Doppelgangers: Learning to Disambiguate Images of Similar Structures
Ruojin Cai Joseph Tung Qianqian Wang Hadar Averbuch-Elor Bharath Hariharan Noah Snavely
We consider the visual disambiguation task of determining whether a pair of
visually similar images depict the same or distinct 3D surfaces (e.g., the same
or opposite sides of a symmetric building). Illusory image matches, where two
images observe distinct but visually similar 3D surfaces, can be challenging
for humans to differentiate, and can also lead 3D reconstruction algorithms to
produce erroneous results. We propose a learning-based approach to visual
disambiguation, formulating it as a binary classification task on image pairs.
To that end, we introduce a new dataset for this problem, Doppelgangers, which
includes image pairs of similar structures with ground truth labels. We also
design a network architecture that takes the spatial distribution of local
keypoints and matches as input, allowing for better reasoning about both local
and global cues. Our evaluation shows that our method can distinguish illusory
matches in difficult cases, and can be integrated into SfM pipelines to produce
correct, disambiguated 3D reconstructions. See our project page for our code,
datasets, and more results: http://doppelgangers-3d.github.io/.