1 code implementation • ICCV 2021 • Jinming Cao, Hanchao Leng, Dani Lischinski, Danny Cohen-Or, Changhe Tu, Yangyan Li
The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.
Ranked #3 on Semantic Segmentation on Stanford2D3D - RGBD
no code implementations • 24 Dec 2020 • Pengdi Huang, Liqiang Lin, Fuyou Xue, Kai Xu, Danny Cohen-Or, Hui Huang
We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels.
no code implementations • ECCV 2018 • Shihao Wu, Hui Huang, Tiziano Portenier, Matan Sela, Danny Cohen-Or, Ron Kimmel, Matthias Zwicker
To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively.
no code implementations • 21 May 2018 • Jinming Cao, Oren Katzir, Peng Jiang, Dani Lischinski, Danny Cohen-Or, Changhe Tu, Yangyan Li
The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.