Automated spacing measurement of formwork system members with 3D point
cloud data
- URL: http://arxiv.org/abs/2305.19275v1
- Date: Tue, 23 May 2023 12:17:31 GMT
- Title: Automated spacing measurement of formwork system members with 3D point
cloud data
- Authors: Keyi Wu, Samuel A. Prieto, Eyob Mengiste, Borja Garc\'ia de Soto
- Abstract summary: The current way to measure the spacing between formwork system members is mostly done using manual measuring tools.
This research proposes a framework to measure the spacing of formwork system members using 3D point cloud data.
- Score: 0.688204255655161
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The formwork system belonging to the temporary structure plays an important
role in the smooth progress and successful completion of a construction
project. Ensuring that the formwork system is installed as designed is
essential for construction safety and quality. The current way to measure the
spacing between formwork system members is mostly done using manual measuring
tools. This research proposes a framework to measure the spacing of formwork
system members using 3D point cloud data to enhance the automation of this
quality inspection. The novelty is not only in the integration of the different
techniques used but in the detection and measurement of key members in the
formwork system without human intervention. The proposed framework was tested
on a real construction site. Five cases were investigated to compare the 3D
point cloud data approach to the manual approach with traditional measuring
tools. The results indicate that the 3D point cloud data approach is a
promising solution and can potentially be an effective alternative to the
manual approach.
Related papers
- System for 3D Acquisition and 3D Reconstruction using Structured Light
for Sewer Line Inspection [1.5854438418597576]
We introduce an innovative system based on single-shot structured light modules that facilitates the detection and classification of spatial defects.
This system creates highly accurate 3D measurements with sub-millimeter resolution of pipe surfaces and fuses them into a holistic 3D model.
The benefit of such a holistic 3D model is twofold: on the one hand, it facilitates the accurate manual sewer pipe assessment, on the other, it simplifies the detection of defects in downstream automatic systems.
arXiv Detail & Related papers (2023-03-06T09:10:55Z) - 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D
Point Clouds [95.54285993019843]
We propose a method for joint detection and tracking of multiple objects in 3D point clouds.
Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
arXiv Detail & Related papers (2022-11-01T20:59:38Z) - From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction [80.67873933010783]
We argue that MDP is currently witnessing benchmark over-fitting and relying on metrics that are only partially helpful to gauge the usefulness of the predictions for 3D applications.
This limits the design and development of novel methods that are truly aware of - and improving towards estimating - the 3D structure of the scene rather than optimizing 2D-based distances.
We propose a set of metrics well suited to evaluate the 3D geometry of MDP approaches and a novel indoor benchmark, RIO-D3D, crucial for the proposed evaluation methodology.
arXiv Detail & Related papers (2022-03-15T17:50:54Z) - Survey and Systematization of 3D Object Detection Models and Methods [3.472931603805115]
We provide a comprehensive survey of recent developments from 2012-2021 in 3D object detection.
We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade.
We propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation and application activities.
arXiv Detail & Related papers (2022-01-23T20:06:07Z) - Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
We propose a unified and learning based approach to the 3D MOT problem.
We employ a Neural Message Passing network for data association that is fully trainable.
We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
arXiv Detail & Related papers (2021-04-23T17:59:28Z) - 3D Human Body Reshaping with Anthropometric Modeling [59.51820187982793]
Reshaping accurate and realistic 3D human bodies from anthropometric parameters poses a fundamental challenge for person identification, online shopping and virtual reality.
Existing approaches for creating such 3D shapes often suffer from complex measurement by range cameras or high-end scanners.
This paper proposes a novel feature-selection-based local mapping technique, which enables automatic anthropometric parameter modeling for each body facet.
arXiv Detail & Related papers (2021-04-05T04:09:39Z) - Learning the Next Best View for 3D Point Clouds via Topological Features [4.447259318741305]
We introduce a reinforcement learning approach for directing the next best view of a noisy 3D sensor.
The metric combines the disjoint sections of an observed surface to focus on high-detail features such as holes and concave sections.
arXiv Detail & Related papers (2021-03-04T02:19:12Z) - Three dimensional unique identifier based automated georeferencing and
coregistration of point clouds in underground environment [0.0]
This study aims at overcoming practical challenges in underground or indoor laser scanning.
The developed approach involves automatically and uniquely identifiable three dimensional unique identifiers (3DUIDs) in laser scans and a 3D registration (3DReG) workflow.
The developed 3DUID can be used in roadway profile extraction, guided automation, sensor calibration, reference targets for routine survey and deformation monitoring.
arXiv Detail & Related papers (2021-02-22T01:47:50Z) - 3D Registration for Self-Occluded Objects in Context [66.41922513553367]
We introduce the first deep learning framework capable of effectively handling this scenario.
Our method consists of an instance segmentation module followed by a pose estimation one.
It allows us to perform 3D registration in a one-shot manner, without requiring an expensive iterative procedure.
arXiv Detail & Related papers (2020-11-23T08:05:28Z) - InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic
Information Modeling [65.47126868838836]
We propose a novel 3D object detection framework with dynamic information modeling.
Coarse predictions are generated in the first stage via a voxel-based region proposal network.
Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
arXiv Detail & Related papers (2020-07-16T18:27:08Z) - Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless
Approach [32.15405927679048]
We propose a targetless and structureless camera-DAR calibration method.
Our method combines a closed-form solution with a structureless bundle where the coarse-to-fine approach does not require an initial adjustment on the temporal parameters.
We demonstrate the accuracy and robustness of the proposed method through both simulation and real data experiments.
arXiv Detail & Related papers (2020-01-17T07:25:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.