Towards Anytime Optical Flow Estimation with Event Cameras

• URL: http://arxiv.org/abs/2307.05033v2
• Date: Thu, 19 Oct 2023 13:36:13 GMT
• Title: Towards Anytime Optical Flow Estimation with Event Cameras
• Authors: Yaozu Ye, Hao Shi, Kailun Yang, Ze Wang, Xiaoting Yin, Yining Lin, Mao Liu, Yaonan Wang, Kaiwei Wang
• Abstract summary: Event cameras are capable of responding to log-brightness changes in microseconds. Existing datasets collected via event cameras provide limited frame rate optical flow ground truth. We propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow.
• Score: 35.685866753715416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical flow estimation is a fundamental task in the field of autonomous driving. Event cameras are capable of responding to log-brightness changes in microseconds. Its characteristic of producing responses only to the changing region is particularly suitable for optical flow estimation. In contrast to the super low-latency response speed of event cameras, existing datasets collected via event cameras, however, only provide limited frame rate optical flow ground truth, (e.g., at 10Hz), greatly restricting the potential of event-driven optical flow. To address this challenge, we put forward a high-frame-rate, low-latency event representation Unified Voxel Grid, sequentially fed into the network bin by bin. We then propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow with only low-frame-rate optical flow ground truth for supervision. The key component of our EVA-Flow is the stacked Spatiotemporal Motion Refinement (SMR) module, which predicts temporally dense optical flow and enhances the accuracy via spatial-temporal motion refinement. The time-dense feature warping utilized in the SMR module provides implicit supervision for the intermediate optical flow. Additionally, we introduce the Rectified Flow Warp Loss (RFWL) for the unsupervised evaluation of intermediate optical flow in the absence of ground truth. This is, to the best of our knowledge, the first work focusing on anytime optical flow estimation via event cameras. A comprehensive variety of experiments on MVSEC, DESC, and our EVA-FlowSet demonstrates that EVA-Flow achieves competitive performance, super-low-latency (5ms), fastest inference (9.2ms), time-dense motion estimation (200Hz), and strong generalization. Our code will be available at https://github.com/Yaozhuwa/EVA-Flow.

Related papers

• SDformerFlow: Spatiotemporal swin spikeformer for event-based optical flow estimation [10.696635172502141]
  Event cameras generate asynchronous and sparse event streams capturing changes in light intensity. Spiking neural networks (SNNs) share similar asynchronous and sparse characteristics and are well-suited for event cameras. We propose two solutions for fast and robust optical flow estimation for event cameras: STTFlowNet and SDFlowformer.
  arXiv  Detail & Related papers  (2024-09-06T07:48:18Z)
• AccFlow: Backward Accumulation for Long-Range Optical Flow [70.4251045372285]
  This paper proposes a novel recurrent framework called AccFlow for long-range optical flow estimation. We demonstrate the superiority of backward accumulation over conventional forward accumulation. Experiments validate the effectiveness of AccFlow in handling long-range optical flow estimation.
  arXiv  Detail & Related papers  (2023-08-25T01:51:26Z)
• BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation [76.66876888943385]
  Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception. We present a novel simulator, BlinkSim, for the fast generation of large-scale data for event-based optical flow.
  arXiv  Detail & Related papers  (2023-03-14T09:03:54Z)
• Learning Dense and Continuous Optical Flow from an Event Camera [28.77846425802558]
  Event cameras such as DAVIS can simultaneously output high temporal resolution events and low frame-rate intensity images. Most of the existing optical flow estimation methods are based on two consecutive image frames and can only estimate discrete flow at a fixed time interval. We propose a novel deep learning-based dense and continuous optical flow estimation framework from a single image with event streams.
  arXiv  Detail & Related papers  (2022-11-16T17:53:18Z)
• Event-based Temporally Dense Optical Flow Estimation with Sequential Learning [11.026299772309796]
  Event cameras capture fast-moving objects without a motion blur. We show that a temporally dense flow estimation at 100Hz can be achieved by treating the flow estimation as a problem.
  arXiv  Detail & Related papers  (2022-10-03T21:50:14Z)
• GMFlow: Learning Optical Flow via Global Matching [124.57850500778277]
  We propose a GMFlow framework for learning optical flow estimation. It consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation. Our new framework outperforms 32-iteration RAFT's performance on the challenging Sintel benchmark.
  arXiv  Detail & Related papers  (2021-11-26T18:59:56Z)
• SCFlow: Optical Flow Estimation for Spiking Camera [50.770803466875364]
  Spiking camera has enormous potential in real applications, especially for motion estimation in high-speed scenes. Optical flow estimation has achieved remarkable success in image-based and event-based vision, but % existing methods cannot be directly applied in spike stream from spiking camera. This paper presents, SCFlow, a novel deep learning pipeline for optical flow estimation for spiking camera.
  arXiv  Detail & Related papers  (2021-10-08T06:16:45Z)
• Dense Optical Flow from Event Cameras [55.79329250951028]
  We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Our proposed approach computes dense optical flow and reduces the end-point error by 23% on MVSEC.
  arXiv  Detail & Related papers  (2021-08-24T07:39:08Z)
• Unsupervised Motion Representation Enhanced Network for Action Recognition [4.42249337449125]
  Motion representation between consecutive frames has proven to have great promotion to video understanding. TV-L1 method, an effective optical flow solver, is time-consuming and expensive in storage for caching the extracted optical flow. We propose UF-TSN, a novel end-to-end action recognition approach enhanced with an embedded lightweight unsupervised optical flow estimator.
  arXiv  Detail & Related papers  (2021-03-05T04:14:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.