Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.Comment: ECCV 201

Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect

Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices. This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists that are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and Image Understanding (CVIU

Recent advances in transient imaging: A computer graphics and vision perspective

Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation

Recent advances in transient imaging: A computer graphics and vision perspective

Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation

Interaktive Raumzeitrekonstruktion in der Computergraphik

High-quality dense spatial and/or temporal reconstructions and correspondence maps from camera images, be it optical flow, stereo or scene flow, are an essential prerequisite for a multitude of computer vision and graphics tasks, e.g. scene editing or view interpolation in visual media production. Due to the ill-posed nature of the estimation problem in typical setups (i.e. limited amount of cameras, limited frame rate), automated estimation approaches are prone to erroneous correspondences and subsequent quality degradation in many non-trivial cases such as occlusions, ambiguous movements, long displacements, or low texture. While improving estimation algorithms is one obvious possible direction, this thesis complementarily concerns itself with creating intuitive, high-level user interactions that lead to improved correspondence maps and scene reconstructions. Where visually convincing results are essential, rendering artifacts resulting from estimation errors are usually repaired by hand with image editing tools, which is time consuming and therefore costly. My new user interactions, which integrate human scene recognition capabilities to guide a semi-automatic correspondence or scene reconstruction algorithm, save considerable effort and enable faster and more efficient production of visually convincing rendered images.Raumzeit-Rekonstruktion in Form von dichten räumlichen und/oder zeitlichen Korrespondenzen zwischen Kamerabildern, sei es optischer Fluss, Stereo oder Szenenfluss, ist eine wesentliche Voraussetzung für eine Vielzahl von Aufgaben in der Computergraphik, zum Beispiel zum Editieren von Szenen oder Bildinterpolation. Da sowohl die Anzahl der Kameras als auch die Bildfrequenz begrenzt sind, ist das Rekonstruktionsproblem unterbestimmt, weswegen automatisierte Schätzungen häufig fehlerhafte Korrespondenzen für nichttriviale Fälle wie Verdeckungen, mehrdeutige oder große Bewegungen, oder einheitliche Texturen enthalten; jede Bildsynthese basierend auf den partiell falschen Schätzungen muß daher Qualitätseinbußen in Kauf nehmen. Man kann nun zum einen versuchen, die Schätzungsalgorithmen zu verbessern. Komplementär dazu kann man möglichst effiziente Interaktionsmöglichkeiten entwickeln, die die Qualität der Rekonstruktion drastisch verbessern. Dies ist das Ziel dieser Dissertation. Für visuell überzeugende Resultate müssen Bildsynthesefehler bislang manuell in einem aufwändigen Nachbearbeitungsschritt mit Hilfe von Bildbearbeitungswerkzeugen korrigiert werden. Meine neuen Benutzerinteraktionen, welche menschliches Szenenverständnis in halbautomatische Algorithmen integrieren, verringern den Nachbearbeitungsaufwand beträchtlich und ermöglichen so eine schnellere und effizientere Produktion qualitativ hochwertiger synthetisierter Bilder

Performance Metrics and Test Data Generation for Depth Estimation Algorithms

This thesis investigates performance metrics and test datasets used for the evaluation of depth estimation algorithms. Stereo and light field algorithms take structured camera images as input to reconstruct a depth map of the depicted scene. Such depth estimation algorithms are employed in a multitude of practical applications such as industrial inspection and the movie industry. Recently, they have also been used for safety-relevant applications such as driver assistance and computer assisted surgery. Despite this increasing practical relevance, depth estimation algorithms are still evaluated with simple error measures and on small academic datasets. To develop and select suitable and safe algorithms, it is essential to gain a thorough understanding of their respective strengths and weaknesses. In this thesis, I demonstrate that computing average pixel errors of depth estimation algorithms is not sufficient for a thorough and reliable performance analysis. The analysis must also take into account the specific requirements of the given applications as well as the characteristics of the available test data. I propose metrics to explicitly quantify depth estimation results at continuous surfaces, depth discontinuities, and fine structures. These geometric entities are particularly relevant for many applications and challenging for algorithms. In contrast to prevalent metrics, the proposed metrics take into account that pixels are neither spatially independent within an image nor uniformly challenging nor equally relevant. Apart from performance metrics, test datasets play an important role for evaluation. Their availability is typically limited in quantity, quality, and diversity. I show how test data deficiencies can be overcome by using specific metrics, additional annotations, and stratified test data. Using systematic test cases, a user study, and a comprehensive case study, I demonstrate that the proposed metrics, test datasets, and visualizations allow for a meaningful quantitative analysis of the strengths and weaknesses of different algorithms. In contrast to existing evaluation methodologies, application-specific priorities can be taken into account to identify the most suitable algorithms

Analysis and Modeling of Passive Stereo and Time-of-Flight Imaging

This thesis is concerned with the analysis and modeling of effects which cause errors in passive stereo and Time-of-Flight imaging systems. The main topics are covered in four chapters: I commence with a treatment of a system combining Time-of-Flight imaging with passive stereo and show how commonly used fusion models relate to the measurements of the individual modalities. In addition, I present novel fusion techniques capable of improving the depth reconstruction over those obtained separately by either modality. Next, I present a pipeline and uncertainty analysis for the generation of large amounts of reference data for quantitative stereo evaluation. The resulting datasets not only contain reference geometry, but also per pixel measures of reference data uncertainty. The next two parts deal with individual effects observed: Time-of-Flight cameras suffer from range ambiguity if the scene extends beyond a certain distance. I show that it is possible to extend the valid range by changing design parameters of the underlying measurement system. Finally, I present methods that make it possible to amend model violation errors in stereo due to reflections. This is done by means of modeling a limited level of light transport and material properties in the scene