Kang Chen
Yizhou Yu
Shi-Min Hu
Skip Abstract Section
Skip Supplemental Material SectionAbstract
Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.
Supplemental Material
Available for Download
zip
a232-chen.zip (90 MB)
Supplemental files.
- An, X., and Pellacini, F. 2008. Appprop: all-pairs appearance-space edit propagation. ACM Trans. Graph. 27, 3, 40:1--40:9. Google Scholar
Digital Library
- Asha, V., Bhajantri, N. U., and Nagabhushan, P. 2011. Glcm-based chi-square histogram distance for automatic detection of defects on patterned textures. Int. J. Comput. Vision Robot. 2, 4 (Feb.), 302--313. Google ScholarDigital Library
- Bell, S., Upchurch, P., Snavely, N., and Bala, K. 2013. Opensurfaces: A richly annotated catalog of surface appearance. ACM Trans. Graph. 32, 4 (July), 111:1--111:17. Google ScholarDigital Library
- Bell, S., Upchurch, P., Snavely, N., and Bala, K. 2015. Material recognition in the wild with the materials in context database. Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Chajdas, M. G., Lefebvre, S., and Stamminger, M. 2010. Assisted texture assignment. In Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, ACM, New York, NY, USA, I3D '10, 173--179. Google ScholarDigital Library
- Chen, T., Zhu, Z., Shamir, A., Hu, S.-M., and Cohen-Or, D. 2013. 3-sweep: Extracting editable objects from a single photo. ACM Trans. Graph. 32, 6 (Nov.), 195:1--195:10. Google ScholarDigital Library
- Chen, K., Lai, Y.-K., Wu, Y.-X., Martin, R., and Hu, S.-M. 2014. Automatic semantic modeling of indoor scenes from low-quality rgb-d data using contextual information. ACM Trans. Graph. 33, 6 (Nov.), 208:1--208:12. Google ScholarDigital Library
- Chia, A. Y.-S., Zhuo, S., Gupta, R. K., Tai, Y.-W., Cho, S.-Y., Tan, P., and Lin, S. 2011. Semantic colorization with internet images. ACM Trans. Graph. 30, 6 (Dec.), 156:1--156:8. Google ScholarDigital Library
- Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., and Xu, Y.-Q. 2006. Color harmonization. ACM Trans. Graph. 25, 3 (July), 624--630. Google ScholarDigital Library
- Csurka, G., Skaff, S., Marchesotti, L., and Saunders, C. 2011. Building look & feel concept models from color combinations. The Visual Computer 27, 12, 1039--1053. Google ScholarDigital Library
- Endres, I., Farhadi, A., Hoiem, D., and Forsyth, D. 2010. The benefits and challenges of collecting richer object annotations. In CVPR. Workshops, 2010 IEEE Computer Society Conference on, 1--8.Google Scholar
- Faridul, H. S., Pouli, T., Chamaret, C., Stauder, J., Trémeau, A., Reinhard, E., et al. 2014. A survey of color mapping and its applications. In Eurographics 2014-State of the Art Reports, The Eurographics Association, 43--67.Google Scholar
- Fisher, M., and Hanrahan, P. 2010. Context-based search for 3d models. ACM Trans. Graph. 29, 6 (Dec.), 182:1--182:10. Google ScholarDigital Library
- Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., and Hanrahan, P. 2012. Example-based synthesis of 3d object arrangements. ACM Trans. Graph. 31, 6 (Nov.), 135:1--135:11. Google ScholarDigital Library
- Huang, H.-Z., Zhang, S.-H., Martin, R. R., and Hu, S.-M. 2014. Learning natural colors for image recoloring. Computer Graphics Forum 33, 7, 299--308. Google ScholarDigital Library
- Huang, Q., Wang, H., and Koltun, V. 2015. Single-view reconstruction via joint analysis of image and shape collections. ACM Trans. Graph. 34, 4 (July), 87:1--87:10. Google ScholarDigital Library
- Jain, A., Thormählen, T., Ritschel, T., and Seidel, H.-P. 2012. Material memex: Automatic material suggestions for 3d objects. ACM Trans. Graph. 31, 6 (Nov.), 143:1--143:8. Google ScholarDigital Library
- Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. 1983. Optimization by simulated annealing. SCIENCE 220, 4598, 671--680.Google Scholar
- Leifman, G., and Tal, A. 2012. Mesh colorization. Computer Graphics Forum 31, 2pt2, 421--430. Google ScholarDigital Library
- Levin, A., Lischinski, D., and Weiss, Y. 2004. Colorization using optimization. ACM Trans. Graph. 23, 3, 689--694. Google ScholarDigital Library
- Lin, S., Ritchie, D., Fisher, M., and Hanrahan, P. 2013. Probabilistic color-by-numbers: Suggesting pattern colorizations using factor graphs. ACM Trans. Graph. 32, 4 (July), 37:1--37:12. Google ScholarDigital Library
- Liu, T., McCann, J., Li, W., and Funkhouser, T. 2015. Composition-aware scene optimization for product images. Computer Graphics Forum 34, 2, 13--24. Google ScholarDigital Library
- Merrell, P., Schkufza, E., and Koltun, V. 2010. Computer-generated residential building layouts. ACM Trans. Graph. 29, 6 (Dec.), 181:1--181:12. Google ScholarDigital Library
- Miao, Y., Hu, F., Zhang, X., Chen, J., and Pajarola, R. 2015. Symmsketch: Creating symmetric 3D free-form shapes from 2D sketches. Computational Visual Media 1, 1, 3--16.Google ScholarCross Ref
- Nguyen, C. H., Ritschel, T., Myszkowski, K., Eisemann, E., and Seidel, H.-P. 2012. 3D Material Style Transfer. Computer Graphics Forum (Proc. EUROGRAPHICS 2012) 2, 31. Google ScholarDigital Library
- O'Donovan, P., Agarwala, A., and Hertzmann, A. 2011. Color compatibility from large datasets. ACM Trans. Graph. 30, 4 (July), 63:1--63:12. Google ScholarDigital Library
- Oliva, A., and Torralba, A. 2001. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42, 145--175. Google ScholarDigital Library
- Qin, X., and Yang, Y.-H. 2005. Basic gray level aura matrices: theory and its application to texture synthesis. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, vol. 1, 128--135 Vol. 1. Google ScholarDigital Library
- Russell, B., Torralba, A., Murphy, K., and Freeman, W. 2008. Labelme: A database and web-based tool for image annotation. IJCV. 77, 1--3, 157--173. Google ScholarDigital Library
- Tibshirani, R. 1996. Regression Shrinkage and Selection Via the Lasso. J. Royal. Statist. Soc B. 58, 1, 267--288.Google ScholarCross Ref
- Wang, B., Yu, Y., Wong, T.-T., Chen, C., and Xu, Y.-Q. 2010. Data-driven image color theme enhancement. ACM Trans. Graph. 29, 6 (Dec.), 146:1--146:10. Google ScholarDigital Library
- Welsh, T., Ashikhmin, M., and Mueller, K. 2002. Transferring color to greyscale images. ACM Trans. Graph. 21, 3, 277--280. Google ScholarDigital Library
- Xu, K., Li, Y., Ju, T., Hu, S.-M., and Liu, T.-Q. 2009. Efficient affinity-based edit propagation using k-d tree. ACM Trans. Graph. 28, 5, 118:1--118:6. Google ScholarDigital Library
- Xu, K., Zheng, H., Zhang, H., Cohen-Or, D., Liu, L., and Xiong, Y. 2011. Photo-inspired model-driven 3d object modeling. ACM Trans. Graph. 30, 4, 80:1--10. Google ScholarDigital Library
- Xu, K., Chen, K., Fu, H., Sun, W.-L., and Hu, S.-M. 2013. Sketch2Scene: Sketch-based co-retrieval and co-placement of 3D models. ACM Trans. Graph. 32, 4, 123:1--123:15. Google ScholarDigital Library
- Yu, L.-F., Yeung, S.-K., Tang, C.-K., Terzopoulos, D., Chan, T. F., and Osher, S. J. 2011. Make it home: automatic optimization of furniture arrangement. ACM Trans. Graph. 30, 4, 86:1--86:12. Google ScholarDigital Library
- Yu, L.-F., Yeung, S. K., Terzopoulos, D., and Chan, T. F. 2012. Dressup!: Outfit synthesis through automatic optimization. ACM Trans. Graph. 31, 6, 134:1--134:14. Google ScholarDigital Library
Index Terms
- Magic decorator: automatic material suggestion for indoor digital scenes
Recommendations
Deep learning based 3D target detection for indoor scenes
Abstract3D target detection is a research hotspot in recent years. In the field of autonomous driving, 3D target detection is mainly targeted at outdoor scenes that the camera height is constant. In a few indoor scenes, 3D target detection is mostly at ...
Rendering evolution at industrial light & magic
EGSR'04: Proceedings of the Fifteenth Eurographics conference on Rendering TechniquesFrom Jurassic Park to Van Helsing, the rendering technologies at ILM have evolved over the last 10 years, both to satisfy the high demands of our clients and also those of the general public. State-of-the-art rendering techniques such as volume ...
Indoor scene understanding based on manhattan and non-manhattan projection of spatial right-angles
AbstractUnderstanding of indoor scenes has considerable value in computer vision. Most previous methods infer indoor scenes via manhattan assumption. However, attic ceilings do not satisfy manhattan assumption and understanding them remains a ...
Highlights- A method to understand indoor scenes including non-Manhattan ceilings is presented.
Comments