Omkar2118/datasets huggingface dataset:
DTTD-Mobile

Are current 3D object tracking methods truely robust enough for low-fidelity depth sensors like the iPhone LiDAR?
We introduce DTTD-Mobile (fully compatible w/ YCB toolbox), a new benchmark built on real-world data captured from mobile devices; 18 objects observed in 100 videos with 47,668 sampled frames and 114,143 object annotations. We evaluate several popular methods—including BundleSDF, ES6D, MegaPose, and DenseFusion—and highlight their limitations in this challenging setting.

Keywords: ObjectPoseEstimation, MobileAI, EdgeAI, ARVR, CVPR

Introducing paper: Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset