用生成模型从单张线稿还原3D线框，误差仅5.3%。
Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation
- 将3D重建转为条件深度估计，用潜在扩散模型处理投影模糊
- 在百万级图像-深度对上训练，平均深度误差5.3%
- 适合手绘线稿转3D建模，尤其对复杂形状表现稳健
将2D自由手绘草图转换为3D模型仍是计算机视觉中的关键挑战，有助于弥合流畅绘图与CAD之间的差距。传统单目深度重建方法不适用于线稿解析。我们提出一种生成式方法，将重建问题定义为条件密集深度估计任务。为此，采用带有条件框架的潜在扩散模型（LDM），以解决正交投影带来的固有歧义。模型在超过一百万对图像-深度数据上进行训练，跨不同形状复杂度表现出稳健性能，平均深度误差为5.3%。
原文摘要 · Abstract (English)
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.
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