When obtaining interior 3D voxel data from triangular meshes, most existing methods fail to handle low quality meshes which happens to take up a big portion on the internet. In this work we present a robust voxelization method that is based on tetrahedral mesh generation within a user defined error bound. Comparing to other tetrahedral mesh generation methods, our method produces much higher quality tetrahedral meshes as the intermediate outcome, which allows us to utilize a faster voxelization algorithm that is based on a stronger assumption. We show the results comparing to various methods including the state-of-the-art. Our contribution includes a framework which takes triangular mesh as an input and produces voxelized data, a proof to an unproved algorithm that performs better than the state-of-the-art, and various experiments including parallelization built on the GPU and CPU. We further tested our method on various dataset including Princeton ModelNet and Thingi10k to show the robustness of the framework, where near 100% availability is achieved, while others can only achieve around 50%.
翻译：当从三角梅什获取 3D voxel 内部 3D voxel 数据时, 大多数现有方法都无法处理 低质量的 meshes, 这恰好在互联网上占很大比例 。 在这项工作中, 我们提出了一个基于用户定义错误中生成四相网状网格的稳健的 voxelization 方法。 比较其他四相网状网状网格生成的方法, 我们的方法作为中间结果, 产生质量高得多的四相网状网状网状网状网列, 从而使我们能够使用基于更强的假设的更快的 voxelization 算法 。 我们展示了与包括最新技术在内的各种方法相比较的结果 。 我们的贡献包括一个框架, 将三角网状网格作为输入, 并生成 voxelized 数据, 证明一个未验证的算法比 状态网格和 CPU 的平行化方法要好得多 。 我们还在包括普林斯顿 模型 Net 和 Thingi10k 中测试了我们的方法, 来显示框架的坚固性,, 近 100% 能够 实现 。 而其他人只能 达到 50% 。
相关内容
微信扫码咨询专知VIP会员