A Convolutional Neural Deferred Shader for Physics Based Rendering

2025-12-22

AI Summary

To address the challenges of excessive MLP parameter counts, poor generalization under low-light conditions, and insufficient physical constraints in neural rendering, this paper proposes PBNDs+, a CNN-based physically guided deferred shading architecture. PBNDs+ integrates physics-driven rendering pipelines with data-driven modeling and introduces the first energy-regularized mechanism to explicitly enforce reflectance energy conservation under dark illumination. Compared to state-of-the-art neural shading and diffusion-based methods, PBNDs+ achieves superior relighting fidelity and shadow realism across diverse lighting conditions—including low-illumination scenarios—while reducing model parameters by 62% and accelerating inference by 3.1×. Notably, it attains strong generalization without requiring large-scale illumination datasets. Key contributions include: (1) the first CNN-based physically grounded deferred shading framework; (2) an energy-conservation regularization scheme; and (3) a lightweight, efficient solution for photorealistic relighting.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Reduces parameters and improves performance in neural shading
• Addresses data imbalance issues in dark illumination conditions
• Enhances photorealistic relighting without material ground truth

Innovation

• Convolutional neural networks reduce parameters and improve performance
• Energy regularization restricts model reflection in dark illumination
• Physics-based neural deferred shading pipeline for realistic rendering