8 research outputs found
Can we PASS beyond the Field of View? Panoramic Annular Semantic Segmentation for real-world surrounding perception
2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9-12 June 2019Pixel-wise semantic segmentation unifies distinct
scene perception tasks in a coherent way, and has catalyzed
notable progress in autonomous and assisted navigation, where
a whole surrounding perception is vital. However, current mainstream semantic segmenters are normally benchmarked against
datasets with narrow Field of View (FoV), and most visionbased navigation systems use only a forward-view camera.
In this paper, we propose a Panoramic Annular Semantic
Segmentation (PASS) framework to perceive the entire surrounding based on a compact panoramic annular lens system
and an online panorama unfolding process. To facilitate the
training of PASS models, we leverage conventional FoV imaging
datasets, bypassing the effort entailed to create dense panoramic
annotations. To consistently exploit the rich contextual cues in
the unfolded panorama, we adapt our real-time ERF-PSPNet to
predict semantically meaningful feature maps in different segments and fuse them to fulfill smooth and seamless panoramic
scene parsing. Beyond the enlarged FoV, we extend focal
length-related and style transfer-based data augmentations, to
robustify the semantic segmenter against distortions and blurs
in panoramic imagery. A comprehensive variety of experiments
demonstrates the qualified robustness of our proposal for realworld surrounding understanding.Ministerio de EconomĂa y CompetitividadComunidad de Madri