Performance Evaluation of Learned 3D Features
This work addresses a domain-specific challenge in 3D Computer Vision for applications like object recognition and surface registration, but it is incremental as it builds on existing learning-based methods.
The paper tackles the problem of identifying effective detector-descriptor pairs for 3D surface matching by evaluating learned 3D detectors paired with popular descriptors, focusing on object recognition and surface registration tasks.
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.