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.

翻译：光场相机凭借微透镜阵列的独特优势，能够捕捉精细的角度与空间细节。这种从多角度获取复杂光模式与细节的能力，显著增强了图像语义分割的精度——这是视觉智能中场景理解的关键环节。然而，光场相机包含的大量角度信息中存在冗余数据，对智能车辆有限的硬件资源构成沉重负担。此外，不恰当的压缩会导致信息损坏与数据丢失。为挖掘具有代表性的信息，我们提出一种新范式——全孔径融合模型（OAFuser），该模型利用中心视图的密集上下文信息，并从子孔径图像中挖掘角度信息，生成语义一致的结果。为避免网络传播过程中的特征丢失，同时精简光场相机产生的冗余信息，我们提出一种简洁高效的子孔径融合模块（SAFM），在无需额外内存成本的情况下将子孔径图像嵌入角度特征。此外，为解决跨视角空间信息不匹配问题，我们提出中心角度校正模块（CARM），实现特征重排并防止由非对称信息引起的特征遮挡。所提出的OAFuser在UrbanLF-Real与UrbanLF-Syn数据集上取得了当前最优性能，并在UrbanLF-Real扩展数据集上以84.93%的mIoU刷新记录，提升幅度达+4.53%。OAFuser的源代码将发布于https://github.com/FeiBryantkit/OAFuser。