10 accepted papers
Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce deterministic predictions that implicitly average over possible futures, while existing generative world models remain computationally expensive. Recent work demonstrates that predi
Precise and real-time visual localization is critical for applications like AR/VR and robotics, especially on resource-constrained edge devices such as smart glasses, where battery life and heat dissipation can be primary concerns. While many efficient models exist, further reducing compute without
Thousands of photos of Earth are taken every day by astronauts from the International Space Station. Localizing these photos, which has been performed manually for decades, has recently been approached through image retrieval solutions: given an astronaut photo, the goal is to find its most similar…
Astronaut photography spanning six decades of human spaceflight presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite its significance accurately localizing the geographical extent of these images crucial for effective utilization…
In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scale dependency, and lack flexibility. This paper introduces a novel approach that leverages Morse theory and persistent ho…
Visual Place recognition is commonly addressed as an image retrieval problem. However, retrieval methods are impractical to scale to large datasets, densely sampled from city-wide maps, since their dimension impact negatively on the inference time. Using approximate nearest neighbour search for retr…
Visual Place Recognition is a task that aims to predict the place of an image (called query) based solely on its visual features. This is typically done through image retrieval, where the query is matched to the most similar images from a large database of geotagged photos, using learned global des…
In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is…
Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perform on a real-world city-wide VG application, we build San Francisco eXtra Large, a n…
In this paper we propose a novel method for image matching based on dense local features and tailored for visual geolocalization. Dense local features matching is robust against changes in illumination and occlusions, but not against viewpoint shifts which are a fundamental aspect of geolocalization…