no code implementations • 13 Aug 2020 • Hao Huang, Jianchun Chen, Xiang Li, Lingjing Wang, Yi Fang
Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching.
1 code implementation • NeurIPS 2019 • Jianchun Chen, Lingjing Wang, Xiang Li, Yi Fang
To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment.
3 code implementations • 7 Jun 2019 • Lingjing Wang, Xiang Li, Jianchun Chen, Yi Fang
In contrast to previous efforts (e. g. coherent point drift), CPD-Net can learn displacement field function to estimate geometric transformation from a training dataset, consequently, to predict the desired geometric transformation for the alignment of previously unseen pairs without any additional iterative optimization process.
1 code implementation • 2 Apr 2019 • Lingjing Wang, Jianchun Chen, Xiang Li, Yi Fang
In contrast, the proposed point registration neural network (PR-Net) actively learns the registration pattern as a parametric function from a training dataset, consequently predict the desired geometric transformation to align a pair of point sets.