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.

本数据集由内窥镜RGB图像与稠密、度量精确的真值深度图（ground truth depth map）配对构成。其场景采用多样化的测试对象以测试模型泛化能力，包括基础几何体模、复杂手术任务模拟器、柔软织物、多孔泡沫以及离体组织（ex-vivo tissue）。真值深度数据通过高精度3D扫描仪与多阶段处理流程采集，以保障2D图像与3D数据间具备高保真度与精准对齐效果。HFD-3D旨在全面评测算法重建细粒度细节，以及处理具有复杂光交互特性（如半透明与镜面反射）的表面的能力。