OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler,Ali Osman Ulusoy,Andreas Geiger +2 more
- pp 6620-6629
TLDR
The utility of the OctNet representation is demonstrated by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.Abstract:
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.read more
Citations
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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
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Frustum PointNets for 3D Object Detection from RGB-D Data
TL;DR: This work directly operates on raw point clouds by popping up RGBD scans and leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects.
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KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas,Charles R. Qi,Jean-Emmanuel Deschaud,Beatriz Marcotegui,François Goulette,Leonidas J. Guibas +5 more
TL;DR: KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.
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PointCNN: convolution on Χ -transformed points
TL;DR: This work proposes to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order.
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