Individual tree measurement in tropical environment using terrestrial laser scanning

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models

Geometric Structure Extraction and Reconstruction

Geometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

Peer reviewe

Automatic Retrieval of Skeletal Structures of Trees from Terrestrial Laser Scanner Data

Research on forest ecosystems receives high attention, especially nowadays with regard to sustainable management of renewable resources and the climate change. In particular, accurate information on the 3D structure of a tree is important for forest science and bioclimatology, but also in the scope of commercial applications. Conventional methods to measure geometric plant features are labor- and time-intensive. For detailed analysis, trees have to be cut down, which is often undesirable. Here, Terrestrial Laser Scanning (TLS) provides a particularly attractive tool because of its contactless measurement technique. The object geometry is reproduced as a 3D point cloud. The objective of this thesis is the automatic retrieval of the spatial structure of trees from TLS data. We focus on forest scenes with comparably high stand density and with many occlusions resulting from it. The varying level of detail of TLS data poses a big challenge. We present two fully automatic methods to obtain skeletal structures from scanned trees that have complementary properties. First, we explain a method that retrieves the entire tree skeleton from 3D data of co-registered scans. The branching structure is obtained from a voxel space representation by searching paths from branch tips to the trunk. The trunk is determined in advance from the 3D points. The skeleton of a tree is generated as a 3D line graph. Besides 3D coordinates and range, a scan provides 2D indices from the intensity image for each measurement. This is exploited in the second method that processes individual scans. Furthermore, we introduce a novel concept to manage TLS data that facilitated the researchwork. Initially, the range image is segmented into connected components. We describe a procedure to retrieve the boundary of a component that is capable of tracing inner depth discontinuities. A 2D skeleton is generated from the boundary information and used to decompose the component into sub components. A Principal Curve is computed from the 3D point set that is associated with a sub component. The skeletal structure of a connected component is summarized as a set of polylines. Objective evaluation of the results remains an open problem because the task itself is ill-defined: There exists no clear definition of what the true skeleton should be w.r.t. a given point set. Consequently, we are not able to assess the correctness of the methods quantitatively, but have to rely on visual assessment of results and provide a thorough discussion of the particularities of both methods. We present experiment results of both methods. The first method efficiently retrieves full skeletons of trees, which approximate the branching structure. The level of detail is mainly governed by the voxel space and therefore, smaller branches are reproduced inadequately. The second method retrieves partial skeletons of a tree with high reproduction accuracy. The method is sensitive to noise in the boundary, but the results are very promising. There are plenty of possibilities to enhance the method’s robustness. The combination of the strengths of both presented methods needs to be investigated further and may lead to a robust way to obtain complete tree skeletons from TLS data automatically.Die Erforschung des ÖkosystemsWald spielt gerade heutzutage im Hinblick auf den nachhaltigen Umgang mit nachwachsenden Rohstoffen und den Klimawandel eine große Rolle. Insbesondere die exakte Beschreibung der dreidimensionalen Struktur eines Baumes ist wichtig für die Forstwissenschaften und Bioklimatologie, aber auch im Rahmen kommerzieller Anwendungen. Die konventionellen Methoden um geometrische Pflanzenmerkmale zu messen sind arbeitsintensiv und zeitaufwändig. Für eine genaue Analyse müssen Bäume gefällt werden, was oft unerwünscht ist. Hierbei bietet sich das Terrestrische Laserscanning (TLS) als besonders attraktives Werkzeug aufgrund seines kontaktlosen Messprinzips an. Die Objektgeometrie wird als 3D-Punktwolke wiedergegeben. Basierend darauf ist das Ziel der Arbeit die automatische Bestimmung der räumlichen Baumstruktur aus TLS-Daten. Der Fokus liegt dabei auf Waldszenen mit vergleichsweise hoher Bestandesdichte und mit zahlreichen daraus resultierenden Verdeckungen. Die Auswertung dieser TLS-Daten, die einen unterschiedlichen Grad an Detailreichtum aufweisen, stellt eine große Herausforderung dar. Zwei vollautomatische Methoden zur Generierung von Skelettstrukturen von gescannten Bäumen, welche komplementäre Eigenschaften besitzen, werden vorgestellt. Bei der ersten Methode wird das Gesamtskelett eines Baumes aus 3D-Daten von registrierten Scans bestimmt. Die Aststruktur wird von einer Voxelraum-Repräsentation abgeleitet indem Pfade von Astspitzen zum Stamm gesucht werden. Der Stamm wird im Voraus aus den 3D-Punkten rekonstruiert. Das Baumskelett wird als 3D-Liniengraph erzeugt. Für jeden gemessenen Punkt stellt ein Scan neben 3D-Koordinaten und Distanzwerten auch 2D-Indizes zur Verfügung, die sich aus dem Intensitätsbild ergeben. Bei der zweiten Methode, die auf Einzelscans arbeitet, wird dies ausgenutzt. Außerdem wird ein neuartiges Konzept zum Management von TLS-Daten beschrieben, welches die Forschungsarbeit erleichtert hat. Zunächst wird das Tiefenbild in Komponenten aufgeteilt. Es wird eine Prozedur zur Bestimmung von Komponentenkonturen vorgestellt, die in der Lage ist innere Tiefendiskontinuitäten zu verfolgen. Von der Konturinformation wird ein 2D-Skelett generiert, welches benutzt wird um die Komponente in Teilkomponenten zu zerlegen. Von der 3D-Punktmenge, die mit einer Teilkomponente assoziiert ist, wird eine Principal Curve berechnet. Die Skelettstruktur einer Komponente im Tiefenbild wird als Menge von Polylinien zusammengefasst. Die objektive Evaluation der Resultate stellt weiterhin ein ungelöstes Problem dar, weil die Aufgabe selbst nicht klar erfassbar ist: Es existiert keine eindeutige Definition davon was das wahre Skelett in Bezug auf eine gegebene Punktmenge sein sollte. Die Korrektheit der Methoden kann daher nicht quantitativ beschrieben werden. Aus diesem Grund, können die Ergebnisse nur visuell beurteiltwerden. Weiterhinwerden die Charakteristiken beider Methoden eingehend diskutiert. Es werden Experimentresultate beider Methoden vorgestellt. Die erste Methode bestimmt effizient das Skelett eines Baumes, welches die Aststruktur approximiert. Der Detaillierungsgrad wird hauptsächlich durch den Voxelraum bestimmt, weshalb kleinere Äste nicht angemessen reproduziert werden. Die zweite Methode rekonstruiert Teilskelette eines Baums mit hoher Detailtreue. Die Methode reagiert sensibel auf Rauschen in der Kontur, dennoch sind die Ergebnisse vielversprechend. Es gibt eine Vielzahl von Möglichkeiten die Robustheit der Methode zu verbessern. Die Kombination der Stärken von beiden präsentierten Methoden sollte weiter untersucht werden und kann zu einem robusteren Ansatz führen um vollständige Baumskelette automatisch aus TLS-Daten zu generieren

Quantitative Tree Reconstruction from Terrestrial Laser Scanning Data and Applications

Understanding the structure and dynamics of trees and forest is key in studying the environment and understanding current and future climates. Development has been fast in measurement technology for these purposes, as it is currently possible to measure forest terrestrially with photography-based instruments or either static or mobile laser scanning, and airborne using drones, helicopters or aeroplanes, and even from space using satellitemounted instruments. However, as all these measurements are indirect presentations of the key attributes to study, they require powerful analysis methods to accompany them. This thesis focuses on terrestrial laser scanning data and presents a method for reconstructing comprehensive, quantitative structure models of trees from such data. The method is designed to be a tool for understanding tree and forest structure, as well as, dynamics and functionality, without the need for destructive measurements. The reconstructed models provide access to tree attributes previously impossible or laborious to measure, either at a single tree-scale, at forest-plot-scale or even at forest-scale. The thesis will present the reconstruction method and will focus on two of its applications: automatic tree species recognition and augmenting the produced structure models with leaves or needles, enabling more accurate simulations involving light propagation and plant interaction with the atmosphere. Additionally, parts of the thesis describe forms of dissemination used to promote the reconstruction method and its applications, increasing the rate of adoption into operational use. The dissemination approaches include several animations, interactive 3D models and open-source software

Reconstruction de formes tubulaires à partir de nuages de points : application à l’estimation de la géométrie forestière

Les capacités des technologies de télédétection ont augmenté exponentiellement au cours des dernières années : de nouveaux scanners fournissent maintenant une représentation géométrique de leur environnement sous la forme de nuage de points avec une précision jusqu'ici inégalée. Le traitement de nuages de points est donc devenu une discipline à part entière avec ses problématiques propres et de nombreux défis à relever. Le coeur de cette thèse porte sur la modélisation géométrique et introduit une méthode robuste d'extraction de formes tubulaires à partir de nuages de points. Nous avons choisi de tester nos méthodes dans le contexte applicatif difficile de la foresterie pour mettre en valeur la robustesse de nos algorithmes et leur application à des données volumineuses. Nos méthodes intègrent les normales aux points comme information supplémentaire pour atteindre les objectifs de performance nécessaire au traitement de nuages de points volumineux.Cependant, ces normales ne sont généralement pas fournies par les capteurs, il est donc nécessaire de les pré-calculer.Pour préserver la rapidité d'exécution, notre premier développement a donc consisté à présenter une méthode rapide d'estimation de normales. Pour ce faire nous avons approximé localement la géométrie du nuage de points en utilisant des "patchs" lisses dont la taille s'adapte à la complexité locale des nuages de points. Nos travaux se sont ensuite concentrés sur l’extraction robuste de formes tubulaires dans des nuages de points denses, occlus, bruités et de densité inhomogène. Dans cette optique, nous avons développé une variante de la transformée de Hough dont la complexité est réduite grâce aux normales calculées. Nous avons ensuite couplé ces travaux à une proposition de contours actifs indépendants de leur paramétrisation. Cette combinaison assure la cohérence interne des formes reconstruites et s’affranchit ainsi des problèmes liés à l'occlusion, au bruit et aux variations de densité. Notre méthode a été validée en environnement complexe forestier pour reconstruire des troncs d'arbre afin d'en relever les qualités par comparaison à des méthodes existantes. La reconstruction de troncs d'arbre ouvre d'autres questions à mi-chemin entre foresterie et géométrie. La segmentation des arbres d'une placette forestière est l'une d’entre elles. C'est pourquoi nous proposons également une méthode de segmentation conçue pour contourner les défauts des nuages de points forestiers et isoler les différents objets d'un jeu de données. Durant nos travaux nous avons utilisé des approches de modélisation pour répondre à des questions géométriques, et nous les avons appliqué à des problématiques forestières.Il en résulte un pipeline de traitements cohérent qui, bien qu'illustré sur des données forestières, est applicable dans des contextes variés.Abstract : The potential of remote sensing technologies has recently increased exponentially: new sensors now provide a geometric representation of their environment in the form of point clouds with unrivalled accuracy. Point cloud processing hence became a full discipline, including specific problems and many challenges to face. The core of this thesis concerns geometric modelling and introduces a fast and robust method for the extraction of tubular shapes from point clouds. We hence chose to test our method in the difficult applicative context of forestry in order to highlight the robustness of our algorithms and their application to large data sets. Our methods integrate normal vectors as a supplementary geometric information in order to achieve the performance goal necessary for large point cloud processing. However, remote sensing techniques do not commonly provide normal vectors, thus they have to be computed. Our first development hence consisted in the development of a fast normal estimation method on point cloud in order to reduce the computing time on large point clouds. To do so, we locally approximated the point cloud geometry using smooth ''patches`` of points which size adapts to the local complexity of the point cloud geometry. We then focused our work on the robust extraction of tubular shapes from dense, occluded, noisy point clouds suffering from non-homogeneous sampling density. For this objective, we developed a variant of the Hough transform which complexity is reduced thanks to the computed normal vectors. We then combined this research with a new definition of parametrisation-invariant active contours. This combination ensures the internal coherence of the reconstructed shapes and alleviates issues related to occlusion, noise and variation of sampling density. We validated our method in complex forest environments with the reconstruction of tree stems to emphasize its advantages and compare it to existing methods. Tree stem reconstruction also opens new perspectives halfway in between forestry and geometry. One of them is the segmentation of trees from a forest plot. Therefore we also propose a segmentation approach designed to overcome the defects of forest point clouds and capable of isolating objects inside a point cloud. During our work we used modelling approaches to answer geometric questions and we applied our methods to forestry problems. Therefore, our studies result in a processing pipeline adapted to forest point cloud analyses, but the general geometric algorithms we propose can also be applied in various contexts