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Item Convolution Shadow Maps(The Eurographics Association, 2007) Annen, Thomas; Mertens, Tom; Bekaert, Philippe; Seidel, Hans-Peter; Kautz, Jan; Jan Kautz and Sumanta PattanaikWe present Convolution Shadow Maps, a novel shadow representation that affords efficient arbitrary linear filtering of shadows. Traditional shadow mapping is inherently non-linear w.r.t. the stored depth values, due to the binary shadow test. We linearize the problem by approximating shadow test as a weighted summation of basis terms. We demonstrate the usefulness of this representation, and show that hardware-accelerated anti-aliasing techniques, such as tri-linear filtering, can be applied naturally to Convolution Shadow Maps. Our approach can be implemented very efficiently in current generation graphics hardware, and offers real-time frame rates.Item Self-Similarity-Based Compression of Point Clouds, with Application to Ray Tracing(The Eurographics Association, 2007) Hubo, Erik; Mertens, Tom; Haber, Tom; Bekaert, Philippe; M. Botsch and R. Pajarola and B. Chen and M. ZwickerMany 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.