Depth estimation.
In this experiment, we evaluate our patch-level features on three monocular depth
estimation benchmarks: NYUd, KITTI and zero-shot transfer from NYUd to SUN3d. We follow the evalu-
ation protocol of Li et al. (2022b). We consider three diﬀerent setups for this evaluation. lin. 1: we extract
the last layer of the frozen transformer and concatenate the [CLS] token to each patch token. Then we
bi-linearly upsample the tokens by a factor of 4 to increase the resolution. Finally we train a simple linear
layer using a classiﬁcation loss by dividing the depth prediction range in 256 uniformly distributed bins and
use a linear normalization following Bhat et al. (2021). lin. 4: we use the same protocol that we use with
one layer, but concatenate the tokens from layers l = {3, 6, 9, 12} for ViT-S/B, l = {5, 12, 18, 24} for ViT-L,
and l = {10, 20, 30, 40} for ViT-g. DPT: we use the DPT decoder (Ranftl et al., 2021) on top of our frozen
DINOv2- Learning Robust Visual Features without Supervision