Search Results for author: Ying He

Found 18 papers, 7 papers with code

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

no code implementations5 Jan 2022 Ganlin Zhang, Dongheng Zhang, Ying He, Jinbo Chen, Fang Zhou, Yan Chen

The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices.

Indoor Localization

High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces

no code implementations1 Dec 2021 Ying He, Dongheng Zhang, Yan Chen

Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices.

Quantization SSIM +1

Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds

no code implementations28 Jul 2021 Wei Zhou, Xin Cao, Xiaodan Zhang, Xingxing Hao, Dekui Wang, Ying He

Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MPVConv improves the accuracy of the backbone (PointNet) by up to \textbf{36\%}, and achieves higher accuracy than the voxel-based model with up to \textbf{34}$\times$ speedups.

PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows

no code implementations13 Jul 2021 Aihua Mao, Zihui Du, Junhui Hou, Yaqi Duan, Yong-Jin Liu, Ying He

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets.

Multi Voxel-Point Neurons Convolution (MVPConv) for Fast and Accurate 3D Deep Learning

no code implementations30 Apr 2021 Wei Zhou, Xin Cao, Xiaodan Zhang, Xingxing Hao, Dekui Wang, Ying He

Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MVPConv improves the accuracy of the backbone (PointNet) by up to 36%, and achieves higher accuracy than the voxel-based model with up to 34 times speedup.

CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds

1 code implementation CVPR 2021 Yiming Zeng, Yue Qian, Zhiyu Zhu, Junhui Hou, Hui Yuan, Ying He

The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence.

Ranked #3 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)

3D Dense Shape Correspondence

ParaNet: Deep Regular Representation for 3D Point Clouds

no code implementations5 Dec 2020 Qijian Zhang, Junhui Hou, Yue Qian, Juyong Zhang, Ying He

Although convolutional neural networks have achieved remarkable success in analyzing 2D images/videos, it is still non-trivial to apply the well-developed 2D techniques in regular domains to the irregular 3D point cloud data.

Deep Magnification-Flexible Upsampling over 3D Point Clouds

1 code implementation25 Nov 2020 Yue Qian, Junhui Hou, Sam Kwong, Ying He

In addition, we propose a simple yet effective training strategy to drive such a flexible ability.

Deep Patch-based Human Segmentation

no code implementations11 Jul 2020 Dongbo Zhang, Zheng Fang, Xuequan Lu, Hong Qin, Antonio Robles-Kelly, Chao Zhang, Ying He

3D human segmentation has seen noticeable progress in re-cent years.

MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling

1 code implementation1 May 2020 Yue Qian, Junhui Hou, Qijian Zhang, Yiming Zeng, Sam Kwong, Ying He

This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applications applied to the downsampled sparse points as much as possible.

Point Cloud Classification

PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling

1 code implementation ECCV 2020 Yue Qian, Junhui Hou, Sam Kwong, Ying He

Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.

Point Cloud Super Resolution Surface Reconstruction

Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling

no code implementations14 Feb 2020 Dongbo Zhang, Xuequan Lu, Hong Qin, Ying He

In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving sharp features and geometric details.

Graphics

Spotting Macro- and Micro-expression Intervals in Long Video Sequences

2 code implementations18 Dec 2019 Ying He, Su-Jing Wang, Jingting Li, Moi Hoon Yap

Both macro- and micro-expression intervals in CAS(ME)$^2$ and SAMM Long Videos are spotted by employing the method of Main Directional Maximal Difference Analysis (MDMD).

Micro-Expression Spotting Optical Flow Estimation

HLO: Half-kernel Laplacian Operator for Surface Smoothing

1 code implementation12 May 2019 Wei Pan, Xuequan Lu, Yuanhao Gong, Wenming Tang, Jun Liu, Ying He, Guoping Qiu

This paper presents a simple yet effective method for feature-preserving surface smoothing.

Computational Geometry Graphics

Blur Removal via Blurred-Noisy Image Pair

no code implementations26 Mar 2019 Chunzhi Gu, Xuequan Lu, Ying He, Chao Zhang

Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images.

Deblurring Image Deblurring +1

Parallel and Scalable Heat Methods for Geodesic Distance Computation

1 code implementation14 Dec 2018 Jiong Tao, Juyong Zhang, Bailin Deng, Zheng Fang, Yue Peng, Ying He

In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes.

Graphics

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