Abstract
We present ReFlow, a unified framework for monocular dynamic scene reconstruction that learns 3D motion in a novel self-correction manner from raw video. Existing methods often suffer from incomplete scene initialization for dynamic regions, leading to unstable reconstruction and motion estimation, which often resorts to external dense motion guidance such as pre-computed optical flow to further stabilize and constrain the reconstruction of dynamic components. However, this introduces additional complexity and potential error propagation. To address these issues, ReFlow integrates a Complete Canonical Space Construction module for enhanced initialization of both static and dynamic regions, and a Separation-Based Dynamic Scene Modeling module that decouples static and dynamic components for targeted motion supervision. The core of ReFlow is a novel self-correction flow matching mechanism, consisting of Full Flow Matching to align 3D scene flow with time-varying 2D observations, and Camera Flow Matching to enforce multi-view consistency for static objects. Together, these modules enable robust and accurate dynamic scene reconstruction. Extensive experiments across diverse scenarios demonstrate that ReFlow achieves superior reconstruction quality and robustness, establishing a novel self-correction paradigm for monocular 4D reconstruction.
Abstract (translated)
我们提出ReFlow，一个用于单目动态场景重建的统一框架，它以新颖的自校正方式从原始视频中学习3D运动。现有方法常因动态区域场景初始化不完整而导致重建与运动估计不稳定，往往依赖外部稠密运动引导（如预计算光流）来进一步稳定和约束动态部件的重建。然而这引入了额外的复杂性和潜在误差传播。为应对这些问题，ReFlow集成了完整规范空间构建模块以增强静态与动态区域的初始化，并采用基于分离的动态场景建模模块解耦静态与动态组件以实现针对性运动监督。ReFlow的核心是一种新颖的自校正流匹配机制，包含全流匹配（将3D场景流与时变2D观测对齐）和相机流匹配（对静态物体施加多视图一致性）。这些模块共同实现了鲁棒准确的动态场景重建。在多样化场景上的大量实验表明，ReFlow达到了卓越的重建质量与鲁棒性，为单目4D重建建立了新颖的自校正范式。
URL
https://arxiv.org/abs/2604.01561
