https://lpanaf.github.io/ Linfei Pan, Johannes Schönberger, Marc Pollefeys - Abstract Structure-from-Motion -- the process of simultaneously estimating camera poses and 3D scene structure from a collection of images -- remains a central challenge in computer vision, with many open problems yet to be solved. Recent advances in feedforward 3D reconstruction have made significant strides in overcoming persistent failure cases... Thu, 13 Nov 2025 00:00:00 +0000 https://lpanaf.github.io/cvpr26_gluemap/ https://lpanaf.github.io/cvpr26_gluemap/ Thu, 14 Nov 2024 00:00:00 +0000 https://lpanaf.github.io/cvpr25_gensfm/ https://lpanaf.github.io/cvpr25_gensfm/ Linfei Pan, Dániel Baráth, Marc Pollefeys, Johannes L. Schönberger - Abstract Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM). Solutions to this problem are categorized into incremental and global approaches. Until now, the most popular systems follow the incremental paradigm due to its superior accuracy... Thu, 07 Mar 2024 00:00:00 +0000 https://lpanaf.github.io/eccv24_glomap/ https://lpanaf.github.io/eccv24_glomap/ Linfei Pan, Marc Pollefeys, Dániel Baráth - Abstract Reconstructing a 3D scene from unordered images is pivotal in computer vision and robotics, with applications spanning crowd-sourced mapping and beyond. While global Structure-from-Motion (SfM) techniques are scalable and fast, they often compromise on accuracy. To address this, we introduce a principled approach that integrates gravity direction into the... Wed, 06 Mar 2024 00:00:00 +0000 https://lpanaf.github.io/eccv24_ra1dof/ https://lpanaf.github.io/eccv24_ra1dof/ Linfei Pan, Johannes L. Schönberger, Viktor Larsson, Marc Pollefeys - Abstract Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structured-based localization. This leads to a significant loss... Sun, 05 Mar 2023 00:00:00 +0000 https://lpanaf.github.io/iccv23_privacy_permute/ https://lpanaf.github.io/iccv23_privacy_permute/ Linfei Pan, Marc Pollefeys, Viktor Larsson - Abstract Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric... Tue, 16 Nov 2021 00:00:00 +0000 https://lpanaf.github.io/cvpr22_implicit_camera/ https://lpanaf.github.io/cvpr22_implicit_camera/ Linfei Pan, Ľubor Ladický, Marc Pollefeys - Abstract Recent progress in consumer hardware allowed for the collection of a large amount of animated point cloud data, which is on the one hand highly redundant and on the other hand incomplete. Our goal is to bridge this gap and find a low dimensional representation capable of approximation to... Fri, 31 Jul 2020 00:00:00 +0000 https://lpanaf.github.io/3dv20_nonrigid/ https://lpanaf.github.io/3dv20_nonrigid/