Manuel López Antequera — accepted AI-conference papers

2021

CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization

Ara Jafarzadeh, Manuel López Antequera, Pau Gargallo, Yubin Kuang, Carl Toft, Fredrik Kahl +1

ICCV 2021poster

Visual localization is the problem of estimating the position and orientation from which a given image (or a sequence of images) is taken in a known scene. It is an important part of a wide range of computer vision and robotics applications, from self-driving cars to augmented/virtual reality system…

Cited by 21 PDF ↗Scholar ↗

2020

Mapillary Planet-Scale Depth Dataset

Manuel López Antequera, Pau Gargallo, Markus Hofinger, Samuel Rota Bulò, Yubin Kuang, Peter Kontschieder

ECCV 2020poster

Learning-based methods produce remarkable results on single image depth tasks when trained on well-established benchmarks, however, there is a large gap from these benchmarks to real-world performance that is usually obscured by the common practice of fine-tuning on the target dataset. We introduce…

Cited by 41 Source ↗PDF ↗code ↗Scholar ↗