【Reading】Ditto-Building Digital Twins of Articulated Objects from Interaction

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This paper propose a way to form articulation model of articulated objects by encoding the features and find the correspondence of static and mobile part via visual observation before and after the interaction.

Workflow-In Brief

Two Stream Encoder

• Given point cloud observations before and after interaction:

• Encode them with PointNet++ Encoder : , . . is the number of the sub-sampled points, and is the dimension of the sub-sampled point features.

• Fuse the features with attention layer: , , .

• The fused feature is decoded by two PointNet++ decoder , , and get , . are point features aligned with

• Feature encoding based on ConvONet.

  

is projected into 2D feature planes and is projected into voxel grids as in the ConvONets. The points that fall into the same pixel cell or voxel cell are aggregated together via max pooling.

Training

Revolute joint: