Corpus ID: 208527241

Tabulated MLP for Fast Point Feature Embedding

@article{Sekikawa2019TabulatedMF,
  title={Tabulated MLP for Fast Point Feature Embedding},
  author={Yusuke Sekikawa and Teppei Suzuki},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.00790}
}
Aiming at a drastic speedup for point-data embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptron (MLP) and look-up table (LUT) to transform point-coordinate inputs into high-dimensional features. When compared with PointNet's feature embedding part realized by MLP that requires millions of dot products, ours at test time requires no such layers of matrix-vector products but requires only looking up the nearest entities followed by interpolation, from the… Expand
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References

SHOWING 1-10 OF 30 REFERENCES
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
  • Hang Su, V. Jampani, +4 authors J. Kautz
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
Local Spectral Graph Convolution for Point Set Feature Learning
Dynamic Graph CNN for Learning on Point Clouds
Deep Closest Point: Learning Representations for Point Cloud Registration
  • Yue Wang, J. Solomon
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2019
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet
Spatial Transformer Networks
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
  • Yin Zhou, Oncel Tuzel
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
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