用运动场先验提升动态场景重建质量，支持多种模型融合。
ReMatching Dynamic Reconstruction Flow
- 基于速度场设计先验，通过匹配机制增强重建流程。
- 在合成与真实动态场景上均显著提升重建精度。
- 适配多种动态表示，适合希望改进重建质量的研究者。
从图像输入重构动态场景是计算机视觉中的基础任务，具有广泛的应用前景。尽管近期取得进展，现有方法在未见视角和时间点上的重建质量仍不理想。本文提出ReMatching框架，通过引入形变先验来提升动态重建模型的性能。该方法采用基于速度场的先验，并设计了一种匹配过程，可无缝集成至现有动态重建流程中。框架具备高度可扩展性，适用于多种动态表示形式，支持多类先验融合，且可通过组合简单先验生成复杂先验类别。在包含合成与真实场景的多个主流基准测试中，将当前最先进方法与本框架结合后，重建精度得到明显提升。
原文摘要 · Abstract (English)
Reconstructing a dynamic scene from image inputs is a fundamental computer vision task with many downstream applications. Despite recent advancements, existing approaches still struggle to achieve high-quality reconstructions from unseen viewpoints and timestamps. This work introduces the ReMatching framework, designed to improve reconstruction quality by incorporating deformation priors into dynamic reconstruction models. Our approach advocates for velocity-field based priors, for which we suggest a matching procedure that can seamlessly supplement existing dynamic reconstruction pipelines. The framework is highly adaptable and can be applied to various dynamic representations. Moreover, it supports integrating multiple types of model priors and enables combining simpler ones to create more complex classes. Our evaluations on popular benchmarks involving both synthetic and real-world dynamic scenes demonstrate that augmenting current state-of-the-art methods with our approach leads to a clear improvement in reconstruction accuracy.
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