Piecewise smooth surface reconstruction

Hoppe, Hugues; DeRose, Tony; Duchamp, Tom; Halstead, Mark A.; Jin, Hubert; McDonald, John A.; Schweitzer, Jean; Stuetzle, Werner

doi:10.1145/192161.192233

Cited by 516 publications

(387 citation statements)

References 24 publications

(31 reference statements)

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“…A few papers described modeling of 3D data acquired from longer range sampling of buildings and entire rooms. The papers descrbing modeling techniques are the following: Hoppe et al [39], Welch and Witkin [81], Krishnamurthy and Levoy [51], Zhao and Mohr [90], Lindstrom et al [60], Leonardis et al [57], Luebke and Erikson [63], Cignoni et al [15], Hirsch et al [37], Dorai and Jain [20], Bors and Pitas [11], Witzgall et al [85,86,87]. There are a number of papers that describe modeling techniques applied to range data.…”

Section: Object Identificationmentioning

confidence: 99%

Construction object identification from LADAR scans:

Gilsinn¹,

Witzgall²,

Cheok³

et al. 2005

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Laser Scanning devices (a.k.a. LADAR for Laser Detection and Ranging) are primarily used in construction projects to capture as-built data for documentation and re-engineering. These systems can rapidly generate unstructured point clouds of scene data within the field of view and line-of-sight of the LADAR. The Construction Metrology and Automation Group (CMAG) at NIST is interested in using these high-resolution scanned data to identify and locate construction objects of interest. The test operation is large-scale pick-and-place of structural steel, where the data from the scans are used to identify the general pose, position and orientation of a target object for initial positioning of a robotic crane. Close-in control of the crane using high framerate flash LADAR is a separate but related area of CMAG research.This initial study describes an experiment in which an I-beam on a concrete floor surface is scanned, and the resulting point cloud data are used to calculate its pose. Multiple scans of two sizes of I-beams were taken with the I-beam placed at various angles relative to the scanner. Two approaches for segmenting potential target objects are described: binning and triangular irregular networks (TINs). Using the method of principal components analysis, the pose of potential target objects is determined. Bounding boxes are then formed around these objects and compared to an ideal bounding box generated from the known geometric specifications of the Ibeam of interest. A measure of best fit is used to determine which scanned object most closely fits the bounding box of the ideal I-beam. A separate laser-based site measurement system (SMS) was used to measure points on the I-beams to form reference data for estimating the closeness of fit of computed pose of the I-beam to measured pose of the I-beam. Three spheres were located in the scan field as a means of registering the scan and SMS axes to one another.

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Section: Object Identificationmentioning

confidence: 99%

Construction object identification from LADAR scans:

Gilsinn¹,

Witzgall²,

Cheok³

et al. 2005

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“…A commonly used method in connection with B-splines is extra knot insertion. We employ a more general method here based on work of Hoppe [4] for subdivision surfaces. It allows us to "tag" control points, allowing different averaging masks to be used at these points.…”

Section: Piecewise Smooth Subdivision Curvesmentioning

confidence: 99%

“…This is inspired by the work of Hoppe [4], who successfully used tagged subdivision surfaces for fitting piecewise smooth surfaces to 3D point clouds. The curve fitting literature includes use of algebraic curves [5,6], piecewise polynomials [7,8,9], point curves [10], and B-splines [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Subdivision curves are simple to implement and provide a flexible way of representing curves of any type, including all kinds of B-splines and extending to functions without analytic representation [14]. In [4], Hoppe introduces piecewise smooth subdivision surfaces, allowing to model sharp features such as creases and corners by tagging the corresponding control points. We apply the tagging concept to subdivision curves to represent piecewise smooth curves.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MCMC-Based Multiview Reconstruction of Piecewise Smooth Subdivision Curves with a Variable Number of Control Points

Kaess

Zboinski

Dellaert

2004

Lecture Notes in Computer Science

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Abstract. We investigate the automated reconstruction of piecewise smooth 3D curves, using subdivision curves as a simple but flexible curve representation. This representation allows tagging corners to model nonsmooth features along otherwise smooth curves. We present a reversible jump Markov chain Monte Carlo approach which obtains an approximate posterior distribution over the number of control points and tags. In a Rao-Blackwellization scheme, we integrate out the control point locations, reducing the variance of the resulting sampler. We apply this general methodology to the reconstruction of piecewise smooth curves from multiple calibrated views, in which the object is segmented from the background using a Markov random field approach. Results are shown for multiple images of two pot shards as would be encountered in archaeological applications.

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“…Hoppe et al [6] presented a method to reconstruct piecewise smooth surface models from scattered range data using a variation of Loop's scheme [7]. Morin et al [8] addressed the issue of reconstructing rotational features in surfaces as special cases.…”

Section: Introductionmentioning

confidence: 99%

A New Class of Non-stationary Interpolatory Subdivision Schemes Based on Exponential Polynomials

Choi

Lee

Yoon

et al. 2006

Geometric Modeling and Processing - GMP 2006

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Abstract. We present a new class of non-stationary, interpolatory subdivision schemes that can exactly reconstruct parametric surfaces including exponential polynomials. The subdivision rules in our scheme are interpolatory and are obtained using the property of reproducing exponential polynomials which constitute a shift-invariant space. It enables our scheme to exactly reproduce rotational features in surfaces which have trigonometric polynomials in their parametric equations. And the mask of our scheme converges to that of the polynomial-based scheme, so that the analytical smoothness of our scheme can be inferred from the smoothness of the polynomial based scheme.

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Piecewise smooth surface reconstruction

Cited by 516 publications

References 24 publications

Construction object identification from LADAR scans:

Construction object identification from LADAR scans:

MCMC-Based Multiview Reconstruction of Piecewise Smooth Subdivision Curves with a Variable Number of Control Points

A New Class of Non-stationary Interpolatory Subdivision Schemes Based on Exponential Polynomials

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