Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules (2412.04457v2)

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.

• The paper establishes a structured evaluation framework that isolates motion representation and optimization challenges in dynamic scene reconstruction.
• The paper demonstrates that while Gaussian splatting offers fast computation, it suffers from reconstruction brittleness compared to hybrid neural field methods.
• The paper finds that dataset variability significantly impacts performance, highlighting the need for robust and standardized evaluation benchmarks.

An Analysis of Monocular Dynamic Gaussian Splatting: Limitations and Opportunities

The paper "Monocular Dynamic Gaussian Splatting is Fast and Brittle but Smooth Motion Helps" presents an empirical paper of Gaussian splatting methods in the context of view synthesis for dynamic scenes using monocular data. As numerous methods emerge, claiming superior performance due to subtle methodological differences, this research makes significant strides by offering a structured evaluation framework and an instructive synthetic dataset designed to isolate factors affecting reconstruction quality. This work critically assesses the strengths and limitations of these methods, highlighting essential findings that may guide future developments in the field.

Core Contributions and Methodological Framework

Gaussian splatting is utilized as a method for scene representation, allowing for efficient view synthesis given the challenge of monocular inputs. The paper focuses on evaluating the efficacy of various Gaussian splatting techniques, systematically categorizing them by motion representation types. It assesses these methods using both pre-existing datasets and a newly designed synthetic dataset that controls for scene complexity and motion.

The proposed framework capitalizes on a comprehensive set of experiments to identify the underlying factors affecting reconstruction quality, such as motion model locality and the brittleness inherent to Gaussian-based optimization techniques. A central contribution of the paper is the provision of an empirical snapshot corroborated by "apples-to-apples" comparisons across multiple methods and datasets, addressing a significant gap in existing research.

Findings and Implications

1. Comparison with Hybrid Neural Fields: The paper paints a sobering picture by demonstrating that the non-Gaussian method, TiNeuVox, often surpasses Gaussian methods in image quality metrics. While Gaussian methods are faster due to rasterization advantages, they fall short in rendering quality and face challenges with noise-prone optimization.
2. Impact of Motion Representation: Empirically, simpler, low-dimensional motion representations intertwined with Gaussian splatting appear to offer better performance than more complex, less constrained representations. However, the results suggest that the enhanced expressiveness of the 4D Gaussian methods often comes at a cost of efficiency.
3. Variability across Datasets: Despite claims from individual papers, the paper indicates that dataset variations dominate method variations. The difficulty in rank-ordering methods uniformly stems from this variability, suggesting a need for more robust evaluation benchmarks.
4. Brittleness of Adaptive Density Control: Adaptive density control, while adding expressivity, introduces optimization instability. The algorithm's efficiency and susceptibility to overfitting vary across scenes, often leading to catastrophic failures in scene reconstruction.
5. Challenges in Monocular Settings: When assessed against the iPhone dataset, Gaussian-based methods exhibit pronounced limitations compared to NeRF-like methods, emphasizing the complexity of monocular dynamic scenes and the necessity for multiview cues.
6. Effect of Motion and Camera Baselines: Effects of camera motion and baselines are significant, revealing that decreasing baselines and increasing object motion adversely affect reconstruction quality.
7. Specular Objects Complexity: The presence of reflective surfaces presents challenges across methods, indicating a need for refined handling of specular effects during reconstruction.
8. Foreground-Background Dynamics: The paper clearly delineates the competencies of dynamic methods over static counterparts, reinforcing that dynamic scene representations are superior in capturing moving elements within a scene.

Conclusion and Future Directions

This research highlights critical challenges and best practices for dynamic scene reconstruction using Gaussian splatting in monocular conditions. The presented findings are poised to direct future research, particularly in optimizing motion representation complexity and addressing the brittleness challenges through improved methodological innovations. As the field evolves, establishing comprehensive benchmarks and standardization across datasets could provide the foundation for more accurate and consistent performance evaluations.

Looking forward, the combination of Gaussian splatting with learned deformation fields or neural field representations may hold promise in enhancing robustness and quality. Despite its current challenges, the continued exploration of Gaussian splatting as a viable technique for dynamic view synthesis remains pivotal for advancing applications in video editing, 3D scene modeling, and augmented reality, among others.