Characterizing three-dimensional surface structures from visual images

Wang, Y.F.

doi:10.1109/34.67630

Cited by 35 publications

(12 citation statements)

References 26 publications

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“…(27) Assume that the local window W is of size -r < x < r and -s < y < s. Dropping higher-order terms from Equation 27, and rewriting the ratio (-a/c, -b/c) as (p q)-the surface gradients, we can solve for the surface gradients using which represents another "observable-prediction-model" structure. Using the texture gradient constraint, the potential energy on the entire image plane 1 composed of many local windows is Ptexture = ff ((I -z42 + (12 _ z)2) dx dy , (29) and the governing equation can be shown to be…”

Section: Physical Model Using the Texture Gradient Constraintmentioning

confidence: 99%

<title>Surface reconstruction method using deformable templates</title>

Wang

1991

Sensor Fusion III: 3D Perception and Recognition

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In this paper, we propose a unification framework for three-dimensional shape reconstruction using physicallybased models. Most shape-from-X techniques use an "observable" (e.g., disparity, intensity, and texture gradient) and a model, which is based on specific domain knowledge (e.g., triangulation principle, reflectance function, and texture distortion equation) to predict the observable, in 3-D shape reconstruction. We show that all these "observable-prediction-model" types of techniques can be incorporated into our framework of energy constraint on a flexible, deformable image frame. In our algorithm, if the observable does not confirm with that predicted by the corresponding model, a large "error" potential results. The error potential gradient forces the flexible image frame to deform in space. The deformation brings the flexible image frame to "wrap" onto the surface of the imaged 3-D object. Surface reconstruction is thus achieved through a "package wrapping" process by minimizing the discrepancy in the observable and the model prediction. The dynamics of such a wrapping process are governed by the least action principle which is physically correct. A physically-based model is essential in this general shape reconstruction framework because of its capability to recover the desired 3-D shape, to provide an animation sequence of the reconstruction, and to include the regularization principle into the theory.

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Section: Physical Model Using the Texture Gradient Constraintmentioning

confidence: 99%

<title>Surface reconstruction method using deformable templates</title>

Wang

1991

Sensor Fusion III: 3D Perception and Recognition

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“…These sensor measurements are used to determine the value of the "state vector" (i.e., the positions of the control vertices). Sensor measurements employed the active structured-lighting technique to infer surface orientation and curvature [46], [49] in shape construction.…”

Section: The Modeling Schemementioning

confidence: 99%

“…If the principal surface curvatures, κ 1 and κ 2 , and their directions, are inferred using, say, the structured light technique reported in [49], then constraints on the second surface derivatives, S uu and S vv , can also be derived. More precisely, we first apply Euler's theorem [21], [41] to compute the normal surface curvatures, κ un and κ vn , along the S u and S v directions…”

Section: The Modeling Schemementioning

confidence: 99%

“…by the underlying surface structure, hence, it should be possible to relate the pattern distortion to the structure of the imaged object. In [46], [49], we developed methods for inferring 3D surface orientation and curvature from 2D image pattern orientation and curvature. Such techniques made available orientation and curvature constraints for 3D shape modeling.…”

Section: Experiments With Real Image Sequencesmentioning

confidence: 99%

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On 3D Model Construction by Fusing Heterogeneous Sensor Data

Wang¹

1994

Computer Vision and Image Understanding

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In this paper, we propose a scheme for 3D model construction by fusing heterogeneous sensor data. The proposed scheme is intended for use in an environment where multiple, heterogeneous sensors operate asynchronously. Surface depth, orientation, and curvature measurements obtained from multiple sensors and vantage points are incorporated to construct a computer description of the imaged object. The proposed scheme uses Kalman filter as the sensor data integration tool and hierarchical spline surface as the recording data structure. Kalman filter is used to obtain statistically optimal estimates of the imaged surface structure based on possibly noisy sensor measurements. Hierarchical spline surface is used as the representation scheme because it maintains high-order surface derivative continuity, may be adaptively refined, and is storage efficient. We show in this paper how these mathematical tools can be used in designing a modeling scheme to fuse heterogeneous sensor data.

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“…The slit-ray projection method [10][11][12][13] (i.e., shape from structured light) is widely used in industrial applications and robot vision systems. It has high measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

Handheld three-dimensional pipe measurement system with a slit-ray projector

2013

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We propose a point cloud data acquisition system that employs slit ray projection. In this system, a slit laser projector and a high-resolution CCD camera are connected to a Microsoft Kinect Sensor. The system is sufficiently compact that it can be hand held. In measurements of pipes, the user directs the laser slit ray at the measurement target. Kinect then detects point cloud data while the CCD camera simultaneously detects the laser streak generated on the target surface. The user manually scans the system by directing the laser slit ray along the measurement pipe. The point cloud data obtained by Kinect is used to determine the movement of the system by adjusting overlapping data in consecutive frames using the ICP (Iterative Closest Point) algorithm. This permits the system to be freely scanned. The pipe cross section is estimated from data obtained by the slit-ray projection method. The three-dimensional shape of the pipe is constructed on a computer from the obtained cross sections.

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Characterizing three-dimensional surface structures from visual images

Cited by 35 publications

References 26 publications

<title>Surface reconstruction method using deformable templates</title>

<title>Surface reconstruction method using deformable templates</title>

On 3D Model Construction by Fusing Heterogeneous Sensor Data

Handheld three-dimensional pipe measurement system with a slit-ray projector

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