In this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our

approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality

blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive

sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is

also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue-noise

properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior

to the current state-of-the-art approaches.