Real-time bas-relief generation from depth-and-normal maps on GPU

To design a bas-relief from a 3D scene is an inherently interactive task in many scenarios. The user normally needs to get instant feedback to select a proper viewpoint. However, current methods are too slow to facilitate this interaction. This paper proposes a two-scale bas-relief modeling method, which is computationally efficient and easy to produce different styles of bas-reliefs. The input 3D scene is first rendered into two textures, one recording the depth information and the other recording the normal information. The depth map is then compressed to produce a base surface with level-of-depth, and the normal map is used to extract local details with two different schemes. One scheme provides certain freedom to design bas-reliefs with different visual appearances, and the other provides a control over the level of detail. Finally, the local feature details are added into the base surface to produce the final result. Our approach allows for real-time computation due to its implementation on graphics hardware. Experiments with a wide range of 3D models and scenes show that our approach can effectively generate digital bas-reliefs in real time

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral

Photo2Relief: Let Human in the Photograph Stand Out

In this paper, we propose a technique for making humans in photographs protrude like reliefs. Unlike previous methods which mostly focus on the face and head, our method aims to generate art works that describe the whole body activity of the character. One challenge is that there is no ground-truth for supervised deep learning. We introduce a sigmoid variant function to manipulate gradients tactfully and train our neural networks by equipping with a loss function defined in gradient domain. The second challenge is that actual photographs often across different light conditions. We used image-based rendering technique to address this challenge and acquire rendering images and depth data under different lighting conditions. To make a clear division of labor in network modules, a two-scale architecture is proposed to create high-quality relief from a single photograph. Extensive experimental results on a variety of scenes show that our method is a highly effective solution for generating digital 2.5D artwork from photographs.Comment: 10 pages, 11 figure

Bas-relief modelling from enriched detail and geometry with deep normal transfer

Detail-and-geometry richness is essential to bas-relief modelling. However, existing image-based and model-based bas-relief modelling techniques commonly suffer from detail monotony or geometry loss. In this paper, we introduce a new bas-relief modelling framework for detail abundance with visual attention based mask generation and geometry preservation, which benefits from our two key contributions. For detail richness, we propose a novel semantic neural network of normal transfer to enrich the texture styles on bas-reliefs. For geometry preservation, we introduce a normal decomposition scheme based on Domain Transfer Recursive Filter (DTRF). Experimental results demonstrate that our approach is advantageous on producing bas-relief modellings with both fine details and geometry preservation

Ink-and-Ray: Bas-Relief Meshes for Adding Global Illumination Effects to Hand-Drawn Characters

We present a new approach for generating global illumination renderings of hand-drawn characters using only a small set of simple annotations. Our system exploits the concept of bas-relief sculptures, making it possible to generate 3D proxies suitable for rendering without requiring side-views or extensive user input. We formulate an optimization process that automatically constructs approximate geometry sufficient to evoke the impression of a consistent 3D shape. The resulting renders provide the richer stylization capabilities of 3D global illumination while still retaining the 2D handdrawn look-and-feel. We demonstrate our approach on a varied set of handdrawn images and animations, showing that even in comparison to ground truth renderings of full 3D objects, our bas-relief approximation is able to produce convincing global illumination effects, including self-shadowing, glossy reflections, and diffuse color bleeding

Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Of assembling small sculptures and disassembling large geometry

This thesis describes the research results and contributions that have been achieved during the author’s doctoral work. It is divided into two independent parts, each of which is devoted to a particular research aspect. The first part covers the true-to-detail creation of digital pieces of art, so-called relief sculptures, from given 3D models. The main goal is to limit the depth of the contained objects with respect to a certain perspective without compromising the initial three-dimensional impression. Here, the preservation of significant features and especially their sharpness is crucial. Therefore, it is necessary to overemphasize fine surface details to ensure their perceptibility in the more complanate relief. Our developments are aimed at amending the flexibility and user-friendliness during the generation process. The main focus is on providing real-time solutions with intuitive usability that make it possible to create precise, lifelike and aesthetic results. These goals are reached by a GPU implementation, the use of efficient filtering techniques, and the replacement of user defined parameters by adaptive values. Our methods are capable of processing dynamic scenes and allow the generation of seamless artistic reliefs which can be composed of multiple elements. The second part addresses the analysis of repetitive structures, so-called symmetries, within very large data sets. The automatic recognition of components and their patterns is a complex correspondence problem which has numerous applications ranging from information visualization over compression to automatic scene understanding. Recent algorithms reach their limits with a growing amount of data, since their runtimes rise quadratically. Our aim is to make even massive data sets manageable. Therefore, it is necessary to abstract features and to develop a suitable, low-dimensional descriptor which ensures an efficient, robust, and purposive search. A simple inspection of the proximity within the descriptor space helps to significantly reduce the number of necessary pairwise comparisons. Our method scales quasi-linearly and allows a rapid analysis of data sets which could not be handled by prior approaches because of their size.Die vorgelegte Arbeit beschreibt die wissenschaftlichen Ergebnisse und Beiträge, die während der vergangenen Promotionsphase entstanden sind. Sie gliedert sich in zwei voneinander unabhängige Teile, von denen jeder einem eigenen Forschungsschwerpunkt gewidmet ist. Der erste Teil beschäftigt sich mit der detailgetreuen Erzeugung digitaler Kunstwerke, sogenannter Reliefplastiken, aus gegebenen 3D-Modellen. Das Ziel ist es, die Objekte, abhängig von der Perspektive, stark in ihrer Tiefe zu limitieren, ohne dass der Eindruck der räumlichen Ausdehnung verloren geht. Hierbei kommt dem Aufrechterhalten der Schärfe signifikanter Merkmale besondere Bedeutung zu. Dafür ist es notwendig, die feinen Details der Objektoberfläche überzubetonen, um ihre Sichtbarkeit im flacheren Relief zu gewährleisten. Unsere Weiterentwicklungen zielen auf die Verbesserung der Flexibilität und Benutzerfreundlichkeit während des Enstehungsprozesses ab. Der Fokus liegt dabei auf dem Bereitstellen intuitiv bedienbarer Echtzeitlösungen, die die Erzeugung präziser, naturgetreuer und visuell ansprechender Resultate ermöglichen. Diese Ziele werden durch eine GPU-Implementierung, den Einsatz effizienter Filtertechniken sowie das Ersetzen benutzergesteuerter Parameter durch adaptive Werte erreicht. Unsere Methoden erlauben das Verarbeiten dynamischer Szenen und die Erstellung nahtloser, kunstvoller Reliefs, die aus mehreren Elementen und Perspektiven zusammengesetzt sein können. Der zweite Teil behandelt die Analyse wiederkehrender Stukturen, sogenannter Symmetrien, innerhalb sehr großer Datensätze. Das automatische Erkennen von Komponenten und deren Muster ist ein komplexes Korrespondenzproblem mit zahlreichen Anwendungen, von der Informationsvisualisierung über Kompression bis hin zum automatischen Verstehen. Mit zunehmender Datenmenge geraten die etablierten Algorithmen an ihre Grenzen, da ihre Laufzeiten quadratisch ansteigen. Unser Ziel ist es, auch massive Datensätze handhabbar zu machen. Dazu ist es notwendig, Merkmale zu abstrahieren und einen passenden niedrigdimensionalen Deskriptor zu entwickeln, der eine effiziente, robuste und zielführende Suche erlaubt. Eine simple Betrachtung der Nachbarschaft innerhalb der Deskriptoren hilft dabei, die Anzahl notwendiger paarweiser Vergleiche signifikant zu reduzieren. Unser Verfahren skaliert quasi-linear und ermöglicht somit eine rasche Auswertung auch auf Daten, die für bisherige Methoden zu groß waren