Performance Evaluation of Learned 3D Features

TL;DR

This paper evaluates the performance of learned 3D detector-descriptor pairs in surface matching tasks, focusing on object recognition and surface registration to identify effective combinations.

Contribution

It presents a comprehensive evaluation of learned 3D detector-descriptor pairs, highlighting their effectiveness in specific 3D computer vision applications.

Findings

Learned detector-descriptor pairs improve surface matching accuracy.

Certain detector-descriptor combinations outperform traditional methods.

The evaluation guides selecting optimal pairs for object recognition and registration.

Abstract

Matching surfaces is a challenging 3D Computer Vision problem typically addressed by local features. Although a variety of 3D feature detectors and descriptors has been proposed in literature, they have seldom been proposed together and it is yet not clear how to identify the most effective detector-descriptor pair for a specific application. A promising solution is to leverage machine learning to learn the optimal 3D detector for any given 3D descriptor [15]. In this paper, we report a performance evaluation of the detector-descriptor pairs obtained by learning a paired 3D detector for the most popular 3D descriptors. In particular, we address experimental settings dealing with object recognition and surface registration.