Daniel Ritchie

2.5k citations
47 papers · 1.7k · h-index 16

Impact in

Papers in

Daniel Ritchie

42 papers receiving 1.6k citations

Peers

Daniel Ritchie
Comparison fields: 5 of 96
  • Computer Graphics and Computer-Aided Design 500
  • Geology 284
  • Computer Vision and Pattern Recognition 877
  • Computational Mechanics 693
  • Architecture 17
Replace Shiqing Xin with:
Shiqing Xin China
Ruizhen Hu China
Youyi Zheng China
Hung‐Kuo Chu Taiwan
Patrick Min United States
Masaki Hilaga Japan
Tianjia Shao China
Nathan Carr United States
Klaus Hildebrandt Netherlands
Oliver van Kaick Canada
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Citations per field
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Shiqing Xin · 1×
Citations per year

Countries citing papers authored by Daniel Ritchie

Since Specialization
Citations

This map shows the geographic impact of Daniel Ritchie's research. 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 Daniel Ritchie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Ritchie more than expected).

Fields of papers citing papers by Daniel Ritchie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Ritchie. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Ritchie. The network helps show where Daniel Ritchie may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Ritchie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Ritchie Line = papers co-authored together Daniel Ritchie links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012254
2 2018219
3 2018161
4 2019141
5 2009139
6 2010117
7 201366
8 201163
9 202058
10 201852
11 202051
12 201549
13
Learning to Infer Graphics Programs from Hand-Drawn Images
201847
14 202137
15 202021
16 202119
17
Learning to Describe Scenes with Programs
201816
18 201014
19 202312
20 201812

About Daniel Ritchie

Daniel Ritchie is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Computational Mechanics, Geology and Industrial and Manufacturing Engineering, having authored 47 papers that have together received 1.7k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (23 papers), Computer Graphics and Visualization Techniques (18 papers), Advanced Vision and Imaging (7 papers), Image Processing and 3D Reconstruction (7 papers), 3D Surveying and Cultural Heritage (7 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Manufacturing Process and Optimization (4 papers) and Computational Geometry and Mesh Generation (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (500 citations), Geology (284 citations), Computer Vision and Pattern Recognition (877 citations), Computational Mechanics (693 citations) and Architecture (17 citations). Daniel Ritchie has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Manolis Savva, Pat Hanrahan, Matthew Fisher, Anne Lynn S. Chang, Thomas A. Funkhouser, James F. O’Brien, Jonathan Richard Shewchuk, Angela Dai, Martin Bokeloh and Matthias Nießner. Their work appears in journals such as ACM Transactions on Graphics, Computer Graphics Forum, Journal of Educational Psychology, ACM SIGPLAN Notices and Lecture notes in computer science.

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.

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