.png){kind=icon}
.png){kind=icon}
Return
Gradient-Guided Density Redistribution Network for Light Field Full-View Depth Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSVT.2026.3664607.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Light field (LF) full-view depth estimation aims to recover dense and coherent depth maps for all sub-aperture views, which is crucial for applications such as 3D reconstruction, LF editing and virtual reality. However, directly extending center-view volume-based methods to the full-view is computationally infeasible, as it requires constructing a separate cost volume for each view. Besides, existing full-view propagation-based approaches, while more efficient, frequently suffer from edge fattening and cross-view inconsistencies in the presence of occlusions. In this paper, we propose a gradient-guided density redistribution network (GDRNet), a novel end-to-end framework that efficiently generates full-view depth maps by constructing a single plane-density volume and a multi-plane depth image, which are then propagated to all angular views. To resolve ambiguous estimates at occlusion edges, we perform a direction-aware gradient-guided density redistribution only inside a dilated edge narrow band. For each center pixel in edge regions, a guidance gradient is derived from the initial depth map to determine the normal and tangent directions. Then, density in edge fattening regions can be redistributed via sampling along the normal direction, while similarity along the tangent direction can fill bad pixels with inconsistencies. Furthermore, an adaptive edge extraction module with four directional learnable Sobel kernels is designed to jointly exploit spatial and angular gradients, enabling robust detection and localizing the refinement band. Extensive experiments on synthetic and real-world LF datasets demonstrate that GDRNet achieves state-of-the-art accuracy and edge sharpness in both quantitative and qualitative evaluations, while maintaining computational efficiency compared to full-view methods.
Keywords:
Light field
full-view depth estimation
gradient-guided
density redistribution
consistency
Journal
IF:
11.1
Papers:
612
Citations:
3.1W