A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modeling

Ingman, Matias; White, Joanne C.; Hyyppä, Hannu

doi:10.3390/rs12162624

Cited by 10 publications

(6 citation statements)

References 47 publications

(81 reference statements)

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“…The study aims to evaluate the quality, accuracy and useability of these methods as digital twin models. Terrestrial laser scanners (TLS) provide good quality point clouds, but data collection requires careful planning and is time consuming [13]. This is especially true in indoor spaces where visibility is restricted by walls and other clutter that raise the number of needed scanning locations up to a large number.…”

Section: Methodsmentioning

confidence: 99%

The use of digital twin models to document cultural heritage monuments

Themistocleous

Evagorou²,

Mettas³

et al. 2022

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

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A digital twin model can be defined as a connection between a virtual model and its real-life counterpart, by providing an interactive virtual environment that represents the real world. The use of digital twin models in cultural heritage enables better decision-making about preventive maintenance, heritage management, and interpretation, as well as accurate digital documentation. In cultural heritage, digital twin models are important in order to convey the real-life experience of the space through an accurate and detailed virtual representation. The use of a realistic virtual environment depends on the theory that people perceive and respond to virtual environments in the same manner as the real world. Therefore, a very realistic virtual environment is expected to create the emotion in the person as they would have achieved in the physical site. In this study, a digital twin model was created using Structure for Motion photogrammetry and a depth camera mapping system and compared to a model generated using laser scanning. The use of in-depth camera mapping utilizes panoramic image walkthroughs by facilitating panoramic image views by rendering an overview of the space. The use of digital twin models in cultural heritage provides a 4th dimension in space by providing additional semantic and geometric information to the user.

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“…Current photogrammetric software can automatically reconstruct 3D mesh models (Furukawa, Curless, Seitz, & Szeliski, 2009;Jancosek & Pajdla, 2011;Romanoni, Delaunoy, Pollefeys, & Matteucci, 2016). For indoor modelling, various photogrammetric methods have been used, such as 3D mapping systems (El-Hakim, Boulanger, Blais, & Beraldin, 1997), videogrammetry-based 3D modelling (Haggrén & Mattila, 1997), structured indoor modelling (Ikehata, Yang, & Furukawa, 2015), cloud-based indoor 3D modelling (Ingman, Virtanen, Vaaja, & Hyyppä, 2020) and other indoor measuring methods (Georgantas, Brédif, & Pierrot-Desseilligny, 2012;Lehtola et al, 2017;León-Vega & Rodríguez-Laitón, 2019). In addition, high dynamic range (HDR) photogrammetry has been used for luminance mapping of the sky and the sun (Cai, 2015) and a laser-scanned point cloud have been coloured with luminance values in a nighttime road environment (Vaaja et al, 2015;Vaaja et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Applying photogrammetry to reconstruct 3D luminance point clouds of indoor environments

Kurkela

Maksimainen

Julin

et al. 2020

Architectural Engineering and Design Management

Self Cite

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“…Moreover, the sensor has been the basis of other RGB-D cameras such as Asus Xtion [ 13 ], Orbbec Astra 3D [ 14 ] or Occipital Structure Sensor [ 15 ]. All them are used in many high accuracy applications, including indoor 3D modeling [ 16 , 17 , 18 , 19 ], simultaneous localization and mapping (SLAM) [ 20 , 21 , 22 ], or augmented reality [ 23 ], which require a rigorous calibration and error modeling of RGB-D camera data to produce high quality information [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras

Vila

Boada

Raba³

et al. 2021

Sensors

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Although low cost red-green-blue-depth (RGB-D) cameras are factory calibrated, to meet the accuracy requirements needed in many industrial applications proper calibration strategies have to be applied. Generally, these strategies do not consider the effect of temperature on the camera measurements. The aim of this paper is to evaluate this effect considering an Orbbec Astra camera. To analyze this camera performance, an experimental study in a thermal chamber has been carried out. From this experiment, it has been seen that produced errors can be modeled as an hyperbolic paraboloid function. To compensate for this error, a two-step method that first computes the error and then corrects it has been proposed. To compute the error two possible strategies are proposed, one based on the infrared distortion map and the other on the depth map. The proposed method has been tested in an experimental scenario with different Orbbec Astra cameras and also in a real environment. In both cases, its good performance has been demonstrated. In addition, the method has been compared with the Kinect v1 achieving similar results. Therefore, the proposed method corrects the error due to temperature, is simple, requires a low computational cost and might be applicable to other similar cameras.

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“…However, since the launch of low-price depth cameras in 2011 [3], the number of users of low-price depth cameras has gradually increased, and the application of depth cameras has become increasingly common. The combination of depth and RGB images can be used in action recognition [4], simultaneous localization and mapping (SLAM) [5], 3D reconstruction [6], augmented reality (AR) [7], and other geographic information applications.…”

Section: Introductionmentioning

confidence: 99%

Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

Zhang¹,

Xia²,

Liu³

et al. 2021

IJGI

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Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each image in the training set is calculated through feature extraction and description, image registration, and pose map optimization. Then, in the image retrieval stage, the training set and the query set are clustered to generate the vector of local aggregated descriptors (VLAD) description vector. In order to overcome the problem that the description vector loses the image color information and improve the retrieval accuracy under different lighting conditions, the opponent color information and depth information are added to the description vector for retrieval. Finally, using the point cloud corresponding to the retrieval result image and its pose, the pose of the retrieved image is calculated by perspective-n-point (PnP) method. The results of indoor scene positioning under different illumination conditions show that the proposed method not only improves the positioning accuracy compared with the original VLAD and ORB-SLAM2, but also has high computational efficiency.

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“…However, since the launch of Kinect’s low-price depth cameras in 2011 [ 1 ], the number of users of low-price depth cameras has gradually increased, and the application of depth cameras has become increasingly common. The combination of depth and RGB images can be used in action recognition [ 2 , 3 , 4 ], simultaneous localization and mapping (SLAM) [ 5 , 6 , 7 ], 3D reconstruction [ 8 , 9 , 10 , 11 ], augmented reality [ 12 , 13 , 14 ] and other geographic information applications. The resolution of a depth image collected by the early Kinect depth sensor was only 640 × 480 pixels; hence, the data were not ideal.…”

Section: Introductionmentioning

confidence: 99%

Texture Synthesis Repair of RealSense D435i Depth Images with Object-Oriented RGB Image Segmentation

Zhang

Xia

Qiao

2020

Sensors

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A depth camera is a kind of sensor that can directly collect distance information between an object and the camera. The RealSense D435i is a low-cost depth camera that is currently in widespread use. When collecting data, an RGB image and a depth image are acquired simultaneously. The quality of the RGB image is good, whereas the depth image typically has many holes. In a lot of applications using depth images, these holes can lead to serious problems. In this study, a repair method of depth images was proposed. The depth image is repaired using the texture synthesis algorithm with the RGB image, which is segmented through a multi-scale object-oriented method. The object difference parameter is added to the process of selecting the best sample block. In contrast with previous methods, the experimental results show that the proposed method avoids the error filling of holes, the edge of the filled holes is consistent with the edge of RGB images, and the repair accuracy is better. The root mean square error, peak signal-to-noise ratio, and structural similarity index measure from the repaired depth images and ground-truth image were better than those obtained by two other methods. We believe that the repair of the depth image can improve the effects of depth image applications.

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A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modeling

Cited by 10 publications

References 47 publications

The use of digital twin models to document cultural heritage monuments

Applying photogrammetry to reconstruct 3D luminance point clouds of indoor environments

A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras

Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

Texture Synthesis Repair of RealSense D435i Depth Images with Object-Oriented RGB Image Segmentation

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