  Leveraging the rich information extracted from light field (LF) cameras is
instrumental for dense prediction tasks. However, adapting light field data to
enhance Salient Object Detection (SOD) still follows the traditional RGB
methods and remains under-explored in the community. Previous approaches
predominantly employ a custom two-stream design to discover the implicit
angular feature within light field cameras, leading to significant information
isolation between different LF representations. In this study, we propose an
efficient paradigm (LF Tracy) to address this limitation. We eschew the
conventional specialized fusion and decoder architecture for a dual-stream
backbone in favor of a unified, single-pipeline approach. This comprises
firstly a simple yet effective data augmentation strategy called MixLD to
bridge the connection of spatial, depth, and implicit angular information under
different LF representations. A highly efficient information aggregation (IA)
module is then introduced to boost asymmetric feature-wise information fusion.
Owing to this innovative approach, our model surpasses the existing
state-of-the-art methods, particularly demonstrating a 23% improvement over
previous results on the latest large-scale PKU dataset. By utilizing only 28.9M
parameters, the model achieves a 10% increase in accuracy with 3M additional
parameters compared to its backbone using RGB images and an 86% rise to its
backbone using LF images. The source code will be made publicly available at
https://github.com/FeiBryantkit/LF-Tracy.
