Journal ArticleDOI
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors
TLDR
This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling and presents the performance results of these descriptors when combined with different 3D keypoint detection methods.Abstract:
A number of 3D local feature descriptors have been proposed in the literature. It is however, unclear which descriptors are more appropriate for a particular application. A good descriptor should be descriptive, compact, and robust to a set of nuisances. This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling. We first evaluate the descriptiveness of these descriptors on eight popular datasets which were acquired using different techniques. We then analyze their compactness using the recall of feature matching per each float value in the descriptor. We also test the robustness of the selected descriptors with respect to support radius variations, Gaussian noise, shot noise, varying mesh resolution, distance to the mesh boundary, keypoint localization error, occlusion, clutter, and dataset size. Moreover, we present the performance results of these descriptors when combined with different 3D keypoint detection methods. We finally analyze the computational efficiency for generating each descriptor.read more
Citations
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Book ChapterDOI
Fast Global Registration
TL;DR: An algorithm for fast global registration of partially overlapping 3D surfaces that provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.
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Image Matching from Handcrafted to Deep Features: A Survey
TL;DR: This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
Proceedings ArticleDOI
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
TL;DR: Qualitative and quantitative evaluations of the PPFNet network suggest increased recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance.
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Fully Convolutional Geometric Features
TL;DR: This work presents fully-convolutional geometric features, computed in a single pass by a 3D fully- Convolutional network, which achieve state-of-the-art accuracy without requiring prepossessing, are compact, and are 290 times faster than the most accurate prior method.
Proceedings ArticleDOI
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects
Tomas Hodan,Pavel Haluza,Stepan Obdrzalek,Jiri Matas,Manolis I. A. Lourakis,Xenophon Zabulis +5 more
TL;DR: T-LESS as discussed by the authors is a dataset for estimating the 6D pose of texture-less rigid objects with no significant texture and no discriminative color or reflectance properties, but some of the objects are parts of others.
References
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Proceedings ArticleDOI
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