Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

Hübner, Patrick; Wursthorn, Sven; Weinmann, Martin

doi:10.5194/isprs-annals-v-4-2022-121-2022

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…in the ISPRS benchmark on indoor modelling (Khoshelham et al, 2021). A discrete version of this metric suitable for voxel data has been proposed in (Hübner et al, 2022). However, this metric relies on the availability of reference geometry, either in the form of a point cloud (Anil et al, 2013;Bonduel et al, 2017;Chen et al, 2018;Assali et al, 2019) or a manually created building model (Khoshelham et al, 2018) which, in turn, is derived from a point cloud or manual in-situ measurements.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Geometry of an Indoor Scenario With Occlusions Based on Total Station Measurements of Wall Elements

Schmidt,

Volland,

Hübner

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Scan2BIM approaches, i.e. the automated reconstruction of building models from point cloud data, is typically evaluated against the same point clouds which are used as input for the reconstruction process. In doing so, the point clouds are often used as ground truth without considering their own inaccuracies. Thus, in this research, we investigate the manual creation of an accurate ground truth, with a process which takes into account the measurement accuracy as well as the modeling accuracy. Therefore we created a ground truth to an existing laser scan data with a total station, based on the assumption that a total station generally measures points more reliably. In addition, a manual selection and classification of points on the wall surfaces during the measurement, serves a reliable detection of the walls via plane fitting. This allows for the creation of a more reliable ground truth, which is determined by the intersection of the planes from corners and edges. The ground truth is aligned parallel to the axes of a local coordinate system. From MLS and TLS point clouds of the same building area, walls are manually classified and corners and edges are determined in a similar way to the total station. These TLS and MLS corners are registered to this ground truth using least squares optimisation at the vertices. The transformation thus determined is used to transform the laser scanning point clouds as well. The resulting errors in the corners and the whole point cloud are evaluated. We conclude that the standard deviation of wall surfaces alone isn’t sufficient to determine the quality of the reconstructed building model. Despite low measurement noise in single wall surfaces, deviations in the reconstructed room model may arise.

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Section: Related Workmentioning

confidence: 99%

Evaluating Geometry of an Indoor Scenario With Occlusions Based on Total Station Measurements of Wall Elements

Schmidt,

Volland,

Hübner

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Other works have studied segmentation techniques applied to HoloLens, such as voxel-based methods. Code and datasets are available on GitHub (Hübner et al, 2020b(Hübner et al, , 2022.…”

Section: Review Of Related Workmentioning

confidence: 99%

Interactive Capture and Labelling of Point Clouds With Hololens2 for Semantic Segmentation

González,

González-Collazo,

Balado

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Low-cost capture systems allow faster data acquisition compared to other systems, although obtained resolution is lower. These systems can be used to acquire low-quality point clouds to classify with simple segmentation algorithms. In this work, we use Microsoft HoloLens 2 sensors to scan indoor 3D environments and hand tracking to position label tags that will support the point cloud segmentation based on heuristic algorithms. Thus, from each tag, several algorithms have been designed to segment doors, windows, columns, walls, ceilings, and floors. The method was tested on 3 real case studies, obtaining F1-Score between 0.61 and 0.96 depending on the object class. These results reinforce the idea that, by combining Mixed Reality with basic point cloud processing algorithms, low-quality point cloud data can be correctly processed without resorting to complex Artificial Intelligence techniques and without labelling large amounts of samples.

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“…Kankare et al, used the point cloud down-sampling method to increase the processing speed and keep the accuracy steady [ 19 ]. Hubner et al, proposed a method of indoor point cloud classification based on a normal vector to improve the classification accuracy by 10% [ 20 ]. Therefore, the combination of the deep learning method and 3D point cloud would provide a new feasible way for higher accuracy of filled rice grain classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Filled/Unfilled Grain Classification Based on Structured Light Imaging and Improved PointNet++

Huang

Lü

Shi

et al. 2023

Sensors

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China is the largest producer and consumer of rice, and the classification of filled/unfilled rice grains is of great significance for rice breeding and genetic analysis. The traditional method for filled/unfilled rice grain identification was generally manual, which had the disadvantages of low efficiency, poor repeatability, and low precision. In this study, we have proposed a novel method for filled/unfilled grain classification based on structured light imaging and Improved PointNet++. Firstly, the 3D point cloud data of rice grains were obtained by structured light imaging. And then the specified processing algorithms were developed for the single grain segmentation, and data enhancement with normal vector. Finally, the PointNet++ network was improved by adding an additional Set Abstraction layer and combining the maximum pooling of normal vectors to realize filled/unfilled rice grain point cloud classification. To verify the model performance, the Improved PointNet++ was compared with six machine learning methods, PointNet and PointConv. The results showed that the optimal machine learning model is XGboost, with a classification accuracy of 91.99%, while the classification accuracy of Improved PointNet++ was 98.50% outperforming the PointNet 93.75% and PointConv 92.25%. In conclusion, this study has demonstrated a novel and effective method for filled/unfilled grain recognition.

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Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

Cited by 3 publications

References 54 publications

Evaluating Geometry of an Indoor Scenario With Occlusions Based on Total Station Measurements of Wall Elements

Evaluating Geometry of an Indoor Scenario With Occlusions Based on Total Station Measurements of Wall Elements

Interactive Capture and Labelling of Point Clouds With Hololens2 for Semantic Segmentation

A Novel Method for Filled/Unfilled Grain Classification Based on Structured Light Imaging and Improved PointNet++

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