We propose a method for self-supervised image representation learning under
the guidance of 3D geometric consistency. Our intuition is that 3D geometric
consistency priors such as smooth regions and surface discontinuities may imply
consistent semantics or object boundaries, and can act as strong cues to guide
the learning of 2D image representations without semantic labels. Specifically,
we introduce 3D geometric consistency into a contrastive learning framework to
enforce the feature consistency within image views. We propose to use geometric
consistency sets as constraints and adapt the InfoNCE loss accordingly. We show
that our learned image representations are general. By fine-tuning our
pre-trained representations for various 2D image-based downstream tasks,
including semantic segmentation, object detection, and instance segmentation on
real-world indoor scene datasets, we achieve superior performance compared with
state-of-the-art methods.

本文提出了一种自监督的图像表示学习方法，利用三维几何一致性来指导学习过程，并在对比学习框架中引入三维几何一致性作为约束，成功学习出既具有一般性，又能够在各种下游任务中取得优越性能的图像表示。