Abstract The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model
fitting. The method interleaves sampling and consolidation of the current data interpretation …

Consistent correspondences between point clouds are vital to 3D vision tasks such as
registration and recognition. In this paper, we present a mutual voting method for ranking 3D …

Geometric model fitting is a challenging but fundamental computer vision problem. Recently,
quantum optimization has been shown to enhance robust fitting for the case of a single …

In this paper, we propose a simple and effective geometric model fitting method to fit and
segment multi-structure data even in the presence of severe outliers. We cast the task of …

This article presents a simple yet effective method for 3-D correspondence selection and
point cloud registration. It first models the initial correspondence set as a graph with nodes …

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting-
the problem of interpreting the input data as a mixture of noisy observations originating from …

We address the problem of recovering multiple structures of different classes in a dataset
contaminated by noise and outliers. In particular, we consider geometric structures defined …

Existing two-view multi-model fitting methods typically follow a two-step manner, ie, model
generation and selection, without considering their interaction. Therefore, in the first step …

This paper presents a novel multi-motion segmentation framework by combining the
geometric model fitting and optical flow. More precisely, we use the spatial information …

We propose Progressive-X+, a new algorithm for finding an unknown number of geometric
models, eg., homographies. The problem is formalized as finding dominant model instances …