Material Recognition Meets 3D Reconstruction : Novel Tools for Efficient, Automatic Acquisition Systems

For decades, the accurate acquisition of geometry and reflectance properties has represented one of the major objectives in computer vision and computer graphics with many applications in industry, entertainment and cultural heritage. Reproducing even the finest details of surface geometry and surface reflectance has become a ubiquitous prerequisite in visual prototyping, advertisement or digital preservation of objects. However, today's acquisition methods are typically designed for only a rather small range of material types. Furthermore, there is still a lack of accurate reconstruction methods for objects with a more complex surface reflectance behavior beyond diffuse reflectance. In addition to accurate acquisition techniques, the demand for creating large quantities of digital contents also pushes the focus towards fully automatic and highly efficient solutions that allow for masses of objects to be acquired as fast as possible. This thesis is dedicated to the investigation of basic components that allow an efficient, automatic acquisition process. We argue that such an efficient, automatic acquisition can be realized when material recognition "meets" 3D reconstruction and we will demonstrate that reliably recognizing the materials of the considered object allows a more efficient geometry acquisition. Therefore, the main objectives of this thesis are given by the development of novel, robust geometry acquisition techniques for surface materials beyond diffuse surface reflectance, and the development of novel, robust techniques for material recognition. In the context of 3D geometry acquisition, we introduce an improvement of structured light systems, which are capable of robustly acquiring objects ranging from diffuse surface reflectance to even specular surface reflectance with a sufficient diffuse component. We demonstrate that the resolution of the reconstruction can be increased significantly for multi-camera, multi-projector structured light systems by using overlappings of patterns that have been projected under different projector poses. As the reconstructions obtained by applying such triangulation-based techniques still contain high-frequency noise due to inaccurately localized correspondences established for images acquired under different viewpoints, we furthermore introduce a novel geometry acquisition technique that complements the structured light system with additional photometric normals and results in significantly more accurate reconstructions. In addition, we also present a novel method to acquire the 3D shape of mirroring objects with complex surface geometry. The aforementioned investigations on 3D reconstruction are accompanied by the development of novel tools for reliable material recognition which can be used in an initial step to recognize the present surface materials and, hence, to efficiently select the subsequently applied appropriate acquisition techniques based on these classified materials. In the scope of this thesis, we therefore focus on material recognition for scenarios with controlled illumination as given in lab environments as well as scenarios with natural illumination that are given in photographs of typical daily life scenes. Finally, based on the techniques developed in this thesis, we provide novel concepts towards efficient, automatic acquisition systems

Perceived Depth Control in Stereoscopic Cinematography

Despite the recent explosion of interest in the stereoscopic 3D (S3D) technology, the ultimate prevailing of the S3D medium is still significantly hindered by adverse effects regarding the S3D viewing discomfort. This thesis attempts to improve the S3D viewing experience by investigating perceived depth control methods in stereoscopic cinematography on desktop 3D displays. The main contributions of this work are: (1) A new method was developed to carry out human factors studies on identifying the practical limits of the 3D Comfort Zone on a given 3D display. Our results suggest that it is necessary for cinematographers to identify the specific limits of 3D Comfort Zone on the target 3D display as different 3D systems have different ranges for the 3D Comfort Zone. (2) A new dynamic depth mapping approach was proposed to improve the depth perception in stereoscopic cinematography. The results of a human-based experiment confirmed its advantages in controlling the perceived depth in viewing 3D motion pictures over the existing depth mapping methods. (3) The practicability of employing the Depth of Field (DoF) blur technique in S3D was also investigated. Our results indicate that applying the DoF blur simulation on stereoscopic content may not improve the S3D viewing experience without the real time information about what the viewer is looking at. Finally, a basic guideline for stereoscopic cinematography was introduced to summarise the new findings of this thesis alongside several well-known key factors in 3D cinematography. It is our assumption that this guideline will be of particular interest not only to 3D filmmaking but also to 3D gaming, sports broadcasting, and TV production

A PCA approach to the object constancy for faces using view-based models of the face

The analysis of object and face recognition by humans attracts a great deal of interest, mainly because of its many applications in various fields, including psychology, security, computer technology, medicine and computer graphics. The aim of this work is to investigate whether a PCA-based mapping approach can offer a new perspective on models of object constancy for faces in human vision. An existing system for facial motion capture and animation developed for performance-driven animation of avatars is adapted, improved and repurposed to study face representation in the context of viewpoint and lighting invariance. The main goal of the thesis is to develop and evaluate a new approach to viewpoint invariance that is view-based and allows mapping of facial variation between different views to construct a multi-view representation of the face. The thesis describes a computer implementation of a model that uses PCA to generate example- based models of the face. The work explores the joint encoding of expression and viewpoint using PCA and the mapping between viewspecific PCA spaces. The simultaneous, synchronised video recording of 6 views of the face was used to construct multi-view representations, which helped to investigate how well multiple views could be recovered from a single view via the content addressable memory property of PCA. A similar approach was taken to lighting invariance. Finally, the possibility of constructing a multi-view representation from asynchronous view-based data was explored. The results of this thesis have implications for a continuing research problem in computer vision – the problem of recognising faces and objects from different perspectives and in different lighting. It also provides a new approach to understanding viewpoint invariance and lighting invariance in human observers

Analysis of Human Faces Using a Measurement-Based Skin Reflectance Model

We have measured 3D face geometry, skin reflectance, and subsurface scattering using custom-built devices for 149 subjects of varying age, gender, and race. We developed a novel skin reflectance model whose parameters can be estimated from measurements. The model decomposes the large amount of measured skin data into a spatially-varying analytic BRDF, a diffuse albedo map, and diffuse subsurface scattering. Our model is intuitive, physically plausible, and -- since we do not use the original measured data -- easy to edit as well. High-quality renderings come close to reproducing real photographs. The analysis of the model parameters for our sample population reveals variations according to subject age, gender, skin type, and external factors (e.g., sweat, cold, or makeup). Using our statistics, a user can edit the overall appearance of a face (e.g., changing skin type and age) or change small-scale features using texture synthesis (e.g., adding moles and freckles). We are making the collected statistics publicly available to the research community for applications in face synthesis and analysis.Engineering and Applied Science

A Process for the Semi-Automated Generation of Life-Sized, Interactive 3D Character Models for Holographic Projection

By mixing digital data into the real world, Augmented Reality (AR) can deliver potent immersive and interactive experience to its users. In many application contexts, this requires the capability to deploy animated, high fidelity 3D character models. In this paper, we propose a novel approach to efficiently transform – using 3D scanning – an actor to a photorealistic, animated character. This generated 3D assistant must be able to move to perform recorded motion capture data, and it must be able to generate dialogue with lip sync to naturally interact with the users. The approach we propose for creating these virtual AR assistants utilizes photogrammetric scanning, motion capture, and free viewpoint video for their integration in Unity. We deploy the Occipital Structure sensor to acquire static high-resolution textured surfaces, and a Vicon motion capture system to track series of movements. The proposed capturing process consists of the steps scanning, reconstruction with Wrap 3 and Maya, editing texture maps to reduce artefacts with Photoshop, and rigging with Maya and Motion Builder to render the models fit for animation and lip-sync using LipSyncPro. We test the approach in Unity by scanning two human models with 23 captured animations each. Our findings indicate that the major factors affecting the result quality are environment setup, lighting, and processing constraints

Capturing and Reconstructing the Appearance of Complex {3D} Scenes

In this thesis, we present our research on new acquisition methods for reflectance properties of real-world objects. Specifically, we first show a method for acquiring spatially varying densities in volumes of translucent, gaseous material with just a single image. This makes the method applicable to constantly changing phenomena like smoke without the use of high-speed camera equipment. Furthermore, we investigated how two well known techniques -- synthetic aperture confocal imaging and algorithmic descattering -- can be combined to help looking through a translucent medium like fog or murky water. We show that the depth at which we can still see an object embedded in the scattering medium is increased. In a related publication, we show how polarization and descattering based on phase-shifting can be combined for efficient 3D~scanning of translucent objects. Normally, subsurface scattering hinders the range estimation by offsetting the peak intensity beneath the surface away from the point of incidence. With our method, the subsurface scattering is reduced to a minimum and therefore reliable 3D~scanning is made possible. Finally, we present a system which recovers surface geometry, reflectance properties of opaque objects, and prevailing lighting conditions at the time of image capture from just a small number of input photographs. While there exist previous approaches to recover reflectance properties, our system is the first to work on images taken under almost arbitrary, changing lighting conditions. This enables us to use images we took from a community photo collection website