3D shape recognition and reconstruction based on line element geometry

Höfer, Michael; Odehnal, Boris; Pottmann, Helmut; Steiner, Tibor; Wallner, Johannes

doi:10.1109/iccv.2005.2

Cited by 32 publications

(55 citation statements)

References 18 publications

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“…Thus this method tends to accept some points that match the motion, but are not conceptually part of a coherent sweep surface. We may perform some light filtering using morphological operators to remove narrow disconnected surface strips, as suggested by [14]. However, some user correction may still be needed in cases such as the one shown in Figure 2, because the erroneously accepted regions may be fairly large, and there is no well-defined metric reason to reject them.…”

Section: "Stationary" or Generalized Rotational Sweepsmentioning

confidence: 99%

Interactive Inverse 3D Modeling

Andrews

Jin

Séquin

2012

Computer-Aided Design and Applications

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ABSTRACT"Interactive Inverse 3D Modeling" is a user-guided approach to shape construction and redesign that extracts well-structured, parameterized, procedural descriptions from unstructured, hierarchically flat input data, such as point clouds, boundary representation meshes, or even multiple pictorial views of a given inspirational prototype. This approach combines traditional "forward" 3D modeling tools with a system of user-guided extraction modules and optimization routines. With a few cursor strokes users can express their preferences of the type of modeling primitives to be used in a particular area of the given prototype to be approximated, and they can also select the degree of parameterization associated with each modeling routine. The results are then pliable, structured descriptions that are well suited to implement the particular design modifications intended by the user.

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Section: "Stationary" or Generalized Rotational Sweepsmentioning

confidence: 99%

Interactive Inverse 3D Modeling

Andrews

Jin

Séquin

2012

Computer-Aided Design and Applications

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“…Some systems augment this core fitting method by iterative re-weighting (for example to downweight outliers) [10,2] or subsequent application of a general, non-linear fitting technique (which requires a reasonable "initial estimate" from the core fitting method to perform well) [4].…”

Section: Common Fitting Methodsmentioning

confidence: 99%

“…Kinematic surfaces are a class of primitive surfaces notable for their general applicability. They can be used to classify a wide range of common surfaces: spheres, planes, cylinders, cones, surfaces of revolution, logarithmic spirals, helices, and more [2]. By chaining simple kinematic surfaces, even more interesting surfaces can be fit, including profile and developable surfaces [3].…”

Section: Introductionmentioning

confidence: 99%

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Generalized, basis-independent kinematic surface fitting

Andrews

Séquin

2013

Computer-Aided Design

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Kinematic surfaces form a general class of surfaces, including surfaces of revolution, helices, spirals, and more. Standard methods for fitting such surfaces are either specialized to a small subset of these surface types (either focusing exclusively on cylinders or exclusively on surfaces of revolution) or otherwise are basis-dependent (leading to scale-dependent results). Previous work has suggested re-scaling data to a fixed size bounding box to avoid the basis-dependence issues. We show that this method fails on some simple, common cases such as a box or a cone with small noise. We propose instead adapting a well-studied approximate maximum-likelihood method to the kinematic surface fitting problem, which solves the basis-dependence issue. Because this technique is not designed for a specific type of kinematic surface, it also opens the door to the possibility of new variants of kinematic surfaces, such as affinely-scaled surfaces of revolution.Keywords: kinematic surface fitting, reverse engineering, slippable surfaces, velocity fields, approximate maximum likelihood, Taubin's method

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“…For instance, Schnabel et al [23] present an efficient RANSAC approach to detect primitive shapes from point clouds. Hofer et al [13] adopt line geometry for the recognition and reconstruction of 3D surfaces. Many methods of fitting primitives are designed for reverse engineering such as [1].…”

Section: Modeling From Point Cloudsmentioning

confidence: 99%

Pipe-Run Extraction and Reconstruction from Point Clouds

Qiu

Zhou

Neumann

2014

Computer Vision – ECCV 2014

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Abstract. This paper presents automatic methods to extract and reconstruct industrial site pipe-runs from large-scale point clouds. We observe three key characteristics in this modeling problem, namely, primitives, similarities, and joints. While primitives capture the dominant cylindric shapes, similarities reveal the inter-primitive relations intrinsic to industrial structures because of human design and construction. Statistical analysis over point normals discovers primitive similarities from raw data to guide primitive fitting, increasing robustness to data noise and incompleteness. Finally, joints are automatically detected to close gaps and propagate connectivity information. The resulting model is more than a collection of 3D triangles, as it contains semantic labels for pipes as well as their connectivity.

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3D shape recognition and reconstruction based on line element geometry

Cited by 32 publications

References 18 publications

Interactive Inverse 3D Modeling

Interactive Inverse 3D Modeling

Generalized, basis-independent kinematic surface fitting

Pipe-Run Extraction and Reconstruction from Point Clouds

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