# Blur aware metric depth estimation with multi-focus plenoptic cameras

arXiv: https://arxiv.org/abs/2308.04252 · HTML: https://mlsanity.com/paper/2308.04252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 66 references that are in the map.
- First posted on arXiv 8 Aug 2023.

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

- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [LiFF: Light Field Features in Scale and Depth](https://mlsanity.com/paper/1901.03916/) (2019)
- [Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields](https://mlsanity.com/paper/2106.03043/) (2021)
- [EPI-based Oriented Relation Networks for Light Field Depth Estimation](https://mlsanity.com/paper/2007.04538/) (2020)
- [Blur Aware Calibration of Multi-Focus Plenoptic Camera](https://mlsanity.com/paper/2004.07745/) (2020)
- [Leveraging blur information for plenoptic camera calibration](https://mlsanity.com/paper/2111.05226/) (2021)
- [Baseline and Triangulation Geometry in a Standard Plenoptic Camera](https://mlsanity.com/paper/2010.04638/) (2020)
- [VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images](https://mlsanity.com/paper/1811.07124/) (2018)
- [Rapid Light Field Depth Estimation with Semi-Global Matching](https://mlsanity.com/paper/1907.13449/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Linear Fractional Transformation Model and Calibration Method for Light Field Camera](https://mlsanity.com/paper/2511.03962/) (2025)

## Abstract (arXiv)

While a traditional camera only captures one point of view of a scene, a plenoptic or light-field camera, is able to capture spatial and angular information in a single snapshot, enabling depth estimation from a single acquisition. In this paper, we present a new metric depth estimation algorithm using only raw images from a multi-focus plenoptic camera. The proposed approach is especially suited for the multi-focus configuration where several micro-lenses with different focal lengths are used. The main goal of our blur aware depth estimation (BLADE) approach is to improve disparity estimation for defocus stereo images by integrating both correspondence and defocus cues. We thus leverage blur information where it was previously considered a drawback. We explicitly derive an inverse projection model including the defocus blur providing depth estimates up to a scale factor. A method to calibrate the inverse model is then proposed. We thus take into account depth scaling to achieve precise and accurate metric depth estimates. Our results show that introducing defocus cues improves the depth estimation. We demonstrate the effectiveness of our framework and depth scaling calibration on relative depth estimation setups and on real-world 3D complex scenes with ground truth acquired with a 3D lidar scanner.