A Survey of Surface Reconstruction from Point Clouds

International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction

Multi-Scale Surface Reconstruction from Images

Many surface reconstruction algorithms have been developed to process point data originating from laser scans. Because laser scanning is a very expensive technique and not available to everyone, 3D reconstruction from images (using, e.g., multi-view stereo) is a promising alternative. In recent years a lot of progress has been made in the computer vision domain and nowadays algorithms are capable of reconstructing large 3D scenes from consumer photographs. Whereas laser scans are very controlled and typically only a few scans are taken, images may be subject to more uncontrolled variations. Standard multi-view stereo algorithms give rise to multi-scale data points due to different camera resolutions, focal lengths, or various distances to the object. When reconstructing a surface from this data, the multi-scale property has to be taken into account because the assumption that the points are samples from the true surface might be violated. This thesis presents two surface reconstruction algorithms that take resolution and scale differences into account. In the first approach we model the uncertainty of each sample point according to its footprint, the surface area that was taken into account during multi-view stereo. With an adaptive volumetric resolution, also steered by the footprints of the sample points, we achieve detailed reconstructions even for large-scale scenes. Then, a general wavelet-based surface reconstruction framework is presented. The multi-scale sample points are characterized by a convolution kernel and the points are fused in frequency space while preserving locality. We suggest a specific implementation for 2.5D surfaces that incorporates our theoretic findings about sample points originating from multi-view stereo and shows promising results on real-world data sets. The other part of the thesis analyzes the scale characteristics of patch-based depth reconstruction as used in many (multi-view) stereo techniques. It is driven by the question how the reconstruction preserves surface details or high frequencies. We introduce an intuitive model for the reconstruction process, prove that it yields a linear system and determine the modulation transfer function. This allows us to predict the amplitude loss of high frequencies in connection with the used patch-size and the internal and external camera parameters. Experiments on synthetic and real-world data demonstrate the accuracy of our model but also show the limitations. Finally, we propose a generalization of the model allowing for weighted patch fitting. The reconstructed points can then be described by a convolution of the original surface and we show how weighting the pixels during photo-consistency optimization affects the smoothing kernel. In this way we are able to connect a standard notion of smoothing to multi-view stereo reconstruction. In summary, this thesis provides a profound analysis of patch-based (multi-view) stereo reconstruction and introduces new concepts for surface reconstruction from the resulting multi-scale sample points

Automatic Fracture Orientation Extraction from SfM Point Clouds

Geology seeks to understand the history of the Earth and its surface processes through charac- terisation of surface formations and rock units. Chief among the geologists’ tools are rock unit orientation measurements, such as Strike, Dip and Dip Direction. These allow an understanding of both surface and sub-structure on both the local and macro scale. Although the way these techniques can be used to characterise geology are well understood, the need to collect these measurements by hand adds time and expense to the work of the geologist, precludes spontaneity in field work, and coverage is limited to where the geologist can physically reach. In robotics and computer vision, multi-view geometry techniques such as Structure from Motion (SfM) allows reconstructions of objects and scenes using multiple camera views. SfM-based techniques provide advantages over Lidar-type techniques, in areas such as cost and flexibility of use in more varied environmental conditions, while sacrificing extreme levels of fidelity. Regardless of this, camera based techniques such as SfM, have developed to the point where accuracy is possible in the decimetre range. Here is presented a system to automate the measurement of Strike, Dip and Dip Direction using multi-view geometry from video. Rather than deriving measurements using a method applied to the images, such as the Hough Transform, this method takes measurements directly from the software generated point cloud. Point cloud noise is mitigated using a Mahalanobis distance implementation. Significant structure is characterised using a k-nearest neighbour region growing algorithm, and final surface orientations are quantified using the plane, and normal direction cosines

Combining Features and Semantics for Low-level Computer Vision

Visual perception of depth and motion plays a significant role in understanding and navigating the environment. Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving. The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information. Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel-based graphical model and demonstrate its benefits especially in reflective regions. Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects. Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network's output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.Die visuelle Wahrnehmung von Tiefe und Bewegung spielt eine wichtige Rolle bei dem Verständnis und der Navigation in unserer Umwelt. Die 3D Rekonstruktion von Szenen im Freien und die Schätzung der Bewegung von Videokameras sind von größter Bedeutung für Anwendungen, wie das autonome Fahren. Die Erforschung der entsprechenden Probleme des maschinellen Sehens hat in den letzten Jahrzehnten enorme Fortschritte gemacht, jedoch bleiben einige Aspekte heute noch ungelöst. Beispiele hierfür sind reflektierende und texturlose Oberflächen oder große Bewegungen, bei denen herkömmliche lokale Methoden häufig scheitern. Weitere Herausforderungen sind niedrige Bildraten, Verdeckungen, große Verzerrungen und schwierige Lichtverhältnisse. In dieser Arbeit schlagen wir vor nicht-lokale Interaktionen zu modellieren, die semantische und kontextbezogene Informationen nutzen, um diese Herausforderungen zu meistern. Für die binokulare Stereo Schätzung schlagen wir zuallererst vor zusammenhängende Bereiche mit objektklassen-spezifischen Disparitäts Vorschlägen zu regularisieren, die wir mit inversen Grafik Techniken auf der Grundlage einer spärlichen Disparitätsschätzung und semantischen Segmentierung des Bildes erhalten. Die Disparitäts Vorschläge kodieren die Tatsache, dass die Gegenstände bestimmter Kategorien nicht willkürlich geformt sind, sondern typischerweise regelmäßige Strukturen aufweisen. Wir integrieren sie für die komplexe Objektklasse 'Auto' in Form eines nicht-lokalen Regularisierungsterm in ein Superpixel-basiertes grafisches Modell und zeigen die Vorteile vor allem in reflektierenden Bereichen. Zweitens nutzen wir für die 3D-Rekonstruktion die Tatsache, dass mit der Größe der rekonstruierten Fläche auch die Wahrscheinlichkeit steigt, Objekte von ähnlicher Art und Form in der Szene zu enthalten. Dies gilt besonders für Szenen im Freien, in denen Gebäude und Fahrzeuge oft vorkommen, die unter fehlender Textur oder Reflexionen leiden aber ähnlichkeit in der Form aufweisen. Wir nutzen diese ähnlichkeiten zur Lokalisierung von Objekten mit Detektoren und zur gemeinsamen Rekonstruktion indem ein volumetrisches Modell ihrer Form erlernt wird. Dies ermöglicht auftretendes Rauschen zu reduzieren, während fehlende Flächen vervollständigt werden, da Objekte ähnlicher Form von allen Beobachtungen der jeweiligen Kategorie profitieren. Die Evaluierung auf einem neuen, herausfordernden vorstädtischen Datensatz in Anbetracht von LIDAR-Entfernungsdaten zeigt die Vorteile der Modellierung von strukturellen Abhängigkeiten zwischen Objekten. Zuletzt, motiviert durch den Erfolg von Deep Learning Techniken bei der Mustererkennung, präsentieren wir eine Methode zum Erlernen von kontextbezogenen Merkmalen zur Lösung des optischen Flusses mittels diskreter Optimierung. Dazu stellen wir eine effiziente Methode vor um zusätzlich zu einem Lokalen Netzwerk ein Kontext-Netzwerk zu erlernen, das mit Hilfe von erweiterter Faltung auf Patches ein großes rezeptives Feld besitzt. Für das Feature Matching vergleichen wir mit schnellen GPU-Matrixmultiplikation jedes Pixel im Referenzbild mit jedem Pixel im Zielbild. Das aus dem Netzwerk resultierende Matching Kostenvolumen bildet den Datenterm für eine diskrete MAP Inferenz in einem paarweisen Markov Random Field. Eine umfangreiche Evaluierung zeigt die Relevanz des Kontextes für das Feature Matching

Object-Aware Tracking and Mapping

Reasoning about geometric properties of digital cameras and optical physics enabled researchers to build methods that localise cameras in 3D space from a video stream, while – often simultaneously – constructing a model of the environment. Related techniques have evolved substantially since the 1980s, leading to increasingly accurate estimations. Traditionally, however, the quality of results is strongly affected by the presence of moving objects, incomplete data, or difficult surfaces – i.e. surfaces that are not Lambertian or lack texture. One insight of this work is that these problems can be addressed by going beyond geometrical and optical constraints, in favour of object level and semantic constraints. Incorporating specific types of prior knowledge in the inference process, such as motion or shape priors, leads to approaches with distinct advantages and disadvantages. After introducing relevant concepts in Chapter 1 and Chapter 2, methods for building object-centric maps in dynamic environments using motion priors are investigated in Chapter 5. Chapter 6 addresses the same problem as Chapter 5, but presents an approach which relies on semantic priors rather than motion cues. To fully exploit semantic information, Chapter 7 discusses the conditioning of shape representations on prior knowledge and the practical application to monocular, object-aware reconstruction systems

Automated Indoor Mapping with Point Clouds

This dissertation examines the current state of automated indoor mapping and modeling using point cloud data produced by close range remote sensing systems. The first part looks at reality capture techniques that convert the physical form of indoor spaces into point clouds of millions of measured points, each with an (x,y,z) coordinate value. The second part examines methods for teasing out geometries from these point clouds -- often complicated by noise and voids -- and converting them into 3D geometric models. The final part examines techniques for merging the coordinate reference systems of these indoor maps and models with those of the outdoor world, resulting in a seamless representation of space. Lessons learned in this study revealed that theories, techniques, and practices in indoor mapping remain relatively elementary compared to those for the outdoors, yet they also present significant opportunities for future research propelled by emerging developments in remote sensing and a growing demand for indoor maps

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results