用几何感知的高斯表面点实现快速高保真物体重建
ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction
- 用高斯表面点统一表示几何与外观，支持渐进式重建
- 在复杂物体上实现90%以上表面覆盖率，扫描时间减少40%
- 适用于文物等精细物体的实时主动扫描，适合机器人视觉应用
自主高保真物体重建是创建数字资产和弥合机器人仿真与现实差距的基础。我们提出ObjSplat，一种利用高斯表面点作为统一表示的主动重建框架，能够逐步重建未知物体的逼真外观与精确几何结构。针对传统透明度或深度线索的局限，引入几何感知视角评估流程，显式建模背面可见性与遮挡感知的多视角共视性，可靠识别几何复杂物体上的未充分重建区域。此外，为克服贪婪规划策略的不足，ObjSplat采用多步前瞻的下一步最佳路径（NBP）规划器，在动态构建的空间图上联合优化信息增益与移动成本，生成全局高效轨迹。大量模拟与真实世界文化艺术品实验表明，ObjSplat可在数分钟内生成物理一致模型，相比现有方法显著提升重建保真度与表面完整性，同时大幅缩短扫描时间和路径长度。
原文摘要 · Abstract (English)
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/ .
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