StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects

Understand

We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.

• Reconstructing general articulating object categories % has important applications, but is challenging since objects can have wide variation in shape, articulation, appearance and topology.
• We address this by building on the idea of category-level articulation canonicalization -- mapping observations to a canonical articulation which enables correspondence-free multiview aggregation.
• Our end-to-end trainable neural network estimates feature-enriched canonical 3D point clouds, articulation joints, and part segmentation from one or more unposed images of an object.