5 accepted papers
Visual Place Recognition (VPR) is a fundamental task in robotics and computer vision, enabling systems to identify locations seen in the past using visual information. Previous state-of-the-art approach focuses on encoding and retrieving semantically meaningful supersegment representations of images
Visual Place Recognition (VPR) is a fundamental task in robotics and computer vision, enabling systems to identify locations seen in the past using visual information. Previous state-of-the-art approach - SegVLAD - focuses on encoding and retrieving semantically meaningful supersegment representatio…
Accurate perception is critical for vehicle safety, with LiDAR as a key enabler in autonomous driving. To ensure robust performance across environments, sensor types, and weather conditions without costly re-annotation, domain generalization in LiDAR-based 3D semantic segmentation is essential. Howe…
Existing vision-language models (VLMs) are tailored for pinhole imagery, stitching multiple narrow field-of-view inputs to piece together a complete omni-scene understanding. Yet, such multi-view perception overlooks the holistic spatial and contextual relationships that a single panorama inherently
Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a