Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image (1905.02722v1)

Abstract: We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we create a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. To train this network, we augment the SUNCG indoor scene dataset with real-world materials and render them with a fast, high-quality, physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Our inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance. Experiments show that our framework outperforms previous methods for estimating individual scene components, which also enables various novel applications for augmented reality, such as photorealistic object insertion and material editing. Code and data will be made publicly available.

Inverse Rendering for Complex Indoor Scenes: An Advanced Computational Approach

The paper "Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image" introduces a robust framework for the inverse rendering of indoor environments from a single RGB image. The paper significantly advances the field of computer vision by estimating comprehensive scene details—geometry, spatially-varying lighting, and non-Lambertian reflectance properties—using deep convolutional neural networks (CNNs).

Inverse rendering is a challenging problem primarily due to its ill-posed nature; multiple scene parameterizations can yield the same observed image. Traditional methods have only addressed subsets of this problem, focusing on individual aspects like shape reconstruction or lighting estimation. This paper distinguishes itself by tackling all significant components simultaneously within a unified framework, providing a holistic scene understanding that is critical for implementing practical applications in augmented reality and beyond.

Methodological Contributions and Numerical Results

The authors innovate by creating a new training dataset that integrates realistic material properties into the SUNCG dataset using a high-quality rendering approach and complex spatially-varying Bidirectional Reflectance Distribution Functions (SVBRDFs). This approach allows the exploitation of a vast array of photorealistic materials, significantly enhancing the visual fidelity of the generated scenes.

A key innovation is the incorporation of a spatially-varying spherical Gaussian lighting model that efficiently captures the lighting intricacies of indoor environments. Additionally, the design of a differentiable rendering layer enables the effective backpropagation of appearance errors, fostering concurrent modeling of shape, lighting, and material characteristics.

The experiments demonstrate that the proposed network outperforms existing methods in estimating these scene properties. Specific improvements are noted in the diffuse albedo recovery, normal estimation, and rendering quality, enabling new capabilities like photorealistic object insertion and material editing, as evidenced by the qualitative and quantitative metrics supplied in the evaluations.

Broader Implications and Future Directions

The presented method enhances our capability to understand and simulate complex scenes, paving the way for significant developments in areas such as augmented reality, virtual reality, and interior design. The ability to accurately and automatically decompose a scene into its physical components from a single image extends potential applications in robust scene manipulation and interactive environments.

Future developments could explore the extension of these techniques to outdoor scenes and videos, incorporating dynamic lighting conditions and temporal coherence to render scenes with even greater realism. Additionally, further research into mitigating the inherent scale ambiguity in lighting and albedo estimations would strengthen model robustness and predictive accuracy.

In conclusion, this paper presents a comprehensive approach to inverse rendering, effectively bridging the gap between theoretical models and practical applications. Such advancements hold promise for increasingly automated, intelligent systems capable of seamlessly understanding and interacting with human environments.