LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data
Author:
Affiliation:
1. New Jersey Institute of Technology, United States and Technische Universität Wien, Austria
2. Adobe Research, United Kingdom
3. New Jersey Institute of Technology, United States
Funder
Austrian Science Fund
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3550469.3555425
Reference37 articles.
1. Room Style Estimation for Style-Aware Recommendation
2. Aditya Chattopadhyay Xi Zhang David Paul Wipf Himanshu Arora and René Vidal. 2022. Structured Graph Variational Autoencoders for Indoor Furniture layout Generation. ArXiv abs/2204.04867(2022). Aditya Chattopadhyay Xi Zhang David Paul Wipf Himanshu Arora and René Vidal. 2022. Structured Graph Variational Autoencoders for Indoor Furniture layout Generation. ArXiv abs/2204.04867(2022).
3. Xinhan Di Pengqian Yu Hong Zhu Lei Cai Qiuyan Sheng Changyu Sun and Lingqiang Ran. 2020. Structural Plan of Indoor Scenes with Personalized Preferences. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12538 LNCS. https://doi.org/10.1007/978-3-030-66823-5_27 10.1007/978-3-030-66823-5_27
4. Xinhan Di Pengqian Yu Hong Zhu Lei Cai Qiuyan Sheng Changyu Sun and Lingqiang Ran. 2020. Structural Plan of Indoor Scenes with Personalized Preferences. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12538 LNCS. https://doi.org/10.1007/978-3-030-66823-5_27
5. Example-based synthesis of 3D object arrangements
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