Fangchang Ma
- Computer Vision and Pattern Recognition top 1%
- Media Technology top 1%
- Aerospace Engineering top 5%
- Environmental Engineering
- Instrumentation top 10%
- Co-authors
- Sertaç KaramanGuilherme V. CavalheiroLuca CarloneAngela DaiQi ShanMatthias NießnerZiya ErkoçYawar Siddiqui
- Topics
- Advanced Vision and Imaging (5 papers)Robotics and Sensor-Based Localization (4 papers)Optical measurement and interference techniques (3 papers)
- Journals
- The International Journal of Robotics Research2021 IEEE/CVF International Conference on Computer Vision (ICCV)DSpace@MIT (Massachusetts Institute of Technology)
- Partner nations
- United StatesGermany
In The Last Decade
Fangchang Ma
8 papers receiving 762 citations
Hit Papers
Peers
Comparison fields: 5 of 42
- Computer Vision and Pattern Recognition 698
- Media Technology 276
- Aerospace Engineering 270
- Environmental Engineering 70
- Instrumentation 68
Countries citing papers authored by Fangchang Ma
This map shows the geographic impact of Fangchang Ma'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 Fangchang Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fangchang Ma more than expected).
Fields of papers citing papers by Fangchang Ma
This network shows the impact of papers produced by Fangchang Ma. 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 Fangchang Ma. The network helps show where Fangchang Ma may publish in the future.
Co-authorship network of co-authors of Fangchang Ma
This figure shows the co-authorship network connecting the top 25 collaborators of Fangchang Ma. A scholar is included among the top collaborators of Fangchang Ma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fangchang Ma. Fangchang Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 31 | |
| 2 | 14 | |
| 3 | 18 | |
| 4 | Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camerabreakdown → | 280 |
| 5 | 12 | |
| 6 | Invertibility of Convolutional Generative Networks from Partial Measurements | 20 |
| 7 | Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagebreakdown → | 378 |
| 8 | 22 |
About Fangchang Ma
Fangchang Ma is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Instrumentation, having authored 8 papers that have together received 775 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (5 papers), Robotics and Sensor-Based Localization (4 papers) and Optical measurement and interference techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (698 citations), Media Technology (276 citations) and Instrumentation (68 citations). Fangchang Ma has collaborated with scholars based in United States and Germany. Frequent co-authors include Sertaç Karaman, Guilherme V. Cavalheiro, Luca Carlone, Angela Dai, Qi Shan, Matthias Nießner, Ziya Erkoç, Yawar Siddiqui, Matthias Niesner and Justus Thies. Their work appears in journals such as The International Journal of Robotics Research, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and DSpace@MIT (Massachusetts Institute of Technology).
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.