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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 2019Show all

17 papers · 1 filter

cs.HC201932 cited

Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning

Chris Xiaoxuan Lu, Bowen Du, Hongkai Wen +5

Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile pla…

cs.CV2019

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Qingyong Hu, Bo Yang, Linhai Xie +5

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

cs.CV2019

SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation

Yasin Almalioglu, Mehmet Turan, Alp Eren Sari +4

In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimat…

eess.SP2019

FootSLAM meets Adaptive Thresholding

Johan Wahlstrom, Andrew Markham, Niki Trigoni

Calibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained c…

eess.SP2019

See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar

Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao +5

This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility…

cs.CV20191 cited

AtLoc: Attention Guided Camera Localization

Bing Wang, Changhao Chen, Chris Xiaoxuan Lu +3

Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To…