Jens Kerber

Animation cartography—intrinsic reconstruction of shape and motion

In this article, we consider the problem of animation reconstruction, that is, the reconstruction of shape and motion of a deformable object from dynamic 3D scanner data, without using user-provided template models. Unlike previous work that addressed this problem, we do not rely on locally convergent optimization but present a system that can handle fast motion, temporally disrupted input, and can correctly match objects that disappear for extended time periods in acquisition holes due to occlusion. Our approach is motivated by cartography: We first estimate a few landmark correspondences, which are extended to a dense matching and then used to reconstruct geometry and motion. We propose a number of algorithmic building blocks: a scheme for tracking landmarks in temporally coherent and incoherent data, an algorithm for robust estimation of dense correspondences under topological noise, and the integration of local matching techniques to refine the result. We describe and evaluate the individual components and propose a complete animation reconstruction pipeline based on these ideas. We evaluate our method on a number of standard benchmark datasets and show that we can obtain correct reconstructions in situations where other techniques fail completely or require additional user guidance such as a template model.

Feature preserving depth compression of range images

In this paper we present a new and efficient method for the depth-compression of range images in a feature preserving way. Given a range image (a depth field), the problem studied in the paper consists of achieving a high compression of the depth data while preserving (or even enhancing) perceptually important features of the image. Our approach works in the gradient domain. It combines a linear rescaling scheme with a simple enhancing technique applied to the gradient of the image. The new depth field is obtained from the enhanced and rescaled derivatives of initial range image. By four parameters a user can steer the compression ratio and the amount of details to be perceivable in the outcome. Experiments have shown that our method works very well even for high compression ratios. Applications can be of artistic nature e.g. embossment, engraving or carving.

Computer Assisted Relief Generation—A Survey

In this paper, we present an overview of the achievements accomplished to date in the field of computer‐aided relief generation. We delineate the problem, classify different solutions, analyse similarities, investigate developments and review the approaches according to their particular relative strengths and weaknesses. Moreover, we describe remaining challenges and point out prospective extensions. In consequence, this survey is addressed to both researchers and artists, through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application.

Feature sensitive bas relief generation

Among all forms of sculpture, bas-relief is arguably the closest to painting. Although inherently a two dimensional sculpture, a bas-relief suggests a visual spatial extension of the scene in depth through the combination of composition, perspective, and shading. Most recently, there have been significant results on digital bas-relief generation but many of the existing techniques may wash out high level surface detail during the compression process. The primary goal of this work is to address the problem of fine features by tailoring a filtering technique that achieves good compression without compromising the quality of surface details. As a secondary application we explore the generation of artistic relief which mimic cubism in painting and we show how it could be used for generating Picasso like portraits.

Scalable Symmetry Detection for Urban Scenes

In this paper, we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which has only been demonstrated on data sets of a few hundred megabytes maximum, our method scales to very large scenes: We map the detection problem to a nearest‐neighbour problem in a low‐dimensional feature space, and follow this with a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real‐world scanner data, obtaining a recognition performance comparable to that of state‐of‐the‐art methods. In practice, it scales linearly with scene size and achieves a high absolute throughput, processing half a terabyte of scanner data overnight on a dual socket commodity PC.

A framework for digital sunken relief generation based on 3D geometric models

Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of relief, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. We have modified the existing techniques of line drawings and relief generation, and then combine them organically for this particular purpose.

Relief stylization from 3D models using featured lines

Digital reliefs mimic the style of their real life counterparts, and hence offer an elegant depiction of a 3D shape. Despite an increasing amount of research on digital relief generation in computer graphics, little has been reported on the generation of abstract sculpture forms using pure engraved lines. In this paper, we present a method to create a relief-like sculpture using lines extracted from 3D Objects. Our relief generation process is different from other previous works: our method uses lines to present the geometric shape while others rely on the smooth surface to convey the shape information. Such a relief-like art piece can be easily machined since only the lines need to be carved.

Partial Symmetry Detection in Volume Data

In this paper, we present an algorithm for detecting partial Euclidean symmetries in volume data. Our algorithm finds subsets in voxel data that map to each other approximately under translations, rotations, and reflections. We implement the search for partial symmetries efficiently and robustly using a feature-based approach: We first reduce the volume to salient line features and then create transformation candidates from matching only local configurations of these line networks. Afterwards, only a shortlist of transformation candidates need to be verified using expensive dense volume matching. We apply our technique on both synthetic test scenes as well as real CT scans and show that we can recover a large amount of partial symmetries for complexly structured volume data

Feature Preserving Sketching of Volume Data

In this paper, we present a novel method for extracting feature lines from volume data sets. This leads to a reduction of visual complexity and provides an abstraction of the original data to important structural features. We employ a new iteratively reweighted least-squares approach that allows us to detect sharp creases and to preserve important features such as corners or intersection of feature lines accurately. Traditional least-squares methods This is important for both visual quality as well as reliable further processing in feature detection algorithms. Our algorithm is efficient and easy to implement, and nevertheless effective and robust to noise. We show results for a number of different data sets.

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.