ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting
This work addresses the challenge of efficient and accurate 3D scene reconstruction for robotics or computer vision applications, representing an incremental advancement by combining existing techniques in a novel framework.
The paper tackles the problem of autonomous high-fidelity scene reconstruction by proposing ActiveSplat, a system that integrates Gaussian splatting with active viewpoint selection and path planning, resulting in improved reconstruction accuracy, data coverage, and exploration efficiency as validated through experiments.
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/.