Learning Depth from Single Monocular Images

586 indexed citations

Abstract

loading...

About

This paper, published in 2005, received 586 indexed citations. Written by Ashutosh Saxena, Sung Heon Chung and Andrew Y. Ng covering the research area of Media Technology and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (547 citations), Media Technology (276 citations) and Aerospace Engineering (133 citations). Published in Neural Information Processing Systems.

In The Last Decade

doi.org/w3158142 →

Countries where authors are citing Learning Depth from Single Monocular Images

Specialization
Citations

This map shows the geographic impact of Learning Depth from Single Monocular Images. 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 Learning Depth from Single Monocular Images with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning Depth from Single Monocular Images more than expected).

Fields of papers citing Learning Depth from Single Monocular Images

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Learning Depth from Single Monocular Images. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning Depth from Single Monocular Images.

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/w3158142.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026