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This thesis investigates the use of sampled CAD models for training and calibrating a semantic segmentation model, RandLA-Net, with the ultimate goal of localizing modules for digital twinning (the process of creating digital twins). A significant contribution is the development of the Random Placement of Component Generator (RPCG), a synthetic dataset generator that randomly places CAD models within scenes while preserving contextual information such as typical height above ground. Training and testing on datasets generated by RPCG demonstrated its ability to recognize class modules in various randomly generated scenes. Various hyperparameters related to the loss function and pre-processing steps were explored to improve RandLA-Net’s generalization to different contextual settings. Notably, using a class-weighted α in the focal loss showed promise in correctly classifying infrequent classes and reducing network overconfidence under domain shifts with similar prior probability distributions. The semantic segmentation results were promising for the RPCG test set, achieving a mean True Positive Rate (mTPR) of 98% and a mean Intersection over Union(mIoU) of 93.6%. However, the performance on a sampled version of a CAD model representing an installation named Undercentral was comparatively lower, with a mTPR of 41.1% and a mIoU of 33.4%, indicating the need for further adaptation to varied contextual environments. Proposed improvements include enhancing RPCG with an occupancy grid to better simulate compact scenes and evaluating different subsampling rates in RandLA-Net’s random sampling layers. For confidence calibration, the thesis finds that averaging multiple Monte Carlo (MC) dropout evaluations effectively reduces network overconfidence and improves model reliability. Although this work addresses only a portion of the overall digital twinning process, it highlights the potential of synthetic data generation in enhancing semantic segmentation models and contributes towards the localization of modules in digital twin creation.

[pt] No presente trabalho propomos uma nova forma de extrair a silhueta de uma nuvem de pontos, via reconstrução local de uma superfície descrita implicitamente por uma função polinomial. Esta reconstrução é baseada nos métodos Gradient one fitting e Ridge regression. A curva silhueta fica definida implicitamente por um sistema de equações não-lineares e sua geração é feita por continuação numérica. Como resultado, verificamos que nosso método se mostrou adequado para tratar dados com ruídos. Além disso, apresentamos um método para a extração local de linhas laplacianas de uma nuvem de pontos baseado na reconstrução local utilizando a triangulação de Delaunay. / [en] In this work we propose a new method for silhouette extraction of a point cloud, via local reconstruction of a surface described implicitly by a polynomial function. This reconstruction is based on the Gradient one fitting and Ridge regression methods. The curve silhouette is implicitly defined by a system of nonlinear equations, and is obtained using numerical continuation. As a result, we observe that our method is suitable to handle noisy data. In addition, we present a method for extracting Laplacian Lines of a point cloud based on local reconstruction using the Delaunay triangulation.

Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to analyze 2D images, videos, and even point clouds that contain 3D data points. On the first two problems, CNNs have achieved remarkable progress, but on point cloud segmentation, the results are less satisfactory due to challenges such as limited memory resource and difficulties in 3D point annotation. One of the research studies carried out by the Computer Vision Lab at Linköping University was aiming to ease the semantic segmentation of 3D point cloud. The idea is that by first projecting 3D data points to 2D space and then focusing only on the analysis of 2D images, we can reduce the overall workload for the segmentation process as well as exploit the existing well-developed 2D semantic segmentation techniques. In order to improve the performance of CNNs for 2D semantic segmentation, the study has used input data derived from different modalities. However, how different modalities can be optimally fused is still an open question. Based on the above-mentioned study, this thesis aims to improve the multistream framework architecture. More concretely, we investigate how different singlestream architectures impact the multistream framework with a given fusion method, and how different fusion methods contribute to the overall performance of a given multistream framework. As a result, our proposed fusion architecture outperformed all the investigated traditional fusion methods. Along with the best singlestream candidate and few additional training techniques, our final proposed multistream framework obtained a relative gain of 7.3\% mIoU compared to the baseline on the semantic3D point cloud test set, increasing the ranking from 12th to 5th position on the benchmark leaderboard.

Nos últimos anos, acompanhar o desenvolvimento de uma cultura tem se tornado cada vez mais imprescindível para a tomada de decisões. Sistemas aéreos remotamente pilotados são muito promissores em aplicações de monitoramento. Sua flexibilidade, facilidade de operação e construção relativamente barata os tornam os melhores candidatos para monitorar atividades na agricultura de precisão, onde as reações imediatas de manejo às doenças das plantas, à falta de nutrientes das plantas e às mudanças ambientais são o ponto focal para eficiência e produtividade das plantações. No entanto, no Brasil a utilização desta tecnologia ainda é limitada e o número de publicações científicas sobre o assunto é escasso. No caso específico da cana-de-açúcar, a utilização de aeronave remotamente pilotada (RPA) é bastante promissora e publicações científicas internacionais são limitadas. O objetivo deste trabalho foi avaliar a potencialidade de imagens obtidas a partir de câmeras com diferentes bandas espectrais embarcadas em RPA para obtenção de modelos tridimensionais para estimativa de altura, produtividade e variabilidade espacial. As coletas foram realizadas ao longo da safra 2014/2015, durante o período de um ano. Foi utilizada uma aeronave remotamente pilotada equipada com uma câmera digital com sensibilidade na região espectral do visível (RGB) e outra na região espectral do infravermelho próximo (IVP) sincronizadas com um sistema de navegação global por satélite (GNSS). Este sistema possibilitou a aquisição de imagens com altíssima resolução (3 cm pixel-1) e permitiu a geração de orto-mosaicos e modelos digitais de superfícies (MDS) através de métodos de reconstrução automática em 3D, ajustados por pontos de controle em solo. O RPA seguiu um plano de voo pré-determinado sobre o local do estudo para garantir a aquisição de imagens com cruzamento e sobreposição superior a 90%. O método de validação foi conduzido a partir das medidas de altura obtidas a campo com o auxílio de régua topográfica. Após o processamento das imagens aéreas foi possível a identificação das áreas com ausência de fechamento de dossel, observando também a relação desses locais com o baixo desenvolvimento da altura das plantas ao longo de seu ciclo. A regressão entre os valores da estimativa de altura obtidas com as simulações apresentou erro relativo inferior a 13%, já a estimativa da produtividade apresentou erro na faixa de 6%. A estimativa de altura e produtividade demonstram o alto potencial para o monitoramento e avaliação de talhões de cana-de-açúcar, podendo ser uma ferramenta utilizada no apoio a gestão destas áreas. / In the last few years, monitoring the development of a culture has become increasingly imperative for decision-making. Remotely piloted aircraft systems (RPA) are very promising in monitoring applications. Their flexibility, ease of operation, and relatively inexpensive construction make them the best candidates to monitor precision farming activities where immediate management responses to plant diseases, lack of plant nutrients, and environmental changes are the focal point for efficiency And productivity of plantations. However in Brazil the use of this technology is still limited and the number of scientific publications on the subject is scarce. In the specific case of sugarcane the use of RPA is very promising and international scientific publications are limited. The objective of this work was to evaluate the potentiality of images obtained from cameras with different spectral bands embedded in RPA to obtain three - dimensional models for estimation of height, productivity and spatial variability. The collections were carried out during the 2014/2015 harvest, during a period of one year, using a remotely piloted aircraft equipped with a digital camera with sensitivity in the visible spectral region (RGB) and another in the near infrared spectral region (NIR) Synchronized with a GNSS. This system allowed the acquisition of images with very high resolution (3 cm pixel-1) allowing the generation of ortho-mosaics and digital surface models (DSM), through automatic 3D reconstruction methods adjusted by control points in soil. The RPA followed a pre-determined flight plan on the study site to ensure cross-over and overlapping acquisition of over 90%. The validation method was carried out from the height measurements obtained in the field with the aid of topography. After the aerial images processing, it was possible to identify the areas of crop failure, also observing the relation of these locations with the low development of plant height throughout its cycle. The regression between the values of the height estimation obtained with the simulations resulted in a relative error of less than 13%. The results obtained demonstrate the high potential of this technique for monitoring and evaluation of sugarcane fields, and can be a tool used to support the management of these areas.

Submitted by Maicon Juliano Schmidt (maicons) on 2015-03-16T13:44:12Z No. of bitstreams: 1 00000AC7.pdf: 4983854 bytes, checksum: 354b89932347d08ba4263cd1c545ef4f (MD5) / Made available in DSpace on 2015-03-16T13:44:12Z (GMT). No. of bitstreams: 1 00000AC7.pdf: 4983854 bytes, checksum: 354b89932347d08ba4263cd1c545ef4f (MD5) Previous issue date: 2014-01-01 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Diversas áreas do conhecimento utilizam técnicas de coleta de dados espaciais com grande resolução e precisão. Em especial, a Geologia tem utilizado novos equipamentos para a automatização de levantamentos e mapeamentos geológicos. A coleta digital feita com lasers scanners terrestres registra não só posicionamento e cor, como também a intensidade de retorno do sinal emitido. Ao utilizar as informações de intensidade de retorno do laser, pode ocorrer o efeito de borda, que é quando o laser colide parcialmente com o alvo e parte do sinal é perdido ou, ainda, colide com outros objetos não desejados ao fundo. Esse efeito faz com que a intensidade de retorno seja uma intensidade incorreta, quando analisa-se a reflectância dos materiais que compõem o alvo escaneado. Para resolver este problema, um novo algoritmo foi desenvolvido utilizando os dados conhecidos do laser, como a posição e a divergência do sinal para recuperar a intensidade de retorno do pulso laser correta quando o efeito de borda é detectado nos dados coletados. Os resultados mostram que esta técnica é uma possível solução para recuperar a intensidade de retorno do pulso de acordo com a reflectância dos materiais que compõem o afloramento. Um estudo adicional é necessário para realizar a otimização do algoritmo e para realizar uma análise estatística das intensidades corrigidas. / Several areas of knowledge use digital techniques to collect spacial data with higher resolution and precision. In special, Geology is using new equipaments to automate geological surveys and mappings. The digital acquisition made with terrestrial laser scanners records not only the target’s position and color, but also the return of the emitted signal’s intensity. When using the laser’s intensity return information, it may occur the edge effect, that is when the laser collides parcially with the target and part of the signal is lost or, also, collides with other undesired objects in the background. This effect makes the laser’s return intensity to be incorrect, when the reflectance of the materials that compose the target being analized. To solve this issue, a new algorithm was developed using the known data as the laser scanner’s position and signal’s divergence to recover the correct laser’s intensity when an edge effect is detected in the collected data. The results show that this technique is a possible solution to recover the correct laser’s return intensity according to the reflectance of the outcrop materials. Additional research is needed to optimize the algorithm and make a statistical analysis of the corrected laser intensity data.

3D point cloud processing has been a critical task due to the increasing demand of a variety of applications such as urban planning and management, as-built mapping of industrial sites, infrastructure monitoring, and road safety inspection. Point clouds are mainly acquired from two sources, laser scanning and optical imaging systems. However, the original point clouds usually do not provide explicit semantic information, and the collected data needs to undergo a sequence of processing steps to derive and extract the required information. Moreover, according to application requirements, the outcomes from the point cloud processing could be different. This dissertation presents two tiers of data processing. The first tier proposes an adaptive data processing framework to deal with multi-source and multi-platform point clouds. The second tier introduces two point clouds processing strategies targeting applications mainly from urban environments and transportation corridors.<div><br></div><div>For the first tier of data processing, the internal characteristics (e.g., noise level and local point density) of data should be considered first since point clouds might come from a variety of sources/platforms. The acquired point clouds may have a large number of points. Data processing (e.g., segmentation) of such large datasets is time-consuming. Hence, to attain high computational efficiency, this dissertation presents a down-sampling approach while considering the internal characteristics of data and maintaining the nature of the local surface. Moreover, point cloud segmentation is one of the essential steps in the initial data processing chain to derive the semantic information and model point clouds. Therefore, a multi-class simultaneous segmentation procedure is proposed to partition point cloud into planar, linear/cylindrical, and rough features. Since segmentation outcomes could suffer from some artifacts, a series of quality control procedures are introduced to evaluate and improve the quality of the results.<br></div><div><br></div><div>For the second tier of data processing, this dissertation focuses on two applications for high human activity areas, urban environments and transportation corridors. For urban environments, a new framework is introduced to generate digital building models with accurate right-angle, multi-orientation, and curved boundary from building hypotheses which are derived from the proposed segmentation approach. For transportation corridors, this dissertation presents an approach to derive accurate lane width estimates using point clouds acquired from a calibrated mobile mapping system. In summary, this dissertation provides two tiers of data processing. The first tier of data processing, adaptive down-sampling and segmentation, can be utilized for all kinds of point clouds. The second tier of data processing aims at digital building model generation and lane width estimation applications.<br></div>

In this thesis a methodology for representing 3D subjects and their deformations in adverse situations is studied. The study is focused in providing methods based on registration techniques to improve the data in situations where the sensor is working in the limit of its sensitivity. In order to do this, it is proposed two methods to overcome the problems which can difficult the process in these conditions. First a rigid registration based on model registration is presented, where the model of 3D planar markers is used. This model is estimated using a proposed method which improves its quality by taking into account prior knowledge of the marker. To study the deformations, it is proposed a framework to combine multiple spaces in a non-rigid registration technique. This proposal improves the quality of the alignment with a more robust matching process that makes use of all available input data. Moreover, this framework allows the registration of multiple spaces simultaneously providing a more general technique. Concretely, it is instantiated using colour and location in the matching process for 3D location registration.

Orchards demand large areas of land, thus they are often situated far from major population centres. As a result it is often difficult to obtain the necessary personnel, limiting both growth and productivity. However, if autonomous robots could be integrated into the operation of the orchard, the manpower demand could be reduced. A key problem for any autonomous robot is localisation; how does the robot know where it is? In agriculture robots, the most common approach is to use GPS positioning. However, in an orchard environment, the dense and tall vegetation restricts the usage to large robots that reach above the surroundings. In order to enable the use of smaller robots, it is instead necessary to use a GPS independent system. However, due to the similarity of the environment and the lack of strong recognisable features, it appears unlikely that typical non-GPS solutions will prove successful. Therefore we present a GPS independent localisation system, specifically aimed for orchards, that utilises the inherent structure of the surroundings. Furthermore, we examine and individually evaluate three related sub-problems. The proposed system utilises a 3D point cloud created from a 2D LIDAR and the robot’s movement. First, we show how the data can be segmented into individual trees using a Hidden Semi-Markov Model. Second, we introduce a set of descriptors for describing the geometric characteristics of the individual trees. Third, we present a robust localisation method based on Hidden Markov Models. Finally, we propose a method for detecting segmentation errors when associating new tree measurements with previously measured trees. Evaluation shows that the proposed segmentation method is accurate and yields very few segmentation errors. Furthermore, the introduced descriptors are determined to be consistent and informative enough to allow localisation. Third, we show that the presented localisation method is robust both to noise and segmentation errors. Finally it is shown that a significant majority of all segmentation errors can be detected without falsely labeling correct segmentations as incorrect. / Eftersom fruktodlingar kräver stora markområden är de ofta belägna långt från större befolkningscentra. Detta gör det svårt att finna tillräckligt med arbetskraft och begränsar expansionsmöjligheterna. Genom att integrera autonoma robotar i drivandet av odlingarna skulle arbetet kunna effektiviseras och behovet av arbetskraft minska. Ett nyckelproblem för alla autonoma robotar är lokalisering; hur vet roboten var den är? I jordbruksrobotar är standardlösningen att använda GPS-positionering. Detta är dock problematiskt i fruktodlingar, då den höga och täta vegetationen begränsar användandet till större robotar som når ovanför omgivningen. För att möjliggöra användandet av mindre robotar är det istället nödvändigt att använda ett GPS-oberoende lokaliseringssystem. Detta problematiseras dock av den likartade omgivningen och bristen på distinkta riktpunkter, varför det framstår som osannolikt att existerande standardlösningar kommer fungera i denna omgivning. Därför presenterar vi ett GPS-oberoende lokaliseringssystem, speciellt riktat mot fruktodlingar, som utnyttjar den naturliga strukturen hos omgivningen.Därutöver undersöker vi och utvärderar tre relaterade delproblem. Det föreslagna systemet använder ett 3D-punktmoln skapat av en 2D-LIDAR och robotens rörelse. Först visas hur en dold semi-markovmodell kan användas för att segmentera datasetet i enskilda träd. Därefter introducerar vi ett antal deskriptorer för att beskriva trädens geometriska form. Vi visar därefter hur detta kan kombineras med en dold markovmodell för att skapa ett robust lokaliseringssystem.Slutligen föreslår vi en metod för att detektera segmenteringsfel när nya mätningar av träd associeras med tidigare uppmätta träd. De föreslagna metoderna utvärderas individuellt och visar på goda resultat. Den föreslagna segmenteringsmetoden visas vara noggrann och ge upphov till få segmenteringsfel. Därutöver visas att de introducerade deskriptorerna är tillräckligt konsistenta och informativa för att möjliggöra lokalisering. Ytterligare visas att den presenterade lokaliseringsmetoden är robust både mot brus och segmenteringsfel. Slutligen visas att en signifikant majoritet av alla segmenteringsfel kan detekteras utan att felaktigt beteckna korrekta segmenteringar som inkorrekta.

Magistro baigiamajam darbui pasirinkta tema yra Trimačiai objektai: atvaizdavimo ir deformacijos algoritmai. Ši tema nagrinėja paviršiaus rekonstrukciją iš taškų debesies ir galimybes pritaikyti paviršiaus deformacijos algoritmus. Analizės etapo metu išsiaiškinta, kad pagrindinė paviršiaus atstatymo iš taškų debesies problema yra lėtas algoritmų veikimas. Šiame darbe siūlomas atvirkštinės inžinerijos metodas, veikiantis 2D Delaunay trianguliacijos pagrindu. Pateikiami algoritmai padalina taškų debesį į kelias dalis, tada iš trimatės erdvės taškų debesies dalys yra transformuojamos į dvimatę erdvę, suskaičiuojama 2D Delaunay trianguliacija ir gautas trikampių tinklelis vėl transformuojamas į trimatę erdvę. Taip pat pateikiamos teorinės galimybės gautą paviršių transformuoti jau žinomu algoritmu. Po algoritmų praktinio įgyvendinimo buvo nustatyta, kad jie veikia taip kaip tikėtasi, rezultatas gaunamas greičiau nei naudojant kitus žinomus algoritmus. Taip pat buvo pastebėta, kad 2D Delaunay trianguliaciją geriau naudoti kai taškų skaičius taškų debesyje yra labai didelis, o kai taškų skaičius neviršija 2000 geriau naudoti 3D Delaunay trianguliaciją. / The chosen theme of the Master of Science degree paper is “Three dimensional objects: visualization and deformation algorithms“. This subject considers surface reconstruction from point clouds and the possibilities to apply surface deformation algorithms. During the analysis phase we found that the main problem of the algorithms of surface reconstruction from scanned point clouds is the lack of speed. So in this paper a method, based on 2D Delaunay triangulation, for reverse engineering is proposed. This method divides point clouds into several parts, and then maps all the points of those point cloud parts to the plane. Then a 2D Delaunay triangulation is computed and the mesh is mapped back to the point cloud. We also give theoretical possibilities to apply a known algorithm for surface deformation. During the implementation phase we found that our algorithms work as expected, but quicker than the other methods proposed earlier. We also noticed that it’s better to use 2D Delaunay triangulation for bigger point clouds and 3D Delaunay triangulation for point clouds, which contains no more than approximately 2000 points.