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Datasets/DTTD-Mobile

DTTD-Mobile

3DImagesVideosMITIntroduced 2023-09-24

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

Benchmarks

1 Image, 2*2 Stitchi/ADD AUC1 Image, 2*2 Stitchi/ADD-S AUC1 Image, 2*2 Stitchi/AR CoU1 Image, 2*2 Stitchi/AR CH1 Image, 2*2 Stitchi/AR pCH16k/ADD AUC16k/ADD-S AUC2D Classification/ADD AUC2D Classification/ADD-S AUC2D Object Detection/ADD AUC2D Object Detection/ADD-S AUC3D/ADD AUC3D/ADD-S AUC3D/AR CoU3D/AR CH3D/AR pCH3D Object Detection/ADD AUC3D Object Detection/ADD-S AUC6D Pose Estimation/ADD AUC6D Pose Estimation/ADD-S AUC6D Pose Estimation/AR CoU6D Pose Estimation/AR CH6D Pose Estimation/AR pCHObject Detection/ADD AUCObject Detection/ADD-S AUCPose Estimation/ADD AUCPose Estimation/ADD-S AUCPose Estimation/AR CoUPose Estimation/AR CHPose Estimation/AR pCH

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Papers
8
Benchmarks
30

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Tasks

1 Image, 2*2 Stitchi16k2D Classification2D Object Detection2D Semantic Segmentation3D3D Depth Estimation3D Object Detection3D Object Reconstruction3D Object Tracking3D Semantic Segmentation6D Pose EstimationDepth EstimationMonocular 3D Object LocalizationMonocular Depth EstimationMulti-Object TrackingObject DetectionPose EstimationRobotic GraspingSemantic SLAMSemantic SegmentationVideo GroundingVideo InpaintingVideo Object DetectionVideo Object Tracking