Image: Gojic et al
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Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking.
Ranked #1 on 3D Feature Matching on 3DMatch Benchmark
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
Ranked #6 on Point Cloud Registration on 3DMatch Benchmark
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
Ranked #1 on 3D Point Cloud Matching on Faust (using extra training data)
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Ranked #2 on 3D Object Classification on ModelNet40
Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized.
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching.