Andrea Cohen — accepted AI-conference papers

2025

Credulous Acceptance in High-Order Argumentation Frameworks with Necessities: An Incremental Approach (Abstract Reprint)

Gianvincenzo Alfano, Andrea Cohen, Sebastian Gottifredi, Sergio Greco, Francesco Parisi, Guillermo R. Simari

IJCAI 2025

Argumentation is an important research area in the field of AI. There is a substantial amount of work on different aspects of Dung's abstract Argumentation Framework (AF). Two relevant aspects considered separately so far are: i) extending the framework to account for recursive attacks and supports,

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2022

PlaneSDF-Based Change Detection for Long-Term Dense Mapping

Jiahui Fu, Chengyuan Lin, Yuichi Taguchi, Andrea Cohen, Yifu Zhang, Stephen Mylabathula +1

RA-L 2022

The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environ

Cited by 10 Source ↗Scholar ↗

2019

Hybrid Scene Compression for Visual Localization

Federico Camposeco, Andrea Cohen, Marc Pollefeys, Torsten Sattler

CVPR 2019poster

Localizing an image w.r.t. a 3D scene model represents a core task for many computer vision applications. An increasing number of real-world applications of visual localization on mobile devices, e.g., Augmented Reality or autonomous robots such as drones or self-driving cars, demand localization ap…

Cited by 70 PDF ↗Scholar ↗

2018

Hybrid Camera Pose Estimation

Federico Camposeco, Andrea Cohen, Marc Pollefeys, Torsten Sattler

CVPR 2018poster

In this paper, we aim to solve the pose estimation problem of calibrated pinhole and generalized cameras w.r.t. a Structure-from-Motion (SfM) model by leveraging both 2D-3D correspondences as well as 2D-2D correspondences. Traditional approaches either focus on the use of 2D-3D matches, known as str…

Cited by 76 Source ↗PDF ↗code ↗Scholar ↗

2017

Toroidal Constraints for Two-Point Localization Under High Outlier Ratios

Federico Camposeco, Torsten Sattler, Andrea Cohen, Andreas Geiger, Marc Pollefeys

CVPR 2017poster

Localizing a query image against a 3D model at large scale is a hard problem, since 2D-3D matches become more and more ambiguous as the model size increases. This creates a need for pose estimation strategies that can handle very low inlier ratios. In this paper, we draw new insights on the geometri…

Cited by 42 PDF ↗Scholar ↗

2015

Merging the Unmatchable: Stitching Visually Disconnected SfM Models

Andrea Cohen, Torsten Sattler, Marc Pollefeys

ICCV 2015poster

Recent advances in Structure-from-Motion not only enable the reconstruction of large scale scenes, but are also able to detect ambiguous structures caused by repeating elements that might result in incorrect reconstructions. Yet, it is not always possible to fully reconstruct a scene. The images req…

Cited by 38 PDF ↗Scholar ↗

2015

The Likelihood-Ratio Test and Efficient Robust Estimation

Andrea Cohen, Christopher Zach

ICCV 2015poster

Robust estimation of model parameters in the presence of outliers is a key problem in computer vision. RANSAC inspired techniques are widely used in this context, although their application might be limited due to the need of a priori knowledge on the inlier noise level. We propose a new approach fo…

Cited by 15 PDF ↗Scholar ↗