Neural rendering has established itself as the state-of-the-art approach for scene reconstruction and novel view synthesis (NVS) tasks. However, its reliance on precise camera poses presents a significant limitation. Since 2023, Gaussian Splatting (GS) has emerged as a promising approach for volumetric rendering, gaining traction in the 3D computer vision and graphics community due to its efficiency and real-time rendering capabilities. While COLMAP-free GS methods have been proposed to address camera pose dependency, they often struggle with “useless frames” - frames that do not introduce information gain about the rendered surface and/or have low resolution - leading to slower reconstruction and inefficient use of computational resources, potentially causing out-of-memory issues on mid-tier machines which does not have extraordinary computational power. To address these challenges, we propose a frame filtering method for efficient NVS based on COLMAP-free GS. Our approach enables scene reconstruction under computational resource constraints while maintaining high rendering quality. Experimental results demonstrate that our method achieves an approximately 30–50% reduction in GPU VRAM usage and a 20–30% decrease in training time for scene reconstruction, offering a more efficient solution for NVS tasks.