We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to other representations, SDFs have the advantage that they can represent shapes with arbitrary topology, and that they guarantee watertight surfaces. We apply our approach to the problem of multi-view 3D reconstruction, where we achieve high reconstruction quality and can capture complex topology of 3D objects. In addition, we employ a multi-resolution strategy to obtain a robust optimization algorithm. We further demonstrate that our SDF-based differentiable renderer can be integrated with deep learning models, which opens up options for learning approaches on 3D objects without 3D supervision. In particular, we apply our method to single-view 3D reconstruction and achieve state-of-the-art results.

翻译：我们提出SDFDiff, 这是一种利用签名远程功能( SDFs) 代表的3D形状的不同形状的新型优化图像化方法。 与其他表达方式相比, SDFs的优势是,它们可以代表任意的地形形状,保证水密表面。 我们用我们的方法来解决多视角3D重建问题,在那里,我们达到高重建质量并能够捕捉3D物体的复杂地形。 此外,我们使用一种多分辨率战略来获得一种强大的优化算法。 我们还进一步证明,我们的基于 SDF 的3D可变构件可以与深层次的学习模型相结合,这为在没有3D监督的情况下学习3D对象的方法开辟了选项。 特别是,我们用我们的方法来单视角3D重建并实现最先进的结果。