The perception of the surrounding environment is a key requirement for autonomous driving
systems, yet the computation of an accurate semantic representation of the scene starting …

Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an
increased demand for external sensor calibration. Despite several proven calibration …

The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs)
imagery heavily relies on the availability of annotated high-resolution aerial data. However …

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift
in real-world Semantic Segmentation (SS) without compromising the private nature of the …

In this paper, we address panoramic semantic segmentation which is under-explored due to
two critical challenges:(1) image distortions and object deformations on panoramas;(2) lack …

We propose a novel multi-modal-based Unsupervised Domain Adaptation (UDA) method for
semantic segmentation. Recently, depth has proven to be a relevent property for providing …

In this work, we introduce panoramic panoptic segmentation, as the most holistic scene
understanding, both in terms of Field of View (FoV) and image-level understanding for …

With the increasing availability of depth sensors, multimodal frameworks that combine color
information with depth data are gaining interest. However, ground truth data for semantic …

In this paper, we present an unsupervised, completely blind, no-reference (NR) stereoscopic
(S3D) image quality prediction model to assess the perceptual quality of natural S3D …