Spatiotemporal oriented energies for spacetime stereo

This paper presents a novel approach to recovering tem-porally coherent estimates of 3D structure of a dynamic scene from a sequence of binocular stereo images. The approach is based on matching spatiotemporal orientation distributions between left and right temporal image streams, which encapsulates both local spatial and temporal struc-ture for disparity estimation. By capturing spatial and tem-poral structure in this unified fashion, both sources of in-formation combine to yield disparity estimates that are nat-urally temporal coherent, while helping to resolve matches that might be ambiguous when either source is considered alone. Further, by allowing subsets of the orientation mea-surements to support different disparity estimates, an ap-proach to recovering multilayer disparity from spacetime stereo is realized. The approach has been implemented with real-time performance on commodity GPUs. Empir-ical evaluation shows that the approach yields qualitatively and quantitatively superior disparity estimates in compari-son to various alternative approaches, including the ability to provide accurate multilayer estimates in the presence of (semi)transparent and specular surfaces. 1

Temporally coherent 4D reconstruction of complex dynamic scenes

This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 . Video available at: https://www.youtube.com/watch?v=bm_P13_-Ds

Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixel Measurement Models

This paper proposes a method for fusing data acquired by a ToF camera and a stereo pair based on a model for depth measurement by ToF cameras which accounts also for depth discontinuity artifacts due to the mixed pixel effect. Such model is exploited within both a ML and a MAP-MRF frameworks for ToF and stereo data fusion. The proposed MAP-MRF framework is characterized by site-dependent range values, a rather important feature since it can be used both to improve the accuracy and to decrease the computational complexity of standard MAP-MRF approaches. This paper, in order to optimize the site dependent global cost function characteristic of the proposed MAP-MRF approach, also introduces an extension to Loopy Belief Propagation which can be used in other contexts. Experimental data validate the proposed ToF measurements model and the effectiveness of the proposed fusion techniques

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

Self-calibrated, multi-spectral photometric stereo for 3D face capture

This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head's motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data