Gaussian Splatting for Real-Time Scene Reconstruction: Methods, Systems, and Trade-offs
cs.CV
Submitted: 2026-10-13
Updated: 2026-10-13
Comments: Accepted version to Els Pattern Recognition
Journal ref: Pattern Recognition, 172:115120, 2026
DOI: 10.1016/j.patcog.2026.115120
Project page: https://pup3dgs.github.io
License: http://creativecommons.org/licenses/by/4.0/
The gist: 3D Gaussian Splatting (3DGS) allows for high-quality real-time novel-view synthesis.
Terminology
Abstract
3D Gaussian Splatting (3DGS) allows for high-quality real-time novel-view synthesis. Since its inception in 2023, its explicit and highly configurable scene representation has been extended toward sparse-view generation, dynamic reconstruction, efficiency and compression, streaming, and simultaneous localization and mapping (SLAM). Although Gaussian Splatting can provide real-time rendering, rendering an existing representation is distinct from constructing, updating, or maintaining the scene itself. In this survey, we synthesize current literature, categorize them and provide a connection of current approaches and tools towards an end-to-end pipeline. Explicitly, we categorize GS scene reconstruction into five overlapping families: single-shot generation, dynamic representations, efficiency and compression, streaming and immersive systems, and Gaussian Splatting SLAM. We distinguish render-only throughput from task-specific scene generation, update, tracking, mapping, and optimization costs, and combine controlled sparse-view benchmarking with native evaluations to identify cross-family quality, latency, memory, and update-cost trade-offs.
Sources
- 3D Gaussian Splatting in Robotics: A Survey
- Efficient Differentiable Hardware Rasterization for 3D Gaussian Splatting
- MTGS: Multi-Traversal Gaussian Splatting
- WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes
- Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
- Towards Accurate Generative Models of Video: A New Metric & Challenges
- Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
- InstantSplat: Sparse-view Gaussian Splatting in Seconds
- GoDe: Gaussians on Demand for Backbone-Agnostic Progressive Compression
- P-4DGS: Predictive 4D Gaussian Splatting with 90$\times$ Compression
- The Replica Dataset: A Digital Replica of Indoor Spaces
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models