ReMatching Dynamic Reconstruction Flow

2024-11-01

arXiv.org

AI Summary

Insufficient generalization to novel viewpoints and timestamps remains a core challenge in dynamic scene reconstruction. To address this, we propose ReMatching—a novel framework that introduces a plug-and-play matching mechanism guided by a velocity-field prior. This mechanism enforces motion consistency via optical flow constraints and supports flexible integration and composition of diverse priors (e.g., physical plausibility, temporal smoothness). ReMatching is architecture-agnostic and seamlessly integrates with mainstream dynamic reconstruction frameworks—including dynamic NeRF—via standardized interfaces. Evaluated on both synthetic and real-world dynamic datasets, ReMatching achieves significant improvements in PSNR and SSIM, enhances cross-view and cross-temporal reconstruction accuracy, and improves geometric consistency. Crucially, it strengthens the generalization capability and robustness of dynamic scene representations, particularly under unseen camera poses and time instants.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Improves dynamic scene reconstruction quality
• Incorporates deformation priors into models
• Enhances reconstruction accuracy from unseen viewpoints

Innovation

• Incorporates deformation priors for dynamic reconstruction
• Uses velocity-field based priors with matching procedure
• Enhances reconstruction accuracy across diverse dynamic scenes