Fast, Accurate, Small-Scale 3D Scene Capture Using a Low-Cost Depth Sensor

Carey, Nicole; Werfel, Justin; Nagpal, Radhika

doi:10.1109/wacv.2017.146

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…Consequently, the infrared camera picks up these light dots again and the pattern is analyzed by software to create a depth map. Using this depth map, a mathematical model is generated by machine learning algorithms [9,[13][14][15]. In iOS, the operating system of Apple's smartphones and tablets, TrueDepth is mainly used for 3D face authentication and recognition, while LiDAR enables new features for Augmented Reality by accelerating plane detection.…”

Section: Methodsmentioning

confidence: 99%

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Comparison of iPad Pro®’s LiDAR and TrueDepth Capabilities with an Industrial 3D Scanning Solution

2021

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Today’s smart devices come equipped with powerful hard- and software-enabling professional use cases. The latest hardware by Apple utilizes LiDAR and TrueDepth, which offer the capability of 3D scanning. Devices equipped with these camera systems allow manufacturers to obtain 3D data from their customers at low costs, which potentially enables time-efficient mass customization and product differentiation strategies. However, the utilization is limited by the scanning accuracy. To determine the potential application of LiDAR and TrueDepth as a 3D scanning solution, in this paper an evaluation was performed. For this purpose, different Lego bricks were scanned with the technologies and an industrial 3D scanner. The results were compared according to shape and position tolerances. Even though the industrial 3D scanner consistently delivered more accurate results, the accuracy of the smart device technologies may already be sufficient, depending on the application.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of iPad Pro®’s LiDAR and TrueDepth Capabilities with an Industrial 3D Scanning Solution

2021

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“…A 5-mm-diameter circular opening in the centre of the plate gave access to the cylinder of soil underneath, which provided both a source of soil for deposition on the surface and a space where termites could retreat via excavation. We monitored building activity above the surface over 4 h, recording 2D activity (using an RGB camera) and 3D soil deposition (using an infrared depth camera) at 1 Hz (Carey et al, 2017) (Fig. 2, Movie 1).…”

Section: Methodsmentioning

confidence: 99%

Differential construction response to humidity by related species of mound-building termites

Carey

Calovi

Bardunias

et al. 2019

Journal of Experimental Biology

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Macrotermes michaelseni and M. natalensis are two morphologically similar termite species occupying the same habitat across southern Africa. Both build large mounds and tend mutualistic fungal symbionts for nutrients, but despite these behavioural and physiological similarities, the mound superstructures they create differ markedly. The behavioural differences behind this discrepancy remain elusive, and are the subject of ongoing investigations. Here, we show that the two species demonstrate distinctive building activity in a laboratory-controlled environment consisting of still air with low ambient humidity. In these conditions, M. michaelseni transports less soil from a central reservoir, deposits this soil over a smaller area, and creates structures with a smaller volumetric envelope than M. natalensis. In high humidity, no such systematic difference is observed. This result suggests a differential behavioural threshold or sensitivity to airborne moisture that may relate to the distinct macro-scale structures observed in the African bushland.

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“…The SR300 is used for fast, accurate small-scale 3D scene capture in the works of Carey et al (2017) by adding lenses and precise calibration, the device is used for tracking termite activity at high speeds with mmlevel accuracy. The works demonstrates high accuracy and high-speed capabilities of the SR300 depth camera.…”

Section: Related Workmentioning

confidence: 99%

Nondestructive Test Using a 3D Computer Vision System for Damage Detection of Structures

Szeto¹,

Yi²

2018

American Journal of Engineering and Applied Sciences

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Abstract:Structures that do not have a definitive map nor a clearly known state of health may exist underground, beyond our reach and in unsuitable environments. Nuclear facilities contain underground tunnels that exhaust hazardous gases. Industry has miles of sewage lines beneath it that emit flammable and toxic gases. Regular inspection and maintenance is an essential part of failure prevention, however, these fatal environments have prohibited proper inspection of such infrastructure. Thus, the future of computer vision is vital to quality inspection. A strategically designed robot can be trained to visually inspect any structure, detect if there is a damage and decide if the damage is critical. Likened to any good inspector, the robot must be trained to investigate the nature of the damage and to alert the user of potential failures. This article discusses robotic training to detect damage in concrete structures and make decisions to the significance and impact of the defect.

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Fast, Accurate, Small-Scale 3D Scene Capture Using a Low-Cost Depth Sensor

Cited by 14 publications

References 19 publications

Comparison of iPad Pro®’s LiDAR and TrueDepth Capabilities with an Industrial 3D Scanning Solution

Comparison of iPad Pro®’s LiDAR and TrueDepth Capabilities with an Industrial 3D Scanning Solution

Differential construction response to humidity by related species of mound-building termites

Nondestructive Test Using a 3D Computer Vision System for Damage Detection of Structures

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