  We present a unified formulation and model for three motion and 3D perception
tasks: optical flow, rectified stereo matching and unrectified stereo depth
estimation from posed images. Unlike previous specialized architectures for
each specific task, we formulate all three tasks as a unified dense
correspondence matching problem, which can be solved with a single model by
directly comparing feature similarities. Such a formulation calls for
discriminative feature representations, which we achieve using a Transformer,
in particular the cross-attention mechanism. We demonstrate that
cross-attention enables integration of knowledge from another image via
cross-view interactions, which greatly improves the quality of the extracted
features. Our unified model naturally enables cross-task transfer since the
model architecture and parameters are shared across tasks. We outperform RAFT
with our unified model on the challenging Sintel dataset, and our final model
that uses a few additional task-specific refinement steps outperforms or
compares favorably to recent state-of-the-art methods on 10 popular flow,
stereo and depth datasets, while being simpler and more efficient in terms of
model design and inference speed.
