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

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 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.