· 2020
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image
- 239citations
- 2020year
Short summary
A deep inverse rendering network reconstructs shape, spatially-varying lighting, and SVBRDF materials from a single indoor scene image.
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Key points
- Reconstructs shape, spatially-varying lighting, and SVBRDF from a single indoor image.
- Uses a novel network incorporating spherical Gaussian lighting, differentiable rendering, and a cascade structure.
- Introduces a new photorealistic indoor dataset and a physically-based renderer for training.
- Outperforms previous methods in scene reconstruction and enables new applications.
AI-generated from the title and abstract; the full text is not read.
Abstract
We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel 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. Since no existing dataset provides ground truth high quality spatially-varying material and spatially-varying lighting, we propose novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Experiments show that our framework outperforms previous methods and enables various novel applications like photorealistic object insertion and material editing.
The authors' abstract, as published at the source., 2020 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science