Publications related to Point-set registration

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics. It is an ill-posed inverse problem, since-without additional prior assumpti ...

WILEY2023

Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks

Jan Skaloud, Davide Antonio Cucci, Aurélien Arnaud Brun

With the objective of improving the registration of lidar point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point–to–point corresponden ...

2022

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Beril Besbinar

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

Local Non-Rigid Structure-from-Motion from Locally Diffeomorphic Mappings

Pascal Fua, Mathieu Salzmann, Shaifali Parashar

We propose a new formulation to the non-rigid structure-from-motion problem that only requires the deforming surface to meaning that its differential structure is preserved. This is a much weaker assumption than the traditional ones of isometry or conforma ...

2020

Reconciling Matching Networks of Conceptual Models

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Zoltán Miklós

Conceptual models such as database schemas, ontologies or process models have been established as a means for effective engineering of information systems. Yet, for complex systems, conceptual models are created by a variety of stakeholders, which calls fo ...

2019

Semantic Attribute Matching Networks

Seungryong Kim

We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations ...

IEEE2019

Segmentation-driven 6D Object Pose Estimation

Pascal Fua, Mathieu Salzmann, Joachim Ludovic Hugonot, Yinlin Hu

The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. I ...

IEEE2019

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón, Okan Altingövde

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

Institute of Electrical and Electronics Engineers2020

Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses

Pascal Fua, Mathieu Salzmann, Kwang Moo Yi, Zheng Dang, Yinlin Hu, Fei Wang

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by ﬁnding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear sy ...

2018

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Eduard Trulls Fortuny, Vincent Lepetit, Kwang Moo Yi, Yuki Ono

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

2018

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny, Yuki Ono

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

IEEE2018

Registration with the Point Cloud Library A Modular Framework for Aligning in 3-D

Alexandru Eugen Ichim

The open-source point cloud library (PCL) and the tools available for point cloud registration is presented. Pairwise registration is usually carried out by means of one of the several variants of the ICP algorithm. Due to the nonconvexity of the optimizat ...

2015

Coupled Structure-from-Motion and 3D Symmetry Detection for Urban Facades

Mark Pauly, Duygu Ceylan

Repeated structures are ubiquitous in urban facades. Such repetitions lead to ambiguity in establishing correspondences across sets of unordered images. A decoupled structure-from-motion reconstruction followed by symmetry detection often produces errors: ...

2014

Sparse Iterative Closest Point

Mark Pauly, Sofien Bouaziz, Andrea Tagliasacchi

Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. Thes ...

2013

Stochastic Exploration of Ambiguities for Non-Rigid Shape Recovery

Pascal Fua

Recovering the 3D shape of deformable surfaces from single images is known to be a highly ambiguous problem because many different shapes may have very similar projections. This is commonly addressed by restricting the set of possible shapes to linear comb ...

Institute of Electrical and Electronics Engineers2013

Resolving Ambiguities in Monocular 3D Reconstruction of Deformable Surfaces

Aydin Varol

In this thesis, we focus on the problem of recovering 3D shapes of deformable surfaces from a single camera. This problem is known to be ill-posed as for a given 2D input image there exist many 3D shapes that give visually identical projections. We present ...

EPFL2012

Joint Statistical Analysis of Images and Keywords with Applications in Semantic Image Enhancement

Sabine Süsstrunk, Albrecht Johannes Lindner, Appu Shaji

With the advent of social image-sharing communities, millions of images with associated semantic tags are now available online for free and allow us to exploit this abundant data in new ways. We present a fast non-parametric statistical framework designed ...

2012

Vision-Based Scene Understanding with Sparsity Promoting Priors

Alexandre Massoud Alahi

Human beings are interested in understanding their environments and the dynamic content that fills their surroundings. For applications ranging from security to marketing, people have installed networks of cameras to capture the dynamic elements of scenes. ...

EPFL2011

Geometric Registration for Deformable Shapes

Mark Pauly, Hao Li

This tutorial overviews recent exciting and rapidly evolving research on registration and correspondence finding across multiple deforming shapes. We address the basic techniques of three main topic areas: <br>Pair-wise local optimization methods &lt ...

The Eurographics Association2010

Exploring Ambiguities for Monocular Non-Rigid Shape Estimation

Pascal Fua

Recovering the 3D shape of deformable surfaces from single images is difficult because many different shapes have very similar projections. This is commonly addressed by restricting the set of possible shapes to linear combinations of deformation modes and ...

2010