Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
Image-based localization is a crucial step in many 3D computer vision applications, e.g., self-driving cars, robotics, and augmented reality among others. Unfortunately, many image-based-localization applications require the storage of large scenes, and many camera pose estimators struggle to scale when the scene representation is large. To alleviate the aforementioned problems, many applications compress a scene representation by reducing the number of 3D points of a point cloud. The state-of-the-art compresses a scene representation by using a K-cover-based algorithm. While the state-of-the-art selects a subset of 3D points that maximizes the probability of accurately estimating the camera pose of a new image, the state-of-the-art does not guarantee an optimal compression and has parameters that are hard to tune. We propose to compress a scene representation by means of a constrained quadratic program that resembles a one-class support vector machine (SVM). Thanks to this resemblance, we derived a variant of the sequential minimal optimization, a widely adopted algorithm to train SVMs. The proposed method uses the points corresponding to the support vectors as the subset to represent a scene. Our experiments on publicly large-scale image-based localization show that our proposed approach delivers four times fewer failed localizations than that of the state-of-the-art while scaling on average two orders of magnitude more favorably.
Smith, Benjamin Robert, "Optimal Compression of Point Clouds" (2019). Graduate Theses, Dissertations, and Problem Reports. 4090.