Exploiting Symmetry and/or Manhattan Properties for
3D Object Structure Estimation from Single and Multiple Images
Abstract
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 singleor 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 multipleimage 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.