BISAC COM004000 Intelligence (AI) & Semantics
Since the creation of computers, there has been a lingering problem of data storing and creation for various tasks. In terms of computer graphics and video games, there has been a constant need in assets. Although nowadays the issue of space is not one of the developers' prime concerns, the need in being able to automate asset creation is still relevant. The graphical fidelity, that the modern audiences and applications demand requires a lot of work on the artists' and designers' front, which costs a lot. The automatic generation of 3D scenes is of critical importance in the tasks of Artificial Intelligent (AI) robotics training, where the amount of generated data during training cannot even be viewed by a single person due to the large amount of data needed for machine learning algorithms. A completely separate, but nevertheless necessary task for an integrated solution, is furniture generation and placement, material and lighting randomisation. In this paper we propose interior generator for computer graphics and robotics learning applications. The suggested framework is able to generate and render interiors with furniture at photo-realistic quality. We combined the existing algorithms for generating plans and arranging interiors and then finally add material and lighting randomization. Our solution contains semantic database of 3D models and materials, which allows generator to get realistic scenes with randomization and per-pixel mask for training detection and segmentation algorithms.
procedural generation, machine learning, AI training, light-processing, tesselation, modeling
1. Merrell P., Schkufza E., Koltun V. Computer-generated residential building layouts //ACM SIGGRAPH Asia 2010 papers. – 2010. – S. 1-12.
2. Bengtsson D., Melin J. Constrained procedural floor plan generation for game environments. – 2016.
3. Cerny Green M., Khalifa A., Alsoughayer A., Surana D., Liapis A., Togelius J. Two-step Constructive Approaches for Dungeon Generation. – 2019.
4. Firaxis Games Sid Meier’s Civilization VI. - 2016.
5. Triumph Studios Age of Wonders III. - 2014.
6. Koenig R., Knecht K. Comparing two evolutionary algorithm based methods for layout generation: Dense packing versus subdivision. - 2014.
7. Zifeng Guo, Biao Li Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system. - 2017.
8. Martin J. Procedural House Generation: A method for dynamically generating floor plans. - 2016.
9. Fernando M. Automatic Real-Time Generation of Floor Plans Based on Squarified Treemaps Algorithm. - 2010.
10. L.-F. Yu, S.-K. Yeung, C.-K. Tang, D. Terzopoulos, T. F.Chan, and S. J. Osher. Make It Home: Automatic Optimization of Furniture Arrangement. In SIGGRAPH 2011, 2011.
11. Matthew Fisher, Daniel Ritchie, Manolis Savva, Thomas Funkhouser, and Pat Hanrahan. 2012. Example-based Synthesis of 3D Object Arrangements. In SIGGRAPH Asia 2012.
12. Paul Henderson and Vittorio Ferrari. 2017. A Generative Model of 3D Object Layouts in Apartments
13. Qiang Fu, Xiaowu Chen, Xiaotian Wang, Sijia Wen, Bin Zhou, and Hongbo Fu. 2017. Adaptive Synthesis of Indoor Scenes via Activity-associated Object Relation Graphs.
14. S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser. Semantic Scene Completion from a Single D. Image.
15. V. F. Paul Henderson, Kartic Subr. Automatic Generation of Constrained Furniture Layouts.
16. Qi, Siyuan and Zhu, Yixin and Huang, Siyuan and Jiang, Chenfanfu and Zhu, Song-Chun. Human-centric Indoor Scene Synthesis Using Stochastic Grammar
17. Kai Wang, Manolis Savva, Angel X. Chang, and Daniel [Razryv obtekaniya teksta]Ritchie. Deep Convolutional Priors for Indoor Scene Synthesis. In SIGGRAPH 2018
18. Daniel Ritchie, Kai Wang and Yu-an Lin. Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models.
19. Pavel Kirsanov, Airat Gaskarov, Filipp Konokhov, Konstantin Sofiiuk, Anna Vorontsova, Igor Slinko, Dmitry Zhukov, Sergey Bykov, Olga Barinova, Anton Konushin. DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation. arXiv:1909.12146. September 2019.
20. Frolov V., Sanzharov V., Galaktionov V. Open Source rendering system Hydra Renderer. https://github.com/Ray-Tracing-Systems/HydraAPI
21. S.V. Ershov, D.D. Zhdanov, A.G. Voloboy, V.A. Galaktionov. Two denoising algorithms for bi-directional Monte Carlo ray tracing // Mathematica Montisnigri, Vol. XLIII, 2018, p. 78-100. https://lppm3.ru/files/journal/XLIII/MathMontXLIII-Ershov.pdf
22. V.V. Sanzharov, V.F. Frolov. Level of Detail for Precomputed Procedural Textures // Programming and Computer Software, 2019, V. 45, Issue 4, pp. 187-195 DOI:10.1134/S0361768819040078