Shape implicit neural representations (INRs) have recently shown to be
effective in shape analysis and reconstruction tasks. Existing INRs require
point coordinates to learn the implicit level sets of the shape. When a normal
vector is available for each point, a higher fidelity representation can be
learned, however normal vectors are often not provided as raw data.
Furthermore, the method's initialization has been shown to play a crucial role
for surface reconstruction. In this paper, we propose a divergence guided shape
representation learning approach that does not require normal vectors as input.
We show that incorporating a soft constraint on the divergence of the distance
function favours smooth solutions that reliably orients gradients to match the
unknown normal at each point, in some cases even better than approaches that
use ground truth normal vectors directly. Additionally, we introduce a novel
geometric initialization method for sinusoidal INRs that further improves
convergence to the desired solution. We evaluate the effectiveness of our
approach on the task of surface reconstruction and shape space learning and
show SOTA performance compared to other unoriented methods. Code and model
parameters available at our project page this https URL

本文提出了一种基于散度导向的形状表示学习方法，不需要输入法向量，并且引入了一种新颖的几何初始化方法，使得 INR 网络在表面重建和形状空间学习任务中取得了比当前最先进的朝向无关方法更好的性能。