Popsift

Abstract

The keypoint detector and descriptor Scalable Invariant Feature Transform (SIFT) [8] is famous for its ability to extract and describe keypoints in 2D images of natural scenes. It is used in ranging from object recognition to 3D reconstruction. However, SIFT is considered compute-heavy. This has led to the development of many keypoint extraction and description methods that sacrifice the wide applicability of SIFT for higher speed. We present our CUDA implementation named PopSift that does not sacrifice any detail of the SIFT algorithm, achieves a keypoint extraction and description performance that is as accurate as the best existing implementations, and runs at least 100x faster on a high-end consumer GPU than existing CPU implementations on a desktop CPU. Without any algorithmic trade-offs and short-cuts that sacrifice quality for speed, we extract at >25 fps from 1080p images with upscaling to 3840x2160 pixels on a high-end consumer GPU.