HO-3D_v3: Improving the Accuracy of Hand-Object Annotations of the HO-3D Dataset
This work provides a more accurate dataset for researchers in computer vision and robotics focusing on hand-object interaction, but it is incremental as it builds on an existing dataset and method.
The authors tackled the problem of inaccurate hand-object interaction annotations in the HO-3D dataset by improving the HOnnotate method, resulting in HO-3D_v3 with 4mm higher accuracy for hand poses and better contact region estimates compared to HO-3D_v2.
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.