面向多视图三维材质重建的本征图像融合
Intrinsic Image Fusion for Multi-View 3D Material Reconstruction
将扩散模型产生的多视图材质预测通过低维参数化函数和鲁棒优化融合为一致的材质空间，为逆渲染中欠约束问题提供了可复用的先验融合框架。
原文依据 We introduce Intrinsic Image Fusion, a method that reconstructs high-quality physically based materials from multi-view images.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出本征图像融合，一种从多视图图像重建高质量基于物理材质的方法。材质重建高度欠约束，通常依赖分析合成，需要昂贵且含噪的路径追踪。为更好地约束优化，我们将单视图先验融入重建过程。我们利用基于扩散的材质估计器，为每个视图生成多个但往往不一致的候选分解。为减少不一致性，我们对预测拟合一个显式的低维参数函数。然后，我们提出一个鲁棒优化框架，使用软性逐视图预测选择以及基于置信度的软多视图内点集，将最置信视图的最一致预测融合到一致的参数化材质空间。最后，我们使用逆路径追踪优化低维参数。我们的结果在合成和真实场景的材质解耦上均优于最优方法，产生清晰干净的重建，适用于高质量重光照。
英文原文摘要 1148 字符
We introduce Intrinsic Image Fusion, a method that reconstructs high-quality physically based materials from multi-view images.Material reconstruction is highly underconstrained and typically relies on analysis-by-synthesis, which requires expensive and noisy path tracing. To better constrain the optimization, we incorporate single-view priors into the reconstruction process. We leverage a diffusion-based material estimator that produces multiple, but often inconsistent, candidate decompositions per view.To reduce the inconsistency, we fit an explicit low-dimensional parametric function to the predictions.We then propose a robust optimization framework using soft per-view prediction selection together with confidence-based soft multi-view inlier set to fuse the most consistent predictions of the most confident views into a consistent parametric material space. Finally, we use inverse path tracing to optimize for the low-dimensional parameters. Our results outperform state-of-the-art methods in material disentanglement on both synthetic and real scenes, producing sharp and clean reconstructions suitable for high-quality relighting.