Fast Semi Dense Epipolar Flow Estimation

Abstract

Optical flow computation consists in recovering the apparent motion field between two images with overlapping fields of view. This paper focuses on a subset of optical flow problems, called epipolar flow, where the camera moves inside a scene containing no moving objects. Accurate solutions exist but their high computational complexities make them non suitable for a large panel of real-time applications.We propose a new epipolar flow approach with low computational complexity achieving the best error rate on the non dense KITTI optical flow 2012 benchmark and running 1000× faster than the second ranked approach. On a 4core 3GHz processor, our multi-core implementation computes a semi dense optical flow field of a 450k pixels image in 260ms. It is a significant advance in reducing the running time of accurate optical flow computation.To achieve such results we rely on the epipolar constraints and the local coherence of the optical flow not only to increase accuracy but also to reduce computational complexity.Our contribution is twofold. It is first, the acceleration and the accuracy increase of current RANSAC based visual odometry algorithms via the estimation of a robust sparse flow field, well distributed over the image domain. And then, the estimation of a semi dense flow field leveraging epipolar constraints and a propagation scheme to speedup the estimation and reduce error rates.

Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation

“…RLOF(IM-GM) represents near state of the art in sparse optical flow, making it an excellent candidate for tracking DeGraF points. RLOF is second only to FSDEF [25] which is comtemporary work to that presented here.…”

“…By contrast, computational efficiency is more readily achievable via sparse point tracking whereby flow estimation only takes place on a fraction of the image. Interestingly, the seminal Lucas-Kanade [5] sparse point tracker proposed over 30 years ago still forms the basis for many contemporary state-of-the-art sparse flow techniques [24,25,2]. Robust Local Optical Flow (RLOF) [2] is one such derivative that shows state-of-the-art accuracy on the KITTI benchmark [10,13].…”

“…However these detectors do not guarantee uniform spatial feature distribution as they locate points only on highly textured image regions (e.g corners and edges). To address this issue, a current state of the art sparse flow method, denoted Fast Semi Dense Epipolar Flow (FSDEF) [25], forces uniformity of FAST key-point feature by use of a block-wise selection; this however adversely effects saliency causing increased erroneous matches. Further to this, FAST points do not provide sub-pixel precision so further refinement to the point matches is required to recover accurate optical flow.…”