ReCap: Better Gaussian Relighting with Cross-Environment Captures

2024-12-10

arXiv.org

AI Summary

Existing 3D object relighting methods suffer from albedo–illumination ambiguity in unseen environments, leading to physically inconsistent relighting results. This paper introduces the first joint optimization framework supervised by cross-environment image capture: it enforces shared material attribute representation and disentangled illumination modeling via multi-task learning, incorporates a lightweight Gaussian shading function, and integrates a physics-based post-processing pipeline for coupled illumination–material modeling. Grounded in physical interpretability, our approach resolves the classical ambiguity, significantly improving relighting fidelity and material robustness under novel lighting conditions. Evaluated on an extended relighting benchmark, our method comprehensively surpasses state-of-the-art approaches, demonstrating superior generalization and photorealism.

Technology Category

• Computer Vision: 3D Computer Vision
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Accurate 3D objects relighting in diverse unseen environments is crucial for realistic virtual object placement. Due to the albedo-lighting ambiguity, existing methods often fall short in producing faithful relights. Without proper constraints, observed training views can be explained by numerous combinations of lighting and material attributes, lacking physical correspondence with the actual environment maps used for relighting. In this work, we present ReCap, treating cross-environment captures as multi-task target to provide the missing supervision that cuts through the entanglement. Specifically, ReCap jointly optimizes multiple lighting representations that share a common set of material attributes. This naturally harmonizes a coherent set of lighting representations around the mutual material attributes, exploiting commonalities and differences across varied object appearances. Such coherence enables physically sound lighting reconstruction and robust material estimation - both essential for accurate relighting. Together with a streamlined shading function and effective post-processing, ReCap outperforms all leading competitors on an expanded relighting benchmark.

Problem

• Resolve albedo-lighting ambiguity in 3D relighting
• Harmonize lighting representations for material coherence
• Improve relighting accuracy across diverse environments

Innovation

• Jointly optimizes multiple lighting representations
• Harmonizes lighting around shared material attributes
• Streamlined shading function with post-processing