Plane Segmentation in Outdoor Imagery-Reference-Cited by-同舟云学术

Plane Segmentation in Outdoor Imagery

Published:2023-11-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
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Author:

Liang Haolin¹^ORCID,Newsam Shawn²^ORCID

Affiliation:

1. Computer Science and Engineering, University of California, Merced

2. University of California, Merced

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615886.3627746

Reference12 articles.

1. Angel Chang Angela Dai Thomas Funkhouser Maciej Halber Matthias Nießner Manolis Savva Shuran Song Andy Zeng and Yinda Zhang. 2017. Matterport3D: Learning from RGB-D Data in Indoor Environments. arXiv:1709.06158 [cs] Angel Chang Angela Dai Thomas Funkhouser Maciej Halber Matthias Nießner Manolis Savva Shuran Song Andy Zeng and Yinda Zhang. 2017. Matterport3D: Learning from RGB-D Data in Indoor Environments. arXiv:1709.06158 [cs]

2. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

3. Development of a synthetic dataset generation method for deep learning of real urban landscapes using a 3D model of a non-existing realistic city

4. Yancong Lin , Ruben Wiersma , Silvia L. Pintea , Klaus Hildebrandt , Elmar Eisemann , and Jan C . van Gemert . 2022 . Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish . arXiv:2203.08586 [cs] Yancong Lin, Ruben Wiersma, Silvia L. Pintea, Klaus Hildebrandt, Elmar Eisemann, and Jan C. van Gemert. 2022. Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish. arXiv:2203.08586 [cs]

5. PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image