Detecting objects of a category in range data by comparing to a single geometric prototype

Abstract

Object detection is here considered as the problem of retrieving from scene data segments that belong to objects from the sought category. The method proposed and investigated works with dense range data, as can be acquired with low-cost sensors. It does not require any training, but just a single geometric prototype that may be taken from an internet repository. Experiments with various household and office scenes are reported, and the performance is quantified on a public dataset. One of the tested variants achieves an F-score and average precision of 94% at total recall, and a correct nearest-neighbor rate of 97%.