5 papers · 1 filter
Non-Minimal Sampling and Consensus for Prohibitively Large Datasets
Seong Hun Lee, Patrick Vandewalle, Javier Civera
We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets contaminated with noise and o…
P3P Made Easy
Seong Hun Lee, Patrick Vandewalle, Javier Civera
We revisit the classical Perspective-Three-Point (P3P) problem, which aims to recover the absolute pose of a calibrated camera from three 2D-3D correspondences. It has long been kn…
Robust Single Rotation Averaging Revisited
Seong Hun Lee, Javier Civera
In this work, we propose a novel method for robust single rotation averaging that can efficiently handle an extremely large fraction of outliers. Our approach is to minimize the to…
PCR-99: A Practical Method for Point Cloud Registration with 99 Percent Outliers
Seong Hun Lee, Javier Civera, Patrick Vandewalle
We propose a robust method for point cloud registration that can handle both unknown scales and extreme outlier ratios. Our method, dubbed PCR-99, uses a deterministic 3-point samp…
Alignment Scores: Robust Metrics for Multiview Pose Accuracy Evaluation
Seong Hun Lee, Javier Civera
We propose three novel metrics for evaluating the accuracy of a set of estimated camera poses given the ground truth: Translation Alignment Score (TAS), Rotation Alignment Score (R…