• Computer Science
  • Published in ArXiv 2019

A Dataset for Semantic Segmentation of Point Cloud Sequences

@article{Behley2019ADF,
  title={A Dataset for Semantic Segmentation of Point Cloud Sequences},
  author={Jens Behley and Martin Garbade and Andres Milioto and Jan Quenzel and Sven Behnke and Cyrill Stachniss and Juergen Gall},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.01416}
}
Highlight Information
Semantic scene understanding is important for various applications. [...] Key Method We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete $360^{o}$ field-of-view of the employed automotive LiDAR.Expand Abstract
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SHOWING 1-10 OF 64 REFERENCES

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Semantic Scene Completion from a Single Depth Image

VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Are we ready for autonomous driving? The KITTI vision benchmark suite

VIEW 15 EXCERPTS
HIGHLY INFLUENTIAL

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Tangent Convolutions for Dense Prediction in 3D

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL