activity
20202026
most citedGeometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes

9 citations · 10 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Image-Guided Pavement Defect Recognition in GPR Data with novel 3D Deep Learning Architecture

Yuandong Pan, Linjun Lu, Mudan Wang +6

Ground Penetrating Radar (GPR) is a widely adopted non-destructive sensing technology for subsurface inspection in civil and transportation engineering. Despite its potential for p…

cs.CV2023★ 1 cited

GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue

Zhening Huang, Xiaoyang Wu, Hengshuang Zhao +4

Current point cloud segmentation architectures suffer from limited long-range feature modeling, as they mostly rely on aggregating information with local neighborhoods. Furthermore…

cs.CV2022

NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms

Zhening Huang, Weiwei Chen, Abir Al-Tabbaa +1

Crack detection plays a key role in automated pavement inspection. Although a large number of algorithms have been developed in recent years to further boost performance, there are…

cs.CV2022★ 9 cited

Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes

Eva Agapaki, Ioannis Brilakis

This paper devises, implements and benchmarks a novel shape retrieval method that can accurately match individual labelled point clusters (instances) of existing industrial facilit…

cs.CY2021

CLOI: An Automated Benchmark Framework For Generating Geometric Digital Twins Of Industrial Facilities

Eva Agapaki, Ioannis Brilakis

This paper devises, implements and benchmarks a novel framework, named CLOI, that can accurately generate individual labelled point clusters of the most important shapes of existin…

cs.CV2020

Instance Segmentation of Industrial Point Cloud Data

Eva Agapaki, Ioannis Brilakis

The challenge that this paper addresses is how to efficiently minimize the cost and manual labour for automatically generating object oriented geometric Digital Twins (gDTs) of ind…