Browsing by Author "Martinek, Magdalena"

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    Compressed Bounding Volume Hierarchies for Efficient Ray Tracing of Disperse Hair
    (The Eurographics Association, 2018) Martinek, Magdalena; Stamminger, Marc; Binder, Nikolaus; Keller, Alexander; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    Ray traced human hair is becoming more and more ubiquitous in photorealistic image synthesis. Despite hierarchical data structures for accelerated ray tracing, performance suffers from the bad separability inherent with ensembles of hair strands. We propose a compressed acceleration data structure that improves separability by adaptively subdividing hair fibers. Compression is achieved by storing quantized as well as oriented bounding boxes and an indexing scheme to specify curve segments instead of storing them. We trade memory for speed, as our approach may use more memory, however, in cases of highly curved hair we can double the number of traversed rays per second over prior work. With equal memory we still achieve a speed-up of up to 30%, with equal performance we can reduce memory by up to 30%.
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    Path-Traced Motion Blur using Motion Trees
    (The Eurographics Association, 2020) Martinek, Magdalena; Thiemann, Philip; Stamminger, Marc; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    Motion Blur is an important effect of photo-realistic rendering. Distribution ray tracing can simulate motion blur very well by integrating light, both over the spatial and the temporal domain. However, increasing the problem by the temporal dimension entails many challenges, particularly in cinematic multi-bounce path tracing of complex scenes where heavy-weight geometry with complex lighting and even offscreen elements contribute to the final image. In particular, for fast moving objects, undersampling in the time domain results in severe artefacts. In this paper, we propose the Motion Tree, a novel Level-of-Detail data structure for efficient handling of animated objects, that both filters in the spatial and the temporal domain. The Motion Tree is a compact nesting of a temporal interval binary tree for filtering time consecutive data and a sparse voxel octree (SVO) which simplifies spatially nearby data. It is generated during a pre-process and fits nicely into any conventional physically based path tracer. When used in a production-scale environment it significantly reduces memory requirements allowing for a speedup in rendering performance with user control over the degree of impact on quality.