A Convolutional Learning System for Object Classification in 3-D Lidar Data

By Danil V. Prokhorov. 2010

56 citations recorded by the source corpus.

Subfields: Environmental Engineering, Geology, Instrumentation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. J. Urgen Schmidhuber
2. Suya You
3. Daniel Maturana
4. Jing Huang
5. Sebastian A. Scherer
6. Andreas Geiger

In Geology

1. J. Urgen Schmidhuber
2. Suya You
3. Daniel Maturana
4. Jing Huang
5. Sebastian A. Scherer
6. Lei Tai
7. Andreas Geiger

In Instrumentation

1. J. Urgen Schmidhuber
2. Suya You
3. Daniel Maturana
4. Jing Huang
5. Sebastian A. Scherer

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “To detect human actions in surveillance videos, a 3-dimensional CNN (e.g., Jain & Seung, 2009; Prokhorov, 2010), combined with SVMs, was part of a larger system (Yang et al., 2009) using a bag of features approach (Nowak, Jurie, & Triggs, 2006) to extract regions of interest.”
• Point cloud labeling using 3D Convolutional Neural Network (2016, methodology): “Prokhorov presented a 3D-CNN for categorization of segmented point clouds [9].”
• Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, background): “After being trained, the RBM gives a reliable representation of the input data statistically.”
• VoxNet: A 3D Convolutional Neural Network for real-time object recognition (2015, methodology): “In the LiDAR domain, [27] is an early work that studies a 3D CNN for use with LiDAR data with a binary classification task.”