Here we propose an end-to-end method that directly predicts parametric layouts from an input panorama image.
Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations.
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance.
Such a scheme has two limitations: 1) Storing and running several networks for different tasks are expensive for typical robotic platforms.
In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.
no code implementations • 17 Oct 2020 • Yunchao Wei, Shuai Zheng, Ming-Ming Cheng, Hang Zhao, LiWei Wang, Errui Ding, Yi Yang, Antonio Torralba, Ting Liu, Guolei Sun, Wenguan Wang, Luc van Gool, Wonho Bae, Junhyug Noh, Jinhwan Seo, Gunhee Kim, Hao Zhao, Ming Lu, Anbang Yao, Yiwen Guo, Yurong Chen, Li Zhang, Chuangchuang Tan, Tao Ruan, Guanghua Gu, Shikui Wei, Yao Zhao, Mariia Dobko, Ostap Viniavskyi, Oles Dobosevych, Zhendong Wang, Zhenyuan Chen, Chen Gong, Huanqing Yan, Jun He
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region.
During the training phase, we generate binary weights on-the-fly since what we actually maintain is the policy network, and all the binary weights are used in a burn-after-reading style.
Our method decomposes the semantic style transfer problem into feature reconstruction part and feature decoder part.
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes.