UCGR: Closing the Discretization Gap in Light Field Depth Estimation via Unified Continuous Geometry Representation

Light field (LF) cameras encode dense spatial-angular information for depth estimation, critical for applications such as 3D reconstruction, refocusing, and virtual reality. However, current deep learning methods for LF depth estimation still face significant challenges due to the discretization gap between the continuous geometry of real scenes and the discrete sampling of digital images, limiting their effectiveness in high-precision application. This gap manifests in two complementary forms: spatial discretization leads to structural ambiguities and information loss, while depth discretization introduces inaccuracies due to fixed, discrete depth sampling. To address these challenges, we propose Unified Continuous Geometry Representation (UCGR), a unified representation that models scene geometry as a continuous field over image coordinates and depth. UCGR treats spatial and depth discretization as two facets of the same problem and realized by two complementary operators: (1) Adaptive Plane Sampling Operator, which learns edge-aware planar priors to preserve geometric details and mitigate spatial discretization. (2) Contextual Depth Correction Operator, which utilizes contextual information for depth correction, ensuring continuous depth estimation and suppressing artifacts. Building on UCGR, we propose a Continuous Geometry Network (CGNet) that collaboratively optimizes both spatial and depth discretization for accurate and consistent LF depth estimation. Extensive experiments on synthetic and real-world LF datasets demonstrate that CGNet achieves state-of-the-art performance, significantly outperforming existing LF depth estimation methods in terms of both accuracy and robustness.