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Holistic 3D Scene Parsing and Reconstruction


Abstract

We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model. Specifically, we introduce a Holistic Scene Grammar (HSG) to represent the 3D scene structure, which characterizes a joint distribution over the functional and geometric space of indoor scenes. The proposed Holistic Scene Grammar (HSG) captures three essential and often latent dimensions of the indoor scenes: i) latent human context, describing the affordance and the functionality of a room arrangement, ii) geometric constraints over the scene configurations, and iii) physical constraints that guarantee physically plausible parsing and reconstruction. We solve this joint parsing and reconstruction problem in an analysis-by-synthesis fashion, seeking to minimize the differences between the input image and the rendered images generated by our 3D representation, over the space of depth, surface normal, and object segmentation map. The optimal configuration, represented by a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses through the non-differentiable solution space, jointly optimizing object localization, 3D layout, and hidden human context. Experimental results demonstrate that the proposed algorithm improves the generalization ability and significantly outperforms prior methods on 3D layout estimation, 3D object detection, and holistic scene understanding.

BibTeX

Please cite our paper if you use our code or data.

					
@inproceedings{huang2018holistic,
    title={Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image},
    author={Huang, Siyuan and Qi, Siyuan and Zhu, Yixin and Xiao, Yinxue and Xu, Yuanlu and Zhu, Song-Chun},
    booktitle={Proceedings of the 15th European Conference on Computer Vision (ECCV)},
    pages={187--203},
    year={2018}
}
					
				
Acknowledgements

We thank Professor Ying Nian Wu from UCLA Statistics Department for helpful discussions. This work is supported by DARPA XAI N66001-17-2-4029, MURI ONR N00014-16-1-2007, SPAWAR N66001-17-2-3602, and ARO W911NF-18-1-0296.