Ao Gao

Ao Gao, Jingyu Gong, Xin Tan et al.

While 3D Gaussian Splatting (3DGS) has revolutionized real-time rendering, its performance degrades significantly under sparse-view extrapolation, manifesting as severe geometric voids and artifacts. Existing solutions primarily rely on an iterative "Repair-then-Distill" paradigm, which is inherently unstable and prone to overfitting. In this work, we propose GSCompleter, a distillation-free plugin that shifts scene completion to a stable "Generate-then-Register" workflow. Our approach first synthesizes plausible 2D reference images and explicitly lifts them into metric-scale 3D primitives via a robust Stereo-Anchor mechanism. These primitives are then seamlessly integrated into the global context through a novel Ray-Constrained Registration strategy. This shift to a rapid registration paradigm delivers superior 3DGS completion performance across three distinct benchmarks, enhancing the quality and efficiency of various baselines and achieving new SOTA results.

Ao Gao, Luosong Guo, Tao Chen et al.

3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper, we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling. Instead of using SfM for scene initialization, we employ a novel method to release the power of large-scale pointmap approaches. Specifically, we propose an efficient grouping strategy based on view similarity, and use robust pointmap priors to obtain high-quality point clouds and camera poses for 3D scene initialization. After obtaining a reliable scene structure, we propose a novel densification approach that adaptively splits Gaussian primitives based on the average shape of neighboring Gaussian ellipsoids, utilizing KNN scheme. In this way, the proposed method tackles the limitation on initialization and optimization, leading to an efficient and accurate 3DGS modeling. Extensive experiments demonstrate that EasySplat outperforms the current state-of-the-art (SOTA) in handling novel view synthesis.