# Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields

arXiv: https://arxiv.org/abs/2107.02967 · HTML: https://mlsanity.com/paper/2107.02967/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [DeepMVS: Learning Multi-view Stereopsis](https://mlsanity.com/paper/1804.00650/) (2018)
- [Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network](https://mlsanity.com/paper/1808.00150/) (2018)
- [Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints](https://mlsanity.com/paper/1910.06727/) (2019)
- [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://mlsanity.com/paper/1811.01791/) (2018)
- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions](https://mlsanity.com/paper/1708.01964/) (2017)
- [EPI-based Oriented Relation Networks for Light Field Depth Estimation](https://mlsanity.com/paper/2007.04538/) (2020)
- [Fast and Accurate Depth Estimation from Sparse Light Fields](https://mlsanity.com/paper/1812.06856/) (2018)
- [View-consistent 4D Light Field Depth Estimation](https://mlsanity.com/paper/2009.04065/) (2020)

## Built on by (most cited first, 5 of 5)

- [Time-Efficient Light-Field Acquisition Using Coded Aperture and Events](https://mlsanity.com/paper/2403.07244/) (2024)
- [Light Field Depth Estimation via Stitched Epipolar Plane Images](https://mlsanity.com/paper/2203.15201/) (2022)
- [Coded-E2LF: Coded Aperture Light Field Imaging from Events](https://mlsanity.com/paper/2602.22620/) (2026)
- [Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues](https://mlsanity.com/paper/2403.02043/) (2024)
- [Stereo-Knowledge Distillation from dpMV to Dual Pixels for Light Field Video Reconstruction](https://mlsanity.com/paper/2405.11823/) (2024)

## Abstract (arXiv)

We present an algorithm to estimate fast and accurate depth maps from light fields via a sparse set of depth edges and gradients. Our proposed approach is based around the idea that true depth edges are more sensitive than texture edges to local constraints, and so they can be reliably disambiguated through a bidirectional diffusion process. First, we use epipolar-plane images to estimate sub-pixel disparity at a sparse set of pixels. To find sparse points efficiently, we propose an entropy-based refinement approach to a line estimate from a limited set of oriented filter banks. Next, to estimate the diffusion direction away from sparse points, we optimize constraints at these points via our bidirectional diffusion method. This resolves the ambiguity of which surface the edge belongs to and reliably separates depth from texture edges, allowing us to diffuse the sparse set in a depth-edge and occlusion-aware manner to obtain accurate dense depth maps.