Decoupling Mask Quality From Completion Design: A Diagnostic Framework for Occlusion-Aware LiDAR 3-D Object Detection

Abstract

LiDAR-based 3D object detection is sensitive to sparse point support and occlusion-induced incomplete bird’s-eye-view (BEV) representations, especially for pedestrians, cyclists, and distant objects. This paper asks how much accurate, target-aligned occlusion guidance can help BEV feature completion and why a geometrically estimated region-of-occlusion map (ROM) fails to reproduce that benefit. We introduce OccBEV-Oracle, a lightweight completion neck inserted between the BEV backbone and dense head of a CenterPoint-style detector. Given an occlusion mask and a density map, it selects low-density occluded tokens, aggregates visible-token context through density-weighted cross-attention, and applies spatially constrained weak-residual fusion. On the KITTI validation split, full-map completion at a strong residual coefficient reduces mean Moderate 3D AP_R40 from 59.42% to 56.65%, whereas oracle GT-ROM-guided masked completion raises mean Moderate and Hard AP_R40 to 64.31% and 60.63%. A matched-strength control shows that weakening the full-map residual recovers only part of this gain (60.97% mean Moderate), so spatial restriction contributes a further 3.34 points that residual strength alone cannot supply. The gains concentrate on Pedestrian and Cyclist and on partly occluded and middle/far-range objects. Raycasting-based estimated ROM variants remain below the baseline. Mismatch, shifted/shuffled, zero-context and mask-only controls, and a direct measurement of where the neck edits the BEV map, show that the aligned GT-ROM mask itself supplies a strong localization prior. A mask-target analysis on nuScenes confirms the failure mode is dataset-independent. OccBEV-Oracle is therefore an upper-bound analysis, not a deployable detector: accurate target-related occlusion localization remains the main bottleneck.