24 research outputs found
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of
consecutive frames into account. While elegant and appealing, the idea of using
more than two frames has not yet produced state-of-the-art results. We present
a simple, yet effective fusion approach for multi-frame optical flow that
benefits from longer-term temporal cues. Our method first warps the optical
flow from previous frames to the current, thereby yielding multiple plausible
estimates. It then fuses the complementary information carried by these
estimates into a new optical flow field. At the time of writing, our method
ranks first among published results in the MPI Sintel and KITTI 2015
benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.Comment: Work accepted at IEEE Winter Conference on Applications of Computer
Vision (WACV 2019
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
Occlusions between consecutive frames have long posed a significant challenge
in optical flow estimation. The inherent ambiguity introduced by occlusions
directly violates the brightness constancy constraint and considerably hinders
pixel-to-pixel matching. To address this issue, multi-frame optical flow
methods leverage adjacent frames to mitigate the local ambiguity. Nevertheless,
prior multi-frame methods predominantly adopt recursive flow estimation,
resulting in a considerable computational overlap. In contrast, we propose a
streamlined in-batch framework that eliminates the need for extensive redundant
recursive computations while concurrently developing effective spatio-temporal
modeling approaches under in-batch estimation constraints. Specifically, we
present a Streamlined In-batch Multi-frame (SIM) pipeline tailored to video
input, attaining a similar level of time efficiency to two-frame networks.
Furthermore, we introduce an efficient Integrative Spatio-temporal Coherence
(ISC) modeling method for effective spatio-temporal modeling during the
encoding phase, which introduces no additional parameter overhead.
Additionally, we devise a Global Temporal Regressor (GTR) that effectively
explores temporal relations during decoding. Benefiting from the efficient SIM
pipeline and effective modules, StreamFlow not only excels in terms of
performance on the challenging KITTI and Sintel datasets, with particular
improvement in occluded areas but also attains a remarkable
enhancement in speed compared with previous multi-frame methods. The code will
be available soon at https://github.com/littlespray/StreamFlow
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Unsupervised learning of optical flow, which leverages the supervision from
view synthesis, has emerged as a promising alternative to supervised methods.
However, the objective of unsupervised learning is likely to be unreliable in
challenging scenes. In this work, we present a framework to use more reliable
supervision from transformations. It simply twists the general unsupervised
learning pipeline by running another forward pass with transformed data from
augmentation, along with using transformed predictions of original data as the
self-supervision signal. Besides, we further introduce a lightweight network
with multiple frames by a highly-shared flow decoder. Our method consistently
gets a leap of performance on several benchmarks with the best accuracy among
deep unsupervised methods. Also, our method achieves competitive results to
recent fully supervised methods while with much fewer parameters.Comment: Accepted to CVPR 2020, https://github.com/lliuz/ARFlo