Active Gaussian Scene Reconstruction via Policy Learning on Hybrid Voxel Maps

Active reconstruction requires an agent to decide where to move and where to look, so as to explore unknown regions and refine poorly reconstructed ones. However, existing methods struggle to balance the gains of exploration and refinement and typically select the next view greedily over the full camera pose space, which limits efficient exploration and refinement. To this end, we present a reinforcement learning framework for active reconstruction driven by a hybrid voxel map that couples exploration state with reconstruction uncertainty. This single spatial representation serves both the exploration of unknown regions and the refinement of poorly reconstructed regions, with refinement guided by the Gaussian Fisher uncertainty it carries. We further separate target-position selection from viewing-direction selection, in which the policy network reads its observation from the hybrid voxel map and outputs the target position, while the viewpoint scoring selects the viewing direction on the same map. This separation avoids the prohibitive action space, accelerates training convergence, and simplifies the exploration task. Experiments on the indoor scenes show that our method outperforms state-of-the-art active reconstruction methods under the same observation budget and reconstruction configuration.