GMLight: Lighting Estimation via Geometric Distribution Approximation (2102.10244v2)

Abstract: Inferring the scene illumination from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and generalization. This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, which can be estimated by a regression network. Inspired by the earth mover's distance, we design a novel geometric mover's loss to guide the accurate regression of light distribution parameters. With the estimated light parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and high-frequency details. Extensive experiments show that GMLight achieves accurate illumination estimation and superior fidelity in relighting for 3D object insertion. The codes are available at \href{https://github.com/fnzhan/Illumination-Estimation}{https://github.com/fnzhan/Illumination-Estimation}.

• The paper presents a novel geometric distribution model that fuses regression and generative methods to accurately estimate lighting.
• It introduces a geometric mover’s loss and spherical convolutions to optimize light parameter regression and panoramic illumination synthesis.
• Experimental results on the Laval Indoor HDR dataset demonstrate reduced RMSE and angular errors, enhancing realism in lighting prediction.

Overview of GMLight: Lighting Estimation via Geometric Distribution Approximation

The paper "GMLight: Lighting Estimation via Geometric Distribution Approximation" introduces a novel approach to lighting estimation by proposing a method that integrates regression-based and generation-based techniques to achieve accurate illumination prediction from a single image. The primary contribution lies in utilizing a geometric distribution model to represent illumination and leveraging a generative projector to synthesize panoramic illumination maps with high fidelity.

Methodology

The authors propose a framework named GMLight, which combines a regression network for estimating light parameters and a generative projector for generating illumination maps. The framework aims to improve accuracy in lighting estimation, which is critical for applications like image composition and object relighting in mixed reality environments.

Key innovations in the proposed method include:

1. Geometric Distribution Representation: Illumination scenes are parameterized through a geometric distribution model comprising light distribution, intensity, ambient term, and auxiliary depth. This representation accounts for the real geometry of scenes, which aids in achieving accurate lighting estimation.
2. Geometric Mover's Loss: Inspired by the Earth Mover's Distance, a geometric mover's loss is introduced to optimize the regression of illumination parameters by considering scene geometry. This loss function facilitates the model to learn precise light distribution by minimizing the cost of transforming predicted light parameters to ground-truth lighting.
3. Generative Projector: With the estimated light parameters, the generative projector uses adversarial learning to synthesize panoramic illumination maps. The authors implement spherical convolutions to handle distortions in panoramic images and introduce an adaptive radius strategy to refine illumination generation.
4. Spatially-Varying Illumination: The method incorporates scene depth information to restore illumination conditions at various spatial positions within a scene, broadening its applicability to dynamic environments.

Experimental Evaluation

The paper demonstrates the efficacy of GMLight on the Laval Indoor HDR dataset, establishing it as a competitive solution by outperforming several state-of-the-art methods across several metrics. Highlights of the evaluation include:

• Quantitative Metrics: The model achieves lower root mean square error (RMSE), scale-invariant RMSE (si-RMSE), and angular error compared to other methods, signifying better performance in both light intensity and direction estimation.
• User Study: In user studies assessing realism, GMLight-generated illumination maps were preferred more frequently, illustrating their visual fidelity and perceived authenticity in practical edge cases.

Implications and Future Directions

GMLight introduces a significant advancement in lighting estimation by successfully integrating geometric information into model training and prediction. The geometric mover's loss notably improves the capacity to predict realistic illumination maps. This development has profound implications in various fields such as virtual reality (VR), augmented reality (AR), and 3D content creation, where accurate relighting is essential for creating immersive and realistic experiences.

Looking forward, further exploration into applying this model to outdoor lighting conditions could broaden its applicability. Additionally, extending this approach to include the dynamic lighting variations in more complex environments would propel further innovation in AI-driven scene understanding and object simulation.

In conclusion, GMLight contributes to the field of lighting estimation by leveraging geometric distributions and adversarial learning to significantly enhance the accuracy and realism of illumination maps. This sets a precedent for future research in AI-based image synthesis and computer vision applications.