Neural marching cubes
Neural marching cubes
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DOI：
10.1145/3478513.3480518
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Zhiqin Chen;Hao Zhang
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作者：
Zhiqin Chen;Hao Zhang
我们介绍神经Marching立方体，一个数据驱动的方法，用于从离散隐式场提取三角形网格。我们基于我们的网格化方法的Marching Cubes（MC），由于其输入的简单性，即一个均匀的网格的有符号的距离或occupancy，这经常出现在表面重建和神经隐式模型。然而，经典的MC是由孤立于各个立方体的粗镶嵌模板定义的。虽然有几种MC变体提出了更精细的镶嵌，但在确定每个立方体中的顶点位置和局部网格拓扑时，它们都做出了启发式假设，例如三线性。原则上，这些方法中没有一种可以重建揭示附近立方体之间的相干性或依赖性的几何特征（例如，锐边），因为这样的信息未被考虑，导致对真实的潜在隐式场的差的估计。为了解决这些挑战，我们从深度学习的角度重新铸造MC，通过设计更适合保留几何特征的镶嵌模板，并从训练网格中学习顶点位置和网格拓扑，以考虑附近立方体的上下文信息。我们开发了一个紧凑的每立方体参数化来表示输出三角形网格，同时与神经处理兼容，以便可以采用简单的3D卷积网络进行训练。我们表明，所有拓扑情况下，在每个立方体，适用于我们的设计可以很容易地得出使用我们的表示，由此产生的镶嵌也可以自然和有效地获得以下几个设计准则。此外，我们的网络学习具有有限感受野的局部特征，因此它可以很好地推广到新的形状和新的数据集。我们评估我们的神经MC方法的定量和定性比较所有知名的MC变体。特别是，我们展示了我们的网络恢复边缘和角落等尖锐特征的能力，这是MC及其变体的一个长期问题。我们的网络还重建本地网状拓扑结构更准确地比以前的方法。代码和数据可在https://github.com/czq142857/NMC上获得。
We introduce Neural Marching Cubes, a data-driven approach for extracting a triangle mesh from a discretized implicit field. We base our meshing approach on Marching Cubes (MC), due to the simplicity of its input, namely a uniform grid of signed distances or occupancies, which frequently arise in surface reconstruction and from neural implicit models. However, classical MC is defined by coarse tessellation templates isolated to individual cubes. While more refined tessellations have been proposed by several MC variants, they all make heuristic assumptions, such as trilinearity, when determining the vertex positions and local mesh topologies in each cube. In principle, none of these approaches can reconstruct geometric features that reveal coherence or dependencies between nearby cubes (e.g., a sharp edge), as such information is unaccounted for, resulting in poor estimates of the true underlying implicit field. To tackle these challenges, we re-cast MC from a deep learning perspective, by designing tessellation templates more apt at preserving geometric features, and learning the vertex positions and mesh topologies from training meshes, to account for contextual information from nearby cubes. We develop a compact per-cube parameterization to represent the output triangle mesh, while being compatible with neural processing, so that a simple 3D convolutional network can be employed for the training. We show that all topological cases in each cube that are applicable to our design can be easily derived using our representation, and the resulting tessellations can also be obtained naturally and efficiently by following a few design guidelines. In addition, our network learns local features with limited receptive fields, hence it generalizes well to new shapes and new datasets. We evaluate our neural MC approach by quantitative and qualitative comparisons to all well-known MC variants. In particular, we demonstrate the ability of our network to recover sharp features such as edges and corners, a long-standing issue of MC and its variants. Our network also reconstructs local mesh topologies more accurately than previous approaches. Code and data are available at https://github.com/czq142857/NMC.