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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 ofimages is getting larger and larger. In this paper, we proposed a GPUaccelerated image matching method with improved Cascade Hashing. Firstly, wepropose a Disk-Memory-GPU data exchange strategy and optimize the load order ofdata, so that the proposed method can deal with big data. Next, we parallelizethe Cascade Hashing method on GPU. An improved parallel reduction and animproved parallel hashing ranking are proposed to fulfill this task. Finally,extensive experiments show that our image matching is about 20 times fasterthan SiftGPU on the same graphics card, nearly 100 times faster than the CPUCasHash method and hundreds of times faster than the CPU Kd-Tree based matchingmethod. Further more, we introduce the epipolar constraint to the proposedmethod, and use the epipolar geometry to guide the feature matching procedure,which further reduces the matching cost.

GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D Reconstruction