YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos

3D semantic occupancy prediction is crucial for fine-grainedscene understanding, yet its advancement in privacy-sensitive indoor en-vironments is fundamentally hindered by the scarcity of large-scale an-notated 3D data. To overcome this limitation, we explore learning indoor3D semantic occupancy prediction from abundant, uncalibrated in-the-wild internet videos while simultaneously bypassing the extensive manualannotation. Specifically, we introduce YouTube-Occ, including an auto-mated data pipeline that leverages 2D and 3D foundation models to pro-cess raw web videos, estimating camera geometry, reconstructing scenepoint clouds, and enriching them with dense semantic pseudo-labels.However, these plausible pseudo-labels fail to yield performance gains un-der naive supervision. To address this impasse, we further propose a pre-training framework driven by feature distillation with a dual-alignmentstrategy. Within it, an intra-frame alignment utilizes a voxel-anchoredGaussianization module to align 3D features with corresponding 2D pri-ors, whereas a cross-scene alignment achieves global semantic consistencyvia class-prototype distillation. Empirically, YouTube-Occ delivers con-sistent gains across three mainstream architectures on the NYUv2 andOcc-ScanNet benchmarks, especially under limited-data conditions. Wewill publicly release our code and data, hoping to inspire future research.