arXiv:2605.10707 · v2

ObjView-Bench: Rethinking Difficulty and Deployment for Object-Centric View Planning

ObjView-Bench：重新审视以物体为中心的视角规划中的难度与部署问题

首次提交 2026-05-11 23:20

版本更新 2026-11-07 02:48

雷达观测 2026-11-09 13:22

摘要

Object-centric view planning is a core component of active geometric 3D reconstruction in robotics, yet existing evaluations often conflate object complexity, planning difficulty, budget assumptions, and physical reachability constraints. As a result, conclusions drawn from idealized view-planning evaluations may not reliably predict performance under realistic reconstruction settings. We introduce ObjView-Bench, an evaluation framework for rethinking difficulty and deployment in object-centric view planning. First, we disentangle three quantities underlying view-planning evaluation: omnidirectional self-occlusion as an object-side attribute, observation saturation difficulty, and protocol-dependent planning difficulty defined through a set-cover formulation. This separation supports controlled dataset construction, analysis of slow-saturation objects, and a case study showing that planning difficulty-aware sampling can improve learned view planners. Second, we design deployment-oriented evaluation protocols that reveal how budget regimes and reachable-view constraints alter method behavior. Across classical, learned, and hybrid planners, ObjView-Bench shows that difficulty, budget, and reachability constraints substantially change method rankings and failure modes. Project page: https://objview-bench.github.io/

作者备注：Accepted to NeurIPS 2026