activity
20192021
most citedMiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation

51 citations · 57 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2021

Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification

Wenming Tang Guoping Qiu

This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic proc…

eess.IV20214 cited

Quarter Laplacian Filter for Edge Aware Image Processing

Yuanhao Gong, Wenming Tang, Lebin Zhou +2

This paper presents a quarter Laplacian filter that can preserve corners and edges during image smoothing. Its support region is , which is smaller than the su…

cs.CV20212 cited

A Discrete Scheme for Computing Image's Weighted Gaussian Curvature

Yuanhao Gong, Wenming Tang, Lebin Zhou +2

Weighted Gaussian Curvature is an important measurement for images. However, its conventional computation scheme has low performance, low accuracy and requires that the input image…

cs.CV202051 cited

MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation

Jun Liu, Qing Li, Rui Cao +2

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently,…

cs.GR2020

Gaussian Curvature Filter on 3D Meshes

Wenming Tang, Yuanhao Gong, Kanglin Liu +4

Minimizing the Gaussian curvature of meshes can play a fundamental role in 3D mesh processing. However, there is a lack of computationally efficient and robust Gaussian curvature o…

cs.LG2019

Spectral Regularization for Combating Mode Collapse in GANs

Kanglin Liu, Wenming Tang, Fei Zhou +1

Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularizati…