Lap3R: Large-Scale Panoptic 3D Reconstruction from Aerial Imagery

Large-scale panoptic 3D reconstruction from aerial imagery requires globally consistent geometry and persistent object identities across distant views. Existing feed-forward methods struggle with both: depth inconsistencies distort local reconstructions, repetitive layouts confound loop closure, and cross-view instance supervision is scarce. We present Lap3R, a framework that combines semantic-prior geometry rectification, spatially and temporally verified loop mining, and cross-view co-visibility mask linking. Lap3R first rectifies local geometric distortions using semantic priors. It then verifies long-range loop candidates to retain genuine revisits while rejecting false matches caused by repetitive aerial layouts. To establish persistent instance identities, Lap3R aggregates mask agreement across co-visible views, without temporal tracking or manual cross-view annotations. On large scenes, Lap3R achieves the lowest trajectory error and Chamfer distance among the evaluated methods, while substantially improving semantic and instance reconstruction over feed-forward baselines. We further use Lap3R to generate pseudo-labels from aerial videos; a model trained solely on these labels surpasses the evaluated panoptic baselines on all reported metrics.