Recognition of one class of surfaces from structured point cloud

U određenim oblastima industrije postoji potreba za generisanjem kompjuterskih modela objekata samo na osnovu njihove fizičke realizacije, a bez unapred poznatih konstrukcionih ili tehnoloških informacija. Pri realizaciji ovakvih zahteva istaknuto mesto zauzimaju tzv. tehnike reverznog inženjerstva geometrijskih modela. Bitnu fazu primene navedenih tehnika predstavlja prepoznavanje geometrijskih primitiva od kojih se posmatrani objekat sastoji. U ovom radu predstavljen je metod za segmentaciju i prepoznavanje G1 kontinualnih površina koje su u skenirnim linijama struktuiranog oblaka predstavljene eliptičnim segmentima. Predloženi algoritam je pre svega namenjen za prepoznavanje eliptičkih cilindara, elipsoida i eliptičkih torusa, ali se u zavisnosti od načina skeniranja dela, može koristiti i za prepoznavanje još nekih površi drugog reda. Proces segmentacije je zasnovan na prepoznavanju eliptičkih segmenata u skeniranim linijama, a na osnovu osobina singulariteta informacione matrice pri regresionoj analizi metodom najmanjih kvadrata. Verifikacija predloženog metoda je izvršena procesiranjem tri sintetizovana, kao i jednog realnog oblaka tačaka.This paper presents a method for recognition of surfaces represented by elliptical segments in structured three dimensional (3D) point clouds. The method is based on direct least squares fitting of ellipses in scanned lines. By recognizing elliptical segments in both directions of structured cloud it is possible to efficiently allocate G1 (and higher) continuous regions which represent a certain class of surfaces. The proposed method is primarily developed for recognition of elliptical cylinders and ellipsoids, including cylinders and spheres. Depending on scanning mode, the method can be employed for recognition of other second degree surfaces like cones. Besides, as presented in the paper, the method can be utilized for recognition of certain class of higher degree surfaces such as elliptical tori. The proposed method is experimentally verified using several synthesized point clouds as well as using a real world case study

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

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

Recognition of one class of quadric surfaces from unstructured point cloud

Critical elements of the state of the art three-dimensional (3D) point cloud processing software are the algorithms for retrieval of high level geometric primitives from raw data. This paper presents a method for recognition of a class of quadric surfaces, in particular for recognition of cylinders, elliptical cylinders, and ellipsoids from 3D point clouds. The method is based on direct least squares fitting of ellipsoids, and it exploits the closeness of scatter matrix to singular in the case when data are sampled for an approximate ellipsoid. This method belongs to the class of region growing methods, and the region is expanded using region growing strategy that is also proposed in this paper. Presented recognition procedure is suitable for segmentation of regions with G1 or higher continuality, and this is its advantage when compared to similar methods. Besides, recognition of quadric surfaces can be performed on unstructured, as well as on structured point clouds. The applicability of the method is illustrated and experimentally verified using two examples that contain G1 continuous surfaces from the considered class. The first example represents synthesized, and the second real-world scanned point cloud

Segmentacija jedne klase površi drugog reda iz struktuiranog oblaka tačaka

U radu se predlaže metod za segmentaciju jedne klase površi drugog reda (kvadrika) iz struktuiranog oblaka tačaka. Metod je zasnovan na segmentaciji elipsi iz skeniranih linija direktnom regresijom metodom najmanjih kvadrata. Segmentacijom elipsi u oba pravca struktuiranog oblaka mogu se efikasno izdvojiti G1 (i više) kontinualni regioni koji odgovaraju određenim površima drugog reda. Predloženi metod je pre svega namenjen segmentaciji eliptičkih cilindara i elipsoida čije posebne slučajeve predstavljaju cilindar i sfera, a u zavisnosti od načina skeniranja može se upotrebiti i za segmentaciju drugih kvadrika (na primer konusa). Pored toga, u radu se pokazuje da metod daje dobre rezultate i u segmentaciji površi višeg reda u odnosu na kvadrike – na primer eliptičkih torusa. Predloženi metod je eksperimentalno verifikovan na većem broju sintetizovanih oblaka tačaka kao i na primeru skeniranog dela iz realnog sveta

Recognition of one class of quadrics from 3D point clouds

Within cyber physical production systems 3D vision as a source of information from real-world provides enormous possibilities. While the hardware of contemporary 3D scanners is characterized by high speed along with high resolution and accuracy, there is a lack of real-time online data processing algorithms that would give certain elements of intelligence to the sensory system. Critical elements of data processing software are efficient, real-time applicable methods for fully automatic recognition of high level geometric primitives from point cloud (surface segmentation and fitting). This paper presents a method for recognition of one class of quadrics from 3D point clouds, in particular for recognition of cylinders, elliptical cylinders and ellipsoids. The method is based on the properties of scatter matrix during direct least squares fitting of ellipsoids. Presented recognition procedure can be employed for segmentation of regions with G1 or higher continuity, and this is its comparative advantage to similar methods. The applicability of the method is illustrated and experimentally verified using two case studies. First case study refers to a synthesized, and the second to a real-world scanned point cloud

Recognition of one class of quadrics from 3D point clouds

Within cyber physical production systems 3D vision as a source of information from real-world provides enormous possibilities. While the hardware of contemporary 3D scanners is characterized by high speed along with high resolution and accuracy, there is a lack of real-time online data processing algorithms that would give certain elements of intelligence to the sensory system. Critical elements of data processing software are efficient, real-time applicable methods for fully automatic recognition of high level geometric primitives from point cloud (surface segmentation and fitting). This paper presents a method for recognition of one class of quadrics from 3D point clouds, in particular for recognition of cylinders, elliptical cylinders and ellipsoids. The method is based on the properties of scatter matrix during direct least squares fitting of ellipsoids. Presented recognition procedure can be employed for segmentation of regions with G1 or higher continuity, and this is its comparative advantage to similar methods. The applicability of the method is illustrated and experimentally verified using two case studies. First case study refers to a synthesized, and the second to a real-world scanned point cloud

Recognition of quadrics from 3d point clouds generated by scanning of rotational parts

This paper presents a method for recognition of second order surfaces (quadrics) from point clouds containing information about scanned rotational parts. The method is region growing method that exploits the scatter of data during least squares fitting of quadrics as a region growing criterion. The presented procedure is convenient for segmentation of regions with high (G1 or higher) continuity. Besides, the region seed point is automatically selected which is its comparative advantage to a number of existing methods. The applicability of the proposed method is evaluated using two case studies; the first case study refers to a synthesized signal, and the second presents the applicability of the method on a real world example.*Ovaj rad je izabran sa konferencije 12th International Scientific Conference MMA 2015 - Flexible Technologies, i publikovan u casopisu Journal of Production Engineering

Recognition of quadrics from 3d point clouds generated by scanning of rotational parts

This paper presents a method for recognition of second order surfaces (quadrics) from point clouds containing information about scanned rotational parts. The method is region growing method that exploits the scatter of data during least squares fitting of quadrics as a region growing criterion. The presented procedure is convenient for segmentation of regions with high (G1 or higher) continuity. Besides, the region seed point is automatically selected which is its comparative advantage to a number of existing methods. The applicability of the proposed method is evaluated using two case studies; the first case study refers to a synthesized signal, and the second presents the applicability of the method on a real world example

Recognition of quadrics from 3d point clouds generated by scanning of rotational parts

This paper presents a method for recognition of second order surfaces (quadrics) from point clouds containing information about scanned rotational parts. The method is region growing method that exploits the scatter of data during least squares fitting of quadrics as a region growing criterion. The presented procedure is convenient for segmentation of regions with high (G1 or higher) continuity. Besides, the region seed point is automatically selected which is its comparative advantage to a number of existing methods. The applicability of the proposed method is evaluated using two case studies; the first case study refers to a synthesized signal, and the second presents the applicability of the method on a real world example