Johannes Köhler

A full-spherical device for simultaneous geometry and reflectance acquisition

We present OrcaM, a device for exploring new methods in the field of simultaneous acquisition of geometry, color and reflectance properties. OrcaM employs a full-spherical construction, a movable projector-camera unit, 633 individually controllable LEDs and a height-adjustable turntable with a glass carrier. In contrast to state of the art hardware layouts, this design allows data acquisition from all possible directions in a single pass without any user interaction. In this paper we report the challenges we encountered during development. We describe the used calibration algorithms that constitute the basis for all future reconstruction methods and present results computed with the methods we currently use.

Detection and Identification Techniques for Markers Used in Computer Vision

This paper summarizes and compares techniques for detecting and identifying markers in the context of computer vision. Existing approaches use correlation, digital, or topological methods for marker identification. The comparison points out that all marker processing algorithms, which employ sophisticated digital codes are more robust and reliable. Existing bit representation schemes for these codes and marker designs are compared with each other. We illustrate in this context, why the marker processing algorithm introduced in [11] is the best performer regarding marker occlusion and minimal detectable pattern size.

Fully Automatic, Omnidirectional Acquisition of Geometry and Appearance in the Context of Cultural Heritage Preservation

Effective documentation and display of ancient objects is an essential task in the field of cultural heritage conservation. Digitization plays an important role in the process of creating, preserving, and accessing objects in digital space. Up to the present day, industrial scanners are used for this task, which focus mainly on the detailed reconstruction of the object’s geometry only. However, particularly important for a faithful digital presentation of the object is the appearance information—that is, a description of the used materials and how they interact with incident light. Using the world’s first full-spherical scanner, we propose a user-friendly reconstruction process that is specifically tailored to the needs of digitizing and representing cultural heritage artifacts. More precisely, our hardware specifically addresses the problem that invaluable or fragile artifacts may not be turned over during acquisition. Nevertheless, we can digitize the object completely, including its bottom. Further, by integrating appearance information into our digitization, we achieve a far more faithful digital replica with a quality comparable to a real picture of the object. But in contrast to a static picture, our representation allows one to interactively change the viewing and lighting directions freely. In addition, the results are very memory efficient, consuming only several megabytes per scanned object. In cooperation with museums and a private collector, we digitized several cultural heritage artifacts to demonstrate the feasibility of the proposed process.

Robust and Accurate Non-parametric Estimation of Reflectance Using Basis Decomposition and Correction Functions

A common approach to non-parametric BRDF estimation is the approximation of the sparsely measured input using basis decomposition. In this paper we greatly improve the fitting accuracy of such methods by iteratively applying a novel correction function to an initial estimate. We also introduce a basis to efficiently represent such a function. Based on this general concept we propose an iterative algorithm that is able to explicitly identify and treat outliers in the input data. Our method is invariant to different error metrics which alleviates the error-prone choice of an appropriate one for the given input. We evaluate our method based on a large set of experiments generated from 100 real-world BRDFs and 16 newly measured materials. The experiments show that our method outperforms other evaluated state-of-the-art basis decomposition methods by an order of magnitude in the perceptual sense for outlier ratios up to 40%.

Faithful, compact and complete digitization of cultural heritage using a full-spherical scanner

Effective documentation and display of ancient objects is an essential task in the field of cultural heritage conservation. Digitization plays an important role for the process of creating, preserving and accessing objects in digital space. Up to the present day, industrial scanners are used for this task that focus mainly on the detailed reconstruction of the objects geometry only. However, important for a faithful digital presentation of the object is in particular the appearance information, i.e. a description of the used materials and how they interact with incident light. Using the worlds first full-spherical scanner, we propose a user friendly reconstruction process that is specifically tailored to the needs for digitizing and representing cultural heritage artifacts. More precisely, our hardware specifically addresses the problem that invaluable or fragile artifacts may not be turned over during acquisition. Nevertheless, we can digitize the object completely including its bottom. Further, by integrating appearance information into our digitization we achieve a far more faithful digital replica with a quality comparable to a real picture of the object. But in contrast to a static picture, our representation allows to interactively change the viewing and lighting directions freely. In addition, the results are very memory efficient, consuming only several MB per scanned object and hence are suited to be accessed and visualized interactively in a web browser. In cooperation with museums and a private collector, we digitized several cultural heritage artifacts in order to demonstrate the feasibility of the proposed process.

High Quality and Memory Efficient Representation for Image Based 3D Reconstructions

We propose a practicable, fully automatic pipeline that directly computes high quality and memory efficient geometry representations from image based 3D reconstructions. It takes a set of calibrated depth and texture images as input. First, we iteratively compute a low frequency geometry proxy directly from the depth images in a photoconsistent way. Second, the large amount of high frequency detail information from all input images is consistently aggregated in a texture and a normal map. View dependent illumination artifacts such as specular reflections are removed during this process. The separation of the underlying massive amount of data into a general shape proxy and high frequency information maps yields a both efficient and visually pleasant representation. Our output can directly be used by digital artists or integrated in memory critical realtime applications such as computer games or web based visualizations.

Improvement of Phase Unwrapping Algorithms by Epipolar Constraints

Phase unwrapping remains a challenging problem in the context of fast 3D reconstruction based on structured light, in particular for objects with complex geometry. In this paper we suggest to support phase unwrapping algorithms by additional constraints induced by the scanning setup. This is possible when at least two cameras are used, a likely case in practice. The constraints are generalized for two or more cameras by introducing the concept of a candidate map. We claim that this greatly reduces the complexity for any subsequent unwrapping algorithm, their performance is thereby strongly increased. We demonstrate this by exemplarily integrating the candidate map into a local path following and a global minimum norm unwrapping method.

Structure from Motion in the Context of Active Scanning

In this paper, we discuss global device calibration based on Structure from Motion (SfM) (Hartley and Zisserman, 2004) in the context of active scanning systems. Currently, such systems are usually pre-calibrated once and partial, unaligned scans are then registered using mostly variants of the Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992). We demonstrate, that SfM-based registration from visual features yields a significantly higher precision. Moreover, we present a novel matching strategy that reduces the influence of an object’s visual features, which can be of low quality, and introduce novel hardware that allows to apply SfM to untextured objects without visual features.