OAVP-SPLAT: OBJECT-AWARE VIEW PLANNING FOR ACTIVE RECONSTRUCTION WITH SEMANTIC 3D GAUSSIAN SPLATTING

Active reconstruction aims to recover scene geometry and semantic information through autonomous exploration. However, semantic maps reconstructed with 3D Gaussian Splatting (3DGS) can exhibit inconsistent class predictions within individual objects and gaps across otherwise continuous surfaces in semantic renderings. We propose OAVP-Splat, a framework that uses object-level observation value to guide two-stage autonomous view planning and refines semantic maps through semantic contribution reweighting. We perform object-level Gaussian clustering and estimate the value of additional observations from semantic uncertainty, temporal disagreement, and observation history. In the coverage stage, the planner builds an initial map through frontier exploration. In the refinement stage, it balances progress toward the next frontier target with the value of revisiting mapped regions and uses geometric support to locally refine viewing directions. After mapping, we reweight semantic contributions using per-Gaussian class probabilities and support from spatially connected 3D components of the same predicted class to improve semantic rendering. Experiments on Replica and Matterport3D demonstrate that OAVP-Splat improves semantic segmentation accuracy while maintaining favorable geometric accuracy and reconstruction quality, combining autonomous exploration with high-quality semantic reconstruction.