论文标题
Taylor3Dnet：基于里程碑的快速3D形状推断泰勒系列
Taylor3DNet: Fast 3D Shape Inference With Landmark Points Based Taylor Series
论文作者
论文摘要
从连续的表示能力中受益，深层隐式函数可以代表无限分辨率的形状。但是，从隐式函数中提取高分辨率ISO-SURFACE需要对具有大量参数的网络进行众多查询点的前向传播，从而防止了生成速度。受泰勒（Taylor）系列的启发，我们提出了taylo3dnet，以加快隐性形状表示的推断。 Taylor3Dnet利用一组离散的地标点及其相应的Taylor串联系数表示代表3D形状的隐式场，而Landmark点的数量与ISO地表提取的分辨率无关。一旦预测了与地标相对应的系数，每个查询点的网络评估就可以简化为具有几个最近的地标点的低阶泰勒级数计算。基于这种有效的表示，我们的Taylor3DNET比基于网络的隐式函数的推理速度明显快。我们评估了具有各种输入类型的重建任务的方法，结果表明，与最先进的基线相比，我们的方法可以通过较大的边距提高推理速度，而无需牺牲性能。
Benefiting from the continuous representation ability, deep implicit functions can represent a shape at infinite resolution. However, extracting high-resolution iso-surface from an implicit function requires forward-propagating a network with a large number of parameters for numerous query points, thus preventing the generation speed. Inspired by the Taylor series, we propose Taylo3DNet to accelerate the inference of implicit shape representations. Taylor3DNet exploits a set of discrete landmark points and their corresponding Taylor series coefficients to represent the implicit field of a 3D shape, and the number of landmark points is independent of the resolution of the iso-surface extraction. Once the coefficients corresponding to the landmark points are predicted, the network evaluation for each query point can be simplified as a low-order Taylor series calculation with several nearest landmark points. Based on this efficient representation, our Taylor3DNet achieves a significantly faster inference speed than classical network-based implicit functions. We evaluate our approach on reconstruction tasks with various input types, and the results demonstrate that our approach can improve the inference speed by a large margin without sacrificing the performance compared with state-of-the-art baselines.
