  Robust multiple model fitting plays a crucial role in many computer vision
applications. Unlike single model fitting problems, the multi-model fitting has
additional challenges. The unknown number of models and the inlier noise scale
are the two most important of them, which are in general provided by the user
using ground-truth or some other auxiliary information. Mode seeking/
clustering-based approaches crucially depend on the quality of model hypotheses
generated. While preference analysis based guided sampling approaches have
shown remarkable performance, they operate in a time budget framework, and the
user provides the time as a reasonable guess. In this paper, we deviate from
the mode seeking and time budget framework. We propose a concept called Kernel
Residual Density (KRD) and apply it to various components of a multiple-model
fitting pipeline. The Kernel Residual Density act as a key differentiator
between inliers and outliers. We use KRD to guide and automatically stop the
sampling process. The sampling process stops after generating a set of
hypotheses that can explain all the data points. An explanation score is
maintained for each data point, which is updated on-the-fly. We propose two
model selection algorithms, an optimal quadratic program based, and a greedy.
Unlike mode seeking approaches, our model selection algorithms seek to find one
representative hypothesis for each genuine structure present in the data. We
evaluate our method (dubbed as DGSAC) on a wide variety of tasks like planar
segmentation, motion segmentation, vanishing point estimation, plane fitting to
3D point cloud, line, and circle fitting, which shows the effectiveness of our
method and its unified nature.
