A Convolutional Neural Deferred Shader for Physics Based Rendering

• URL: http://arxiv.org/abs/2512.19522v1
• Date: Mon, 22 Dec 2025 16:16:13 GMT
• Title: A Convolutional Neural Deferred Shader for Physics Based Rendering
• Authors: Zhuo He, Yingdong Ru, Qianying Liu, Paul Henderson, Nicolas Pugeault,
• Abstract summary: Recent advances in neural rendering have achieved impressive results on photorealistic shading and relighting.<n>This paper introduces pbnds+: a novel physics-based neural deferred shading pipeline utilizing convolution neural networks to decrease the parameters.
• Score: 9.933770503395117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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