3DIAS：隐式代数曲面的三维形状重建
3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces
摘要：
三维形状表示对三维形状重建有着重要的影响。基于基元的表示主要通过一组简单的隐式基元来近似3D形状，但是基元的低几何复杂性限制了形状分辨率。此外，为任意形状设置足够数量的基元是具有挑战性的。为了克服这些问题，我们提出了一个受约束的隐式代数曲面作为原语，具有较少的可学习系数和较高的几何复杂性，以及一个深层神经网络来产生这些原语。我们的实验表明，我们的方法在表示能力方面的优越性相比，在单一的RGB图像三维形状重建的最先进的方法。此外，我们证明了我们的方法可以以无监督的方式语义学习3D形状的片段。该代码可从https://myavartanoo.github.io/3dias/公开获取。
英文摘要：
3D Shape representation has substantial effects on 3D shape reconstruction. Primitive-based representations approximate a 3D shape mainly by a set of simple implicit primitives, but the low geometrical complexity of the primitives limits the shape resolution. Moreover, setting a sufficient number of primitives for an arbitrary shape is challenging. To overcome these issues, we propose a constrained implicit algebraic surface as the primitive with few learnable coefficients and higher geometrical complexities and a deep neural network to produce these primitives. Our experiments demonstrate the superiorities of our method in terms of representation power compared to the state-of-the-art methods in single RGB image 3D shape reconstruction. Furthermore, we show that our method can semantically learn segments of 3D shapes in an unsupervised manner. The code is publicly available from https://myavartanoo.github.io/3dias/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
