# VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images

arXiv: https://arxiv.org/abs/1811.07124 · HTML: https://mlsanity.com/paper/1811.07124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning](https://mlsanity.com/paper/2103.10179/) (2021)
- [Blur aware metric depth estimation with multi-focus plenoptic cameras](https://mlsanity.com/paper/2308.04252/) (2023)

## Abstract (arXiv)

The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images. In turn, 3D models of the recorded objects can be recovered, which is a great advantage over other imaging system. However, reflective and texture-less areas in light field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retrieve multi-scale features from reflective and texture-less regions for accurate disparity estimation. Meanwhile, our network has achieved similar or better performance in other regions for both synthetic light field images and real-world data compared to the state-of-the-art algorithms. Currently, we achieve the best score for mean squared error (MSE) on HCI 4D Light Field Benchmark.