Hypothesis-Induced Action Fields for Object Localization in Partial Scenes

Object localization in partial scenes requires inferring the position of an unobserved object from incomplete spatial and semantic context. Commonsense relations provide contextual information about potential target locations, but incomplete observations may remain compatible with several spatially separated alternatives. Compressing these alternatives into point estimates can obscure the underlying ambiguity, and aggregating these estimates may yield a prediction with limited support from the observed context. We introduce Hypothesis-Induced Action Fields (HIAF), a method that couples the representation of spatial alternatives with the localization decision. Spatial hypotheses derived from observed objects jointly define an action field that estimates the probability of successful localization at different locations. Learning this field aligns hypothesis generation and scoring with the localization objective, allowing alternatives to inform the final prediction collectively. On Partial ScanNet, HIAF achieves the highest mean success rates and lowest mean successful localization errors among the evaluated methods across three evaluation protocols. At 1 m, query-level success reaches 30.06%, exceeding the strongest evaluated baseline by 4.86 percentage points. At the stricter 0.5 m tolerance, HIAF achieves 13.19% query-level success, compared with 9.54% for the strongest baseline at that tolerance.