An evolutionary approach to the extraction of object construction trees from 3D point clouds

Fayolle, Pierre-Alain; Pasko, Alexander

doi:10.1016/j.cad.2016.01.001

Cited by 29 publications

(14 citation statements)

References 57 publications

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“…To be topologically consistent in the sense of 3D modelling, we treat overlapping similarly to Fayolle and Pasko [51]. We represent a constructive model using a binary (CSG) construction tree structure with primitive solids at the leaves and operations at the internal nodes of the tree.…”

Section: D Aggregation For Scene Modellingmentioning

confidence: 99%

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

et al. 2018

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3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores.

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Section: D Aggregation For Scene Modellingmentioning

confidence: 99%

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

et al. 2018

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“…Detect constituent primitives. Extracting primitives, possibly only partially present, from point clouds is an important problem in surface reconstruction and reverse engineering [3,24,47,51]. Since CSDFs consist of combinations of primitives, one could detect a complete object by its constituent parts, in a similar way as how a set of 2D image descriptors on a 3D object can enable recognizing a particular object and its pose.…”

Section: Use In Object Detection Methodsmentioning

confidence: 99%

“…9), it is often a good approximation close to the surface [26]. Other implementations of the set operations have been derived (see [24,23]); these aim to strike a balance between accurately approximating the Euclidean distance and being differentiable everywhere ( Fig. 10).…”

Section: Modelling Scenesmentioning

confidence: 99%

“…However, although CSDFs can be converted to a mesh or point cloud, generating a CSG structure from a boundary representation is ongoing research. Some recent work include Andrews et al (2013) [1], who semi-automatically reverse-engineer meshes into a CSG tree; and Fayolle et al (2016) [24], who detect primitives from a point cloud, and formulate an optimization problem to recover a CSG tree that combines the primitives to best fit the point cloud.…”

Section: Modelling Toolsmentioning

confidence: 99%

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Continuous Signed Distance Functions for 3D Vision

Haugo¹,

Stahl²,

Brekke³

2017

2017 International Conference on 3D Vision (3DV)

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This paper explores the use of continuous signed distance functions as an object representation for 3D vision. Popularized in procedural computer graphics, this representation defines 3D objects as geometric primitives combined with constructive solid geometry and transformed by nonlinear deformations, scaling, rotation or translation. Unlike their discretized counterpart, that have become important in dense 3D reconstruction, the continuous distance function is not stored as a sampled volume, but as a closed mathematical expression. Through surveys and qualitative studies we argue that this representation can have several benefits for 3D vision, such as being able to describe many classes of indoor and outdoor objects at the order of hundreds of bytes per class, getting parametrized shape variations for free. As a distance function, the representation also has useful computational aspects by defining, at each point in space, the direction and distance to the nearest surface, and whether a point is inside or outside the surface.

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“…The computational complexity and the respective solutions have been studied by numerous authors, as reported in [38]. For the sake of better understanding, the following articles can be noted that show the computational details regarding raster to vector graphics conversion [39][40][41], sketch processing [42][43][44], data registration [9,[45][46][47], noise removal [48,49], data reduction [50,51], curve-based surface reconstruction [42,[52][53][54][55][56][57][58], polygon-based surface reconstruction [59][60][61][62][63], and direct slicing [64][65][66][67][68].…”

Section: Introductionmentioning

confidence: 99%

Geometric Modeling and 3D Printing Using Recursively Generated Point Cloud

Tashi

Ullah

Kubo

2019

MCA

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Previous studies have reported that a recursive process called the point cloud creation algorithm (PCA) that generates a point cloud is useful for reverse engineering a planner shape. This study elucidates the characteristics of the parameters used in the recursive process as well as its ability in geometric modeling and 3D printing of 3D shapes. In the recursive process, three constants (center point, initial distance, and initial angle) and two variables (instantaneous distance and instantaneous rotational angle) are employed. The shape-modeling characteristics of the constants and variables are elucidated using some commonly used shapes (straight-line, circle, ellipses, spiral, astroid, S-shape, and leaf-shape). In addition, the shape-modeling capability of the recursive process as a whole is quantified using two parameters called the radius of curvature and aesthetic value. Moreover, an illustrative example that shows the efficacy of the recursive process in virtual and physical prototyping of a relatively complex 3D object is presented. The results show that reverse engineering performed by the recursive-process-created point cloud is free from computational complexity compared to reverse engineering performed by the 3D-scanner-created point cloud. As such, the outcomes of this study enrich the field of reverse engineering.

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An evolutionary approach to the extraction of object construction trees from 3D point clouds

Cited by 29 publications

References 57 publications

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

Continuous Signed Distance Functions for 3D Vision

Geometric Modeling and 3D Printing Using Recursively Generated Point Cloud

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