Abstract
In this paper, we explore the problem of event-based meshflow estimation, a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start, we review the state-of-the-art in event-based flow estimation, highlighting two key areas for further research: i) the lack of meshflow-specific event datasets and methods, and ii) the underexplored challenge of event data density. First, we generate a large-scale High-Resolution Event Meshflow (HREM) dataset, which showcases its superiority by encompassing the merits of high resolution at 1280x720, handling dynamic objects and complex motion patterns, and offering both optical flow and meshflow labels. These aspects have not been fully explored in previous works. Besides, we propose Efficient Event-based MeshFlow (EEMFlow) network, a lightweight model featuring a specially crafted encoder-decoder architecture to facilitate swift and accurate meshflow estimation. Furthermore, we upgrade EEMFlow network to support dense event optical flow, in which a Confidence-induced Detail Completion (CDC) module is proposed to preserve sharp motion boundaries. We conduct comprehensive experiments to show the exceptional performance and runtime efficiency (30x faster) of our EEMFlow model compared to the recent state-of-the-art flow method. As an extension, we expand HREM into HREM+, a multi-density event dataset contributing to a thorough study of the robustness of existing methods across data with varying densities, and propose an Adaptive Density Module (ADM) to adjust the density of input event data to a more optimal range, enhancing the model's generalization ability. We empirically demonstrate that ADM helps to significantly improve the performance of EEMFlow and EEMFlow+ by 8% and 10%, respectively. Code and dataset are released at this https URL.
Abstract (translated)
在这篇论文中，我们探讨了基于事件的网格流估计问题，这是一个新颖的任务，涉及从事件相机预测出一个空间上平滑且稀疏的运动场。首先，我们回顾了基于事件的流动估计领域的最新进展，并指出了两个关键的研究方向：i) 缺乏专门针对网格流的事件数据集和方法；ii) 未充分探索的事件数据密度挑战。为此，我们生成了一个大规模的高分辨率事件网格流（HREM）数据集，该数据集展示了其优越性，包括在1280x720分辨率下具有高质量图像、处理动态物体以及复杂的运动模式的能力，并提供光学流动和网格流标签。这些方面在之前的工作中并未得到充分探索。 此外，我们提出了一种轻量级的基于事件的网格流（EEMFlow）网络模型，其特征是一个特别设计的编码-解码架构，旨在加速并提高网格流估计的准确性。另外，我们将EEMFlow网络升级为支持密集事件光学流动，在此过程中提出了一个信心诱导细节完成（CDC）模块来保持尖锐的运动边界。 我们进行了全面实验，展示了我们的EEMFlow模型在性能和运行时间效率方面的卓越表现，与最近最先进的流方法相比快30倍。作为扩展，我们将HREM数据集升级为HREM+，这是一个多密度事件数据集，有助于对现有方法在不同密度的数据上鲁棒性的彻底研究，并提出了一个自适应密度模块（ADM），用于调整输入事件数据的密度范围，从而增强模型的泛化能力。 我们通过实证展示了ADM能够显著提升EEMFlow和EEMFlow+的表现，分别提高了8%和10%。代码和数据集可在以下网址获取：[此链接]（请替换为实际发布的URL）。
URL
https://arxiv.org/abs/2510.04111
