Search Results for author: Zhenxiao Liang

Found 6 papers, 3 papers with code

A Condition Number for Joint Optimization of Cycle-Consistent Networks

1 code implementation NeurIPS 2019 Leonidas J. Guibas, Qi-Xing Huang, Zhenxiao Liang

A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly.

Learning Transformation Synchronization

1 code implementation CVPR 2019 Xiangru Huang, Zhenxiao Liang, Xiaowei Zhou, Yao Xie, Leonidas Guibas, Qi-Xing Huang

Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.

Path-Invariant Map Networks

1 code implementation CVPR 2019 Zaiwei Zhang, Zhenxiao Liang, Lemeng Wu, Xiaowei Zhou, Qi-Xing Huang

Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields.

3D Semantic Segmentation Scene Segmentation

Joint Map and Symmetry Synchronization

no code implementations ECCV 2018 Yifan Sun, Zhenxiao Liang, Xiangru Huang, Qi-Xing Huang

Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.

SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions

no code implementations ICML 2018 Chandrajit Bajaj, Tingran Gao, Zihang He, Qi-Xing Huang, Zhenxiao Liang

We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e. g., 2D images or 3D shapes).


Translation Synchronization via Truncated Least Squares

no code implementations NeurIPS 2017 Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qi-Xing Huang

In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.


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