When Absence Is Not Evidence: Occlusion-Type-Aware Censored Fusion for Multi-View 3D Grounding

In multi-view 3D grounding, a low language-conditioned response can indicate that a surface does not belong to the referent or that it is occluded. Conflating these cases can dilute valid evidence or accumulate spurious contradiction. We formulate fusion as censored observation modeling, marginalizing uncertain track–view validity. Exact visibility labels reveal a readout-dependent distinction: partial occlusion can retain semantic evidence through spatially extended response fields, whereas full occlusion carries much weaker membership signal. Our three-component likelihood preserves partial-occlusion evidence and approximates full occlusion with a shared nuisance density. A spatial gate removes instance-grouping labels from inference while geometry and semantic backbones remain frozen. We introduce VisCen3D, a diagnostic benchmark with persistent surface identity, exact visibility, and occlusion-type labels. A preregistered evaluation on 160 fresh scenes and 882 episodes shows that grouping-free fusion on analytic tracks improves AUROC on the most-occluded surface tercile by $+0.050$ under Stable Diffusion and $+0.041$ under SigLIP over development-selected non-censored comparators; scene-level $95\%$ confidence intervals exclude zero. A post-hoc ablation on the same partition shows that the partial/full refinement improves over ordinary censoring. Top-1 target-track selection increases by $3.29$/$16.55$ percentage points under SD/SigLIP, with the SD directional prediction confirmatory and the SigLIP result descriptive. Transfer experiments expose the limits: averaging outperforms every tested likelihood rule on ScanNet++ with windowed responses, while incomplete surface recovery limits fusion with fully estimated geometry. These findings establish ranking and selection benefits on controlled surface support and clarify dependence on semantic readout and surface recovery.