Optimizing 3D Geometry Reconstruction from Implicit Neural Representations

TL;DR

This paper introduces a novel method for 3D geometry reconstruction using implicit neural representations that improves detail preservation and reduces computational costs by integrating advanced activation functions, positional encodings, and normals.

Contribution

The paper presents a new approach combining periodic activations, positional encodings, and normals to enhance 3D shape learning and detail retention in implicit neural representations.

Findings

Enhanced capture of high-frequency details.

Reduced computational expenses.

Improved accuracy in 3D shape reconstruction.

Abstract

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.