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: https://li-yuetao.github.io/ObjSplat-page/ .

翻译：自主高保真物体重建是创建数字资产以及弥合机器人领域仿真与现实鸿沟的基础。本文提出 ObjSplat，一种主动重建框架，它采用高斯面片作为统一表示，在逐步重建未知物体的同时兼顾逼真外观与精确几何。针对传统基于不透明度或深度线索的局限性，我们引入了一种几何感知的视点评估流程，该流程显式建模背面可见性与遮挡感知的多视角共视性，即使在几何复杂的物体上也能可靠地识别出欠重建区域。此外，为了克服贪心规划策略的局限，ObjSplat 采用了一种在动态构建的空间图上执行多步前瞻的次优路径规划器。通过联合优化信息增益与移动成本，该规划器可生成全局高效的轨迹。在仿真环境以及真实世界文化遗物上的广泛实验表明，ObjSplat 能在数分钟内生成物理一致的模型，在实现更优重建保真度与表面完整性的同时，与现有最优方法相比大幅缩短了扫描时间与路径长度。项目页面：https://li-yuetao.github.io/ObjSplat-page/ 。