4 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…
Massive semantically labeled datasets are readily available for 2D images, however, are much harder to achieve for 3D scenes. Objects in 3D repositories like ShapeNet are labeled, but regrettably only in isolation, so without context. 3D scenes can be acquired by range scanners on city-level scale,…