VDGS: 可視性駆動による大規模空中シーン再構成のための3Dガウシアンスプラッティング
VDGS: Visibility-Driven Large-Scale 3D Gaussian Splatting for Aerial Scene Reconstruction
カメラ分布を考慮した可視性統計を導入し、シーンの分割と勾配補償を行うことで、視点が偏った大規模空中シーンの3DGS再構成を効率化・高品質化した手法。
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著者: Haolin Yu, Jiadong Tang, YiXian Wang, Yu Gao, Shi He, Zhilin Lai, Yi Yang, Mengyin Fu
分類: cs.CV
原文アブストラクト
Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In recent years, 3D Gaussian Splatting (3DGS) has demonstrated remarkable advantages in both visual quality and computational efficiency, making it a promising representation for large-scale scene reconstruction. However, it still faces challenges in large-scale scenes, including excessive memory consumption and uneven viewpoint coverage caused by UAV acquisition, limiting its real-world applications. To address this, we propose VDGS, a novel 3DGS framework that incorporates camera distribution into scene modeling. VDGS introduces visibility-driven statistics for scene anchors to quantify supervision strength. These statistics are further leveraged for scene partitioning and for gradient compensation in under-optimized regions, thereby promoting balanced optimization across different regions. Extensive experiments on multiple large-scale aerial scene datasets demonstrate that, under imbalanced viewpoint distributions, VDGS consistently outperforms existing methods, while maintaining competitive performance in scenarios with more uniform view distributions.
関連論文
- HessianForge: 物理情報ニューラル表現と平滑化エネルギー制約によるスケーラブルなLiDAR再構成3D再構成/sim2real
- ES-Gaussian: 誤差空間に基づくガウシアン補完による3Dガウシアンスプラッティングマッピング3D再構成/sim2real
- LI-GS: LiDAR統合ガウシアンスプラッティングによる大規模高精度3次元再構成3D再構成/sim2real
