Progressive ray casting for volumetric models on mobile devices

Mobile devices have experienced an incredible market penetration in the last decade. Currently, medium to premium smartphones are relatively affordable devices. With the increase in screen size and resolution, together with the improvements in performance of mobile CPUs and GPUs, more tasks have become possible. In this paper we explore the rendering of medium to large volumetric models on mobile and low performance devices in general. To do so, we present a progressive ray casting method that is able to obtain interactive frame rates and high quality results for models that not long ago were only supported by desktop computers.Peer ReviewedPostprint (author's final draft

Fehlerkaschierte Bildbasierte Darstellungsverfahren

Creating photo-realistic images has been one of the major goals in computer graphics since its early days. Instead of modeling the complexity of nature with standard modeling tools, image-based approaches aim at exploiting real-world footage directly,as they are photo-realistic by definition. A drawback of these approaches has always been that the composition or combination of different sources is a non-trivial task, often resulting in annoying visible artifacts. In this thesis we focus on different techniques to diminish visible artifacts when combining multiple images in a common image domain. The results are either novel images, when dealing with the composition task of multiple images, or novel video sequences rendered in real-time, when dealing with video footage from multiple cameras.Fotorealismus ist seit jeher eines der großen Ziele in der Computergrafik. Anstatt die Komplexität der Natur mit standardisierten Modellierungswerkzeugen nachzubauen, gehen bildbasierte Ansätze den umgekehrten Weg und verwenden reale Bildaufnahmen zur Modellierung, da diese bereits per Definition fotorealistisch sind. Ein Nachteil dieser Variante ist jedoch, dass die Komposition oder Kombination mehrerer Quellbilder eine nichttriviale Aufgabe darstellt und häufig unangenehm auffallende Artefakte im erzeugten Bild nach sich zieht. In dieser Dissertation werden verschiedene Ansätze verfolgt, um Artefakte zu verhindern oder abzuschwächen, welche durch die Komposition oder Kombination mehrerer Bilder in einer gemeinsamen Bilddomäne entstehen. Im Ergebnis liefern die vorgestellten Verfahren neue Bilder oder neue Ansichten einer Bildsammlung oder Videosequenz, je nachdem, ob die jeweilige Aufgabe die Komposition mehrerer Bilder ist oder die Kombination mehrerer Videos verschiedener Kameras darstellt

Efficient Semantic Segmentation for Resource-Constrained Applications with Lightweight Neural Networks

This thesis focuses on developing lightweight semantic segmentation models tailored for resource-constrained applications, effectively balancing accuracy and computational efficiency. It introduces several novel concepts, including knowledge sharing, dense bottleneck, and feature re-usability, which enhance the feature hierarchy by capturing fine-grained details, long-range dependencies, and diverse geometrical objects within the scene. To achieve precise object localization and improved semantic representations in real-time environments, the thesis introduces multi-stage feature aggregation, feature scaling, and hybrid-path attention methods

3D Shape Understanding and Generation

In recent years, Machine Learning techniques have revolutionized solutions to longstanding image-based problems, like image classification, generation, semantic segmentation, object detection and many others. However, if we want to be able to build agents that can successfully interact with the real world, those techniques need to be capable of reasoning about the world as it truly is: a tridimensional space. There are two main challenges while handling 3D information in machine learning models. First, it is not clear what is the best 3D representation. For images, convolutional neural networks (CNNs) operating on raster images yield the best results in virtually all image-based benchmarks. For 3D data, the best combination of model and representation is still an open question. Second, 3D data is not available on the same scale as images – taking pictures is a common procedure in our daily lives, whereas capturing 3D content is an activity usually restricted to specialized professionals. This thesis is focused on addressing both of these issues. Which model and representation should we use for generating and recognizing 3D data? What are efficient ways of learning 3D representations from a few examples? Is it possible to leverage image data to build models capable of reasoning about the world in 3D? Our research findings show that it is possible to build models that efficiently generate 3D shapes as irregularly structured representations. Those models require significantly less memory while generating higher quality shapes than the ones based on voxels and multi-view representations. We start by developing techniques to generate shapes represented as point clouds. This class of models leads to high quality reconstructions and better unsupervised feature learning. However, since point clouds are not amenable to editing and human manipulation, we also present models capable of generating shapes as sets of shape handles -- simpler primitives that summarize complex 3D shapes and were specifically designed for high-level tasks and user interaction. Despite their effectiveness, those approaches require some form of 3D supervision, which is scarce. We present multiple alternatives to this problem. First, we investigate how approximate convex decomposition techniques can be used as self-supervision to improve recognition models when only a limited number of labels are available. Second, we study how neural network architectures induce shape priors that can be used in multiple reconstruction tasks -- using both volumetric and manifold representations. In this regime, reconstruction is performed from a single example -- either a sparse point cloud or multiple silhouettes. Finally, we demonstrate how to train generative models of 3D shapes without using any 3D supervision by combining differentiable rendering techniques and Generative Adversarial Networks