# Classification reliability of 3D shapes using neural networks in case of partial and noisy models

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/classification-reliability-of-3d-shapes-using-neural-networks-in-case-of-partial/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Simone Paganoni,Emanuele Zappa,Simone Turrisi |
| Citations | 2 |
| DOI | 10.1109/i2mtc43012.2020.9128469 |
| Fields | Computer Science,Earth and Planetary Sciences,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3038314583 |
| Type | conference-paper |
| Year | 2020 |

## Paper authors

- [Simone Paganoni](https://scholariq.org/researchers/simone-paganoni/)

## Paper primary topic

- [3D Surveying and Cultural Heritage](https://scholariq.org/topics/3d-surveying-and-cultural-heritage/)

## Paper topics

- [3D Surveying and Cultural Heritage](https://scholariq.org/topics/3d-surveying-and-cultural-heritage/)
- [Industrial Vision Systems and Defect Detection](https://scholariq.org/topics/industrial-vision-systems-and-defect-detection/)
- [Optical measurement and interference techniques](https://scholariq.org/topics/optical-measurement-and-interference-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.