Depth Estimation Training Dataset With Egocentric Multi View And Temporal Consistency Constraints

via “monocular depth estimation dataset curation and annotation”

Dataset by robbyant. 3,88,267 downloads.

Unique: Integrated directly into HuggingFace Hub ecosystem with 274K+ samples, enabling one-line dataset loading via `datasets.load_dataset()` without manual download/preprocessing; Apache 2.0 license permits commercial use unlike some proprietary depth datasets (NYU Depth v2, KITTI)

vs others: Larger and more accessible than DIODE (10K images) and easier to integrate than raw KITTI depth splits, but smaller and potentially less diverse than indoor/outdoor combinations like ScanNet + Cityscapes

via “depth estimation training dataset with egocentric multi-view and temporal consistency constraints”

Dataset by ropedia-ai. 14,56,180 downloads.

Unique: Combines egocentric video with synchronized depth ground truth and temporal structure, enabling training of depth models that exploit human motion priors and temporal consistency — most depth datasets use arbitrary camera motion or static scenes

vs others: More suitable for egocentric depth learning than NYU Depth or ScanNet because it captures first-person perspective and dynamic scenes; more temporally structured than single-frame depth datasets