Online 6 DOF augmented reality registration from natural features

Chia, Kar Wee; Cheok, Adrian David; Prince, S.J.D.

doi:10.1109/ismar.2002.1115123

Cited by 23 publications

(4 citation statements)

References 17 publications

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“…Early vision-based tracking utilized fiducial markers in prepared AR settings [14]. Currently, vision-based tracking research is based on marker less approach [15][16][17].…”

Section: ) Ar Trackingmentioning

confidence: 99%

Marker-based Augmented Reality Application for Car Basic Maintenance

Eg Su,

Zakaria,

Muhammad Kamarul

et al. 2023

Int J Innov Comp

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In general, Augmented Reality (AR) is a technology that allows the virtual object to complement the real-world environment. The rapid growth of technology has enabled mostly anyone to use AR technology. Thus, applying AR technology in the learning experience should normalize as it is now easy to access and there is much positive feedback based on the past research regarding AR in education. Unlike watching tutorial videos, AR teaching applications would provide users with different learning experiences. Therefore, the aim of this research is to provide more immersive learning experiences to drivers to learn simple car maintenance. This application will illustrate 3D steps and procedures of simple car maintenance for drivers to learn in helping them to self-service their car. Consequently, there are 4 phases of this project that need to be carried out to achieve the objectives of this research. The first phase is to review the AR application concept and understand the car services techniques. The second phase is to design and develop the AR application that will provide simple car services techniques. The third phase is integrating the AR car services manual and finally evaluating the effectives of this application in providing the car basic maintenance tasks and knowledge to new drivers. The evaluation discovered that the drivers have more interaction during learning experience using the AR application.

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“…Early vision-based tracking utilized fiducial markers in prepared AR settings [14]. Currently, vision-based tracking research is based on marker less approach [15][16][17].…”

Section: ) Ar Trackingmentioning

confidence: 99%

Marker-based Augmented Reality Application for Car Basic Maintenance

Eg Su,

Zakaria,

Muhammad Kamarul

et al. 2023

Int J Innov Comp

View full text Add to dashboard Cite

show abstract

“…Some of the most common choices are the Harris corner detector [9], applied in [3,4], and the Kanade-Lucas-Tomasi (KLT) tracker [16], used in [23,7,19]. To automate the initialization and failure recovery of a tracker, reliable wide baseline matching is desired, which in turn imposes a demand for a higher degree of feature invariance.…”

Section: Related Researchmentioning

confidence: 99%

“…In [3] two or more reference views are used to compute current camera pose from epipolar geometry constraints on natural feature correspondences. Markers are still used to pre-calibrate the reference frames with standard calibration tools.…”

Section: Related Researchmentioning

confidence: 99%

What and Where: 3D Object Recognition with Accurate Pose

Gordon

Lowe

2006

Toward Category-Level Object Recognition

114

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Abstract. Many applications of 3D object recognition, such as augmented reality or robotic manipulation, require an accurate solution for the 3D pose of the recognized objects. This is best accomplished by building a metrically accurate 3D model of the object and all its feature locations, and then fitting this model to features detected in new images. In this chapter, we describe a system for constructing 3D metric models from multiple images taken with an uncalibrated handheld camera, recognizing these models in new images, and precisely solving for object pose. This is demonstrated in an augmented reality application where objects must be recognized, tracked, and superimposed on new images taken from arbitrary viewpoints without perceptible jitter. This approach not only provides for accurate pose, but also allows for integration of features from multiple training images into a single model that provides for more reliable recognition 1 .

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“…Some marker-less tracking methods have been introduced which track natural features [4] [24] [1]. These methods are useful when the target objects have rigid and unchanging features, but they suffer from lower reliability than their marker-based counterparts.…”

Section: Introductionmentioning

confidence: 99%

Enabling beyond-surface interactions for interactive surface with an invisible projection

Chan

Kao

et al. 2010

Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology

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This paper presents a programmable infrared (IR) technique that utilizes invisible, programmable markers to support interaction beyond the surface of a diffused-illumination (DI) multi-touch system. We combine an IR projector and a standard color projector to simultaneously project visible content and invisible markers. Mobile devices outfitted with IR cameras can compute their 3D positions based on the markers perceived. Markers are selectively turned off to support multi-touch and direct on-surface tangible input. The proposed techniques enable a collaborative multi-display multitouch tabletop system. We also present three interactive tools: i-m-View, i-m-Lamp, and i-m-Flashlight, which consist of a mobile tablet and projectors that users can freely interact with beyond the main display surface. Early user feedback shows that these interactive devices, combined with a large interactive display, allow more intuitive navigation and are reportedly enjoyable to use.

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Online 6 DOF augmented reality registration from natural features

Cited by 23 publications

References 17 publications

Marker-based Augmented Reality Application for Car Basic Maintenance

Marker-based Augmented Reality Application for Car Basic Maintenance

What and Where: 3D Object Recognition with Accurate Pose

Enabling beyond-surface interactions for interactive surface with an invisible projection

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