PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Authors
- Hao SuKaichun MoLeonidas Guibas
In The Last Decade
doi.org/10.1109/cvpr.2017.16 →Countries where authors are citing PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
This map shows the geographic impact of PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation more than expected).
Fields of papers citing PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
This network shows the impact of PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.
About PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
This paper, published in 2017, received 5.8k indexed citations . Written by Hao Su, Kaichun Mo and Leonidas Guibas covering the research area of Geology, Aerospace Engineering and Computational Mechanics. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (3.0k citations), Computational Mechanics (2.7k citations) and Geology (2.0k citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/cvpr.2017.16.