M
Mingqiang Wei
Researcher at Nanjing University of Aeronautics and Astronautics
Publications - 138
Citations - 1472
Mingqiang Wei is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 17, co-authored 78 publications receiving 721 citations. Previous affiliations of Mingqiang Wei include Chinese Academy of Sciences & The Chinese University of Hong Kong.
Papers
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Proceedings ArticleDOI
Detail-recovery Image Deraining via Context Aggregation Networks
TL;DR: This paper introduces two parallel sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining, and proposes an end-to-end detail-recovery image deraining network (termed a DRDNet) to solve the problem.
Journal ArticleDOI
Bi-Normal Filtering for Mesh Denoising
TL;DR: This paper takes advantage of the piecewise consistent property of the two normal fields of a mesh surface and proposes an effective framework in which they are filtered and integrated using a novel method to guide the denoising process.
Journal ArticleDOI
Mesh Denoising Guided by Patch Normal Co-Filtering via Kernel Low-Rank Recovery
TL;DR: This work proposes a new patch normal co-filter (PcFilter) for mesh denoising, inspired by the geometry statistics which show that surface patches with similar intrinsic properties exist on the underlying surface of a noisy mesh, aiming at removing different levels of noise, yet preserving various surface features.
Journal ArticleDOI
Tensor Voting Guided Mesh Denoising
TL;DR: This work votes on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS) to give a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features.
Posted Content
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end detail-recovery image deraining network (termed a DRD-Net), which combines the squeeze-and-excitation (SE) operation with residual blocks to make full advantage of spatial contextual information.