Comparison of surface normal estimation methods for range sensing applications

By Klaas Klasing, Daniel Althoff, Dirk Wollherr, Martin Buss. IEEE International Conference on Robotics and Automation, 2009

326 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Instrumentation

View Paper: https://www.semanticscholar.org/paper/aceb49d4acc9cbed0fb7e8f14ce7216aba2b572b

How researchers see this paper

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

In Aerospace Engineering

1. Jens Behley
2. Volker Steinhage
3. Armin Bernd Cremers
4. Darwin G. Caldwell
5. Chunhua Shen
6. Wei Yin
7. Youliang Yan
8. Yifan Liu

In Computer Vision and Pattern Recognition

1. Chunhua Shen
2. Yifan Liu
3. Youliang Yan
4. Wei Yin
5. Volker Steinhage
6. Jens Behley
7. Darwin G. Caldwell
8. Armin Bernd Cremers

In Instrumentation

1. Jens Behley

Citations in the corpus

• Enforcing Geometric Constraints of Virtual Normal for Depth Prediction (2019, methodology): “We follow [24] to calculate the surface normal.”
• Performance of histogram descriptors for the classification of 3D laser range data in urban environments (2012, methodology): “For every laser range scan we used an octree to determine the nearest neighbors, and the normals of a scan were estimated using PCA [22] on a neighborhood of 0.”
• Slip Detection and Recovery for Quadruped Robots (2015, background): “This is commonly estimated by vision [17] or by fitting a plane (gradient-based terrain detection) through the feet that are on the ground (stance feet) [26].”
• Indoor mapping using planes extracted from noisy RGB-D sensors (2012, unknown): “Existing normal estimation methods can be divided into optimization-based methods and averaging methods [16].”