To the best of our knowledge, the most recent light detection and ranging (lidar)-based
surveys have been focused only on specific applications such as reconstruction and …

We present an approach to robust estimation of fundamental matrices from noisy data
contaminated by outliers. The problem is cast as a series of weighted homogeneous least …

Building model reconstruction from 3D point clouds has been investigated for several
decades with increasing interests. Building models represented by one or more parametric …

Automatic 3D plane segmentation is necessary for many applications including point cloud
registration, building information model (BIM) reconstruction, simultaneous localization and …

Motion segmentation is an essential task in artificial intelligence and computer vision.
However, scene motion in real-world intelligent systems usually integrates multiple types of …

Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims
to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed …

After decades of investigation, point cloud registration is still a challenging task in practice,
especially when the correspondences are contaminated by a large number of outliers. It may …

High-dimensional geometric patterns appear in many computer vision problems. In this
work, we present high-dimensional convolutional networks for geometric pattern recognition …

Greedy search-based guided sampling is a promising research field in model fitting to data
with multiple structures in the presence of a large number of outliers. However, these greedy …

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 …