Partition-based Nonrigid Registration for 3D Face Model

This paper presents a partition-based surface registration for 3D morphable model(3DMM). In the 3DMM, it often requires to warp a handcrafted template model into different captured models. The proposed method first utilizes the landmarks to partition the template model then scale each part and finally smooth the boundaries. This method is especially effective when the disparity between the template model and the target model is huge. The experiment result shows the method perform well than the traditional warp method and robust to the local minima

3D Shape Measurement of Objects in Motion and Objects with Complex Surfaces

This thesis aims to address the issues caused by high reflective surface and object with motion in the three dimensional (3D) shape measurement based on phase shifting profilometry (PSP). Firstly, the influence of the reflectivity of the object surface on the fringe patterns is analysed. One of the essential factors related to phase precision is modulation index, which has a direct relationship with the surface reflectivity. A comparative study focusing on the modulation index of different materials is presented. The distribution of modulation index for different material samples is statistically analysed, which leads to the conclusion that the modulation index is determined by the diffuse reflectivity. Then the method based on optimized combination of multiple reflected image patterns is proposed to address the saturation issue and improve the accuracy for the reconstruction of object with high reflectivity.A set of phase shifted sinusoidal fringe patterns with different exposure time are projected to the object and then captured by camera. Then a set of masks are generated to select the data for the compositing. Maximalsignal-to-noise ratio combining model is employed to form the composite images pattern. The composite images are then used to phase mapping.Comparing to the method only using the highest intensity of pixels for compositing image, the signal noise ratio (SNR) of composite image is increased due to more efficient use of information carried by the images

Comparative analysis of 3D- depth cameras in industrial bin picking solution

Machine vision is a crucial component of a successful bin picking solution. During the past few years, there has been large advancements in depth sensing technologies. This has led to them receiving a lot of attention, especially in bin picking applications. With reduced costs and greater accessibility, the use of machine vision has rapidly increased. Automated bin picking poses a technical challenge, which is present in numerous industrial processes. Robots need perception from their surroundings, and machine vision attempt to solve this by providing eyes to the machine. The motivation behind solving this challenge is the increased productivity, enabled by automated bin picking. The main goal of this thesis is to address the challenges of bin picking by comparing the performance of different 3D- depth cameras with illustrative case studies and experimental research. The depth cameras are exposed to different ambient conditions and object properties, where the performance of different 3D- imaging technologies is evaluated and compared between each other. The performance of a commercial bin picking solution is also researched through illustrative case studies to evaluate the accuracy, reliability, and flexibility of the solution. Feasibility study is also conducted, and the capabilities of the bin picking solution is demonstrated in two industrial applications. This research work focuses on three different depth sensing technologies. Comparison is done between structured light, stereo vision, and time-of-flight technologies. The main categories for evaluation are ambient light tolerance, reflective surfaces, and how well the depth cameras can detect simple and complex geometric features. The comparison between the depth cameras is limited to opaque objects, ranging from shiny metal blanks to matte connector components and porous surface textures. The performance of each depth camera is evaluated, and the advantages and disadvantages of each technology are discussed. Results of this thesis showed that while all of the technologies are capable of performing in a bin picking solution, structured light performed the best in the evaluation criteria of this thesis. The results from bin picking solution accuracy evaluation also illustrated some of the many challenges of bin picking, and how the true accuracy of the bin picking solution is not dictated purely by the resolution of the vision sensor. Finally, to conclude this thesis the results and future suggestions are discussed.Konenäkö on keskeinen osa automatisoitua kasasta poimintasovellusta. Syvyyskamerateknologiat ovat kehittyneet paljon kuluneiden vuosien aikana, joka on herättänyt paljon keskustelua niiden käyttömahdollisuuksista. Kustannusten alenemisen, sekä paremman saatavuuden myötä konenäön käyttö, erityisesti kasasta poimintasovelluksissa onkin lisääntynyt nopeasti. Automatisoitu kasasta poiminta kuitenkin omaa teknisiä haasteita, jotka ovat läsnä lukuisissa teollisissa prosesseissa. Motivaatio automatisoidun kasasta poiminnan taustalla on tuotettavuuden kasvu, jonka konenäkö mahdollistaa tarjoamalla dataa robotin ympäristöstä. Tämän diplomityön tavoitteina on vastata kasasta poiminnan haasteisiin vertailemalla erilaisten 3D-syvyyskameroiden suorituskykyä tapaustutkimusten sekä kokeellisen tutkimuksen avulla. Syvyyskameroiden toimintaa arvioidaan erilaisissa ympäristöissä sekä erilaisilla kappaleilla, jonka seurauksena 3D-kuvaustekniikoiden suorituskykyä vertaillaan keskenään. Työn aikana arvioidaan myös kaupallisen kasasta poimintasovelluksen suorituskykyä, jossa tutkitaan tapaustutkimusten avulla sovelluksen tarkkuutta, luotettavuutta sekä joustavuutta. Tämän lisäksi sovelluksen toimintaa pilotoidaan, ja ratkaisun ominaisuuksia demonstroidaan kahdessa teollisessa sovelluksessa. Tämä diplomityö keskittyy kolmeen eri syvyyskameratekniikkaan. Vertailu tehdään strukturoidun valon, stereonäön sekä Time-of-Flight tekniikoiden välillä. Arvioinnin pääkategoriat ovat ympäristön valoisuus, geometristen muotojen havainnointikyky, sekä heijastavat pinnat. Syvyyskameroiden välinen vertailu rajoittuu läpinäkymättömiin kappaleisiin, jotka vaihtelevat kiiltävistä metalliaihioista mattapintaisiin liitinkomponentteihin ja huokoisiin pintarakenteisiin. Tutkimuksen tulokset osoittivat, että vaikka kaikki tekniikat kykenevät automatisoituun kasasta poimintaan, strukturoitu valo suoriutui tutkituista teknologioista parhaiten. Kasasta poimintasovelluksen tarkkuuden arviointi havainnollisti myös sen monia haasteita, sekä kuinka sovelluksen todellinen tarkkuus ei riipu ainoastaan syvyyskameran resoluutiosta. Loppupäätelmien lisäksi työ päätetään ehdotuksilla tutkimuksen jatkamiseksi