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Exact and Adaptive Signed Distance FieldsComputation for Rigid and DeformableModels on GPUs

机译:GPU上刚性模型和可变形模型的精确和自适应符号距离场计算

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Most techniques for real-time construction of a signed distance field, whether on a CPU or GPU, involve approximate distances. We use a GPU to build an exact adaptive distance field, constructed from an octree by using the Morton code. We use rectangle-swept spheres to construct a bounding volume hierarchy (BVH) around a triangulated model. To speed up BVH construction, we can use a multi-BVH structure to improve the workload balance between GPU processors. An upper bound on distance to the model provided by the octree itself allows us to reduce the number of BVHs involved in determining the distances from the centers of octree nodes at successively lower levels, prior to an exact distance query involving the remaining BVHs. Distance fields can be constructed 35-64 times as fast as a serial CPU implementation of a similar algorithm, allowing us to simulate a piece of fabric interacting with the Stanford Bunny at 20 frames per second.
机译:无论是在CPU还是GPU上,大多数用于实时构建带符号距离场的技术都涉及近似距离。我们使用GPU来构建精确的自适应距离场,该场是通过使用Morton代码从八叉树构造而成的。我们使用矩形扫描球体围绕三角化模型构建边界体积层次(BVH)。为了加快BVH的构建,我们可以使用多BVH结构来改善GPU处理器之间的工作负载平衡。由八叉树本身提供的模型的距离的上限允许我们在涉及剩余BVH的精确距离查询之前,减少在确定相继较低级别的八叉树节点中心距离时涉及的BVH数量。距离字段的构建速度是类似算法的串行CPU实现的35-64倍,从而使我们能够以每秒20帧的速度模拟一块与Stanford Bunny交互的结构。

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