58 citations · 175 across the 25 of their papers we have counts for
12 papers · 1 filter
StraightPCF: Straight Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +3
Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. State-of-the-art methods remove noise by moving no…
Weighted Point Cloud Normal Estimation
Weijia Wang, Xuequan Lu, Di Shao +4
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures. Also, they usually ignore the contributions of different…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction
Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly +1
Compression of convolutional neural network models has recently been dominated by pruning approaches. A class of previous works focuses solely on pruning the unimportant filters to…
Graph Classification via Discriminative Edge Feature Learning
Yang Yi, Xuequan Lu, Shang Gao +2
Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when a…
A Capsule Network for Hierarchical Multi-Label Image Classification
Khondaker Tasrif Noor, Antonio Robles-Kelly, Brano Kusy
Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arran…
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +1
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and o…
Deep Point Cloud Normal Estimation via Triplet Learning
Weijia Wang, Xuequan Lu, Dasith de Silva Edirimuni +2
Normal estimation on 3D point clouds is a fundamental problem in 3D vision and graphics. Current methods often show limited accuracy in predicting normals at sharp features (e.g.,…
Deep Patch-based Human Segmentation
Dongbo Zhang, Zheng Fang, Xuequan Lu +4
3D human segmentation has seen noticeable progress in re-cent years. It, however, still remains a challenge to date. In this paper, weintroduce a deep patch-based method for 3D hum…
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
Mehrdad Shoeiby, Antonio Robles-Kelly, Ran Wei +1
This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution. The dataset was first released and…
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images
Hao Zhu, Mantang Guo, Hongdong Li +2
Light-field cameras (LFC) have received increasing attention due to their wide-spread applications. However, current LFCs suffer from the well-known spatio-angular trade-off, which…
A Frequency Domain Neural Network for Fast Image Super-resolution
Junxuan Li, Shaodi You, Antonio Robles-Kelly
In this paper, we present a frequency domain neural network for image super-resolution. The network employs the convolution theorem so as to cast convolutions in the spatial domain…