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.

本研究解决了隐式神经表示在保留高频细节和计算成本上的限制。通过整合周期性激活函数、位置编码和法线信息，提出了一种新方法，大幅提升了模型学习3D形状的能力，同时保持复杂细节与锐利特征。这一发现为3D几何重建提供了更高效的解决方案。

优化隐式神经表示的3D几何重建