LargeSceneGaussian: High-Efficiency 3D Gaussian Splatting for Large-Scale Scene Reconstruction

This is the authors' abstract. We don't add key points for this paper.

Recently, significant progress has been made in the field of novel view synthesis. 3D Gaussian Splatting has shown high training efficiency and real-time rendering capabilities in scene modeling. However, large-scale and complex unbounded scenes pose challenges such as data initialization, view dependency, and high memory overhead, affecting rendering efficiency and visual quality. To address these issues, we propose LargeSceneGaussian (LSG), a novel method for efficient large-scale scene reconstruction. LSG incorporates three key strategies: (1) Denoising sparse point cloud data from drone-captured images to enhance quality, (2) K-Means-based partitioning of the scene into balanced sub-blocks for independent training and seamless merging, and (3) View filtering to remove redundant camera views, improving efficiency. The LSG achieves 15% lower memory usage, 10% faster training, and 20% less redundant view data, significantly enhancing rendering quality and efficiency for large-scale 3D modeling.