Freeform 3D interactions in everyday environments

PhD ThesisPersonal computing is continuously moving away from traditional input using mouse and keyboard, as new input technologies emerge. Recently, natural user interfaces (NUI) have led to interactive systems that are inspired by our physical interactions in the real-world, and focus on enabling dexterous freehand input in 2D or 3D. Another recent trend is Augmented Reality (AR), which follows a similar goal to further reduce the gap between the real and the virtual, but predominately focuses on output, by overlaying virtual information onto a tracked real-world 3D scene. Whilst AR and NUI technologies have been developed for both immersive 3D output as well as seamless 3D input, these have mostly been looked at separately. NUI focuses on sensing the user and enabling new forms of input; AR traditionally focuses on capturing the environment around us and enabling new forms of output that are registered to the real world. The output of NUI systems is mainly presented on a 2D display, while the input technologies for AR experiences, such as data gloves and body-worn motion trackers are often uncomfortable and restricting when interacting in the real world. NUI and AR can be seen as very complimentary, and bringing these two fields together can lead to new user experiences that radically change the way we interact with our everyday environments. The aim of this thesis is to enable real-time, low latency, dexterous input and immersive output without heavily instrumenting the user. The main challenge is to retain and to meaningfully combine the positive qualities that are attributed to both NUI and AR systems. I review work in the intersecting research fields of AR and NUI, and explore freehand 3D interactions with varying degrees of expressiveness, directness and mobility in various physical settings. There a number of technical challenges that arise when designing a mixed NUI/AR system, which I will address is this work: What can we capture, and how? How do we represent the real in the virtual? And how do we physically couple input and output? This is achieved by designing new systems, algorithms, and user experiences that explore the combination of AR and NUI

Multiframe Scene Flow with Piecewise Rigid Motion

We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences. In contrast to the competing methods, we take advantage of an oversegmentation of the reference frame and robust optimization techniques. We formulate scene flow recovery as a global non-linear least squares problem which is iteratively solved by a damped Gauss-Newton approach. As a result, we obtain a qualitatively new level of accuracy in RGB-D based scene flow estimation which can potentially run in real-time. Our method can handle challenging cases with rigid, piecewise rigid, articulated and moderate non-rigid motion, and does not rely on prior knowledge about the types of motions and deformations. Extensive experiments on synthetic and real data show that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October 201

Multiframe Scene Flow with Piecewise Rigid Motion

We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences. In contrast to the competing methods, we take advantage of an oversegmentation of the reference frame and robust optimization techniques. We formulate scene flow recovery as a global non-linear least squares problem which is iteratively solved by a damped Gauss-Newton approach. As a result, we obtain a qualitatively new level of accuracy in RGB-D based scene flow estimation which can potentially run in real-time. Our method can handle challenging cases with rigid, piecewise rigid, articulated and moderate non-rigid motion, and does not rely on prior knowledge about the types of motions and deformations. Extensive experiments on synthetic and real data show that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October 201

SLAM-based Dense Surface Reconstruction in Monocular Minimally Invasive Surgery and its Application to Augmented Reality.

While Minimally Invasive Surgery (MIS) offers considerable benefits to patients, it also imposes big challenges on a surgeon's performance due to well-known issues and restrictions associated with the field of view (FOV), hand-eye misalignment and disorientation, as well as the lack of stereoscopic depth perception in monocular endoscopy. Augmented Reality (AR) technology can help to overcome these limitations by augmenting the real scene with annotations, labels, tumour measurements or even a 3D reconstruction of anatomy structures at the target surgical locations. However, previous research attempts of using AR technology in monocular MIS surgical scenes have been mainly focused on the information overlay without addressing correct spatial calibrations, which could lead to incorrect localization of annotations and labels, and inaccurate depth cues and tumour measurements. In this paper, we present a novel intra-operative dense surface reconstruction framework that is capable of providing geometry information from only monocular MIS videos for geometry-aware AR applications such as site measurements and depth cues. We address a number of compelling issues in augmenting a scene for a monocular MIS environment, such as drifting and inaccurate planar mapping. Methods A state-of-the-art Simultaneous Localization And Mapping (SLAM) algorithm used in robotics has been extended to deal with monocular MIS surgical scenes for reliable endoscopic camera tracking and salient point mapping. A robust global 3D surface reconstruction framework has been developed for building a dense surface using only unorganized sparse point clouds extracted from the SLAM. The 3D surface reconstruction framework employs the Moving Least Squares (MLS) smoothing algorithm and the Poisson surface reconstruction framework for real time processing of the point clouds data set. Finally, the 3D geometric information of the surgical scene allows better understanding and accurate placement AR augmentations based on a robust 3D calibration. Results We demonstrate the clinical relevance of our proposed system through two examples: a) measurement of the surface; b) depth cues in monocular endoscopy. The performance and accuracy evaluations of the proposed framework consist of two steps. First, we have created a computer-generated endoscopy simulation video to quantify the accuracy of the camera tracking by comparing the results of the video camera tracking with the recorded ground-truth camera trajectories. The accuracy of the surface reconstruction is assessed by evaluating the Root Mean Square Distance (RMSD) of surface vertices of the reconstructed mesh with that of the ground truth 3D models. An error of 1.24mm for the camera trajectories has been obtained and the RMSD for surface reconstruction is 2.54mm, which compare favourably with previous approaches. Second, \textit{in vivo} laparoscopic videos are used to examine the quality of accurate AR based annotation and measurement, and the creation of depth cues. These results show the potential promise of our geometry-aware AR technology to be used in MIS surgical scenes. Conclusions The results show that the new framework is robust and accurate in dealing with challenging situations such as the rapid endoscopy camera movements in monocular MIS scenes. Both camera tracking and surface reconstruction based on a sparse point cloud are effective and operated in real-time. This demonstrates the potential of our algorithm for accurate AR localization and depth augmentation with geometric cues and correct surface measurements in MIS with monocular endoscopes

Scene Reconstruction Beyond Structure-from-Motion and Multi-View Stereo

Image-based 3D reconstruction has become a robust technology for recovering accurate and realistic models of real-world objects and scenes. A common pipeline for 3D reconstruction is to first apply Structure-from-Motion (SfM), which recovers relative poses for the input images and sparse geometry for the scene, and then apply Multi-view Stereo (MVS), which estimates a dense depthmap for each image. While this two-stage process is quite effective in many 3D modeling scenarios, there are limits to what can be reconstructed. This dissertation focuses on three particular scenarios where the SfM+MVS pipeline fails and introduces new approaches to accomplish each reconstruction task. First, I introduce a novel method to recover dense surface reconstructions of endoscopic video. In this setting, SfM can generally provide sparse surface structure, but the lack of surface texture as well as complex, changing illumination often causes MVS to fail. To overcome these difficulties, I introduce a method that utilizes SfM both to guide surface reflectance estimation and to regularize shading-based depth reconstruction. I also introduce models of reflectance and illumination that improve the final result. Second, I introduce an approach for augmenting 3D reconstructions from large-scale Internet photo-collections by recovering the 3D position of transient objects --- specifically, people --- in the input imagery. Since no two images can be assumed to capture the same person in the same location, the typical triangulation constraints enjoyed by SfM and MVS cannot be directly applied. I introduce an alternative method to approximately triangulate people who stood in similar locations, aided by a height distribution prior and visibility constraints provided by SfM. The scale of the scene, gravity direction, and per-person ground-surface normals are also recovered. Finally, I introduce the concept of using crowd-sourced imagery to create living 3D reconstructions --- visualizations of real places that include dynamic representations of transient objects. A key difficulty here is that SfM+MVS pipelines often poorly reconstruct ground surfaces given Internet images. To address this, I introduce a volumetric reconstruction approach that leverages scene scale and person placements. Crowd simulation is then employed to add virtual pedestrians to the space and bring the reconstruction "to life."Doctor of Philosoph

Compact Environment Modelling from Unconstrained Camera Platforms

Mobile robotic systems need to perceive their surroundings in order to act independently. In this work a perception framework is developed which interprets the data of a binocular camera in order to transform it into a compact, expressive model of the environment. This model enables a mobile system to move in a targeted way and interact with its surroundings. It is shown how the developed methods also provide a solid basis for technical assistive aids for visually impaired people

High quality dynamic reflectance and surface reconstruction from video

The creation of high quality animations of real-world human actors has long been a challenging problem in computer graphics. It involves the modeling of the shape of the virtual actors, creating their motion, and the reproduction of very fine dynamic details. In order to render the actor under arbitrary lighting, it is required that reflectance properties are modeled for each point on the surface. These steps, that are usually performed manually by professional modelers, are time consuming and cumbersome. In this thesis, we show that algorithmic solutions for some of the problems that arise in the creation of high quality animation of real-world people are possible using multi-view video data. First, we present a novel spatio-temporal approach to create a personalized avatar from multi-view video data of a moving person. Thereafter, we propose two enhancements to a method that captures human shape, motion and reflectance properties of amoving human using eightmulti-view video streams. Afterwards we extend this work, and in order to add very fine dynamic details to the geometric models, such as wrinkles and folds in the clothing, we make use of the multi-view video recordings and present a statistical method that can passively capture the fine-grain details of time-varying scene geometry. Finally, in order to reconstruct structured shape and animation of the subject from video, we present a dense 3D correspondence finding method that enables spatiotemporally coherent reconstruction of surface animations directly frommulti-view video data. These algorithmic solutions can be combined to constitute a complete animation pipeline for acquisition, reconstruction and rendering of high quality virtual actors from multi-view video data. They can also be used individually in a system that require the solution of a specific algorithmic sub-problem. The results demonstrate that using multi-view video data it is possible to find the model description that enables realistic appearance of animated virtual actors under different lighting conditions and exhibits high quality dynamic details in the geometry.Die Entwicklung hochqualitativer Animationen von menschlichen Schauspielern ist seit langem ein schwieriges Problem in der Computergrafik. Es beinhaltet das Modellieren einer dreidimensionaler Abbildung des Akteurs, seiner Bewegung und die Wiedergabe sehr feiner dynamischer Details. Um den Schauspieler unter einer beliebigen Beleuchtung zu rendern, müssen auch die Reflektionseigenschaften jedes einzelnen Punktes modelliert werden. Diese Schritte, die gewöhnlich manuell von Berufsmodellierern durchgeführt werden, sind zeitaufwendig und beschwerlich. In dieser These schlagen wir algorithmische Lösungen für einige der Probleme vor, die in der Entwicklung solch hochqualitativen Animationen entstehen. Erstens präsentieren wir einen neuartigen, räumlich-zeitlichen Ansatz um einen Avatar von Mehransicht-Videodaten einer bewegenden Person zu schaffen. Danach beschreiben wir einen videobasierten Modelierungsansatz mit Hilfe einer animierten Schablone eines menschlichen Körpers. Unter Zuhilfenahme einer handvoll synchronisierter Videoaufnahmen berechnen wir die dreidimensionale Abbildung, seine Bewegung und Reflektionseigenschaften der Oberfläche. Um sehr feine dynamische Details, wie Runzeln und Falten in der Kleidung zu den geometrischen Modellen hinzuzufügen, zeigen wir eine statistische Methode, die feinen Details der zeitlich variierenden Szenegeometrie passiv erfassen kann. Und schließlich zeigen wir eine Methode, die dichte 3D Korrespondenzen findet, um die strukturierte Abbildung und die zugehörige Bewegung aus einem Video zu extrahieren. Dies ermöglicht eine räumlich-zeitlich zusammenhängende Rekonstruktion von Oberflächenanimationen direkt aus Mehransicht-Videodaten. Diese algorithmischen Lösungen können kombiniert eingesetzt werden, um eine Animationspipeline für die Erfassung, die Rekonstruktion und das Rendering von Animationen hoher Qualität aus Mehransicht-Videodaten zu ermöglichen. Sie können auch einzeln in einem System verwendet werden, das nach einer Lösung eines spezifischen algorithmischen Teilproblems verlangt. Das Ergebnis ist eine Modelbeschreibung, das realistisches Erscheinen von animierten virtuellen Schauspielern mit dynamischen Details von hoher Qualität unter verschiedenen Lichtverhältnissen ermöglicht