3 accepted papers
Visual SLAM is one of the most fundamental problems in computer vision, with direct applications to real-time localization tasks such as AR/VR, robotics, and 3D scene reconstruction. Although significant progress has been made in both sparse and dense approaches, real-time monocular SLAM remains cha
Outliers are ubiquitous in geometric vision contexts such as pose estimation and mapping, leading to inaccurate estimates. While robust loss functions can tackle outliers, it is challenging to make the estimation robust to the choice of initialization and to estimate the appropriate robust loss shap…
RANSAC-based algorithms are the standard techniques for robust estimation in computer vision. These algorithms are iterative and computationally expensive; they alternate between random sampling of data, computing hypotheses, and running inlier counting. Many authors tried different approaches to im…