SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints

Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task mainly performed through data-hungry deep learning techniques that need very large amounts of data to be trained. Due the high cost performing segmentation labeling, many synthetic datasets have been proposed. However, most them miss multi-sensor nature data, and do not capture significant changes introduced by variation daytime weather conditions. To fill these gaps, we introduce SELMA, novel dataset semantic contains more than 30K unique waypoints acquired 24 different including RGB, depth, cameras LiDARs, in 27 conditions, total 20M samples. SELMA based CARLA, an open-source simulator generating scenarios, modified increase variability diversity scenes class sets, align it with other benchmark datasets. As shown experimental evaluation, allows efficient training standard multi-modal architectures, achieves remarkable results real-world data. free publicly available, thus supporting open science research.