Exploring task-agnostic, ShapeNet-based object recognition for mobile robots

Agnese Chiatti, Gianluca Bardaro, Emanuele Bastianelli, Ilaria Tiddi, Prasenjit Mitra, Enrico Motta

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review


This position paper presents an attempt to improve the scalability of existing object recognition methods, which largely rely on supervision and imply a huge availability of manually-labelled data points. Moreover, in the context of mobile robotics, data sets and experimental settings are often handcrafted based on the specific task the object recognition is aimed at, e.g. object grasping. In this work, we argue instead that publicly available open data such as ShapeNet [8] can be used for object classification first, and then to link objects to their related concepts, leading to task-agnostic knowledge acquisition practices. To this aim, we evaluated five pipelines for object recognition, where target classes were all entities collected from ShapeNet and matching was based on: (i) shape-only features, (ii) RGB histogram comparison, (iii) a combination of shape and colour matching, (iv) image feature descriptors, and (v) inexact, normalised cross-correlation, resembling the Deep, Siamese-like NN architecture of [31]. We discussed the relative impact of shape-derived and colour-derived features, as well as suitability of all tested solutions for future application to real-life use cases.

Original languageEnglish
Title of host publicationProceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference (EDBT/ICDT 2019) Lisbon, Portugal, March 26, 2019
EditorsPaolo Papotti
PublisherCEUR Workshop Proceedings
Number of pages8
Publication statusPublished - 2019
Event2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019 - Lisbon, Portugal
Duration: 26 Mar 2019 → …

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR Workshop Proceedings
ISSN (Print)1613-0073


Conference2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019
Period26/03/19 → …

Bibliographical note

DARLI-AP: Data Analytics Solutions for Real-Life Applications

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