Publications

We present an approach for recovering the reflectance of a static scene with known geometry from a collection of images taken under distant, unknown illumination. In contrast to previous work, we allow the illumination to vary between the images, which greatly increases the applicability of the approach. Using an all-frequency relighting framework based on wavelets, we are able to simultaneously estimate the per-image incident illumination and the per- surface point reflectance. The wavelet framework allows for incorporating various reflection models. We demonstrate the quality of our results for synthetic test cases as well as for several datasets captured under laboratory conditions. Combined with multi-view stereo reconstruction, we are even able to recover the geometry and reflectance of a scene solely using images collected from the Internet.

The shape and reflectance of complex objects, for use in computer graphics applications, cannot always be acquired using specialized equipment due to cost or pratical considerations. We want to provide an easy and cost-effective way for the approximate recovery of both shape and spatially-varying reflectance of objects using commodity hardware. In this paper, we present an image-based technique for recovering 3D shape and spatially-varying reflectance properties from a sparse set of photographs, taken under varying illumination. Our technique models the reflectance with a set of low-parameter BRDFs without knowledge of the location of the light-sources or camera. This results an a flexible and portable system that can be used in the field. We successfully apply the approach to several objects (synthetic and real), recovering shape and reflectance. The acquired information can then be used to render the object with modifications to geometry and lighting via traditional rendering methods.

Many real-world, scanned surfaces contain repetitive structures, like bumps, ridges, creases, and so on. We present a compression technique that exploits self-similarity within a point-sampled surface. Our method replaces similar surface patches with an instance of a representative patch. We use a concise shape descriptor to identify and cluster similar patches. Decoding is achieved through simple instancing of the representative patches. Encoding is efficient, and can be applied to large datasets consisting of millions of points. Moreover, our technique offers random access to the compressed data, making it applicable to ray tracing, and easily allows for storing additional point attributes, like normals.

Both ray tracing and point-based representations provide means to efficiently display very complex 3D models. Computational efficiency has been the main focus of previous work on ray tracing point-sampled surfaces. For very complex models efficient storage in the form of compression becomes necessary in order to avoid costly disk access. However, as ray tracing requires neighborhood queries, existing compression schemes cannot be applied because of their sequential nature. This paper introduces a novel acceleration structure called the Quantized kd-tree, which offers both efficient traversal and storage. The gist of our new representation lies in quantizing the kd-tree splitting plane coordinates. We show that the Quantized kd-tree reduces the memory footprint up to 18 times, not compromising performance. Moreover, the technique can also be employed to provide LOD (Level-Of-Detail) to reduce aliasing problems, with little additional storage cost.

To faithfully display objects consisting of translucent materials such as milk, fruit, wax and marble, one needs to take into account subsurface scattering of light. Accurate renderings require expensive simulation of light transport. Alternatively, the widely-used fast dipole approximation cannot deal with internal visibility issues, and has limited applicability (only homogeneous materials). We present a novel algorithm to plausibly reproduce subsurface scattering based on the diffusion approximation. This yields a relatively simple partial differential equation, which we propose to solve numerically using the multigrid method. The main difficulty in this approach consists of accurately representing interactions near the object's surface, for which we employ the embedded boundary discretization. Also, our method allows us to refine the simulation hierarchically where needed in order to optimize performance and memory usage. The resulting approach is capable of rapidly and accurately computing subsurface scattering in polygonal meshes for both homogeneous and heterogeneous materials. The amount of time spent computing subsurface scattering in a complex object is generally a few minutes.