用卷积神经着色提升多视角3D重建精度，尤其改善暗区和无纹理区域的细节。
Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images
- 采用卷积神经着色器捕捉局部光照变化，克服单点几何信息不足的问题。
- 在多个数据集上重建精度显著优于当前最优方法，尤其在暗部与平滑区域表现更佳。
- 适合需要高保真3D建模的场景，如数字孪生、文化遗产数字化。
我们提出一种卷积神经着色（CNS）新框架，用于从多视角图像中重建高质量三维形状。近年来，神经辐射场等可微渲染方法虽被广泛使用，但依赖表面位置与法向等单点几何信息，导致局部细节缺失。本文通过引入卷积神经着色器，有效捕捉暗区与无纹理区域的细微变化，显著提升几何预测精度。同时，设计细粒度位移网络，利用表面几何的空间相关性，学习邻近渲染坐标间的位移细节，缓解图像边界处的表面不规则问题。大量实验表明，该方法在重建形状与渲染图像质量上均显著超越现有最先进方法。
原文摘要 · Abstract (English)
We propose a convolutional neural shading (CNS), a novel pipeline to reconstruct high-quality 3D shapes from multi-view images. Several recent studies have used neural radiance fields and other neural differentiable rendering methods to understand 3D geometry. However, these approaches rely on single-point geometric information, such as positions and normals of the surface, leading to a lack of detailed local geometry. Our approach addresses the inherent limitations of single-point information by leveraging a neural shader to capture variations even in dark and textureless regions with a convolutional neural shader, resulting in far more accurate geometry predictions. Additionally, our method mitigates surface irregularities at image boundaries by introducing a fine-detail displacement network, which utilizes spatial information of surface geometry and learns fine displacement details by correlating neighboring values in the rendering coordinates. Through extensive experiments, our proposed method has demonstrated significant quality improvements in the reconstructed shapes and rendered images over current state-of-the-art methods.
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