  Light field cameras, by harnessing the power of micro-lens array, are capable
of capturing intricate angular and spatial details. This allows for acquiring
complex light patterns and details from multiple angles, significantly
enhancing the precision of image semantic segmentation, a critical aspect of
scene interpretation in vision intelligence. However, the extensive angular
information of light field cameras contains a large amount of redundant data,
which is overwhelming for the limited hardware resources of intelligent
vehicles. Besides, inappropriate compression leads to information corruption
and data loss. To excavate representative information, we propose a new
paradigm, Omni-Aperture Fusion model (OAFuser), which leverages dense context
from the central view and discovers the angular information from sub-aperture
images to generate a semantically consistent result. To avoid feature loss
during network propagation and simultaneously streamline the redundant
information from the light field camera, we present a simple yet very effective
Sub-Aperture Fusion Module (SAFM) to embed sub-aperture images into angular
features without any additional memory cost. Furthermore, to address the
mismatched spatial information across viewpoints, we present a Center Angular
Rectification Module (CARM) to realize feature resorting and prevent feature
occlusion caused by asymmetric information. Our proposed OAFuser achieves
state-of-the-art performance on the UrbanLF-Real and -Syn datasets and sets a
new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain
of +4.53%. The source code of OAFuser will be available at
https://github.com/FeiBryantkit/OAFuser.
