MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference

2025-10-13

AI Summary

Existing 2D image-based reflectance modeling methods suffer from inadequate multi-view material consistency and inaccurate physical environment modeling, leading to lighting artifacts and poor generalization. This paper proposes a novel reflectance modeling framework tailored for Gaussian splatting. First, we design a multi-view consistent material inference mechanism that jointly optimizes diffuse, specular, and indirect illumination components. Second, we introduce ray-tracing-driven environmental lighting modeling, integrated with reflectance strength prior learning and cross-view photometric variation analysis, to precisely identify highly reflective regions and suppress lighting crosstalk. Finally, we enforce multi-view photometric consistency constraints within a deferred shading pipeline. Evaluated on mainstream novel view synthesis benchmarks, our method achieves state-of-the-art performance, significantly improving geometry and illumination reconstruction accuracy while enhancing rendering realism and visual fidelity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modeling reflections from 2D images is essential for photorealistic rendering and novel view synthesis. Recent approaches enhance Gaussian primitives with reflection-related material attributes to enable physically based rendering (PBR) with Gaussian Splatting. However, the material inference often lacks sufficient constraints, especially under limited environment modeling, resulting in illumination aliasing and reduced generalization. In this work, we revisit the problem from a multi-view perspective and show that multi-view consistent material inference with more physically-based environment modeling is key to learning accurate reflections with Gaussian Splatting. To this end, we enforce 2D Gaussians to produce multi-view consistent material maps during deferred shading. We also track photometric variations across views to identify highly reflective regions, which serve as strong priors for reflection strength terms. To handle indirect illumination caused by inter-object occlusions, we further introduce an environment modeling strategy through ray tracing with 2DGS, enabling photorealistic rendering of indirect radiance. Experiments on widely used benchmarks show that our method faithfully recovers both illumination and geometry, achieving state-of-the-art rendering quality in novel views synthesis.

Problem

• Modeling reflections from 2D images for realistic rendering
• Solving material inference constraints under limited environment modeling
• Handling indirect illumination caused by inter-object occlusions

Innovation

• Multi-view consistent material inference for Gaussian Splatting
• Photometric variation tracking for reflection strength priors
• Ray tracing environment modeling for indirect illumination