Sim2Occ: Scaling Indoor Occupancy Pretraining with Unified Synthetic Supervision

Indoor occupancy models benefit from large-scale supervised learning, but existing real-world datasets provide limited volumetric supervision and are costly to expand while maintaining consistent geometry, semantic labels, and instance identities. We introduce Sim2Occ, an automated framework that turns furnished virtual interiors into a unified source of synthetic occupancy supervision for pretraining and benchmarking. At its core is an entity-consistent scene field that resolves mesh ownership and completes enclosed object bodies, keeping geometry, semantic labels, and instance identities consistent within room-bounded supervision domains. Scene enrichment and room-adaptive view selection produce diverse image–occupancy pairs, with alignment checked against rendered depth. We provide nested semantic training sets of 20k, 50k, and 100k observations, alongside panoptic and embodied occupancy benchmarks. Across five baselines, increasing the synthetic training scale improves both geometric and semantic occupancy prediction, with gains of 5.32–9.37 mIoU from 20k to 100k. When fine-tuned on real-world datasets, models pretrained on our benchmark achieve semantic completion gains of up to 4.30 mIoU on NYUv2 and 3.47 on OccScanNet. Open-vocabulary occupancy also benefits, gaining 4.21 mIoU on NYUv2. Larger single-frame training sources also improve embodied occupancy prediction, while panoptic baselines demonstrate instance-aware completion from the shared annotations. Sim2Occ establishes a scalable resource for studying transferable indoor occupancy representations.