ReCap: Better Gaussian Relighting with Cross-Environment Captures (2412.07534v3)

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.

• The paper introduces ReCap, a multi-task learning method using cross-environment captures to jointly optimize lighting representations and address albedo-lighting ambiguity.
• ReCap employs a novel shading function with a generalized split-sum approximation for flexible material representation and integration of standard HDR maps.
• Evaluations show ReCap achieves state-of-the-art relighting quality, outperforming 3D Gaussian Splatting by 3.4 dB PSNR, with implications for VR/AR.

Review of "ReCap: Better Gaussian Relighting with Cross-Environment Captures"

The paper "ReCap: Better Gaussian Relighting with Cross-Environment Captures" addresses the challenge of realistic 3D object relighting under varied and unseen environmental conditions. The objective is to achieve high fidelity in virtual and augmented reality by accurately reflecting light, casting shadows, and adapting to diverse lighting scenarios.

Main Contributions

The paper introduces ReCap, a method that treats cross-environment captures as a multi-task problem to surmount the limitations of conventional techniques in relighting. The main contributions of the paper are as follows:

1. Multi-task Learning Approach: ReCap uses cross-environment captures to jointly optimize various lighting representations. This approach helps address the albedo-lighting ambiguity by maintaining a shared set of material attributes, thus enhancing the physical validity of lighting reconstructions both in real-time applications and high-quality renderings.
2. Shading Function Innovation: The authors propose a novel shading function that integrates a generalized split-sum approximation, transcending limitations of previous models by improving the optimization process. This configuration allows efficient and flexible material representation, facilitating the inclusion of standard HDR maps.
3. Cross-environment Photometric Supervision: The model leverages additional photometric supervision to relieve the entangled relationship between albedo and lighting estimations, which typically suffers under ambiguous conditions in single capture environments.
4. State-of-the-Art Relighting Performance: ReCap outperforms contemporary methods like 3D Gaussian Splatting by 3.4 dB in PSNR on a robust relighting benchmark. The paper details achieving superior relighting quality while preserving computational efficiency, a critical consideration for practical deployment.

Numerical and Qualitative Results

The performance of ReCap is rigorously evaluated through a comprehensive set of experiments. The method consistently outperforms existing models across diverse lighting conditions in both qualitative (visual representation) and quantitative metrics (PSNR, SSIM, and LPIPS). The results highlight the benefits of cross-environment captures in effective material and light decoupling, proving particularly advantageous for specular objects that pose challenges in view-dependent variations.

Implications and Future Directions

The research indicates substantial implications for both theoretical advancements in neural representation models and practical applications in VR/AR environments. By refining material and lighting decoding, the paper not only enhances the expressive power of neural radiance fields but also broadens the applicability of Gaussian point-based techniques in real-time rendering scenarios.

In future work, the extension of the cross-environment paradigm to include dynamic or temporally varying lighting could further solidify the method's supremacy in relighting tasks. Additionally, addressing limitations such as indirect illumination and subsurface scattering promises to refine the fidelity of rendered virtual scenes.

In conclusion, this paper offers insightful advancements in relighting methodologies, specifically leveraging cross-environment captures to overcome endemic issues in Gaussian-based approaches. As the field continues to evolve, ReCap sets a promising trajectory for future explorations into efficient, physically-consistent relighting techniques in computer graphics.