Processing techniques of point cloud data on small-sized objects with complex free-form surface

The scattered point cloud data, which comes from such small-sized objects with complex free-form surface as shelled shrimp, is processed as error points removing, points filtering, holes filling, clouds segmenting, etc. using CATIA V5 R20. The principle of data processing and skills utilized in the operation mentioned above apply also to the small-sized objects similar to the shelled shrimp in reverse engineering in other fields, such as optical components, irregular parts, ears, nose, etc

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on epsilon insensitive support vector regression (epsilon-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of epsilon-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on epsilon insensitive support vector regression (epsilon-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of epsilon-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

Automated computer vision system for real-time drilling cuttings monitoring

In rotary drilling operations, cuttings are continuously transported to the surface by drilling fluid. Real-time monitoring of cuttings and cavings is crucial for early detection and remediation of drilling problems such as stuck pipe, lost circulation, high torque and drag, reduction in rate of penetration, and other wellbore instability issues. These incidents are large contributors to drilling-related Non-Productive Time (NPT). At the current stage, a mud logger performs monitoring manually. This work proposes to use computer vision techniques to automate this procedure. To achieve this application, specific requirements should be established to design an automated machine vision system to maintain drilling safety and speed. Cuttings ramp has been identified as an ideal location to perform the measurement, where cuttings and caving are sliding down a slope at a steady speed. To accomplish this task, an intelligent image processing system must be able to track cuttings speed, measure volume, analyze size, and generate a surface model. Through a detailed review and testing of available 3D sensing techniques, a system consisting of a 2D high-resolution camera and 3D laser profile scanner was designed. By implementing image processing techniques, the cuttings speed on the ramp was estimated which was then synchronized to the 3D depth data from a laser scanner. Finally, the volume of moving cuttings was estimated and a 3D surface profile was reconstructed using point cloud data. Experimental results in the lab environment validated that such a system can be applied to quantify cuttings volume, size distribution, and reconstruct a 3D profile of cuttings and cavings. This measured result can be stored for further analysis. Overall, this work established a foundation for the design of a sophisticated real-time monitoring system for hole cleaning and wellbore risk reduction.Petroleum and Geosystems Engineerin

Machining tool identification utilizing temporal 3D point clouds

The manufacturing domain is regarded as one of the most important engineering areas. Recently, smart manufacturing merges the use of sensors, intelligent controls, and software to manage each stage in the manufacturing lifecycle. Additionally, the increasing use of point clouds to model real products and machining tools in a virtual space facilitates the more accurate monitoring of the end-to-end production lifecycle. Thus, the conjunction of both, intelligent methods and more accurate 3D models allows the prediction of uncertainties and anomalies in the manufacturing process as well as reduces the final production costs. However, the high complexity of the geometrical structures defined by point clouds and the high accuracy required by the Quality Assurance/Quality control parameters during the process, pave the way for continuous improvements in smart manufacturing methods. This paper addresses a comprehensive analysis of machining tool identification utilizing temporal point cloud data. Specifically, we deal with the identification of machining tools from temporal 3D point clouds. To do that, we propose a process to construct and train intelligent models utilizing such data. Moreover, in our case study, we provide the research community with two labeled temporal 3D point cloud datasets, and we experiment with the pioneering PointNet neural network and three of its variants demonstrating an accuracy of 95% in the identification of the utilized machining tools in a machining process. Finally, we provide a prototype end-to-end intelligent system of machining tool identification

Superquadric representation of scenes from multi-view range data

Object representation denotes representing three-dimensional (3D) real-world objects with known graphic or mathematic primitives recognizable to computers. This research has numerous applications for object-related tasks in areas including computer vision, computer graphics, reverse engineering, etc. Superquadrics, as volumetric and parametric models, have been selected to be the representation primitives throughout this research. Superquadrics are able to represent a large family of solid shapes by a single equation with only a few parameters. This dissertation addresses superquadric representation of multi-part objects and multiobject scenes. Two issues motivate this research. First, superquadric representation of multipart objects or multi-object scenes has been an unsolved problem due to the complex geometry of objects. Second, superquadrics recovered from single-view range data tend to have low confidence and accuracy due to partially scanned object surfaces caused by inherent occlusions. To address these two problems, this dissertation proposes a multi-view superquadric representation algorithm. By incorporating both part decomposition and multi-view range data, the proposed algorithm is able to not only represent multi-part objects or multi-object scenes, but also achieve high confidence and accuracy of recovered superquadrics. The multi-view superquadric representation algorithm consists of (i) initial superquadric model recovery from single-view range data, (ii) pairwise view registration based on recovered superquadric models, (iii) view integration, (iv) part decomposition, and (v) final superquadric fitting for each decomposed part. Within the multi-view superquadric representation framework, this dissertation proposes a 3D part decomposition algorithm to automatically decompose multi-part objects or multiobject scenes into their constituent single parts consistent with human visual perception. Superquadrics can then be recovered for each decomposed single-part object. The proposed part decomposition algorithm is based on curvature analysis, and includes (i) Gaussian curvature estimation, (ii) boundary labeling, (iii) part growing and labeling, and (iv) post-processing. In addition, this dissertation proposes an extended view registration algorithm based on superquadrics. The proposed view registration algorithm is able to handle deformable superquadrics as well as 3D unstructured data sets. For superquadric fitting, two objective functions primarily used in the literature have been comprehensively investigated with respect to noise, viewpoints, sample resolutions, etc. The objective function proved to have better performance has been used throughout this dissertation. In summary, the three algorithms (contributions) proposed in this dissertation are generic and flexible in the sense of handling triangle meshes, which are standard surface primitives in computer vision and graphics. For each proposed algorithm, the dissertation presents both theory and experimental results. The results demonstrate the efficiency of the algorithms using both synthetic and real range data of a large variety of objects and scenes. In addition, the experimental results include comparisons with previous methods from the literature. Finally, the dissertation concludes with a summary of the contributions to the state of the art in superquadric representation, and presents possible future extensions to this research

Digitisation of bamboo culms for structural applications

Reducing the negative environmental impact caused by the intensive manufacturing of industrialised building materials and components requires the adoption of alternative sustainable resources and the development of appropriate procedures to encourage their use in the construction industry. Bamboo in its natural form (culms or poles) is one of the most promising non-conventional sustainable building materials, endemic to most developing countries where high demand for building materials will be driven by the large-scale urbanisation predicted for the coming decades. The use of bamboo poles as structural elements poses multiple challenges starting with the need to define their inherent geometric variability to enable their inclusion in formal design and fabrication processes. This paper describes the details of a non-destructive 3D scanning and modelling workflow developed to capture and process the relevant digital information that describes the geometric properties of bamboo poles. The digitisation of over 230 poles with a combined length of 500 m was carried out confirming the accuracy of the generated geometric models. Also, a small reciprocal frame prototype was successfully developed based on the geometric information extracted from a 3D model of the structure incorporating the digitised poles. The effective digitisation of bamboo poles and its integration into modern platforms can provide the construction industry with the necessary support to design, build and maintain high quality structures that incorporate this sustainable and renewable resource

A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201

08221 Abstracts Collection -- Geometric Modeling

From May 26 to May 30 2008 the Dagstuhl Seminar 08221 ``Geometric Modeling\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available