activity
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 2020Show all

8 papers · 1 filter

cs.RO20202 cited

3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar

Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang +2

Accurate motion capture of aerial robots in 3-D is a key enabler for autonomous operation in indoor environments such as warehouses or factories, as well as driving forward researc…

cs.CV202015 cited

SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

Sheng Ao, Qingyong Hu, Bo Yang +2

Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either…

cs.CV2020

Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors

Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu +2

Positional estimation is of great importance in the public safety sector. Emergency responders such as fire fighters, medical rescue teams, and the police will all benefit from a r…

cs.CV2020

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Qingyong Hu, Bo Yang, Sheikh Khalid +3

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly an…

cs.CV2020106 cited

A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence

Changhao Chen, Bing Wang, Chris Xiaoxuan Lu +2

Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or g…

physics.comp-ph2020114 cited

Solving the wave equation with physics-informed deep learning

Ben Moseley, Andrew Markham, Tarje Nissen-Meyer

We investigate the use of Physics-Informed Neural Networks (PINNs) for solving the wave equation. Whilst PINNs have been successfully applied across many physical systems, the wave…