Martin Rump

Photo-realistic Rendering of Metallic Car Paint from Image-Based Measurements

State‐of‐the‐art car paint shows not only interesting and subtle angular dependency but also significant spatial variation. Especially in sunlight these variations remain visible even for distances up to a few meters and give the coating a strong impression of depth which cannot be reproduced by a single BRDF model and the kind of procedural noise textures typically used. Instead of explicitly modeling the responsible effect particles we propose to use image‐based reflectance measurements of real paint samples and represent their spatial varying part by Bidirectional Texture Functions (BTF). We use classical BRDF models like Cook‐Torrance to represent the reflection behavior of the base paint and the highly specular finish and demonstrate how the parameters of these models can be derived from the BTF measurements. For rendering, the image‐based spatially varying part is compressed and efficiently synthesized. This paper introduces the first hybrid analytical and image‐based representation for car paint and enables the photo‐realistic rendering of all significant effects of highly complex coatings.

Design and Implementation of Practical Bidirectional Texture Function Measurement Devices Focusing on the Developments at the University of Bonn

Understanding as well as realistic reproduction of the appearance of materials play an important role in computer graphics, computer vision and industry. They enable applications such as digital material design, virtual prototyping and faithful virtual surrogates for entertainment, marketing, education or cultural heritage documentation. A particularly fruitful way to obtain the digital appearance is the acquisition of reflectance from real-world material samples. Therefore, a great variety of devices to perform this task has been proposed. In this work, we investigate their practical usefulness. We first idey a set of necessary attributes and establish a general categorization of different designs that have been realized. Subsequently, we provide an in-depth discussion of three particular implementations by our work group, demonstrating advantages and disadvantages of different system designs with respect to the previously established attributes. Finally, we survey the existing literature to compare our implementation with related approaches.

Practical spectral characterization of trichromatic cameras

Simple and effective geometric and radiometric calibration of camera devices has enabled the use of consumer digital cameras for HDR photography, for image based measurement and similar applications requiring a deeper understanding about the camera characteristics. However, to date no such practical methods for estimating the spectral response of cameras are available. Existing approaches require costly hardware and controlled acquisition conditions limiting their applicability. Consequently, even though being highly desirable for color correction and color processing purposes as well as for designing image-based measurement or photographic setups, the spectral response of a camera is rarely considered. Our objective is to close this gap. In this work a practical approach for multi-spectral characterization of trichromatic cameras is presented. Taking photographs of a color chart and measuring the average lighting using a spectrophotometer the effective spectral response of a camera can be estimated for a wide range of out-of-lab environments. By comprehensive cross validation experiments we prove that the new method performs well compared to costly reference measurements. Moreover, we show that our technique can also be used to generate ICC profiles with higher accuracy and less constrained capturing conditions compared to state-of-the-art ICC profilers.

Spectralization: reconstructing spectra from sparse data

Traditional RGB reflectance and light data suffers from the problem of metamerism and is not suitable for rendering purposes where exact color reproduction under many different lighting conditions is needed. Nowadays many setups for cheap and fast acquisition of RGB or similar trichromatic datasets are available. In contrast to this, multi‐ or even hyper‐spectral measurements require costly hardware and have severe limitations in many cases. In this paper, we present an approach to combine efficiently captured RGB data with spectral data that can be captured with small additional effort for example by scanning a single line of an image using a spectral line‐scanner. Our algorithm can infer spectral reflectances and illumination from such sparse spectral and dense RGB data. Unlike other approaches, our method reaches acceptable perceptual errors with only three channels for the dense data and thus enables further use of highly efficient RGB capture systems. This way, we are able to provide an easier and cheaper way to capture spectral textures, BRDFs and environment maps for the use in spectral rendering systems.

Parallelized matrix factorization for fast BTF compression

Dimensionality reduction methods like Principal Component Analysis (PCA) have become commonplace for the compression of large datasets in computer graphics. One important application is the compression of Bidirectional Texture Functions (BTF). However, the use of such techniques has still many limitations that arise from the large size of the input data which results in impractically high compression times. In this paper, we address these shortcomings and present a method which allows for efficient parallelized computation of the PCA of a large BTF matrix. The matrix is first split into several blocks for which the PCA can be performed independently and thus in parallel. We scale the single subproblems in such a way, that they can be solved in-core using the EM-PCA algorithm. This allows us to perform the calculation on current GPUs exploiting their massive parallel computing power. The eigenspaces determined for the individual blocks are then merged to obtain the PCA of the whole dataset. This way nearly arbitrarily sized matrices can be processed considerably faster than by serial algorithms. Thus, BTFs with much higher spatial and angular resolution can be compressed in reasonable time.

Fast Capture of Spectral Image Series.

In recent years there has been an increasing interest in multispectral imaging hardware. Among many other applications is the color-correct reproduction of materials. In this paper, we aim at circumventing the limitations of most devices, namely extensive acquisition times for acceptable signal-to-noise-ratios. For this purpose we propose a novel approach to spectral imaging that combines high-quality RGB data and spatial filtering of extremely noisy and sparsely measured spectral information. The capability of handling noisy spectral data allows a dramatic reduction of overall exposure times. The speed-up we achieve allows for spectral imaging at practical acquisition times. We use the RGB images for constraining the reconstruction of dense spectral information from the filtered noisy spectral data. A further important contribution is the extension of a commonly used radiometric calibration method for determining the camera response in the lowest, noise-dominated range of pixel values. We apply our approach both to capturing single high-quality spectral images, as well as to the acquisition of image-based multispectral surface reflectance. Our results demonstrate that we are able to lower the acquisition times for such multispectral reflectance from several days to the few hours necessary for an RGB-based measurement.