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 propose MAGSAC++ and Progressive NAPSAC sampler, P-NAPSAC in short. In
MAGSAC++, we replace the model quality and polishing functions of the original method by …

A method called, sigma-consensus, is proposed to eliminate the need for a user-defined
inlier-outlier threshold in RANSAC. Instead of estimating the noise sigma, it is marginalized …

A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is
introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local …

We present the Synthetic-to-Real Visual Domain Adaptation (VisDA) Benchmark, a large-
scale testbed for unsupervised domain adaptation across visual domains. The VisDA …

With the proliferation of acquisition devices, gathering massive volumes of 3D data is now
easy. Processing such large masses of pointclouds, however, remains a challenge. This is …

This paper introduces a novel anchor design principle to support anchor-based face
detection for superior scale-invariant performance, especially on tiny faces. To achieve this …

We propose-RANSAC, a generalized differentiable RANSAC that allows learning the entire
randomized robust estimation pipeline. The proposed approach enables the use of …

Abstract We propose the Model Quality Network, MQ-Net in short, for predicting the quality,
eg the pose error of essential matrices, of models generated inside RANSAC. It replaces the …

We propose Graph-Cut RANSAC, GC-RANSAC in short, a new robust geometric model
estimation method where the local optimization step is formulated as energy minimization …