# NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry

arXiv: https://arxiv.org/abs/2107.03610 · HTML: https://mlsanity.com/paper/2107.03610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation](https://mlsanity.com/paper/1805.09806/) (2018)
- [UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss](https://mlsanity.com/paper/1711.07837/) (2017)
- [DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency](https://mlsanity.com/paper/1809.01649/) (2018)
- [SelFlow: Self-Supervised Learning of Optical Flow](https://mlsanity.com/paper/1904.09117/) (2019)
- [What Matters in Unsupervised Optical Flow](https://mlsanity.com/paper/2006.04902/) (2020)
- [Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2003.13045/) (2020)
- [Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness](https://mlsanity.com/paper/1608.05842/) (2016)
- [Occlusion Aware Unsupervised Learning of Optical Flow](https://mlsanity.com/paper/1711.05890/) (2017)
- [DDFlow: Learning Optical Flow with Unlabeled Data Distillation](https://mlsanity.com/paper/1902.09145/) (2019)
- [ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data](https://mlsanity.com/paper/2008.08974/) (2020)
- [Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching](https://mlsanity.com/paper/2004.02138/) (2020)
- [Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes](https://mlsanity.com/paper/1904.03848/) (2019)
- [Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry](https://mlsanity.com/paper/2003.00766/) (2020)
- [VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals](https://mlsanity.com/paper/2104.06789/) (2021)
- [OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning](https://mlsanity.com/paper/2006.16637/) (2020)
- [Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds](https://mlsanity.com/paper/2011.13784/) (2020)
- [Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks](https://mlsanity.com/paper/2104.00431/) (2021)

## Built on by (most cited first, 3 of 3)

- [PanoFlow: Learning 360deg Optical Flow for Surrounding Temporal Understanding](https://mlsanity.com/paper/2202.13388/) (2022)
- [RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation](https://mlsanity.com/paper/2308.05318/) (2023)
- [SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation](https://mlsanity.com/paper/2304.14418/) (2023)

## Abstract (arXiv)

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach.