  In the absence of parallax cues, a learning-based single image depth
estimation (SIDE) model relies heavily on shading and contextual cues in the
image. While this simplicity is attractive, it is necessary to train such
models on large and varied datasets, which are difficult to capture. It has
been shown that using embeddings from pre-trained foundational models, such as
CLIP, improves zero shot transfer in several applications. Taking inspiration
from this, in our paper we explore the use of global image priors generated
from a pre-trained ViT model to provide more detailed contextual information.
We argue that the embedding vector from a ViT model, pre-trained on a large
dataset, captures greater relevant information for SIDE than the usual route of
generating pseudo image captions, followed by CLIP based text embeddings. Based
on this idea, we propose a new SIDE model using a diffusion backbone which is
conditioned on ViT embeddings. Our proposed design establishes a new
state-of-the-art (SOTA) for SIDE on NYUv2 dataset, achieving Abs Rel error of
0.059 (14% improvement) compared to 0.069 by the current SOTA (VPD). And on
KITTI dataset, achieving Sq Rel error of 0.139 (2% improvement) compared to
0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model
trained on NYUv2, we report mean relative improvement of (20%, 23%, 81%, 25%)
over NeWCRFs on (Sun-RGBD, iBims1, DIODE, HyperSim) datasets, compared to (16%,
18%, 45%, 9%) by ZoeDepth. The project page is available at
https://ecodepth-iitd.github.io
