Real-Time Plane Segmentation Using RGB-D Cameras

By D. Holz, Stefan Holzer, Radu Bogdan Rusu, Sven Behnke. Robot Soccer World Cup, 2012

359 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ef22156ebfbe2d572ce56b3c367f5e24c3772135

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. Rongrong Ji
2. Sven Behnke
3. Hannes Schulz
4. Houwen Peng
5. Max Schwarz
6. Weiming Hu

In Computer Vision and Pattern Recognition

1. Houwen Peng
2. Hannes Schulz
3. Rongrong Ji
4. Max Schwarz
5. Weiming Hu
6. Bing Li
7. Tae-Kyeong Lee
8. Se-Young Oh

Citations in the corpus

• RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features (2015, methodology): “Finally, our method generates less feature dimensions (10,192 vs. 188,300) and thus is also faster in the classifier training and recall steps.”
• RGBD Salient Object Detection: A Benchmark and Algorithms (2014, methodology): “To determine whether patches come from one plane, we compare their surface normals following the procedure proposed in [16].”
• Indoor mapping using planes extracted from noisy RGB-D sensors (2012, background): “[14] suggested a real-time plane segmentation algorithm, which calculates the local surface normals using integral images and then cluster points in both normal and distance space.”