We propose to utilize self-supervised techniques in the 2D domain for
fine-grained 3D shape segmentation tasks. This is inspired by the observation
that view-based surface representations are more effective at modeling
high-resolution surface details and texture than their 3D counterparts based on
point clouds or voxel occupancy. Specifically, given a 3D shape, we render it
from multiple views, and set up a dense correspondence learning task within the
contrastive learning framework. As a result, the learned 2D representations are
view-invariant and geometrically consistent, leading to better generalization
when trained on a limited number of labeled shapes compared to alternatives
that utilize self-supervision in 2D or 3D alone. Experiments on textured
(RenderPeople) and untextured (PartNet) 3D datasets show that our method
outperforms state-of-the-art alternatives in fine-grained part segmentation.
The improvements over baselines are greater when only a sparse set of views is
available for training or when shapes are textured, indicating that MvDeCor
benefits from both 2D processing and 3D geometric reasoning.

本研究提出了一种利用自我监督技术在二维领域进行细粒度三维形状分割任务的方法，并实现对 3D 形状的多视角表征，透过对比学习框架中密集对应关系的学习任务，实现对 2D 表征的无视角和几何一致性特性的学习，经实验表明，该方法在纹理和无纹理的三维数据集上的表现优于现有的最先进方法，尤其在训练仅局限于少量标记数据或形状纹理较丰富时的表现更加优异，证明了二维处理和三维几何推断对该方法整体的提升效果。