Automatic Registration of RGB-D Scans via Salient Directions
By Bernhard Zeisl, Kevin Köser, Marc Pollefeys. 2013
51 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
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
- Caifa Zhou
- Jan Dirk Wegner
- Andreas Wieser
- Zan Gojcic
- Xiaoshui Huang
- Guofeng Mei
- Christopher Bongsoo Choy
- Vladlen Koltun
In Computer Vision and Pattern Recognition
In Geology
- Caifa Zhou
- Jan Dirk Wegner
- Andreas Wieser
- Zan Gojcic
- Xiaoshui Huang
- Guofeng Mei
- Christopher Bongsoo Choy
- Vladlen Koltun
Citations in the corpus
- SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, unknown): “There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”
- The Perfect Match: 3D Point Cloud Matching With Smoothed Densities (2018, background): “, [27, 48, 35]), most approaches match corresponding 3D feature descriptors that are custom-tailored for 3D point clouds and usually deFigure 1: 3DSmoothNet generalization ability: our descriptor, trained solely on indoor scenes (top) can seamlessly generalize to outdoor scenes (bottom).”