A new enhancement of RANSAC, the locally optimized RANSAC (LO-RANSAC), is introduced. It has been observed that, to find an optimal solution (with a given probability), the number of samples drawn in RANSAC is significantly higher than predicted from the mathematical model. This is due to the incorrect assumption, that a model with parameters computed from an outlier-free sample is consistent w...

A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability η. A provably optimal model verification strategy is designed for the situation when the contamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized RANSAC algorithms guar...

When mosaicking adjacent nightlight images of a large area that lacks human activities, traditional registration methods have difficulty realizing the tie point registrations due to lack structural information. In order address this issue, study devises an easy-to-implement engineering solution allows for sparse light areas with high efficiency while guaranteeing accuracy in non-sparse areas. T...

Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a n...

Three-dimensional model reconstruction is a pivotal technology in the realm of computer vision. Point cloud registration serves as its integral step, which decisively impacts efficiency and precision entire process. However, existing point algorithms often face issues. These include prolonged processing time, inadequate accuracy, poor robustness. To address these problems, this paper proposes n...

This paper compares the use of RANSAC for the determination of epi-polar geometry for calibrated stereo reconstruction of 3D data with more conventional optimisation schemes. The paper illustrates the poor convergence efficiency of RANSAC which is explained by a theoretical relationship describing its dependency upon the number of model parameters. The need for an a-priori estimate of outlier c...

Point cloud collection forming a 3D scene typically uses information from multiple data scans. The common approach is to register the point pairs consequentially using variant of iterative closest (ICP) algorithm, but most versions ICP algorithm only work correctly for small movement between two clouds. This makes it difficult accumulate Global registration algorithms are also known, which theo...

The accurate and efficient segmentation of large-scale urban point clouds is crucial for many higher-level tasks, such as boundary line extraction, cloud registration, deformation measurement. In this paper, we propose a novel supervoxel approach to address the problem under-segmentation in local regions at various resolutions. Our introduces distance constraints from points planes merging stag...

We present a method that can evaluate a RANSAC hypothesis in constant time, i.e. independent of the size of the data. A key observation here is that correct hypotheses are tightly clustered together in the latent parameter domain. In a manner similar to the generalized Hough transform we seek to find this cluster, only that we need as few as two votes for a successful detection. Rapidly locatin...