A framework, TR2M, uses multimodal inputs to rescale relative depth to metric depth, enhancing performance across various datasets through cross-modality attention and contrastive learning.
This work presents a generalizable framework to transfer relative depth to
metric depth. Current monocular depth estimation methods are mainly divided
into metric depth estimation (MMDE) and relative depth estimation (MRDE). MMDEs
estimate depth in metric scale but are often limited to a specific domain.
MRDEs generalize well across different domains, but with uncertain scales which
hinders downstream applications. To this end, we aim to build up a framework to
solve scale uncertainty and transfer relative depth to metric depth. Previous
methods used language as input and estimated two factors for conducting
rescaling. Our approach, TR2M, utilizes both text description and image as
inputs and estimates two rescale maps to transfer relative depth to metric
depth at pixel level. Features from two modalities are fused with a
cross-modality attention module to better capture scale information. A strategy
is designed to construct and filter confident pseudo metric depth for more
comprehensive supervision. We also develop scale-oriented contrastive learning
to utilize depth distribution as guidance to enforce the model learning about
intrinsic knowledge aligning with the scale distribution. TR2M only exploits a
small number of trainable parameters to train on datasets in various domains
and experiments not only demonstrate TR2M’s great performance in seen datasets
but also reveal superior zero-shot capabilities on five unseen datasets. We
show the huge potential in pixel-wise transferring relative depth to metric
depth with language assistance. (Code is available at:
https://github.com/BeileiCui/TR2M)