Fully Convolutional Geometric Features

Christopher Choy, Jaesik Park, Vladlen Koltun; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 8958-8966

Abstract


Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features with limited receptive field. In this work, we present fully-convolutional geometric features, computed in a single pass by a 3D fully-convolutional network. We also present new metric learning losses that dramatically improve performance. Fully-convolutional geometric features are compact, capture broad spatial context, and scale to large scenes. We experimentally validate our approach on both indoor and outdoor datasets. Fully-convolutional geometric features achieve state-of-the-art accuracy without requiring prepossessing, are compact (32 dimensions), and are 290 times faster than the most accurate prior method.

Related Material


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[bibtex]
@InProceedings{Choy_2019_ICCV,
author = {Choy, Christopher and Park, Jaesik and Koltun, Vladlen},
title = {Fully Convolutional Geometric Features},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}