While recent methods for motion and stereo estimation recover an unprecedented amount of
details, such highly detailed structures are neither adequately reflected in the data of …

In this paper, we propose a novel framework for optical flow estimation that achieves a good
balance between performance and efficiency. Our approach involves disentangling global …

To apply optical flow in practice, it is often necessary to resize the input to smaller
dimensions in order to reduce computational costs. However, downsizing inputs makes the …

Recently, AutoFlow has shown promising results on learning a training set for optical flow,
but requires ground truth labels in the target domain to compute its search metric. Observing …

Attention-based motion aggregation concepts have recently shown their usefulness in
optical flow estimation, in particular when it comes to handling occluded regions. However …

Optical flow estimation is a challenging task consisting of predicting per-pixel motion vectors
between images. Recent methods have employed larger and more complex models to …

In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision
Challenge 2022. It is based on the MS-RAFT method, which successfully integrates several …

Hierarchical concepts have proven useful in many classical and learning-based optical flow
methods regarding both accuracy and robustness. In this paper we show that such concepts …

Despite of exciting advances in image-based rendering and novel view synthesis, it is still
challenging to achieve high-resolution results that can reach production-level quality when …

Finding corresponding pixels within a pair of images is a fundamental computer vision task
with various applications. Due to the specific requirements of different tasks like optical flow …