# Occlusion-Aware Cost Constructor for Light Field Depth Estimation

arXiv: https://arxiv.org/abs/2203.01576 · HTML: https://mlsanity.com/paper/2203.01576/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://mlsanity.com/paper/1912.07849/) (2019)
- [Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields](https://mlsanity.com/paper/2106.03043/) (2021)
- [Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution](https://mlsanity.com/paper/2108.03918/) (2021)
- [EPI-based Oriented Relation Networks for Light Field Depth Estimation](https://mlsanity.com/paper/2007.04538/) (2020)
- [Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift](https://mlsanity.com/paper/1909.09059/) (2019)
- [Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines](https://mlsanity.com/paper/2108.07408/) (2021)

## Built on by (most cited first, 23 of 23)

- [Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation](https://mlsanity.com/paper/2208.09688/) (2022)
- [Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution](https://mlsanity.com/paper/2207.09156/) (2022)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results](https://mlsanity.com/paper/2304.10415/) (2023)
- [Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2510.09320/) (2025)
- [LFSRDiff: Light Field Image Super-Resolution via Diffusion Models](https://mlsanity.com/paper/2311.16517/) (2023)
- [LiFCal: Online Light Field Camera Calibration via Bundle Adjustment](https://mlsanity.com/paper/2408.11682/) (2024)
- [Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction](https://mlsanity.com/paper/2301.08433/) (2023)
- [OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation](https://mlsanity.com/paper/2305.17710/) (2023)
- [Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images](https://mlsanity.com/paper/2311.01292/) (2023)
- [Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution](https://mlsanity.com/paper/2401.00740/) (2024)
- [LIDAR: Lightweight Adaptive Cue-Aware Fusion Vision Mamba for Multimodal Segmentation of Structural Cracks](https://mlsanity.com/paper/2507.22477/) (2025)
- [Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression](https://mlsanity.com/paper/2307.06143/) (2023)
- [Iterative approach to reconstructing neural disparity fields from light-field data](https://mlsanity.com/paper/2407.15380/) (2024)
- [LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction](https://mlsanity.com/paper/2409.17759/) (2024)
- [MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2410.06478/) (2024)
- [LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration](https://mlsanity.com/paper/2510.13729/) (2025)
- [Learning-based Spatial and Angular Information Separation for Light Field Compression](https://mlsanity.com/paper/2304.06322/) (2023)
- [Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues](https://mlsanity.com/paper/2403.02043/) (2024)
- [MDeRainNet: An Efficient Macro-pixel Image Rain Removal Network](https://mlsanity.com/paper/2406.10652/) (2024)
- [Dense Scene Reconstruction from Light-Field Images Affected by Rolling Shutter](https://mlsanity.com/paper/2412.03518/) (2024)
- [PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment](https://mlsanity.com/paper/2607.00176/) (2026)
- [Frequency-Structured Field Learning for Light-Field Disparity Estimation](https://mlsanity.com/paper/2607.14941/) (2026)

## Abstract (arXiv)

Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation methods construct matching cost by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets, which is complex and time-consuming. In this paper, we propose a simple and fast cost constructor to construct matching cost for LF depth estimation. Our cost constructor is composed by a series of convolutions with specifically designed dilation rates. By applying our cost constructor to SAI arrays, pixels under predefined disparities can be integrated and matching cost can be constructed without using any shifting operation. More importantly, the proposed cost constructor is occlusion-aware and can handle occlusions by dynamically modulating pixels from different views. Based on the proposed cost constructor, we develop a deep network for LF depth estimation. Our network ranks first on the commonly used 4D LF benchmark in terms of the mean square error (MSE), and achieves a faster running time than other state-of-the-art methods.