A High-Fidelity 3D Dataset for Fine-Grained Monocular Depth Estimation (HFD-3D)

The dataset comprises endoscopic RGB images paired with dense, metric-accurate ground truth depth maps. The scenes feature a diverse range of objects designed to test model generalization, including basic geometric phantoms, complex surgical task simulators, soft fabrics, porous foams, and ex-vivo tissues. Ground truth depth was acquired using a high-precision 3D scanner and a multi-stage processing pipeline to ensure high fidelity and precise alignment between the 2D images and 3D data. HFD-3D is designed to thoroughly test the ability of algorithms to reconstruct fine-grained details and handle surfaces with complex light interaction, such as translucency and specularity.