# Welsch Based Multiview Disparity Estimation

arXiv: https://arxiv.org/abs/2110.00803 · HTML: https://mlsanity.com/paper/2110.00803/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 2 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Hierarchical Deep Stereo Matching on High-resolution Images](https://mlsanity.com/paper/1912.06704/) (2019)
- [EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection](https://mlsanity.com/paper/1903.01700/) (2019)

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

- [Multi-view Disparity Estimation Using a Novel Gradient Consistency Model](https://mlsanity.com/paper/2405.17029/) (2024)
- [A 3D Reconstruction Benchmark for Asset Inspection](https://mlsanity.com/paper/2603.17358/) (2026)

## Abstract (arXiv)

In this work, we explore disparity estimation from a high number of views. We experimentally identify occlusions as a key challenge for disparity estimation for applications with high numbers of views. In particular, occlusions can actually result in a degradation in accuracy as more views are added to a dataset. We propose the use of a Welsch loss function for the data term in a global variational framework for disparity estimation. We also propose a disciplined warping strategy and a progressive inclusion of views strategy that can reduce the need for coarse to fine strategies that discard high spatial frequency components from the early iterations. Experimental results demonstrate that the proposed approach produces superior and/or more robust estimates than other conventional variational approaches.