For vector fields, we propose a hashing strategy in scale space to accelerate the convolution-based pattern query.
We encode the local flow behavior in scale space using a sequence of hierarchical base descriptors, which are
pre-computed and hashed into a number of hash tables. This ensures a fast fetching of similar occurrences
in the flow and requires only a constant number of table lookups.
For line fields, we present a stream line segmentation algorithm to split long stream lines into globally-consistent
segments, which provides similar segmentations for similar flow structures. It gives the benefit of isolating a
pattern from long and dense stream lines, so that our patterns can be defined sparsely and have a significant
extent, i.e., they are integration-based and not local. This allows for a greater flexibility in defining features of
interest. For user-defined patterns of curve segments, our algorithm finds similar ones that are invariant to similarity
transformations.
Additionally, we present a method for shape recovery from multiple views. This semi-automatic method fits a
template mesh to high-resolution normal data. In contrast to existing 3D reconstruction approaches, we accelerate
the data acquisition time by omitting the structured light scanning step of obtaining low frequency 3D information.