NeRD: Neural Reflectance Decomposition from Image Collections (2012.03918v4)

Abstract: Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination. Though recent work has shown that implicit representations can be used to model the radiance field of an object, most of these techniques only enable view synthesis and not relighting. Additionally, evaluating these radiance fields is resource and time-intensive. We propose a neural reflectance decomposition (NeRD) technique that uses physically-based rendering to decompose the scene into spatially varying BRDF material properties. In contrast to existing techniques, our input images can be captured under different illumination conditions. In addition, we also propose techniques to convert the learned reflectance volume into a relightable textured mesh enabling fast real-time rendering with novel illuminations. We demonstrate the potential of the proposed approach with experiments on both synthetic and real datasets, where we are able to obtain high-quality relightable 3D assets from image collections. The datasets and code is available on the project page: https://markboss.me/publication/2021-nerd/

• The paper presents NeRD’s main contribution by decomposing scenes into SVBRDFs, volumetric geometry, and illumination using a dual-network approach.
• The paper employs multi-layer perceptrons and spherical Gaussians to model spatially-varying reflectance and handle unconstrained illumination effectively.
• The paper demonstrates high-quality relighting and view synthesis, achieving superior PSNR and SSIM compared to traditional NeRF methods.

Analyzing "NeRD: Neural Reflectance Decomposition from Image Collections"

The paper "NeRD: Neural Reflectance Decomposition from Image Collections" addresses a complex challenge in computer vision and computer graphics: decomposing a scene into its shape, reflectance, and illumination, especially under unrestrained environmental illumination. This separation process is analogous to inverse rendering and aims to extract meaningful 3D attributes from image collections. The proposed methodology leverages advances in neural networks, particularly drawing upon the architectural principles of Neural Radiance Fields (NeRF).

Methodology and Approach

The paper introduces Neural Reflectance Decomposition (NeRD), which extends the NeRF framework by embedding reflectance properties instead of direct radiance. NeRD employs a combination of multi-layer perceptrons (MLPs) to estimate spatially-varying bidirectional reflectance distribution functions (SVBRDFs), volumetric geometry, and illumination from image collections captured under diverse lighting conditions. Notably, NeRD decouples the illumination and view-dependent components, allowing for real-time relighting and view synthesis.

A key distinction between NeRD and NeRF is NeRD's focus on relighting capabilities. The architecture involves two networks: a sampling network to determine coarse scene attributes and a finer decomposition network to extract BRDF parameters and geometry normals. The paper applies spherical Gaussians for modeling illumination, providing a smooth approximation of incident light.

Experimental Results

The empirical evaluation in the paper demonstrates NeRD's efficacy on both synthetic and real-world datasets. NeRD successfully generates high-quality relightable 3D models, matching or exceeding current state-of-the-art techniques in similar domains. The paper outlines the method's robustness against fluctuating illumination, a condition that poses significant challenges to traditional methods relying on controlled lab settings.

Performance metrics such as PSNR and SSIM are used to evaluate the model's effectiveness. Compared to NeRF, NeRD attains improved consistency and accuracy in both relighting and novel view synthesis. Furthermore, NeRF's dependency on fixed illumination makes it unsuitable for environments with varying lighting—a limitation suitably addressed by NeRD.

Implications and Future Directions

By proficiently handling unconstrained illumination, NeRD broadens the applicability of neural scene representations in real-world scenarios. This includes fields like virtual reality and augmented reality where lighting conditions are unpredictable. Furthermore, the ability to extract textured meshes with embedded BRDF parameters highlights the potential for interactive applications in graphics pipelines.

The research opens avenues for future work in enhancing the granularity of illumination models. While NeRD successfully deploys spherical Gaussians, exploring implicit representations for high-frequency light effects could boost fidelity. Moreover, integrating explicit shadow modeling could further solidify NeRD's performance in naturally illuminated environments.

Conclusion

In summary, the NeRD framework advances the field of inverse rendering by holistically tackling the decomposition of scenes under varying illumination. Through intelligent architectural adaptations and the innovative use of spherical Gaussians, NeRD achieves remarkable strides in rendering realistic 3D assets. These contributions not only extend the functionality of neural scene representations but also set a precedent for subsequent research efforts aiming to merge physical accuracy with computational efficiency.