用于从多视图图像高质量三维重建的卷积神经着色
Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images
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中文总结 AI 辅助
该研究提出卷积神经着色（CNS）方法，通过卷积神经着色器和细节位移网络解决现有三维重建方法的单点信息局限，提升了多视图图像三维重建的质量。
中文摘要 AI 辅助
我们提出了一种卷积神经着色（CNS），这是一种从多视图图像重建高质量三维形状的新流程。近期多项研究采用神经辐射场及其他神经可微渲染方法来理解三维几何，但这些方法依赖表面位置、法向量等单点几何信息，导致缺乏详细的局部几何。我们的方法通过利用卷积神经着色器捕捉暗区和无纹理区域的变化，解决了单点信息的固有局限，使几何预测更准确。此外，我们引入了利用表面几何空间信息的细节位移网络，通过关联渲染坐标中的相邻值学习精细位移细节，缓解了图像边界处的表面不规则性。大量实验表明，我们的方法在重建形状和渲染图像的质量上较现有最优方法有显著提升。
英文摘要
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.
