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Conservative voxelization

机译:保守体素化

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摘要

We propose a novel hardware-accelerated voxelization algorithm for polygonal models. Compared with previous approaches, our algorithm has a major advantage that it guarantees the conservative correctness in voxelization: every voxel intersecting the input model is correctly recognized. This property is crucial for applications like collision detection, occlusion culling and visibility processing. We also present an efficient and robust implementation of the algorithm in the GPU. Experiments show that our algorithm has a lower memory consumption than previous approaches and is more efficient when the volume resolution is high. In addition, our algorithm requires no preprocessing and is suitable for voxelizing deformable models.
机译:我们为多边形模型提出了一种新颖的硬件加速体素化算法。与以前的方法相比,我们的算法的主要优点是可以保证体素化过程中的保守正确性:与输入模型相交的每个体素都可以正确识别。该属性对于碰撞检测,遮挡剔除和可见性处理等应用至关重要。我们还介绍了该算法在GPU中的高效且强大的实现。实验表明,与以前的方法相比,我们的算法具有更低的内存消耗,并且在体积分辨率较高时效率更高。另外,我们的算法不需要预处理,适合于体素化可变形模型。

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