Author:
Ruppel Philipp,Görner Michael,Hendrich Norman,Zhang Jianwei
Reference16 articles.
1. Albrecht, S.: Transparent object reconstruction and registration confidence measures for 3D point clouds based on data inconsistency and viewpoint analysis. Ph.D. thesis, Osnabrück University (2018)
2. Chen, G., Han, K., Wong, K.Y.K.: TOM-Net: learning transparent object matting from a single image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9233–9241, June 2018
3. Eren, G., et al.: Scanning from heating: 3D shape estimation of transparent objects from local surface heating. Opt. Express 17, 11457–11468 (2009). https://doi.org/10.1364/OE.17.011457
4. Ham, C., Singh, S., Lucey, S.: Occlusions are fleeting – texture is forever: moving past brightness constancy. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 273–281 (2017). https://doi.org/10.1109/WACV.2017.37
5. Ihrke, I., Kutulakos, K., Lensch, H., Magnor, M., Heidrich, W.: Transparent and specular object reconstruction. Comput. Graph. Forum 29, 2400–2426 (2010). https://doi.org/10.1111/j.1467-8659.2010.01753.x