RT info:eu-repo/semantics/article
T1 PASS: Panoramic Annular Semantic Segmentation
A1 Yang, Kailun
A1 Hu, Xinxin
A1 Bergasa Pascual, Luis Miguel
A1 Romera Carmena, Eduardo
A1 Kaiwei, Wang
K1 Semantics
K1 Cameras
K1 Image segmentation
K1 Sensors
K1 Navigation
K1 Task analysis
K1 Benchmark testing
K1 Intelligent vehicles
K1 Scene parsing
K1 Semantic segmentation
K1 Scene understanding
K1 Panoramic annular images
K1 Electrónica
K1 Electronics
AB Pixel-wise semantic segmentation is capable of unifying most of driving scene perception tasks, and has enabled striking progress in the context of navigation assistance, where an entire surrounding sensing is vital. However, current mainstream semantic segmenters are predominantly benchmarked against datasets featuring narrow Field of View (FoV), and a large part of vision-based intelligent vehicles use only a forward-facing camera. In this paper, we propose a Panoramic Annular Semantic Segmentation (PASS) framework to perceive the whole 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 efforts entailed to create fully 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 panoramic scene parsing. The innovation lies in the network adaptation to enable smooth and seamless segmentation, combined with an extended set of heterogeneous data augmentations to attain robustness in panoramic imagery. A comprehensive variety of experiments demonstrates the effectiveness for real-world surrounding perception in a single PASS, while the adaptation proposal is exceptionally positive for state-of-the-art efficient networks.
PB IEEE
SN 1524-9050
YR 2019
FD 2019-09-12
LK http://hdl.handle.net/10017/42859
UL http://hdl.handle.net/10017/42859
LA eng
NO Ministerio de Economía y Competitividad
DS MINDS@UW
RD 24-abr-2024