Intrinsic Decompositions for Image Editing

By Nicolas Bonneel, Balazs Kovacs, Sylvain Paris, Kavita Bala. 2017

101 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Noah Snavely
2. Jitendra Malik
3. Luc Van Gool
4. Zhengqi Li
5. Xiaogang Wang
6. Ming-Hsuan Yang
7. Abhinav Kumar Gupta
8. Dacheng Tao

In Generative Models

1. Noah Snavely
2. Zhengqi Li
3. Kavita Bala
4. Nathan Paczan
5. Mike Roberts

Citations in the corpus

• Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2020, background): “…the color image. alistic synthetic datasets and interactive simulation environments that have spurred rapid progress towards the goal of holistic indoor scene understanding [6, 7, 9, 10, 14, 15, 19, 22, 24, 31, 33, 36, 37, 39, 43, 44, 45, 46, 49, 52, 61, 62, 63, 65, 70, 72, 75, 76, 79, 83, 84].”
• CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering (2018, methodology): “For instance, [7, 10] created a large-scale dataset and benchmark based on video games for the purpose of autonomous driving, and [29, 30] use synthetic imagery to form small benchmarks for intrinsic images.”
• Shading Annotations in the Wild (2017, background): “Finally, synthetic datasets (from rendered CG scenes) also provide a way to obtain ground truth shading for intrinsic images and other problems [8, 4, 7].”