ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction
Yuetao Li, Zhizhou Jia, Yu Zhang, Qun Hao, Shaohui Zhang

TL;DR
ObjSplat introduces a geometry-aware active reconstruction framework using Gaussian surfels, enabling efficient, high-fidelity object modeling with improved view planning and occlusion handling, demonstrated on complex objects in simulation and real-world settings.
Contribution
It proposes a novel geometry-aware viewpoint evaluation and a multi-step lookahead planner for active object reconstruction, improving efficiency and accuracy over prior methods.
Findings
Achieves high-fidelity, complete reconstructions within minutes.
Reduces scan time and path length significantly.
Outperforms state-of-the-art approaches in fidelity and efficiency.
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Robotic Path Planning Algorithms · Robot Manipulation and Learning
