Learning Deep Correspondence through Prior and Posterior Feature Constancy
Year: 2017
Type: preprint
Abstract: Stereo matching algorithms usually consist of four steps, including matchingcost calculation, matching cost aggregation, disparity calculation, and
disparity refinement. Existing CNN-based methods only adopt CNN to solve parts
of the four steps, or use different networks to deal with different steps,
making them difficult to obtain the overall optimal solution. In this paper, we
propose a network architecture to incorporate all steps of stereo matching. The
network consists of three parts. The first part calculates the multi-scale
shared features. The second part performs matching cost calculation, matching
cost aggregation and disparity calculation to estimate the initial disparity
using shared features. The initial disparity and the shared features are used
to calculate the prior and posterior feature constancy. The initial disparity,
the prior and posterior feature constancy are then fed to a sub-network to
refine the initial disparity through a Bayesian inference process. The proposed
method has been evaluated on the Scene Flow and KITTI datasets. It achieves the
state-of-the-art performance on the KITTI 2012 and KITTI 2015 benchmarks while
maintaining a very fast running time.
Source: arXiv (Cornell University)
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2773913336