用新架构提升隐式神经表示的细节还原与效率
Optimizing 3D Geometry Reconstruction from Implicit Neural Representations
- 引入周期激活、位置编码和法向量增强网络表达能力
- 显著改善高频细节保留，计算成本低于传统隐式表示
- 适合需要高精度3D重建的研究者与工业应用
隐式神经表示（INR）在学习3D几何方面展现出强大潜力，相比传统的网格方法具有明显优势。常见的INR将形状边界隐式编码为学习到的连续函数的零水平集，并建立从低维潜在空间到所有可能形状（由符号距离函数表示）的空间映射。然而，大多数INR难以保留高频细节，这对精确的几何描绘至关重要，且计算开销较大。为此，本文提出一种新方法，同时降低计算成本并提升细粒度细节的捕捉能力。该方法在神经网络架构中融合周期激活函数、位置编码和法向量信息，显著增强了模型对整个3D形状空间的学习能力，有效保留了复杂细节与锐利特征，解决了传统表示在这些方面常出现的不足。
原文摘要 · Abstract (English)
Implicit neural representations have emerged as a powerful tool in learning 3D geometry, offering unparalleled advantages over conventional representations like mesh-based methods. A common type of INR implicitly encodes a shape's boundary as the zero-level set of the learned continuous function and learns a mapping from a low-dimensional latent space to the space of all possible shapes represented by its signed distance function. However, most INRs struggle to retain high-frequency details, which are crucial for accurate geometric depiction, and they are computationally expensive. To address these limitations, we present a novel approach that both reduces computational expenses and enhances the capture of fine details. Our method integrates periodic activation functions, positional encodings, and normals into the neural network architecture. This integration significantly enhances the model's ability to learn the entire space of 3D shapes while preserving intricate details and sharp features, areas where conventional representations often fall short.
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