Davide Boscaini

3.8k total citations · 2 hit papers
20 papers, 1.8k citations indexed

About

Davide Boscaini is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Industrial and Manufacturing Engineering. According to data from OpenAlex, Davide Boscaini has authored 20 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 11 papers in Computational Mechanics and 4 papers in Industrial and Manufacturing Engineering. Recurrent topics in Davide Boscaini's work include 3D Shape Modeling and Analysis (11 papers), Image Processing and 3D Reconstruction (4 papers) and Advanced Neural Network Applications (3 papers). Davide Boscaini is often cited by papers focused on 3D Shape Modeling and Analysis (11 papers), Image Processing and 3D Reconstruction (4 papers) and Advanced Neural Network Applications (3 papers). Davide Boscaini collaborates with scholars based in Italy, Switzerland and United Kingdom. Davide Boscaini's co-authors include Michael M. Bronstein, Emanuele Rodolà, Federico Monti, Jonathan Masci, Jan Svoboda, Bruno E. Correia, Freyr Sverrisson, Pablo Gaínza, Fabio Poiesi and Umberto Castellani and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Methods and Medical Image Analysis.

In The Last Decade

Davide Boscaini

18 papers receiving 1.7k citations

Hit Papers

Geometric Deep Learning on Graphs and Manifolds Using Mix... 2017 2026 2020 2023 2017 2019 250 500 750
What are hit papers?

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.

  1. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
    2017 981 citations Federico Monti, Davide Boscaini et al. IRIS Research product catalog (Sapienza University of Rome) profile →
  2. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
    2019 449 citations Pablo Gaínza, Freyr Sverrisson et al. Nature Methods profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Davide Boscaini Italy 7 711 562 456 402 221 20 1.8k
Federico Monti Italy 8 651 0.9× 688 1.2× 230 0.5× 444 1.1× 269 1.2× 16 1.9k
Emanuele Rodolà Italy 26 1.8k 2.5× 666 1.2× 1.2k 2.5× 420 1.0× 268 1.2× 82 3.2k
Christopher Morris United States 14 447 0.6× 566 1.0× 153 0.3× 111 0.3× 100 0.5× 35 1.3k
Martin Heusel Austria 6 1.7k 2.4× 585 1.0× 130 0.3× 304 0.8× 50 0.2× 6 2.5k
Yusu Wang United States 25 461 0.6× 105 0.2× 357 0.8× 302 0.8× 473 2.1× 113 1.6k
Steven Gold United States 12 1.1k 1.5× 321 0.6× 181 0.4× 169 0.4× 82 0.4× 16 1.6k
宏治 津田 Japan 1 867 1.2× 1.1k 1.9× 115 0.3× 70 0.2× 81 0.4× 2 2.0k
Joshua A. Levine United States 19 636 0.9× 193 0.3× 477 1.0× 52 0.1× 189 0.9× 69 1.6k
L.P. Cordella Italy 17 1.2k 1.7× 739 1.3× 45 0.1× 159 0.4× 227 1.0× 53 1.9k
Kayvon Fatahalian United States 24 974 1.4× 444 0.8× 429 0.9× 75 0.2× 103 0.5× 63 3.0k

Countries citing papers authored by Davide Boscaini

Since Specialization
Citations

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

Fields of papers citing papers by Davide Boscaini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Davide Boscaini. 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 Davide Boscaini. The network helps show where Davide Boscaini may publish in the future.

Co-authorship network of co-authors of Davide Boscaini

This figure shows the co-authorship network connecting the top 25 collaborators of Davide Boscaini. A scholar is included among the top collaborators of Davide Boscaini 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 Davide Boscaini. Davide Boscaini 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.
Boscaini, Davide, et al.. (2025). 3D Part Segmentation via Geometric Aggregation of 2D Visual Features. Institutional Research Information System (Università degli Studi di Trento). 3257–3267. 1 indexed citations
2.
Boscaini, Davide, et al.. (2024). Open-vocabulary object 6D pose estimation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 18071–18080. 3 indexed citations
3.
4.
Boscaini, Davide & Fabio Poiesi. (2023). PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding. Image and Vision Computing. 137. 104768–104768. 3 indexed citations
5.
Petit, Laurent, Emanuele Olivetti, Silvio Sarubbo, et al.. (2023). Supervised tractogram filtering using Geometric Deep Learning. Medical Image Analysis. 90. 102893–102893. 5 indexed citations
6.
Boscaini, Davide, et al.. (2023). Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation. Iris (University of Trento). 2095–2104. 4 indexed citations
7.
Boscaini, Davide & Fabio Poiesi. (2023). Patchmixer: Rethinking Network Design to Boost Generalization For 3d Point Cloud Understanding. SSRN Electronic Journal.
8.
Mekhalfi, Mohamed Lamine, Davide Boscaini, André Dias, et al.. (2023). The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios. Institutional Research Information System (Università degli Studi di Trento). 2546–2554. 4 indexed citations
9.
Poiesi, Fabio & Davide Boscaini. (2022). Learning general and distinctive 3D local deep descriptors for point cloud registration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(3). 1–1. 58 indexed citations
10.
Svoboda, Jan, et al.. (2020). Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition. Institutional Research Information System (Università degli Studi di Trento). 1–9. 2 indexed citations
11.
Gaínza, Pablo, Freyr Sverrisson, Federico Monti, et al.. (2019). Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods. 17(2). 184–192. 449 indexed citations breakdown →
12.
Mancini, Massimiliano, et al.. (2019). Structured Domain Adaptation for 3D Keypoint Estimation. IRIS Research product catalog (Sapienza University of Rome). 36. 57–66. 1 indexed citations
13.
Monti, Federico, Davide Boscaini, Jonathan Masci, et al.. (2017). Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. IRIS Research product catalog (Sapienza University of Rome). 5425–5434. 981 indexed citations breakdown →
14.
Masci, Jonathan, Emanuele Rodolà, Davide Boscaini, Michael M. Bronstein, & Hao Li. (2016). Geometric deep learning. IRIS Research product catalog (Sapienza University of Rome). 1–50. 27 indexed citations
15.
Boscaini, Davide, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein, & Daniel Cremers. (2016). Anisotropic Diffusion Descriptors. Computer Graphics Forum. 35(2). 431–441. 81 indexed citations
16.
Boscaini, Davide, Jonathan Masci, Simone Melzi, et al.. (2015). Learning class‐specific descriptors for deformable shapes using localized spectral convolutional networks. Computer Graphics Forum. 34(5). 13–23. 124 indexed citations
17.
Boscaini, Davide, Davide Eynard, Drosos Kourounis, & Michael M. Bronstein. (2015). Shape‐from‐Operator: Recovering Shapes from Intrinsic Operators. Computer Graphics Forum. 34(2). 265–274. 30 indexed citations
18.
Boscaini, Davide, et al.. (2014). Coulomb Shapes: Using Electrostatic Forces for Deformation-invariant Shape Representation. Eurographics. 5 indexed citations
19.
Boscaini, Davide & Umberto Castellani. (2014). A sparse coding approach for local-to-global 3D shape description. The Visual Computer. 30(11). 1233–1245. 2 indexed citations
20.
Boscaini, Davide & Umberto Castellani. (2013). Local Signature Quantization by Sparse Coding. Eurographics. 2 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.

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