Construction Scene Segmentation Using 3D Point Clouds: A Dataset and Challenges
Publication: Construction Research Congress 2024
ABSTRACT
With the purpose of facilitating process tracking such as inspection reports and progress monitoring, the AEC industry has adopted an as-built 3D model that is reconstructed using a 3D scanner during or after construction. In response to the laborious difficulties of converting a point cloud into a semantically rich model, for example, BIM, researchers are attempting to automate this process via machine learning, applying 3D semantic segmentation and parametric modeling. However, there are no publicly accessible 3D datasets that target construction sites, regarded as unstructured and cluttered scenes, thus yielding a barrier to construction scene segmentation development. To this end, this paper aims to generate a 3D construction dataset that can be utilized for machine learning models requiring ground truth and to suggest foundation processing for general scene segmentation on construction datasets. In addition, we identify and discuss several challenges pertaining to construction sites, in terms of 3D semantic segmentation.
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Information & Authors
Information
Published In
Construction Research Congress 2024
Pages: 378 - 385
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer networks
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction management
- Construction sites
- Detection methods
- Engineering fundamentals
- Equipment and machinery
- Methodology (by type)
- Models (by type)
- Three-dimensional models
- Tracking
Authors
Metrics & Citations
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