Articulated objects, composed of multiple rigid parts connected by joints allowing rotational or translational motion, such as doors, cupboards and spectacles are ubiquitous in our daily lives.

The number of part queries is set to 50. SGD optimizer with the initial learning rate of 2e-2 and weight decay of 5e-4 is used. We sample 128 training samples for each object part class. The initial value of the learnable fusion weight is 0.5 . The total batch size is 8, and the training iterations amount to 40k.

OV -P ARTS includes refined versions of two publicly available datasets: Pascal-Part-116 and ADE20K-Part-234.

Then, to tackle the benchmark mentioned above, we present the Point-In-Context (PIC) to explore in-context learning for 3D point clouds.