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A considerable limitation of employing sparse voxels octrees (SVOs) as a model format for ray tracing has been that the octree data structure is inherently static. Due to traversal algorithms dependence on the strict hierarchical structure of octrees, it has been challenging to achieve real-time performance of SVO model animation in ray tracing since the octree data structure would typically have to be regenerated every frame. Presented in this article is a novel method for animation of models specified on the SVO format. The method distinguishes itself by permitting model transformations such as rotation, translation, and anisotropic scaling, while preserving the hierarchical structure of SVO models so that they may be efficiently traversed. Due to its modest memory footprint and straightforward arithmetic operations, the method is well-suited for implementation in hardware. A software ray tracing implementation of animated SVO models demonstrates real-time performance on current-generation desktop GPUs, and shows that the animation method does not substantially slow down the rendering procedure compared to rendering static SVOs.
In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-world smoke plumes. We additionally propose a framework for accurate physics-based reconstructions from a small number of video streams. Central components
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the users body. In doing so, we address several difficult challenges. First, the problem is severe
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well
In this paper, we present an approach to reconstruct 3-D human motion from multi-cameras and track human skeleton using the reconstructed human 3-D point (voxel) cloud. We use an improved and more robust algorithm, probabilistic shape from silhouette
We present a real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerat