1,442 research outputs found
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Fine-grained classification involves distinguishing between similar
sub-categories based on subtle differences in highly localized regions;
therefore, accurate localization of discriminative regions remains a major
challenge. We describe a patch-based framework to address this problem. We
introduce triplets of patches with geometric constraints to improve the
accuracy of patch localization, and automatically mine discriminative
geometrically-constrained triplets for classification. The resulting approach
only requires object bounding boxes. Its effectiveness is demonstrated using
four publicly available fine-grained datasets, on which it outperforms or
achieves comparable performance to the state-of-the-art in classification
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