Light field cameras can provide rich angular and spatial information to enhance image semantic segmentation for scene understanding in the field of autonomous driving. However, the extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resource of intelligent vehicles. Besides, inappropriate compression leads to information corruption and data loss. To excavate representative information, we propose an 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 Center Angular Rectification Module (CARM) realized 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 made publicly available at https://github.com/FeiBryantkit/OAFuser.

翻译：光场相机能够提供丰富的角度与空间信息，以增强自动驾驶领域中用于场景理解的图像语义分割。然而，光场相机庞大的角度信息包含大量冗余数据，对智能车辆有限的硬件资源构成过载负担。此外，不当的压缩会导致信息损坏和数据丢失。为挖掘具有代表性的信息，我们提出了一种全孔径融合模型（OAFuser），该模型利用中心视图的密集上下文，并从子孔径图像中提取角度信息，以生成语义一致的结果。为避免网络传播过程中的特征损失，同时精简光场相机的冗余信息，我们提出了一种简单而极为有效的子孔径融合模块（SAFM），该模块可在不增加额外内存成本的情况下将子孔径图像嵌入为角度特征。此外，为解决不同视点间空间信息不匹配的问题，我们提出了中心角度校正模块（CARM），实现了特征重排序，并防止了由非对称信息导致的特征遮挡。我们提出的OAFuser在UrbanLF-Real和UrbanLF-Syn数据集上取得了最先进的性能，并在UrbanLF-Real Extended数据集上以84.93%的平均交并比（mIoU）刷新了纪录，提升了+4.53%。OAFuser的源代码将在https://github.com/FeiBryantkit/OAFuser公开提供。