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

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

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
74 Downloads (Pure)


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
JournalCEUR Workshop Proceedings
Publication statusPublished - 27 Feb 2019
Event2019 Workshops of the EDBT/ICDT Joint Conference - Lisbon, Portugal
Duration: 26 Mar 2019 → …

ASJC Scopus subject areas

  • Computer Science(all)


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