Large-Scale Light Field Capture and Reconstruction

This thesis discusses approaches and techniques to convert Sparsely-Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) devices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid transformation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. To overcome these three challenges, we propose: (i) a novel self-calibration method, which takes advantage of the geometric constraints from the scene and the cameras, for estimating the rigid transformations from the camera coordinate frame of one Kinect V2 to the camera coordinate frames of 12-nearest RGB cameras; (ii) a novel coarse-to-fine approach for recovering the rigid transformation from the coordinate system of one Kinect to the coordinate system of the other by means of local color and geometry information; (iii) several novel algorithms that can be categorized into two groups for reconstructing a DSLF from an input SSLF, including novel view synthesis methods, which are inspired by the state-of-the-art video frame interpolation algorithms, and Epipolar-Plane Image (EPI) inpainting methods, which are inspired by the Shearlet Transform (ST)-based DSLF reconstruction approaches

Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time

Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust. More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression. Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object

Dynamic Speed and Separation Monitoring with On-Robot Ranging Sensor Arrays for Human and Industrial Robot Collaboration

This research presents a flexible and dynamic implementation of Speed and Separation Monitoring (SSM) safety measure that optimizes the productivity of a task while ensuring human safety during Human-Robot Collaboration (HRC). Unlike the standard static/fixed demarcated 2D safety zones based on 2D scanning LiDARs, this research presents a dynamic sensor setup that changes the safety zones based on the robot pose and motion. The focus of this research is the implementation of a dynamic SSM safety configuration using Time-of-Flight (ToF) laser-ranging sensor arrays placed around the centers of the links of a robot arm. It investigates the viability of on-robot exteroceptive sensors for implementing SSM as a safety measure. Here the implementation of varying dynamic SSM safety configurations based on approaches of measuring human-robot separation distance and relative speeds using the sensor modalities of ToF sensor arrays, a motion-capture system, and a 2D LiDAR is shown. This study presents a comparative analysis of the dynamic SSM safety configurations in terms of safety, performance, and productivity. A system of systems (cyber-physical system) architecture for conducting and analyzing the HRC experiments was proposed and implemented. The robots, objects, and human operators sharing the workspace are represented virtually as part of the system by using a digital-twin setup. This system was capable of controlling the robot motion, monitoring human physiological response, and tracking the progress of the collaborative task. This research conducted experiments with human subjects performing a task while sharing the robot workspace under the proposed dynamic SSM safety configurations. The experiment results showed a preference for the use of ToF sensors and motion capture rather than the 2D LiDAR currently used in the industry. The human subjects felt safe and comfortable using the proposed dynamic SSM safety configuration with ToF sensor arrays. The results for a standard pick and place task showed up to a 40% increase in productivity in comparison to a 2D LiDAR

Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time

Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust. More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression. Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object

Analysis of 3D human gait reconstructed with a depth camera and mirrors

L'évaluation de la démarche humaine est l'une des composantes essentielles dans les soins de santé. Les systèmes à base de marqueurs avec plusieurs caméras sont largement utilisés pour faire cette analyse. Cependant, ces systèmes nécessitent généralement des équipements spécifiques à prix élevé et/ou des moyens de calcul intensif. Afin de réduire le coût de ces dispositifs, nous nous concentrons sur un système d'analyse de la marche qui utilise une seule caméra de profondeur. Le principe de notre travail est similaire aux systèmes multi-caméras, mais l'ensemble de caméras est remplacé par un seul capteur de profondeur et des miroirs. Chaque miroir dans notre configuration joue le rôle d'une caméra qui capture la scène sous un point de vue différent. Puisque nous n'utilisons qu'une seule caméra, il est ainsi possible d'éviter l'étape de synchronisation et également de réduire le coût de l'appareillage. Notre thèse peut être divisée en deux sections: reconstruction 3D et analyse de la marche. Le résultat de la première section est utilisé comme entrée de la seconde. Notre système pour la reconstruction 3D est constitué d'une caméra de profondeur et deux miroirs. Deux types de capteurs de profondeur, qui se distinguent sur la base du mécanisme d'estimation de profondeur, ont été utilisés dans nos travaux. Avec la technique de lumière structurée (SL) intégrée dans le capteur Kinect 1, nous effectuons la reconstruction 3D à partir des principes de l'optique géométrique. Pour augmenter le niveau des détails du modèle reconstruit en 3D, la Kinect 2 qui estime la profondeur par temps de vol (ToF), est ensuite utilisée pour l'acquisition d'images. Cependant, en raison de réflections multiples sur les miroirs, il se produit une distorsion de la profondeur dans notre système. Nous proposons donc une approche simple pour réduire cette distorsion avant d'appliquer les techniques d'optique géométrique pour reconstruire un nuage de points de l'objet 3D. Pour l'analyse de la démarche, nous proposons diverses alternatives centrées sur la normalité de la marche et la mesure de sa symétrie. Cela devrait être utile lors de traitements cliniques pour évaluer, par exemple, la récupération du patient après une intervention chirurgicale. Ces méthodes se composent d'approches avec ou sans modèle qui ont des inconvénients et avantages différents. Dans cette thèse, nous présentons 3 méthodes qui traitent directement les nuages de points reconstruits dans la section précédente. La première utilise la corrélation croisée des demi-corps gauche et droit pour évaluer la symétrie de la démarche, tandis que les deux autres methodes utilisent des autoencodeurs issus de l'apprentissage profond pour mesurer la normalité de la démarche.The problem of assessing human gaits has received a great attention in the literature since gait analysis is one of key components in healthcare. Marker-based and multi-camera systems are widely employed to deal with this problem. However, such systems usually require specific equipments with high price and/or high computational cost. In order to reduce the cost of devices, we focus on a system of gait analysis which employs only one depth sensor. The principle of our work is similar to multi-camera systems, but the collection of cameras is replaced by one depth sensor and mirrors. Each mirror in our setup plays the role of a camera which captures the scene at a different viewpoint. Since we use only one camera, the step of synchronization can thus be avoided and the cost of devices is also reduced. Our studies can be separated into two categories: 3D reconstruction and gait analysis. The result of the former category is used as the input of the latter one. Our system for 3D reconstruction is built with a depth camera and two mirrors. Two types of depth sensor, which are distinguished based on the scheme of depth estimation, have been employed in our works. With the structured light (SL) technique integrated into the Kinect 1, we perform the 3D reconstruction based on geometrical optics. In order to increase the level of details of the 3D reconstructed model, the Kinect 2 with time-of-flight (ToF) depth measurement is used for image acquisition instead of the previous generation. However, due to multiple reflections on the mirrors, depth distortion occurs in our setup. We thus propose a simple approach for reducing such distortion before applying geometrical optics to reconstruct a point cloud of the 3D object. For the task of gait analysis, we propose various alternative approaches focusing on the problem of gait normality/symmetry measurement. They are expected to be useful for clinical treatments such as monitoring patient's recovery after surgery. These methods consist of model-free and model-based approaches that have different cons and pros. In this dissertation, we present 3 methods that directly process point clouds reconstructed from the previous work. The first one uses cross-correlation of left and right half-bodies to assess gait symmetry while the other ones employ deep auto-encoders to measure gait normality