  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. These intermediate estimates are used to generate
a final implicit 3D reconstruction.Our approach reconstructs objects even when
they are observed in different articulations in images with large baselines,
and animation of reconstructed shapes. Quantitative and qualitative evaluations
on different object categories show that our method is able to achieve high
reconstruction accuracy, especially as more views are added.
