This paper proposes a two-view deterministic geometric model fitting method,
termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure
data. SDF starts from superpixel segmentation, which effectively captures prior
information of feature appearances. The feature appearances are beneficial to
reduce the computational complexity for deterministic fitting methods. SDF also
includes two original elements, i.e., a deterministic sampling algorithm and a
novel model selection algorithm. The two algorithms are tightly coupled to
boost the performance of SDF in both speed and accuracy. Specifically, the
proposed sampling algorithm leverages the grouping cues of superpixels to
generate reliable and consistent hypotheses. The proposed model selection
algorithm further makes use of desirable properties of the generated
hypotheses, to improve the conventional fit-and-remove framework for more
efficient and effective performance. The key characteristic of SDF is that it
can efficiently and deterministically estimate the parameters of model
instances in multi-structure data. Experimental results demonstrate that the
proposed SDF shows superiority over several state-of-the-art fitting methods
for real images with single-structure and multiple-structure data.Comment: Accepted by European Conference on Computer Vision (ECCV
