ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction

Abstract:Autonomous high-fidelity object reconstruction is fundamental for creating digital assets and bridging the simulation-to-reality gap in robotics. We present ObjSplat, an active reconstruction framework that leverages Gaussian surfels as a unified representation to progressively reconstruct unknown objects with both photorealistic appearance and accurate geometry. Addressing the limitations of conventional opacity or depth-based cues, we introduce a geometry-aware viewpoint evaluation pipeline that explicitly models back-face visibility and occlusion-aware multi-view covisibility, reliably identifying under-reconstructed regions even on geometrically complex objects. Furthermore, to overcome the limitations of greedy planning strategies, ObjSplat employs a next-best-path (NBP) planner that performs multi-step lookahead on a dynamically constructed spatial graph. By jointly optimizing information gain and movement cost, this planner generates globally efficient trajectories. Extensive experiments in simulation and on real-world cultural artifacts demonstrate that ObjSplat produces physically consistent models within minutes, achieving superior reconstruction fidelity and surface completeness while significantly reducing scan time and path length compared to state-of-the-art approaches. Project page: this https URL .

Accepted to IEEE T-ASE. Code: this https URL , Project Page: this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2601.06997 [cs.RO]

(or arXiv:2601.06997v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2601.06997 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TASE.2026.3700105 Focus to learn more DOI(s) linking to related resources

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