Three-Dimensional Gaussian Splatting (3DGS), an important advancement in the field of computer graphics and 3D vision, has emerged to greatly accelerate the rendering process in novel views’ synthesis. Due to its ability to directly realize the real-time estimation of 3D shapes without neural networks, 3DGS has received a lot of attention in the fields of robotics, urban mapping, autonomous navigation, and virtual reality/augmented reality. In view of the growing popularity of 3DGS, we conduct a systematic review of the relevant literature. We begin by surveying existing work on 3D reconstruction and rendering, outlining the historical development and recent advances from both foundational and innovation-driven perspectives. Next, we summarize the most commonly used datasets and evaluation metrics in 3D reconstruction and rendering. Finally, we summarize the current challenges and suggest potential directions for future research. Through this survey, we aim to provide researchers with a treasure trove of resources in understanding and using techniques related to 3D reconstruction and rendering, in order to promote technological development and application deepening in the field of 3D vision.

Dense simultaneous localization and mapping (SLAM) via 3D Gaussian splatting (3DGS) faces memory bottlenecks due to the explosive growth of primitives during long-sequence mapping. We propose SATA-SLAM, a framework featuring spatial-aware truncation and adaptive anti-hallucination. The online front-end maintains a constant memory footprint via a spatial-aware pruning module (SAPM), which employs a survival scoring function that couples primitive opacity with view-frustum projection coverage and a temporal protection window. Subsequently, an anti-hallucination generative refinement module (AGRM) utilizes texture priors from pretrained diffusion models for offline inpainting of residual regions. In addition, an adaptive gating mechanism to verify and suppress AIGC-induced hallucinations caused by pose drift, ensuring multiview consistency. Experiments on the public Replica dataset show that SATA-SLAM improves rendering quality from 12.5 dB to 37.44 dB (averaged over the Replica room0 and office0 scenes) while using only 26% of the original memory, outperforming the unconstrained baseline. This study provides a pathway toward low-power, high-fidelity environmental perception for mobile robots.