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A Quantitative Assessment Framework for Model Quality Evaluation of 3D Scene under Simulation Platform
Technical Paper
2014-01-0177
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Vision-based Advanced Driver Assistance Systems (Vi-ADAS) has achieved rapid growth in recent years. Since vehicle field testing under various driving scenarios can be costly, tedious, unrepeatable, and often dangerous, simulation has thus become an effective means that reduces or partially replaces the conventional field testing in the early development stage. This paper proposes a quantitative assessment framework for model quality evaluation of 3D scene under simulation platform. An imaging model is first built. The problem of solving the imaging model is then transformed into the problem of intrinsic image decomposition. Based on Retinex theory and Non-local texture analyses, a superior intrinsic image decomposition method is adopted to evaluate the fidelity of the 3D scene model through the degree of deviation to the Reflectance and Shading intrinsic maps respectively. Some preliminary testing results demonstrate that the proposed assessment framework can produce quantitative evaluation on 3D scene models.
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Citation
Wang, Y., Han, F., Kong, Y., and Deng, W., "A Quantitative Assessment Framework for Model Quality Evaluation of 3D Scene under Simulation Platform," SAE Technical Paper 2014-01-0177, 2014, https://doi.org/10.4271/2014-01-0177.Also In
References
- http://www.mobileye.com/
- http://delphi.com/manufacturers/auto/safety/active/
- http://www.civitec.com/solutions/pro-sivicprofessional.html
- https://www.tassinternational.com/prescan
- Heam Donald , Baker M. Pauline Computer Graphics Pearson US Imports & PHIPEs 1996
- Torrance K. and Sparrow E. Theory for Off-Specular Reflection from Rough Surfaces Journal of the Optical Society of America 57 9 1105 1114 1967
- Grapinet Melanie , De Souza Philippe , Smal Jean-Christophe et al. Characterization and Simulation of Optical Sensors Procedia Social and Behavioral Sciences, Transport Research Arena-Europe 962 971 2012
- Barrow , H. G. and Tenenbaum , J. M. Recovering Intrinsic Scene Characteristics from Images Computer Vision Systems 1978
- Land , E. and McCann , J. Lightness and Retinex Theory Journal of the Optical Society of America A 3 1684 1692 1971
- Funt , B. V. , Drew , M. S. and Brockington , M. Recovering Shading from Color Images European Conference on Computer Vision (ECCV) 1992 124 132 Santa Margherita Ligure Italy
- Grosse , R. , Johnson , M. K. , Adelson , E. H. et al. Ground-truth Dataset and Baseline Evaluations for Intrinsic Image Algorithms International Conference on Computer Vision (ICCV) 2009 2335 2342 Kyoto, Japan
- Tappen , M. F. , Freeman , W. T. and Adelson , E. H. Recovering Intrinsic Images from a Single Image IEEE Transactions on Pattern Analysis and Machine Intelligence 27 9 1459 1472 2005
- Shen , J. B. , Yang , X.S. , Jia , Y. D. et al. Intrinsic Images Using Optimization IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011 3481 3487 Colorado, USA
- Zhao , Q. , Tan , P. , Dai , Q. et al. A Closed-form Solution to Retinex with Non-local Texture Constraints IEEE Transactions on Pattern Analysis and Machine Intelligence 34 7 1437 1444 2012
- Buades , A. , Coll , B. and Morel , J. M. A Non-local Algorithm for Image Denoising IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2005 2 60 65 San Diego, USA
- Hinneburg A. , Aggarwal C. C. , Keim D. A. What Is the Nearest Neighbor in High Dimensional Spaces In the 26th VLDB conference 506 515 2000
- Wang , Z. A. , Bovik , C. , Sheikh , H. R. et al. Image quality assessment: From error visibility to structural similarity IEEE Transactions on Image Processing 13 4 600 612 2004