  Humans demonstrate remarkable skill in transferring manipulation abilities
across objects of varying shapes, poses, and appearances, a capability rooted
in their understanding of semantic correspondences between different instances.
To equip robots with a similar high-level comprehension, we present SparseDFF,
a novel DFF for 3D scenes utilizing large 2D vision models to extract semantic
features from sparse RGBD images, a domain where research is limited despite
its relevance to many tasks with fixed-camera setups. SparseDFF generates
view-consistent 3D DFFs, enabling efficient one-shot learning of dexterous
manipulations by mapping image features to a 3D point cloud. Central to
SparseDFF is a feature refinement network, optimized with a contrastive loss
between views and a point-pruning mechanism for feature continuity. This
facilitates the minimization of feature discrepancies w.r.t. end-effector
parameters, bridging demonstrations and target manipulations. Validated in
real-world scenarios with a dexterous hand, SparseDFF proves effective in
manipulating both rigid and deformable objects, demonstrating significant
generalization capabilities across object and scene variations.
