论文标题
GIF：一般形状表示的神经隐式函数
GIFS: Neural Implicit Function for General Shape Representation
论文作者
论文摘要
神经隐式功能的最新发展已在高质量的3D形状重建方面表现出巨大的成功。但是，大多数作品将空间划分为形状的内部和外部，这将其代表力量限制为单层和水密形状。这种限制会导致乏味的数据处理（将非紧密的原始数据转换为水密度），以及代表现实世界中一般对象形状的无能。在这项工作中，我们提出了一种新的方法，可以代表一般形状，包括具有多层表面的非紧密形状和形状。我们介绍了3D形状（GIF）的一般隐式函数，该功能建模了每两个点之间的关系，而不是点和表面之间的关系。 GIF没有将3D空间分为预定义的内部区域，而是编码是否将两个点分开。 Shapenet上的实验表明，在重建质量，渲染效率和视觉保真度方面，GIF的表现优于先前的最先进方法。项目页面可从https://jianglongye.com/gifs获得。
Recent development of neural implicit function has shown tremendous success on high-quality 3D shape reconstruction. However, most works divide the space into inside and outside of the shape, which limits their representing power to single-layer and watertight shapes. This limitation leads to tedious data processing (converting non-watertight raw data to watertight) as well as the incapability of representing general object shapes in the real world. In this work, we propose a novel method to represent general shapes including non-watertight shapes and shapes with multi-layer surfaces. We introduce General Implicit Function for 3D Shape (GIFS), which models the relationships between every two points instead of the relationships between points and surfaces. Instead of dividing 3D space into predefined inside-outside regions, GIFS encodes whether two points are separated by any surface. Experiments on ShapeNet show that GIFS outperforms previous state-of-the-art methods in terms of reconstruction quality, rendering efficiency, and visual fidelity. Project page is available at https://jianglongye.com/gifs .
