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20162022
most citedLearning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling

224 citations · 708 across the 24 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.LG20212 cited

CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals

Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang +2

mmWave FMCW radar has attracted huge amount of research interest for human-centered applications in recent years, such as human gesture/activity recognition. Most existing pipeline…

cs.CV2021224 cited

Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling

Qingyong Hu, Bo Yang, Linhai Xie +5

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing ste…

cs.SD202110 cited

SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform

Yuhang He, Niki Trigoni, Andrew Markham

We present a new framework SoundDet, which is an end-to-end trainable and light-weight framework, for polyphonic moving sound event detection and localization. Prior methods typica…

cs.CV2021

Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks

Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Pedro P. B. de Gusmao +3

Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly de…

cs.RO20211 cited

RadarLoc: Learning to Relocalize in FMCW Radar

Wei Wang, Pedro P. B. de Gusmo, Bo Yang +2

Relocalization is a fundamental task in the field of robotics and computer vision. There is considerable work in the field of deep camera relocalization, which directly estimates p…

cs.CV2021

P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching

Bing Wang, Changhao Chen, Zhaopeng Cui +8

Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D loc…