Abstract
The estimation of optical flow and 6-DoF ego-motion, two fundamental tasks in 3D vision, has typically been addressed independently. For neuromorphic vision (e.g., event cameras), however, the lack of robust data association makes solving the two problems separately an ill-posed challenge, especially in the absence of supervision via ground truth. Existing works mitigate this ill-posedness by either enforcing the smoothness of the flow field via an explicit variational regularizer or leveraging explicit structure-and-motion priors in the parametrization to improve event alignment. The former notably introduces bias in results and computational overhead, while the latter, which parametrizes the optical flow in terms of the scene depth and the camera motion, often converges to suboptimal local minima. To address these issues, we propose an unsupervised framework that jointly optimizes egomotion and optical flow via implicit spatial-temporal and geometric regularization. First, by modeling camera's egomotion as a continuous spline and optical flow as an implicit neural representation, our method inherently embeds spatial-temporal coherence through inductive biases. Second, we incorporate structure-and-motion priors through differential geometric constraints, bypassing explicit depth estimation while maintaining rigorous geometric consistency. As a result, our framework (called E-MoFlow) unifies egomotion and optical flow estimation via implicit regularization under a fully unsupervised paradigm. Experiments demonstrate its versatility to general 6-DoF motion scenarios, achieving state-of-the-art performance among unsupervised methods and competitive even with supervised approaches.
Abstract (translated)
在三维视觉中，光流（optical flow）和六自由度自我运动估计（6-DoF ego-motion）是两个基本任务。通常这两个任务会分别独立处理。但对于仿生视觉系统（例如事件相机），由于缺乏稳健的数据关联性，在没有通过地面实况进行监督的情况下单独解决这两个问题变得非常困难。现有的研究通过在光流场中强制平滑度来减少这种病态性，或者通过参数化中的结构和运动先验信息提高事件对齐的准确性。前者通常会在结果中引入偏差并增加计算负担，而后者，即通过对场景深度及相机运动进行参数化，往往只能达到次优局部最小值。 为了解决这些问题，我们提出了一种无监督框架，该框架通过隐式的时空和几何正则化同时优化自我运动和光流。首先，我们的方法通过将摄像机的自我运动建模为连续样条曲线，并且把光流表示为一个隐式神经网络模型，在此基础上，本方法内在地通过归纳偏差嵌入了时空一致性。其次，我们通过微分几何约束引入结构与运动先验知识，这避免了显式的深度估计同时保持严格的几何一致性。 因此，我们的框架（称为E-MoFlow）在完全无监督的范式下，通过隐式正则化统一自我运动和光流的估计。实验表明，该方法能够处理各种6-DoF运动场景，并在无监督的方法中表现出最先进的性能，在与有监督方法的竞争中也具有竞争力。
URL
https://arxiv.org/abs/2510.12753
