ActiveSplat: ガウススプラッティングによる能動的高精細シーン再構成
ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting
ガウススプラッティングを活用し、オンラインマッピング・視点選択・経路計画を統合した自律的な高精細3D再構成システムを提案。疎なトポロジーと密な予測を組み合わせた階層的計画で効率的な探索を実現する。
著者: Yuetao Li, Zijia Kuang, Ting Li, Qun Hao, Zike Yan, Guyue Zhou, Shaohui Zhang
分類: cs.RO, cs.CV
原文アブストラクト
We propose ActiveSplat, an autonomous high-fidelity reconstruction system leveraging Gaussian splatting. Taking advantage of efficient and realistic rendering, the system establishes a unified framework for online mapping, viewpoint selection, and path planning. The key to ActiveSplat is a hybrid map representation that integrates both dense information about the environment and a sparse abstraction of the workspace. Therefore, the system leverages sparse topology for efficient viewpoint sampling and path planning, while exploiting view-dependent dense prediction for viewpoint selection, facilitating efficient decision-making with promising accuracy and completeness. A hierarchical planning strategy based on the topological map is adopted to mitigate repetitive trajectories and improve local granularity given limited time budgets, ensuring high-fidelity reconstruction with photorealistic view synthesis. Extensive experiments and ablation studies validate the efficacy of the proposed method in terms of reconstruction accuracy, data coverage, and exploration efficiency. The released code will be available on our project page: https://li-yuetao.github.io/ActiveSplat/.
