# FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation

arXiv: https://arxiv.org/abs/1805.03517 · HTML: https://mlsanity.com/paper/1805.03517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

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

- [Attentive Multimodal Fusion for Optical and Scene Flow](https://mlsanity.com/paper/2307.15301/) (2023)
- [FlowNAS: Neural Architecture Search for Optical Flow Estimation](https://mlsanity.com/paper/2207.01271/) (2022)

## Abstract (arXiv)

Optical Flow algorithms are of high importance for many applications. Recently, the Flow Field algorithm and its modifications have shown remarkable results, as they have been evaluated with top accuracy on different data sets. In our analysis of the algorithm we have found that it produces accurate sparse matches, but there is room for improvement in the interpolation. Thus, we propose in this paper FlowFields++, where we combine the accurate matches of Flow Fields with a robust interpolation. In addition, we propose improved variational optimization as post-processing. Our new algorithm is evaluated on the challenging KITTI and MPI Sintel data sets with public top results on both benchmarks.