ObjView-Bench: Rethinking Difficulty and Deployment for Object-Centric View Planning
Sicong Pan, Hao Hu, Xuying Huang, Benno Wingender, Maren Bennewitz

TL;DR
ObjView-Bench is a new evaluation framework that disentangles key factors affecting object-centric view planning, enabling more realistic assessment of methods under practical constraints.
Contribution
It introduces a framework that separates object complexity, planning difficulty, and deployment constraints, improving evaluation realism and analysis of view planning methods.
Findings
Planning difficulty-aware sampling improves learned view planners.
Budget and reachability constraints significantly affect method rankings.
The framework enables controlled dataset construction and analysis of slow-saturation objects.
Abstract
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…
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