Difference-Guided Full-View Volume for Light Field Depth Estimation

Light field imaging provides rich geometric information by simultaneously capturing spatial and angular dimensions, enabling precise depth estimation which is crucial for numerous vision tasks. Existing volume-based depth estimation methods primarily focus on the center view, ignoring consistency across full views due to insufficient angular coherence and computational complexity. To address these limitations, we propose a novel difference-guided full-view volume framework, which integrates a full-view difference volume with a center-view matching volume. Specifically, the full-view difference volume exploits angular diversity, providing robust angular correspondences that help address ambiguities in low-texture regions and improve consistency across views. The center-view volume preserves precise spatial matching correspondences that are essential for accurate estimation. By propagating the center density information across all views and combining them in final rendering, proposed method effectively leverages two complementary correspondences. Furthermore, we introduce an assisted edge-aware module that leverages multi-view pixel visibility to modulate feature weights. It mitigates occlusions and enforcing edge consistency across all views. Finally, we propose a difference-guided full-view density propagation network for light field full-view depth estimation. Experimental results on synthetic and real light field datasets demonstrate the effectiveness of our method in simultaneously estimating high-quality depth maps for all views. Compared to the previous full-view and arbitrary-view models, our framework achieves over a 8% reduction in Mean Square Error and more than a 3% reduction in BadPixel (7). Furthermore, the proposed approach realizes a 3 ×  to 40 ×  speedup in inference time relative to existing techniques.