Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation (2412.15211v1)

Abstract: Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for more specular objects whose appearance strongly depends on the viewing direction. Some prior approaches model appearance variation across images using a per-image embedding vector, while others use physically-based rendering to recover the materials and per-image illumination. Such approaches fail at faithfully recovering view-dependent appearance given significant variation in input illumination and tend to produce mostly diffuse results. We present an approach that reconstructs objects from images taken under different illuminations by first relighting the images under a single reference illumination with a multiview relighting diffusion model and then reconstructing the object's geometry and appearance with a radiance field architecture that is robust to the small remaining inconsistencies among the relit images. We validate our proposed approach on both synthetic and real datasets and demonstrate that it greatly outperforms existing techniques at reconstructing high-fidelity appearance from images taken under extreme illumination variation. Moreover, our approach is particularly effective at recovering view-dependent "shiny" appearance which cannot be reconstructed by prior methods.

• The paper introduces a novel two-stage methodology combining diffusion models and neural radiance fields for high-fidelity 3D reconstruction despite extreme illumination variations.
• A key step involves using multiview diffusion models to relight input images, harmonizing them under a unified reference illumination to reduce ambiguities in 3D structure and material properties.
• The method employs an adapted NeRF with per-image shading embeddings for robust reconstruction, achieving superior performance, particularly in capturing specular reflections on synthetic and real-world data.

Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation

The paper entitled "Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation" introduces a novel methodology in the domain of 3D reconstruction, specifically designed to handle challenges associated with varying illumination conditions across input images. The authors propose a two-stage approach to accurately reconstruct high-fidelity 3D models of objects from images captured under drastically different lighting conditions, leveraging the capabilities of diffusion models and neural radiance fields.

Problem Formulation and Existing Challenges

The problem addressed involves synthesizing novel views of a scene by reconstructing a 3D representation from a set of photographs. Traditional view synthesis approaches often assume consistent illumination across all input images; however, this assumption is violated in real-world scenarios like outdoor scenes with changing weather or images sourced from the internet, which may exhibit extreme variations in lighting. This poses significant challenges, especially for objects with specular surfaces, as the appearance changes with light direction and view angle.

Previous methods attempted to tackle this challenge through per-image latent embeddings or physics-based inverse rendering models, both of which encountered limitations. Per-image embeddings risk absorbing all view-dependent effects, potentially leading to inaccuracies and loss of detail. Physics-based techniques, while offering a more analytical approach, suffer from ambiguities in separating material properties and illumination effects.

Methodology

The proposed solution by the authors involves two main components:

1. Relighting through Diffusion Models: The authors introduce a multiview relighting framework using diffusion models, which simultaneously relight all input images to ensure consistency under a designated reference illumination. This joint approach mitigates ambiguities associated with single-image relighting, as it capitalizes on information from multiple views to disambiguate geometry, materials, and lighting conditions. The diffusion model intelligently estimates the illumination for all images to match a reference image, improving consistency and establishing a robust basis for the subsequent 3D reconstruction phase.
2. Robust 3D Reconstruction via Neural Radiance Fields (NeRF): Once images are harmonized under a unified lighting condition, the authors deploy a radiance field architecture adapted from NeRF-Casting. An innovative aspect of their approach is the use of per-image “shading embeddings,” designed to correct residual inconsistencies in image relighting. These embeddings allow slight perturbations in estimated surface normals to align with actual highlights, maintaining the integrity of specular reflections critical for high-quality reconstructions.

Empirical Validation and Results

The methodology is validated on both synthetic datasets (augmenting assets with highly reflective materials) and real-world datasets (from the NAVI collection). The authors show significant quantitative and qualitative improvements over state-of-the-art techniques, reporting metrics like peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual similarity (LPIPS). The approach demonstrates superior ability in capturing accurate specular highlights and intricate details, outperforming previous models which often result in blurred or inaccurate renderings under complex lighting variations.

Implications and Future Directions

The proposed dual-stage model offers a robust solution for 3D reconstruction under diverse and dynamic real-world lighting scenarios, opening doors for applications in AR/VR, digital content creation, and robotics. The integration of diffusion models with 3D reconstruction pipelines highlights the potential of leveraging generative priors for complex vision tasks. Future work may explore joint optimization frameworks to integrate camera pose estimation alongside relighting and reconstruction, potentially increasing robustness against varying environmental factors and reducing dependency on precalculated inputs.

In summary, this research contributes a notable advancement in the field of 3D vision, showcasing the efficacy of hybridizing contemporary generative models with traditional radiance field techniques to tackle longstanding challenges in view synthesis under variable illumination conditions.