Beyond Static Gaussians: An Empirical Investigation of Architectural Paradigms for Dynamic 3D Scene Reconstruction

2026-05-29

AI Summary

This work addresses the unclear trade-offs among reconstruction quality, model compactness, and rendering speed in dynamic 3D scene reconstruction. It presents the first systematic taxonomy and empirical evaluation of dynamic 3D Gaussian splatting methods, categorizing them into two paradigms: structure-guided approaches (e.g., deformation fields, canonical spaces, and meshes) and Gaussian-centric approaches (e.g., continuous functions and 4D representations). A comprehensive benchmarking study on the D-NeRF dataset reveals that structure-guided methods achieve superior reconstruction fidelity and model compactness, whereas Gaussian-centric methods enable faster rendering—often reaching real-time performance—at the cost of reduced quality stability and higher storage overhead. This study elucidates the fundamental trade-offs inherent in dynamic Gaussian splatting techniques and establishes a clear benchmark for future research.

Abstract

Dynamic scene reconstruction via 3D Gaussian Splatting (3DGS) has emerged as a compelling approach for representing evolving environments, yet understanding trade-offs between methodologies remains crucial. This paper presents a comprehensive analysis of dynamic 3DGS methods, categorizing them into two paradigms: structure-guided methods employing auxiliary representations (deformation fields, canonical spaces, grids) to model temporal changes, and gaussian-centric methods encoding dynamics directly into primitives via continuous functions or 4D representations. We evaluate representative methods from both paradigms on the D-NeRF benchmark. Our findings reveal that structure-guided methods achieve superior reconstruction fidelity and compact model sizes, while gaussian-centric approaches demonstrate significantly higher rendering speeds enabling real-time performance, though with greater quality variability and potentially substantial storage overhead. This analysis highlights a fundamental trade-off between reconstruction quality/compactness versus rendering speed, providing insights to guide future research and application development in dynamic scene reconstruction.

Problem

• dynamic 3D scene reconstruction
• 3D Gaussian Splatting
• architectural paradigms
• reconstruction fidelity
• rendering speed

Innovation

• Dynamic 3D Reconstruction
• 3D Gaussian Splatting
• Structure-Guided Methods
• Gaussian-Centric Methods
• Rendering-Speed vs. Quality Trade-off