Leveraging Diffusion Models for Robust and Generalizable Single-Image Lighting Estimation

Core Concepts

We present a simple yet effective technique to estimate lighting in a single input image by leveraging pre-trained diffusion models to render a chrome ball into the scene. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.

Abstract

The paper presents a novel approach for estimating the lighting conditions in a single input image by leveraging pre-trained diffusion models. The key idea is to inpaint a chrome ball into the input image using a diffusion model and then unwrap the chrome ball to obtain an HDR environment map that represents the scene's lighting. The authors identify two key challenges in this approach: (1) consistently generating high-quality chrome balls using diffusion models, and (2) generating HDR chrome balls from an LDR diffusion model. To address these challenges, the authors propose the following: Iterative inpainting algorithm: The authors observe that the initial noise map used in the diffusion process can significantly impact the quality and consistency of the generated chrome balls. They propose an iterative algorithm that generates multiple chrome balls, computes their median, and then refines the result using SDEdit to obtain a high-quality and consistent chrome ball. LoRA fine-tuning for exposure bracketing: To generate HDR chrome balls, the authors fine-tune the diffusion model using LoRA on a small set of synthetically generated chrome balls with varying exposure values. This allows the model to generate multiple LDR chrome balls with different exposures, which can then be combined to produce an HDR environment map. The authors evaluate their method on standard benchmarks, Laval Indoor and Poly Haven, and demonstrate that it outperforms or is competitive with state-of-the-art methods. They also show that their method generalizes well to diverse in-the-wild scenes, producing convincing lighting estimates, while the baselines fail to do so.

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Key Insights Distilled From

DiffusionLight

by Pakkapon Pho... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2312.09168.pdf

Deeper Inquiries

How can the proposed method be extended to handle more complex lighting scenarios, such as outdoor scenes with dynamic lighting conditions or scenes with multiple light sources

The proposed method can be extended to handle more complex lighting scenarios by incorporating dynamic lighting conditions and multiple light sources. For outdoor scenes with dynamic lighting, the diffusion model can be enhanced to adapt to changing light intensities and directions. This can be achieved by incorporating temporal information into the model, allowing it to track changes in lighting over time. Additionally, the model can be trained on a diverse dataset of outdoor scenes with varying lighting conditions to improve its generalization capabilities. To handle scenes with multiple light sources, the diffusion model can be modified to support the estimation of multiple light contributions in the environment map. This can involve segmenting the image to identify different light sources and their corresponding intensities and directions. By training the model on images with multiple light sources, it can learn to differentiate between different sources and accurately estimate their contributions to the overall lighting in the scene.

What are the potential limitations of using diffusion models for lighting estimation, and how can they be addressed in future work

One potential limitation of using diffusion models for lighting estimation is the computational complexity and time required for training and inference. Diffusion models can be resource-intensive, especially when dealing with high-resolution images or complex scenes. To address this limitation, future work can focus on optimizing the model architecture and training process to improve efficiency without compromising accuracy. Techniques such as model distillation, pruning, or quantization can be explored to reduce the model size and computational requirements. Another limitation is the reliance on synthetic training data or limited real-world datasets, which may not fully capture the diversity of lighting conditions in natural scenes. To overcome this limitation, efforts can be made to collect more diverse and representative datasets for training the diffusion model. This can involve capturing real-world scenes with varying lighting conditions and incorporating them into the training data to improve the model's robustness and generalization capabilities.

How can the proposed approach be integrated with other computer vision tasks, such as virtual object insertion or augmented reality applications, to create a more comprehensive lighting-aware system

The proposed approach can be integrated with other computer vision tasks, such as virtual object insertion or augmented reality applications, to create a more comprehensive lighting-aware system. By combining the lighting estimation capabilities of the diffusion model with object recognition and tracking algorithms, virtual objects can be seamlessly inserted into real-world scenes with accurate lighting conditions. This integration can enhance the realism and coherence of virtual objects in augmented reality applications. Furthermore, the lighting estimation from the diffusion model can be used to dynamically adjust the appearance of virtual objects based on the surrounding lighting conditions. This can improve the visual consistency and realism of virtual objects in different lighting environments. By incorporating the lighting-aware system into augmented reality frameworks, users can experience more immersive and realistic virtual interactions with the physical world.