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Energy-based Self-Collision Culling for Arbitrary Mesh Deformations

机译:基于能量的自碰撞剔除,用于任意网格变形

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In this paper, we accelerate self-collision detection (SCD) for a deforming triangle mesh by exploiting the idea that a mesh cannot self collide unless it deforms enough. Unlike prior work on subspace self-collision culling which is restricted to low-rank deformation subspaces, our energy-based approach supports arbitrary mesh deformations while still being fast. Given a bounding volume hierarchy (BVH) for a triangle mesh, we precompute Energy-based Self-Collision Culling (ESCC) certificates on bounding-volume-related sub-meshes which indicate the amount of deformation energy required for it to self collide. After updating energy values at runtime, many bounding-volume self-collision queries can be culled using the ESCC certificates. We propose an affine-frame Laplacian-based energy definition which sports a highly optimized certificate pre-process, and fast runtime energy evaluation. The latter is performed hierarchically to amortize Laplacian energy and affine-frame estimation computations. ESCC supports both discrete and continuous SCD with detailed and nonsmooth geometry. We observe significant culling on many examples, with SCD speed-ups up to 26 x.
机译:在本文中,我们通过利用网格无法变形的想法来加速变形三角形网格的自碰撞检测(SCD)。与先前针对子空间自碰撞剔除的工作仅限于低秩变形子空间不同,我们基于能量的方法支持任意网格变形,同时仍保持快速。给定三角形网格的边界体积层次(BVH),我们在与边界体积相关的子网格上预先计算了基于能量的自碰撞剔除(ESCC)证书,这些证书指示其自碰撞所需的变形能量。在运行时更新能量值后,可以使用ESCC证书剔除许多边界量自碰撞查询。我们提出了基于仿射帧拉普拉斯算子的能量定义,该定义具有高度优化的证书预处理和快速运行时能量评估的功能。后者被分层执行以摊销拉普拉斯能量和仿射帧估计计算。 ESCC支持离散和连续SCD,并具有详细和不平滑的几何形状。我们观察到在许多示例中都进行了大量剔除,SCD提速高达26倍。

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