Learning to Detect Good 3D Keypoints

Performance Evaluation of Learned 3D Features

“…In particular, the authors cast keypoint detection as a binary classification problem tackled by a Random Forest and show how to generate the training set as well as the feature representation deployed by the classifier. Later, Tonioni et al [15] have demonstrated that this approach can be applied seamlessly and very effectively to other popular descriptors such as SI [4] and FPFH [8].…”

“…In order to carry out the performance evaluation proposed in this paper, for most local descriptors reviewed in section 2 we did learn the corresponding optimal detector according to the keypoint learning methodology [15]. We provide here a brief overview of this methodology and refer the reader to [9,15] for a detailed description.…”

“…More recently, however, [9] and [15] have proposed a machine learning approach that allows for learning an optimal 3D keypoint detector for any given 3D descriptor so as to maximize the end-to-end performance of the overall feature-matching pipeline. The authors show that this approach provides effective pipelines across diverse applications and datasets.…”

“…By enabling an optimal detector to be learned for any descriptor, [15] sets forth a novel paradigm to maximize affinity between 3D detectors and descriptors. This opens arXiv:1909.06884v1 [cs.CV] 15 Sep 2019 up the question of which learned detector-descriptor pair may turn out most effective in the main application areas. This paper tries to answer this question by proposing an experimental evaluation of learned 3D pipelines.…”

“…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.…”