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
MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo
Rongxuan Tan, Qing Wang, Xueyan Wang, Chao Yan, Yang Sun, Youyang Feng
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Ke Fan, Jingshi Lei, Xuelin Qian, Miaopeng Yu, Tianjun Xiao, Tong He, Zheng Zhang, Yanwei Fu
NeRF-Enhanced Outpainting for Faithful Field-of-View Extrapolation
Rui Yu, Jiachen Liu, Zihan Zhou, Sharon X. Huang