Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
2020864 citationsDi Feng, Christian Schütz et al.IEEE Transactions on Intelligent Transportation Systemsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Christian Schütz
Since
Specialization
Citations
This map shows the geographic impact of Christian Schütz'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 Christian Schütz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christian Schütz more than expected).
Fields of papers citing papers by Christian Schütz
This network shows the impact of papers produced by Christian Schütz. 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 Christian Schütz. The network helps show where Christian Schütz may publish in the future.
Co-authorship network of co-authors of Christian Schütz
This figure shows the co-authorship network connecting the top 25 collaborators of Christian Schütz.
A scholar is included among the top collaborators of Christian Schütz 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 Christian Schütz. Christian Schütz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Feng, Di, Christian Schütz, Lars Rosenbaum, et al.. (2020). Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges. IEEE Transactions on Intelligent Transportation Systems. 22(3). 1341–1360.864 indexed citations breakdown →
Schütz, Christian. (1998). Geometric point matching of free-form 3D objects.2 indexed citations
10.
Schütz, Christian, et al.. (1997). Augmented reality using range images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3012. 472–472.6 indexed citations
11.
Hügli, Heinz & Christian Schütz. (1996). Computer vision of free-form 3D objects by geometric matching. 1–5.1 indexed citations
12.
Hügli, Heinz & Christian Schütz. (1996). HOW WELL PERFORMS FREE-FORM 3D OBJECT RECOGNITION FROM RANGE IMAGES?. 2904(9). 66–74.6 indexed citations
Schütz, Christian & Heinz Hügli. (1995). Towards the recognition of 3D free-form objects. 2588. 476–484.1 indexed 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.