  Many man-made objects have intrinsic symmetries and Manhattan structure. By
assuming an orthographic projection model, this paper addresses the estimation
of 3D structures and camera projection using symmetry and/or Manhattan
structure cues, which occur when the input is single- or multiple-image from
the same category, e.g., multiple different cars. Specifically, analysis on the
single image case implies that Manhattan alone is sufficient to recover the
camera projection, and then the 3D structure can be reconstructed uniquely
exploiting symmetry. However, Manhattan structure can be difficult to observe
from a single image due to occlusion. To this end, we extend to the
multiple-image case which can also exploit symmetry but does not require
Manhattan axes. We propose a novel rigid structure from motion method,
exploiting symmetry and using multiple images from the same category as input.
Experimental results on the Pascal3D+ dataset show that our method
significantly outperforms baseline methods.
