# Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition

arXiv: https://arxiv.org/abs/2002.03779 · HTML: https://mlsanity.com/paper/2002.03779/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review](https://mlsanity.com/paper/2306.05978/) (2023)
- [Fine-grained 3D object recognition: an approach and experiments](https://mlsanity.com/paper/2306.15919/) (2023)
- [Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments](https://mlsanity.com/paper/2309.08239/) (2023)
- [THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen Environments](https://mlsanity.com/paper/2408.01579/) (2024)

## Abstract (arXiv)

Despite the recent success of state-of-the-art 3D object recognition approaches, service robots are frequently failed to recognize many objects in real human-centric environments. For these robots, object recognition is a challenging task due to the high demand for accurate and real-time response under changing and unpredictable environmental conditions. Most of the recent approaches use either the shape information only and ignore the role of color information or vice versa. Furthermore, they mainly utilize the Ln Minkowski family functions to measure the similarity of two object views, while there are various distance measures that are applicable to compare two object views. In this paper, we explore the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition. Towards this goal, we extensively evaluate the performance of object recognition approaches in three different configurations, including textitcolor-only, textitshape-only, and textit combinations of color and shape, in both offline and online settings. Experimental results concerning scalability, memory usage, and object recognition performance show that all of the textitcombinations of color and shape yields significant improvements over the textitshape-only and textitcolor-only approaches. The underlying reason is that color information is an important feature to distinguish objects that have very similar geometric properties with different colors and vice versa. Moreover, by combining color and shape information, we demonstrate that the robot can learn new object categories from very few training examples in a real-world setting.