Multi View
Multi-view analysis integrates data from multiple perspectives to improve accuracy and robustness in various applications, primarily aiming to overcome limitations of single-view approaches. Current research focuses on developing efficient algorithms and model architectures, such as transformers and graph neural networks, to handle high-dimensional data and address challenges like data incompleteness, view misalignment, and computational constraints. This field is significant for advancing computer vision, medical image analysis, robotics, and other domains by enabling more accurate and reliable inferences from complex, multi-faceted data.
Papers
Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation
Chaolong Yang, Yuyao Yan, Weiguang Zhao, Jianan Ye, Xi Yang, Amir Hussain, Kaizhu Huang
DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer
High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization
Jiaxin Xie, Hao Ouyang, Jingtan Piao, Chenyang Lei, Qifeng Chen
A Light Touch Approach to Teaching Transformers Multi-view Geometry
Yash Bhalgat, Joao F. Henriques, Andrew Zisserman
Dual Information Enhanced Multi-view Attributed Graph Clustering
Jia-Qi Lin, Man-Sheng Chen, Xi-Ran Zhu, Chang-Dong Wang, Haizhang Zhang