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 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.
我们提出了ActiveSplat，一种利用高斯分裂进行自主高保真重建的系统。该系统利用高效且逼真的渲染功能，构建了在线建图、视角选择和路径规划的统一框架。ActiveSplat的关键在于一种混合地图表示，将环境的密集信息与工作空间的稀疏抽象相结合。因此，该系统利用稀疏拓扑结构进行高效的视角采样和路径规划，同时通过视角依赖的密集预测进行视角选择，从而实现准确且完整的高效决策。基于拓扑地图的分层规划策略能够在预算有限的情况下减轻重复轨迹并提升局部精细度，从而确保高保真重建和逼真视图合成。大量实验和消融研究验证了所提方法在重建精度、数据覆盖率和探索效率方面的有效性。