cao, Elton Lipson, Hod |
| author_facet | Cao, Elton Lipson, Hod |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13549 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation Cao, Elton Lipson, Hod Computer Vision and Pattern Recognition 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. |
| title | Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.13549 |