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Found 9 papers, 2 papers with code
Impact of color and mixing proportion of synthetic point clouds on semantic segmentation
Deep learning (DL)-based point cloud segmentation is essential for understanding built environment.
Scan-to-BIM for As-built Roads: Automatic Road Digital Twinning from Semantically Labeled Point Cloud Data
Creating geometric digital twins (gDT) for as-built roads still faces many challenges, such as low automation level and accuracy, limited asset types and shapes, and reliance on engineering experience.
Cypher4BIM: Releasing the Power of Graph for Building Knowledge Discovery
This study aims to explore graphic building information query and develop a graph query language tailored for IFC-Graph.
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue
For feature aggregation, it improves feature modeling by allowing the network to learn from both local points and neighboring geometry partitions, resulting in an enlarged data-tailored receptive field.
NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms
In the comparison study, crack detection algorithms were trained equally on the largest public crack dataset collected and evaluated on the proposed dataset (NHA12D).
Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes
This paper devises, implements and benchmarks a novel shape retrieval method that can accurately match individual labelled point clusters (instances) of existing industrial facilities with their respective CAD models.
CLOI: An Automated Benchmark Framework For Generating Geometric Digital Twins Of Industrial Facilities
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 existing industrial facilities with minimal manual effort in a generic point-level format.
Instance Segmentation of Industrial Point Cloud Data
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 industrial facilities, so that the benefits provide even more value compared to the initial investment to generate these models.
3D Semantic Parsing of Large-Scale Indoor Spaces
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.
