ReFlow：面向动态场景重建的自校正运动学习
ReFlow: Self-correction Motion Learning for Dynamic Scene Reconstruction
提出自校正流匹配机制，无需外部光流引导即可从单目视频学习3D运动，为动态场景重建提供了一种可复用的无监督运动监督范式。
原文依据 a novel self-correction flow matching mechanism, consisting of Full Flow Matching to align 3D scene flow with time-varying 2D observations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出ReFlow，一个统一的单目动态场景重建框架，能够以新颖的自校正方式从原始视频中学习3D运动。现有方法常常面临动态区域场景初始化不完整的问题，导致重建和运动估计不稳定，因而往往借助外部稠密运动引导（如预计算光流）来进一步稳定和约束动态分量的重建。然而，这引入了额外的复杂性以及潜在的误差传播。为解决这些问题，ReFlow集成了一个完整规范空间构建模块，以增强静态和动态区域的初始化，并集成了一个基于分离的动态场景建模模块，将静态和动态分量解耦以进行有针对性的运动监督。ReFlow的核心是一种新颖的自校正流匹配机制，包括全流匹配（Full Flow Matching）以将3D场景流与时变的2D观测对齐，以及相机流匹配（Camera Flow Matching）以对静态物体施加多视图一致性。这些模块共同实现了稳健且准确的动态场景重建。跨多种场景的大量实验表明，ReFlow取得了更优的重建质量和鲁棒性，为单目4D重建建立了一种新的自校正范式。
英文原文摘要 1355 字符
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.