Multiple planes based registration using 3D Projective Space for Augmented Reality

Uematsu, Yuko; Saitō, Hideo

doi:10.1016/j.imavis.2009.01.003

Cited by 9 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As illustrated in Figure 1, suppose Ȇ is one plane in the space, and the homography corresponding to Ȇ in the two images is H, select any two points P and Q outside of Ȇ, the image points of P and Q in the two images are p 1 q 1 p 2 q 2 . Suppose: Hq q (6) Then, the straight line In the 3D space, any three points out of one straight line can determine a plane. As illustrated in Figure 2, suppose Ȇ is the plane determined by x 1 x 2 and x 3 , and it is not parallel to the baseline.…”

Section: Calculated Homography Based On Three Pairs Of Pointsmentioning

confidence: 99%

“…(6) In the case of direction is not defined, it can not decide to judge the direction in which side of plane Ȇ based on homography constraints, but the space points can be divided into two categories according to the symbol of k, they are located on both sides of Ȇ, thus, when to detect the multiple planes, the number of subsequent modeling will be greatly reduced. On this basis, this article proposes a fast multi-plane detection algorithm.…”

Section: Classification Of Feature Pointsmentioning

confidence: 99%

“…There are many special geometric properties in planes and they are abound in the building structures and artificial scenes, consequently, they are widely used in feature matching and classification [1], camera selfcalibration [2], obstacle detection, 3D reconstruction and scene analysis [3] [4], visual orientation, object recognition, visual measurement, image stitching [5], augmented reality [6], automotive safe driving [7] [8] and other mission of computer vision.…”

Section: Introductionmentioning

confidence: 99%

“…Feature distribution based on homography constrain Operate the vector cross product for (5) can obtain(6).…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Planes Detection Algorithm Based on Feature Distribution

Zhao

2011

2011 Third International Conference on Intelligent Networking and Collaborative Systems

View full text Add to dashboard Cite

For detecting multiple planar regions rapidly in the image sequence, this paper proposed a novel algorithm. First of all, the method uses RANSAC to detect a dominant homography, then, the method calculates the poles corresponding to the two images based on homography constraint and classify all the matching feature points. When detect the homography corresponding to more planes based on RANSAC once more, only three pairs of feature points should be selected from the classified feature points, that is, define the to be determined plane homography model together with a pair of poles, and this can improve the detection efficiency. Simulation and real experiment results show hat the proposed algorithm is robust to the existence of mismatched features and is applicable to either stereo or motion sequence images.

show abstract

Section: Calculated Homography Based On Three Pairs Of Pointsmentioning

confidence: 99%

Section: Classification Of Feature Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Feature distribution based on homography constrain Operate the vector cross product for (5) can obtain(6).…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Planes Detection Algorithm Based on Feature Distribution

Zhao

2011

2011 Third International Conference on Intelligent Networking and Collaborative Systems

View full text Add to dashboard Cite

show abstract

“…For other purposes, plane registration is common in robotics and augmented reality to estimate the position of the camera in the scenario. For example, walls, floor and roof are used to extract the plane models that define them in (Pathak et al, 2009;Lee et al, 2012;Uematsu and Saito, 2009). Those planes are registered along the movement of robots to estimate robot position, but not in order to faithfully reconstruct the scenario.…”

Section: Introductionmentioning

confidence: 99%

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Saval-Calvo

Azorín-López

Fuster-Guilló

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

Many applications including object reconstruction, robot guidance, and scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown an it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the Signal-to-Noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, µ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a * Corresponding author * * Principal corresponding author Email addresses: msaval@dtic.ua.es (Marcelo Saval-Calvo), jazorin@dtic.ua.es (Jorge Azorín-López), fuster@dtic.ua.es (Andrés Fuster-Guilló), hmora@dtic.ua.es The results show the good performance of the µ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

show abstract

Displacement measurement and nonlinear structural system identification: A vision‐based approach with camera motion correction using planar structures

Jiao

Guo

Fujita

et al. 2021

Struct Control Health Monit

View full text Add to dashboard Cite

Summary Knowledge of the camera motion is important in the applications of structural dynamic response measurement using a vision‐based approach because in most of the field measurements, such motion can be non‐trivial and the accuracy of dynamic response measurements is strongly affected by the camera motion. This paper presents a new framework for camera motion estimation and vision‐based displacement measurement, which greatly lowers the barriers to the application of generally positioned cameras from strictly stationary cameras. The contributions in this paper are twofold. First, camera motion as well as reconstructed structural displacement are calculated based on reference planar structures visible in a given scene. In this case, a homography is more effective at describing view changes and planar geometric constraints can be incorporated early in the reconstruction process, thereby improving the quality and effectiveness of the estimates. The second contribution is that the homography of the planar structure is estimated by a newly proposed tracking algorithm that combines RANSAC algorithm and Efficient Second‐order Minimization (ESM) technique, which refines the final estimates to sub‐pixel accuracy and avoids tracking drift and non‐smoothness effectively. Experimental results indicate that the quality of the camera motion estimation and displacement reconstruction can be significantly improved by the judgmatical use of the proposed algorithm for planar structure homography estimation. Furthermore, nonlinear structural system identification is carried out to additionally verify the proposed algorithm using unscented Kalman filter.

show abstract

Multiple planes based registration using 3D Projective Space for Augmented Reality

Cited by 9 publications

References 23 publications

A Planes Detection Algorithm Based on Feature Distribution

A Planes Detection Algorithm Based on Feature Distribution

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Displacement measurement and nonlinear structural system identification: A vision‐based approach with camera motion correction using planar structures

Contact Info

Product

Resources

About