Farthest Point Sampling

Farthest Point Sampling (FPS) is a technique used to select a representative subset of points from a larger point cloud, aiming to maximize the distance between selected points and minimize the overall "fill distance." Current research focuses on improving FPS's efficiency and robustness, particularly for large-scale point clouds and noisy data, through methods like adaptive voxel-based sampling and instance-centric approaches that prioritize informative points. These advancements are crucial for accelerating applications such as 3D object detection, face recognition, and machine learning regression, where efficient and accurate point cloud processing is essential. The ultimate goal is to achieve optimal balance between computational efficiency and the preservation of crucial information within the downsampled point cloud.