  In computer vision domain, how to fast and accurately perform multiview
stereo (MVS) is still a challenging problem. In this paper we present a fast
yet accurate method for 3D dense reconstruction, called AMHMVS, built on the
PatchMatch based stereo algorithm. Different from the regular symmetric
propagation scheme, our approach adopts an asymmetric checkerboard propagation
strategy, which can adaptively make effective hypotheses expand further
according to the confidence of current neighbor hypotheses. In order to
aggregate visual information from multiple images better, we propose the
multi-hypothesis joint view selection for each pixel, which leverages a cost
matrix based on the multiple propagated hypotheses to robustly infer an
appropriate aggregation subset parallel. Combined with the above two steps, our
approach not only has the capacity of massively parallel computation, but also
obtains high accuracy and completeness. Experiments on extensive datasets show
that our method achieves more accurate and robust results, and runs faster than
the competing methods.
