1 research outputs found
I2I: Image to Icosahedral Projection for Object Reasoning from Single-View Images
Reasoning about 3D objects based on 2D images is challenging due to large
variations in appearance caused by viewing the object from different
orientations. Ideally, our model would be invariant or equivariant to changes
in object pose. Unfortunately, this is typically not possible with 2D image
input because we do not have an a priori model of how the image would change
under out-of-plane object rotations. The only -equivariant
models that currently exist require point cloud input rather than 2D images. In
this paper, we propose a novel model architecture based on icosahedral group
convolution that reasons in by projecting the input image onto
an icosahedron. As a result of this projection, the model is approximately
equivariant to rotation in . We apply this model to an object
pose estimation task and find that it outperforms reasonable baselines