Monocular Dynamic Gaussian Splatting is Fast and Brittle but Smooth Motion Helps

2024-12-05

arXiv.org

AI Summary

This work addresses the ill-posed problem of monocular dynamic scene view synthesis by systematically evaluating and analyzing multiple Gaussian splatting–based dynamic modeling approaches. We propose the first standardized benchmark framework tailored for monocular dynamic Gaussian splatting, featuring a controllable synthetic dataset that disentangles motion, occlusion, and illumination factors, alongside joint evaluation across diverse real and synthetic datasets. Key findings include: (1) existing methods achieve high rendering efficiency but suffer from optimization fragility; (2) incorporating motion priors—particularly motion smoothness—significantly improves robustness; and (3) performance rankings are clear on synthetic data but obscured on real-world data due to uncontrolled confounding factors. Based on these insights, we distill seven reproducible, empirically grounded design principles, providing both theoretical foundations and practical guidelines for dynamic Gaussian modeling.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance

Abstract

Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data -- an ill-posed and challenging problem. The fast pace of work in this area has produced multiple simultaneous papers that claim to work best, which cannot all be true. In this work, we organize, benchmark, and analyze many Gaussian-splatting-based methods, providing apples-to-apples comparisons that prior works have lacked. We use multiple existing datasets and a new instructive synthetic dataset designed to isolate factors that affect reconstruction quality. We systematically categorize Gaussian splatting methods into specific motion representation types and quantify how their differences impact performance. Empirically, we find that their rank order is well-defined in synthetic data, but the complexity of real-world data currently overwhelms the differences. Furthermore, the fast rendering speed of all Gaussian-based methods comes at the cost of brittleness in optimization. We summarize our experiments into a list of findings that can help to further progress in this lively problem setting. Project Webpage: https://lynl7130.github.io/MonoDyGauBench.github.io/

Problem

• Benchmarking Gaussian-splatting methods for dynamic scene reconstruction
• Analyzing brittleness in optimization of fast-rendering Gaussian methods
• Evaluating performance impact of motion representation types

Innovation

• Monocular dynamic Gaussian splatting for view synthesis
• Benchmarking and analyzing Gaussian-splatting-based methods
• Synthetic dataset isolates reconstruction quality factors