Improving 3D Reconstruction using Deep Learning Priors

Modeling the 3D geometry of shapes and the environment around us has many practical applications in mapping, navigation, virtual/ augmented reality, and autonomous robots. In general, the acquisition of 3D models relies on using passive images or using active depth sensors such as structured light systems that use external infrared projectors. Although active methods provide very robust and reliable depth information, they have limited use cases and heavy power requirements, which makes passive techniques more suitable for day-to-day user applications. Image-based depth acquisition systems usually face challenges representing thin, textureless, or specular surfaces and regions in shadows or low-light environments. While scene depth information can be extracted from the set of passive images, fusion of depth information from several views into a consistent 3D representation remains a challenging task. The most common challenges in 3D environment capture include the use of efficient scene representation that preserves the details, thin structures, and ensures overall completeness of the reconstruction. In this thesis, we illustrate the use of deep learning techniques to resolve some of the challenges of image-based depth acquisition and 3D scene representation. We use a deep learning framework to learn priors over scene geometry and scene global context for solving several ambiguous and ill-posed problems such as estimating depth on textureless surfaces and producing complete 3D reconstruction for partially observed scenes. More specifically, we propose that using deep learning priors, a simple stereo camera system can be used to reconstruct a typical apartment size indoor scene environments with the fidelity that approaches the quality of a much more expensive state-of-the-art active depth-sensing system. Furthermore, we describe how deep learning priors on local shapes can represent 3D environments more efficiently than with traditional systems while at the same time preserving details and completing surfaces.Doctor of Philosoph

Towards 3D facial morphometry:facial image analysis applications in anesthesiology and 3D spectral nonrigid registration

In anesthesiology, the detection and anticipation of difficult tracheal intubation is crucial for patient safety. When undergoing general anesthesia, a patient who is unexpectedly difficult to intubate risks potential life-threatening complications with poor clinical outcomes, ranging from severe harm to brain damage or death. Conversely, in cases of suspected difficulty, specific equipment and personnel will be called upon to increase safety and the chances of successful intubation. Research in anesthesiology has associated a certain number of morphological features of the face and neck with higher risk of difficult intubation. Detecting and analyzing these and other potential features, thus allowing the prediction of difficulty of tracheal intubation in a robust, objective, and automatic way, may therefore improve the patients' safety. In this thesis, we first present a method to automatically classify images of the mouth cavity according to the visibility of certain oropharyngeal structures. This method is then integrated into a novel and completely automatic method, based on frontal and profile images of the patient's face, to predict the difficulty of intubation. We also provide a new database of three dimensional (3D) facial scans and present the initial steps towards a complete 3D model of the face suitable for facial morphometry applications, which include difficult tracheal intubation prediction. In order to develop and test our proposed method, we collected a large database of multimodal recordings of over 2700 patients undergoing general anesthesia. In the first part of this thesis, using two dimensional (2D) facial image analysis methods, we automatically extract morphological and appearance-based features from these images. These are used to train a classifier, which learns to discriminate between patients as being easy or difficult to intubate. We validate our approach on two different scenarios, one of them being close to a real-world clinical scenario, using 966 patients, and demonstrate that the proposed method achieves performance comparable to medical diagnosis-based predictions by experienced anesthesiologists. In the second part of this thesis, we focus on the development of a new 3D statistical model of the face to overcome some of the limitations of 2D methods. We first present EPFL3DFace, a new database of 3D facial expression scans, containing 120 subjects, performing 35 different facial expressions. Then, we develop a nonrigid alignment method to register the scans and allow for statistical analysis. Our proposed method is based on spectral geometry processing and makes use of an implicit representation of the scans in order to be robust to noise or holes in the surfaces. It presents the significant advantage of reducing the number of free parameters to optimize for in the alignment process by two orders of magnitude. We apply our proposed method on the data collected and discuss qualitative results. At its current level of performance, our fully automatic method to predict difficult intubation already has the potential to reduce the cost, and increase the availability of such predictions, by not relying on qualified anesthesiologists with years of medical training. Further data collection, in order to increase the number of patients who are difficult to intubate, as well as extracting morphological features from a 3D representation of the face are key elements to further improve the performance