  Self-Supervised Learning (SSL) methods operate on unlabeled data to learn
robust representations useful for downstream tasks. Most SSL methods rely on
augmentations obtained by transforming the 2D image pixel map. These
augmentations ignore the fact that biological vision takes place in an
immersive three-dimensional, temporally contiguous environment, and that
low-level biological vision relies heavily on depth cues. Using a signal
provided by a pretrained state-of-the-art monocular RGB-to-depth model (the
\emph{Depth Prediction Transformer}, Ranftl et al., 2021), we explore two
distinct approaches to incorporating depth signals into the SSL framework.
First, we evaluate contrastive learning using an RGB+depth input
representation. Second, we use the depth signal to generate novel views from
slightly different camera positions, thereby producing a 3D augmentation for
contrastive learning. We evaluate these two approaches on three different SSL
methods -- BYOL, SimSiam, and SwAV -- using ImageNette (10 class subset of
ImageNet), ImageNet-100 and ImageNet-1k datasets. We find that both approaches
to incorporating depth signals improve the robustness and generalization of the
baseline SSL methods, though the first approach (with depth-channel
concatenation) is superior. For instance, BYOL with the additional depth
channel leads to an increase in downstream classification accuracy from 85.3\%
to 88.0\% on ImageNette and 84.1\% to 87.0\% on ImageNet-C.
