On the convergence of IRLS and its variants in outlier-robust estimation
Outlier-robust estimation involves estimating some parameters (eg, 3D rotations) from data
samples in the presence of outliers, and is typically formulated as a non-convex and non …
samples in the presence of outliers, and is typically formulated as a non-convex and non …
Scalable 3d registration via truncated entry-wise absolute residuals
Given an input set of 3D point pairs the goal of outlier-robust 3D registration is to compute
some rotation and translation that align as many point pairs as possible. This is an important …
some rotation and translation that align as many point pairs as possible. This is an important …
Essential matrix estimation using convex relaxations in orthogonal space
A Karimian, R Tron - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
We introduce a novel method to estimate the essential matrix for two-view Structure from
Motion (SfM). We show that every 3 by 3 essential matrix can be embedded in a 4 by 4 …
Motion (SfM). We show that every 3 by 3 essential matrix can be embedded in a 4 by 4 …
Imbalanced mixed linear regression
We consider the problem of mixed linear regression (MLR), where each observed sample
belongs to one of $ K $ unknown linear models. In practical applications, the mixture of the …
belongs to one of $ K $ unknown linear models. In practical applications, the mixture of the …
Block coordinate descent on smooth manifolds: Convergence theory and twenty-one examples
Block coordinate descent is an optimization paradigm that iteratively updates one block of
variables at a time, making it quite amenable to big data applications due to its scalability …
variables at a time, making it quite amenable to big data applications due to its scalability …
Unlabeled principal component analysis
We introduce robust principal component analysis from a data matrix in which the entries of
its columns have been corrupted by permutations, termed Unlabeled Principal Component …
its columns have been corrupted by permutations, termed Unlabeled Principal Component …
Unlabeled Principal Component Analysis and Matrix Completion
We introduce robust principal component analysis from a data matrix in which the entries of
its columns have been corrupted by permutations, termed Unlabeled Principal Component …
its columns have been corrupted by permutations, termed Unlabeled Principal Component …
Recovering simultaneously structured data via non-convex iteratively reweighted least squares
C Kümmerle, J Maly - Advances in Neural Information …, 2024 - proceedings.neurips.cc
We propose a new algorithm for the problem of recovering data that adheres to multiple,
heterogenous low-dimensional structures from linear observations. Focussing on data …
heterogenous low-dimensional structures from linear observations. Focussing on data …
Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization
The problem of finding suitable point embedding or geometric configurations given only
Euclidean distance information of point pairs arises both as a core task and as a sub …
Euclidean distance information of point pairs arises both as a core task and as a sub …
Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search
Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D
correspondence set is a crucial procedure in point cloud registration. Existing …
correspondence set is a crucial procedure in point cloud registration. Existing …