Online Reconstruction of Indoor Scenes from RGB-D Streams
By Hao Wang, J. Wang, Liang Wang. 2016
26 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
- Matthias Nießner
- Angela Dai
- Marc Pollefeys
- Thomas A. Funkhouser
- Angel X. Chang
- Torsten Sattler
- Manolis Savva
- Thomas Schöps
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Matthias Nießner
- Abhinav Kumar Gupta
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
- Kristen Grauman
In Geology
- Matthias Nießner
- Angela Dai
- Marc Pollefeys
- Angel X. Chang
- Thomas A. Funkhouser
- Manolis Savva
- Torsten Sattler
- Leonidas J. Guibas
Citations in the corpus
- ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “One of the earliest and most popular datasets for RGB-D scene understanding is NYU v2 [71].”
- BAD SLAM: Bundle Adjusted Direct RGB-D SLAM (2019, methodology): “Pose graph optimization methods [9, 11, 30, 34, 60, 65] build a graph of simplified constraints on the relative transformation between camera pairs.”
- School of Electrical and Computer Engineering Oklahoma State University , Stillwater , OK 74078 , USA (methodology): “In contrast, the main intention of the researchers in the computer vision community is dense modeling and enhancing the quality and fidelity of the reconstructed models [22, 29, 19, 4].”