用卷积网络替代MLP，提升物理渲染的光照重建效率与泛化能力。
A Convolutional Neural Deferred Shader for Physics Based Rendering
- 采用卷积神经网络替代传统MLP，减少参数量并提升渲染速度。
- 在真实场景数据上实现更优的光照重建效果，暗光条件下仍保持稳定。
- 适合需要高效、高保真光照重演的影视与游戏渲染应用。
近期神经渲染进展通过多层感知机（MLP）从真实世界数据中学习渲染方程，实现了逼真的阴影与光照重演，尤其在缺乏材料真值的现实物体上表现优异。然而，MLP的密集连接导致参数量庞大，需高算力支持，训练困难且渲染性能受限。数据驱动方法还需大量数据以保证泛化性，不平衡数据可能使模型忽略特殊光照条件（如暗场景）。本文提出pbnds+：一种新型基于物理的神经延迟着色管道，采用卷积神经网络降低参数量并提升渲染性能；同时引入能量正则化，在暗光条件下限制反射，增强稳定性。大量实验表明，该方法优于经典基线、先进神经着色模型及基于扩散的方法。
原文摘要 · Abstract (English)
Recent advances in neural rendering have achieved impressive results on photorealistic shading and relighting, by using a multilayer perceptron (MLP) as a regression model to learn the rendering equation from a real-world dataset. Such methods show promise for photorealistically relighting real-world objects, which is difficult to classical rendering, as there is no easy-obtained material ground truth. However, significant challenges still remain the dense connections in MLPs result in a large number of parameters, which requires high computation resources, complicating the training, and reducing performance during rendering. Data driven approaches require large amounts of training data for generalization; unbalanced data might bias the model to ignore the unusual illumination conditions, e.g. dark scenes. This paper introduces pbnds+: a novel physics-based neural deferred shading pipeline utilizing convolution neural networks to decrease the parameters and improve the performance in shading and relighting tasks; Energy regularization is also proposed to restrict the model reflection during dark illumination. Extensive experiments demonstrate that our approach outperforms classical baselines, a state-of-the-art neural shading model, and a diffusion-based method.
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