R3RECON: Radiance-Field-Free Active Reconstruction via Renderability

In active reconstruction, an embodied agent must decidewhere to look next to efficiently acquire views that support high-qualitynovel-view rendering. Recent work on active view planning for neuralrendering largely derives next-best-view (NBV) criteria by backpropa-gating through radiance fields or estimating information entropy over3D Gaussian primitives. While effective, these strategies tightly cou-ple view selection to heavy, representation-specific mechanisms and failto account for the computational and resource constraints required forlightweight online deployment. In this paper, we revisit active recon-struction from a renderability-centric perspective. We propose R3CON,a radiance-fields-free active reconstruction framework that induces an im-plicit, pose-conditioned renderability field over SE(3) from a lightweightvoxel map. Our formulation aggregates per-voxel online observation statis-tics into a unified scalar renderability score that is cheap to update andcan be queried in closed form at arbitrary candidate viewpoints in mil-liseconds, without requiring gradients or radiance-field training. This ren-derability field is strongly correlated with image-space reconstruction er-ror, naturally guiding NBV selection. We further introduce a panoramicextension that estimates omnidirectional (360◦ ) view utility to acceleratecandidate evaluation. In the standard indoor Replica dataset, R3CONachieves more uniform novel-view quality and higher 3D Gaussian splat-ting (3DGS) reconstruction accuracy than recent active GS baselineswith matched view and time budgets. .