Go-ICP: Solving 3D Registration Efficiently and Globally Optimally
By Jiaolong Yang, Hongdong Li, Yunde Jia. IEEE International Conference on Computer Vision, 2013
506 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/752017f97cd4200bcee68f97d4edc35419a7dfd4
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
- Dylan Campbell
- Rangaprasad Arun Srivatsan
- Simon Lucey
- Yasuhiro Aoki
- Jiaolong Yang
- Hongdong Li
- Hunter Goforth
- Yunde Jia
In Computer Vision and Pattern Recognition
- Hongdong Li
- Simon Lucey
- Dylan Campbell
- Rangaprasad Arun Srivatsan
- Yasuhiro Aoki
- Jiaolong Yang
- Hunter Goforth
- Yunde Jia
Citations in the corpus
- PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet (2019, methodology): “[46] developed Go-ICP, a branch and bound-based optimization approach to obtain globally optimal pose.”
- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (2016, background): “A preliminary version of this work was presented as a conference paper [24].”
- A self-supervised learning system for object detection using physics simulation and multi-view pose estimation (2017, unknown): “However, in the recently concluded Amazon Picking Challenge [3] where setup includes varying light conditions and cluttered objects, the top performing teams of TU Delft and MIT-Princeton developed vision system [6], [24] comprising of a CNN-based segmentation [11], [16] module followed by pairwise ”