3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hybrid representations that integrate implicit and explicit features offer a way to mitigate these limitations. However, when applied in parallelized block-wise training, two critical issues arise since reconstruction accuracy deteriorates due to reduced data diversity when training each block independently, and parallel training restricts the number of divided blocks to the available number of GPUs. To address these issues, we propose Momentum-GS, a novel approach that leverages momentum-based self-distillation to promote consistency and accuracy across the blocks while decoupling the number of blocks from the physical GPU count. Our method maintains a teacher Gaussian decoder updated with momentum, ensuring a stable reference during training. This teacher provides each block with global guidance in a self-distillation manner, promoting spatial consistency in reconstruction. To further ensure consistency across the blocks, we incorporate block weighting, dynamically adjusting each block's weight according to its reconstruction accuracy. Extensive experiments on large-scale scenes show that our method consistently outperforms existing techniques, achieving a 12.8% improvement in LPIPS over CityGaussian with much fewer divided blocks and establishing a new state of the art. Project page: https://jixuan-fan.github.io/Momentum-GS_Page/

3D Gaussian Splatting (3DGS) has emerged as a high-quality and efficient alternative to Neural Radiance Fields (NeRF), offering distinct advantages in scene representation and suitability for multi-view 3D object detection (MV-3DOD) tasks. However, conventional 3DGS-based detection methods typically employ a reconstruction-then-detection pipeline, which is time-consuming and unsuitable for real-time applications. This approach arises from 3DGS’s original design for reconstruction tasks, which lacks network-based training. In this paper, we propose GS-3Det, an online Gaussian detection framework for MV-3DOD, achieving real-time performance through a single forward pass for scene reconstruction and detection. Specifically, we introduce a detection-aware Gaussian grid that enables directly prediction of explicit 3D Gaussians from multi-view images, enabling efficient and robust 3D scene understanding. Additionally, we propose a Dual-Path Consistency module that leverages 3D constraints to improve the accuracy of Gaussian grid representation and detection. Experiments on the ScanNet V2 dataset demonstrate that GS-3Det surpasses state-of-the-art methods by 3.5% in [email protected] and 2.7% in [email protected], underscoring its generalization capability and real-time performance.