TO-LF: A Texture and Occlusion-Oriented Benchmark Dataset for Light Field Disparity Estimation

Accurate disparity estimation in light field (LF) imaging remains challenging due to the narrow baseline between adjacent sub-aperture images (SAIs) and the occlusion effect. Existing learning-based methods suffer from degraded performance in complex scenarios owing to the scarcity of high-quality and diverse training data. To address this limitation, we propose a Texture and Occlusion-oriented Light Field dataset (TO-LF) containing 78 carefully curated images. Unlike the widely used HCI 4D LF benchmark, TO-LF not only provides more training samples but also introduces a more challenging test set with complex occlusions and significant textureless regions. Furthermore, we present a viewpoint-selective sub-pixel cost volume construction method (VS-Sub), which extends disparity labels to the subpixel level for denser cost volumes, and employs dynamic dilated convolutions to differentiate between occluded and non-occluded viewpoints. Comprehensive experiments demonstrate that our framework achieves state-of-the-art (SOTA) performance in disparity estimation.