Hendrik P. A. Lensch

Image-based reconstruction of spatial appearance and geometric detail

Real-world objects are usually composed of a number of different materials that often show subtle changes even within a single material. Photorealistic rendering of such objects requires accurate measurements of the reflection properties of each material, as well as the spatially varying effects. We present an image-based measuring method that robustly detects the different materials of real objects and fits an average bidirectional reflectance distribution function (BRDF) to each of them. In order to model local changes as well, we project the measured data for each surface point into a basis formed by the recovered BRDFs leading to a truly spatially varying BRDF representation. Real-world objects often also have fine geometric detail that is not represented in an acquired mesh. To increase the detail, we derive normal maps even for non-Lambertian surfaces using our measured BRDFs. A high quality model of a real object can be generated with relatively little input data. The generated model allows for rendering under arbitrary viewing and lighting conditions and realistically reproduces the appearance of the original object.

Dual photography

We present a novel photographic technique called dual photography, which exploits Helmholtz reciprocity to interchange the lights and cameras in a scene. With a video projector providing structured illumination, reciprocity permits us to generate pictures from the viewpoint of the projector, even though no camera was present at that location. The technique is completely image-based, requiring no knowledge of scene geometry or surface properties, and by its nature automatically includes all transport paths, including shadows, inter-reflections and caustics. In its simplest form, the technique can be used to take photographs without a camera; we demonstrate this by capturing a photograph using a projector and a photo-resistor. If the photo-resistor is replaced by a camera, we can produce a 4D dataset that allows for relighting with 2D incident illumination. Using an array of cameras we can produce a 6D slice of the 8D reflectance field that allows for relighting with arbitrary light fields. Since an array of cameras can operate in parallel without interference, whereas an array of light sources cannot, dual photography is fundamentally a more efficient way to capture such a 6D dataset than a system based on multiple projectors and one camera. As an example, we show how dual photography can be used to capture and relight scenes.

Acquisition and Analysis of Bispectral Bidirectional Reflectance Distribution Functions

In fluorescent materials, light from a certain band of incident wavelengths is reradiated at longer wavelengths, i.e., with a reduced per-photon energy. While fluorescent materials are common in everyday life, they have received little attention in computer graphics. Especially, no bidirectional reradiation measurements of fluorescent materials have been available so far. In this paper, we extend the well-known concept of the bidirectional reflectance distribution function (BRDF) to account for energy transfer between wavelengths, resulting in a Bispectral Bidirectional Reflectance and Reradiation Distribution Function (bispectral BRRDF). Using a bidirectional and bispectral measurement setup, we acquire reflectance and reradiation data of a variety of fluorescent materials, including vehicle paints, paper and fabric, and compare their renderings with RGB, RGBxRGB, and spectral BRDFs. Our acquisition is guided by a principal component analysis on complete bispectral data taken under a sparse set of angles. We show that in order to faithfully reproduce the full bispectral information for all other angles, only a very small number of wavelength pairs needs to be measured at a high angular resolution.

Automated texture registration and stitching for real world models

A system is presented which automatically registers and stitches textures acquired from multiple photographic images onto the surface of a given corresponding 3D model. Within this process the camera position, direction and field of view must be determined for each of the images. For this registration, which aligns a 2D image to a 3D model we present an efficient hardware-accelerated silhouette-based algorithm working on different image resolutions that accurately registers each image without any user interaction. Besides the silhouettes, the given texture information can be used to improve accuracy by comparing one stitched texture to already registered images resulting in a global multi-view optimization. After the 3D-2D registration for each part of the 3D models surface the view is determined which provides the best available texture. Textures are blended at the borders of regions assigned to different views.

DISCO: acquisition of translucent objects

Translucent objects are characterized by diffuse light scattering beneath the objects surface. Light enters and leaves an object at possibly distinct surface locations. This paper presents the first method to acquire this transport behavior for arbitrary inhomogeneous objects. Individual surface points are illuminated in our DISCO measurement facility and the objects impulse response is recorded with a high-dynamic range video camera. The acquired data is resampled into a hierarchical model of the objects light scattering properties. Missing values are consistently interpolated resulting in measurement-based, complete and accurate representations of real translucent objects which can be rendered with various algorithms.

A Silhouette-Based Algorithm for Texture Registration and Stitching

In this paper a system is presented that automatically registers and stitches textures acquired from multiple photographic images onto the surface of a given corresponding 3D model. Within this process the camera position, direction, and field of view must be determined for each of the images. For this registration, which aligns a 2D image to a 3D model, we present an efficient hardware-accelerated silhouette-based algorithm working on different image resolutions that accurately registers each image without any user interaction. Besides the silhouettes, the given texture information also can be used to improve accuracy by comparing one stitched texture to already registered images resulting in a global multiview optimization. After the 3D?2D registration for each part of the 3D models surface the view is determined which provides the best available texture. Textures are blended at the borders of regions assigned to different views.

Optimal HDR reconstruction with linear digital cameras

Given a multi-exposure sequence of a scene, our aim is to recover the absolute irradiance falling onto a linear camera sensor. The established approach is to perform a weighted average of the scaled input exposures. However, there is no clear consensus on the appropriate weighting to use. We propose a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise. Our weighting is based on a calibrated camera model that accounts for all noise sources. This model also allows us to simultaneously estimate the irradiance and its uncertainty. We evaluate our method on simulated and real world photographs, and show that we consistently improve the signal-to-noise ratio over previous approaches. Finally, we show the effectiveness of our model for optimal exposure sequence selection and HDR image denoising.

Polarization and Phase-Shifting for 3D Scanning of Translucent Objects

Translucent objects pose a difficult problem for traditional structured light 3D scanning techniques. Subsurface scattering corrupts the range estimation in two ways: by drastically reducing the signal-to-noise ratio and by shifting the intensity peak beneath the surface to a point which does not coincide with the point of incidence. In this paper we analyze and compare two descattering methods in order to obtain reliable 3D coordinates for translucent objects. By using polarization-difference imaging, subsurface scattering can be filtered out because multiple scattering randomizes the polarization direction of light while the surface reflectance partially keeps the polarization direction of the illumination. The descattered reflectance can be used for reliable 3D reconstruction using traditional optical 3D scanning techniques, such as structured light. Phase-shifting is another effective descattering technique if the frequency of the projected pattern is sufficiently high. We demonstrate the performance of these two techniques and the combination of them on scanning real-world translucent objects.

3D Acquisition of mirroring objects using striped patterns

Objects with mirroring optical characteristics are left out of the scope of most 3D scanning methods. We present here a new automatic acquisition approach, shape-from-distortion, that focuses on that category of objects, requires only a still camera and a color monitor, and produces range scans (plus a normal and a reflectance map) of the target. Our technique consists of two steps: first, an improved environment matte is captured for the mirroring object, using the interference of patterns with different frequencies to obtain sub-pixel accuracy. Then, the matte is converted into a normal and a depth map by exploiting the self-coherence of a surface when integrating the normal map along different paths. The results show very high accuracy, capturing even smallest surface details. The acquired depth maps can be further processed using standard techniques to produce a complete 3D mesh of the object.

Transparent and Specular Object Reconstruction

This state of the art report covers reconstruction methods for transparent and specular objects or phenomena. While the 3D acquisition of opaque surfaces with Lambertian reflectance is a well‐studied problem, transparent, refractive, specular and potentially dynamic scenes pose challenging problems for acquisition systems. This report reviews and categorizes the literature in this field.