Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming (2503.08166v1)

Abstract: Representing and rendering dynamic scenes from 2D images is a fundamental yet challenging problem in computer vision and graphics. This survey provides a comprehensive review of the evolution and advancements in dynamic scene representation and rendering, with a particular emphasis on recent progress in Neural Radiance Fields based and 3D Gaussian Splatting based reconstruction methods. We systematically summarize existing approaches, categorize them according to their core principles, compile relevant datasets, compare the performance of various methods on these benchmarks, and explore the challenges and future research directions in this rapidly evolving field. In total, we review over 170 relevant papers, offering a broad perspective on the state of the art in this domain.

Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming

The research paper titled "Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming" is a comprehensive survey of dynamic scene representation and rendering methodologies, with a particular focus on innovations in Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3D-GS). The paper outlines the progress, challenges, and future directions of dynamic scene reconstruction—a pivotal problem in the realms of computer vision and graphics due to the inherent complexities and continuous changes of real-world environments.

Background and Evolution

Dynamic scene reconstruction has undergone significant transformation over the years, beginning with traditional methods such as Structure from Motion (SfM) and Multi-View Stereo (MVS). These techniques attempted to model dynamic scenes by assuming a sequence of static reconstructions, with inherent difficulties in consistency and topology changes. Noteworthy progress arrived with Non-Rigid SfM (NRSfM) and template-based approaches, which incorporated prior shape knowledge to navigate complex motion scenarios. The appearance of consumer-grade RGB-D sensors further revolutionized this domain by enabling real-time dynamic reconstructions.

NeRF represented a substantial leap by introducing the concept of implicitly modeling scenes with neural networks, capturing fine details through a continuous volumetric function tuned via an MLP. However, vanilla NeRF required further adaptations to efficiently handle dynamic scenarios. Parallel to NeRF advancements, 3D Gaussian Splatting emerged as a potent approach for static scene rendering and was explored for its application in dynamic environments.

Techniques in Dynamic Scene Representation

Dynamic NeRF Approaches:

1. 4D-based Dynamic NeRF: These methods extend NeRF's capabilities by incorporating the time dimension directly into the input of the neural network. They exploit forward and backward 3D scene flows to maintain temporal consistency but grapple with data sparsity challenges.
2. Deformation-based Dynamic NeRF: This category introduces a deformation field to warp the canonical scene representation into current frames, thus accommodating motion and topological changes. These methods are essential in capturing varying environments without the strict requirement of rigid geometrical assumptions.
3. Hybrid Dynamic NeRF: These techniques combine explicit geometric structures with implicit neural fields to optimize rendering speed and model interpretability, solving shortcomings related to computational intensity and complexity.

Dynamic 3D-GS Approaches:

1. Deformation Field Methods: These strategies leverage MLPs to predict transformations of Gaussian parameters across frames, emphasizing flexibility and computational efficiency.
2. 4D Primitive Methods: By including the time dimension in the Gaussian Splatting framework, these methods project temporal slices to render dynamic scenes, focusing on achieving real-time performance in dynamic contexts.
3. Per-frame Training Methods: These methods initialize Gaussian parameters and progressively refine them across frames, addressing challenges of real-time adaptability in varying scenes.

Future Directions and Implications

The survey highlights several areas poised for further exploration, such as real-time handling of large-scale dynamic environments, optimizing reconstruction from sparse viewpoints, improving on-the-fly adaptability in training and rendering systems, and the development of novel data structures and primitives for dynamic scenes. Enhancements in efficient representation and seamless integration of scene priors remain crucial for achieving realistic and vivid reconstructions of dynamic scenarios.

This paper lays an important foundation for understanding the current landscape of dynamic scene reconstruction, elucidating both the practical implications for real-time applications such as AR/VR and theoretical advancements in AI-driven modeling. The insights and thorough comparison of methodologies are invaluable for researchers endeavoring to push boundaries in computer vision and dynamic scene rendering.