IllumiNeRF: 3D Relighting without Inverse Rendering
(2406.06527)Abstract
Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination -- are based on inverse rendering, and attempt to disentangle the object geometry, materials, and lighting that explain the input images. Furthermore, this typically involves optimization through differentiable Monte Carlo rendering, which is brittle and computationally-expensive. In this work, we propose a simpler approach: we first relight each input image using an image diffusion model conditioned on lighting and then reconstruct a Neural Radiance Field (NeRF) with these relit images, from which we render novel views under the target lighting. We demonstrate that this strategy is surprisingly competitive and achieves state-of-the-art results on multiple relighting benchmarks. Please see our project page at https://illuminerf.github.io/.
Overview
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IllumiNeRF introduces a novel method for 3D relightable view synthesis, bypassing traditional inverse rendering techniques.
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The technique employs a generative image diffusion model conditioned on lighting and constructs a 'latent NeRF' for rendering new views under target lighting.
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The method demonstrates superior performance on benchmarks, significantly outperforming existing approaches in terms of PSNR, SSIM, and LPIPS metrics.
Insightful Overview of IllumiNeRF: 3D Relighting without Inverse Rendering
The paper "IllumiNeRF: 3D Relighting without Inverse Rendering" presents a novel approach to the challenging problem of 3D relightable view synthesis. The proposed method departs from traditional inverse rendering techniques, offering a significantly different paradigm for generating relit images of 3D objects under new lighting conditions. Here, I will provide an expert overview of the methodology, results, and implications of this research.
Methodology
The core innovation of IllumiNeRF lies in bypassing the computational and structural complexities of inverse rendering. Instead, the approach leverages a generative image diffusion model, which is conditioned on lighting to produce relit images. These generated images are then used to reconstruct a Neural Radiance Field (NeRF), termed here as a "latent NeRF," which can subsequently render novel views under the target lighting.
Problem Formulation and Model Architecture
The problem is formulated as follows: Given a dataset of images $\mathcal{D}$ captured from various viewpoints under unknown illuminations, the goal is to relight the object under desired target lighting $LT$. Traditional methods would disentangle geometry, materials, and lighting to achieve this, but IllumiNeRF proposes to skip this step.
- Relighting Diffusion Model (RDM): The RDM is fine-tuned to generate high-quality relit images given the input images and the target lighting. Specifically, it uses image-space radiance cues derived from a simple shading model applied to the estimated object geometry.
- Latent NeRF Construction: Multiple plausible relit images generated by the RDM are treated as samples of unobserved latent variables. These samples are then used to train a latent NeRF model, which distills these multiple samples into a single consistent 3D representation capable of rendering views under new lighting.
Experimental Results
The method is rigorously evaluated against several baselines using both synthetic and real-world datasets. Quantitative results on the TensoIR benchmark show that IllumiNeRF significantly outperforms existing methods such as NeRFactor, InvRender, and even the top-performing TensoIR, achieving the highest scores across PSNR, SSIM, and LPIPS metrics.
For the real-world Stanford-ORB benchmark, IllumiNeRF again shows strong performance, only marginally trailing the top-performing Neural-PBIR in certain metrics but producing noticeably better visual quality for specular reflections and color fidelity.
Benchmarking and Ablation Studies
In ablation studies, the utility of the latent NeRF over a standard NeRF is clearly demonstrated. The latent NeRF is crucial for reconciling variations in relit images. Increasing the number of diffusion samples ($S$) used in training further enhances the quality of the output, supporting the hypothesis that more samples lead to a better-fitting latent model.
Implications and Future Directions
The implications of IllumiNeRF are both practical and theoretical:
- Practical Implications: In practice, this method can democratize high-quality 3D content creation, making it accessible for applications in virtual reality, augmented reality, game development, filmmaking, and more. The reduction in computational complexity compared to inverse rendering is a significant advantage.
- Theoretical Implications: The success of IllumiNeRF underscores the potential of neural generative models in interpreting and synthesizing complex 3D scenes. It challenges the traditional reliance on physical-based rendering and opens avenues for further research into leveraging diffusion models for various other tasks in computer vision and graphics.
Conclusion
IllumiNeRF represents a significant shift in the approach to 3D relightable view synthesis. By employing a generative image diffusion model combined with a latent NeRF, the method not only simplifies the process but also achieves state-of-the-art results. Future developments may explore real-time implementations and adaptations to more dynamic and complex scenes, broadening the scope and applicability of this innovative approach.
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