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arxiv 2107.00887 v1 pith:6XB6B4E6 submitted 2021-07-02 cs.CV cs.HC

HO-3D_v3: Improving the Accuracy of Hand-Object Annotations of the HO-3D Dataset

Shreyas Hampali , Sayan Deb Sarkar , Vincent Lepetit This is my paper
classification cs.CV cs.HC
keywords ho-3dhandobjectaccuracyannotationscontactdatasethand-object
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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HO-3D is a dataset providing image sequences of various hand-object interaction scenarios annotated with the 3D pose of the hand and the object and was originally introduced as HO-3D_v2. The annotations were obtained automatically using an optimization method, 'HOnnotate', introduced in the original paper. HO-3D_v3 provides more accurate annotations for both the hand and object poses thus resulting in better estimates of contact regions between the hand and the object. In this report, we elaborate on the improvements to the HOnnotate method and provide evaluations to compare the accuracy of HO-3D_v2 and HO-3D_v3. HO-3D_v3 results in 4mm higher accuracy compared to HO-3D_v2 for hand poses while exhibiting higher contact regions with the object surface.

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Cited by 3 Pith papers

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  2. UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation

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  3. Grasp in Gaussians: Fast Monocular Reconstruction of Dynamic Hand-Object Interactions

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    GraG reconstructs dynamic 3D hand-object interactions from monocular video 6.4x faster than prior work by using compact Sum-of-Gaussians tracking initialized from large models and refined with 2D losses.