  Image feature point matching is a key step in Structure from Motion(SFM).
However, it is becoming more and more time consuming because the number of
images is getting larger and larger. In this paper, we proposed a GPU
accelerated image matching method with improved Cascade Hashing. Firstly, we
propose a Disk-Memory-GPU data exchange strategy and optimize the load order of
data, so that the proposed method can deal with big data. Next, we parallelize
the Cascade Hashing method on GPU. An improved parallel reduction and an
improved parallel hashing ranking are proposed to fulfill this task. Finally,
extensive experiments show that our image matching is about 20 times faster
than SiftGPU on the same graphics card, nearly 100 times faster than the CPU
CasHash method and hundreds of times faster than the CPU Kd-Tree based matching
method. Further more, we introduce the epipolar constraint to the proposed
method, and use the epipolar geometry to guide the feature matching procedure,
which further reduces the matching cost.
