Article: Colour-spin: a novel PCL descriptor for 3D object recognition and detection Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.17 No.2 pp.178 - 198 Abstract: This paper addresses the challenge of three-dimensional (3D) object recognition and detection in real-world scenes by introducing a new feature descriptor, the colour-spin (CSpin). CSpin combines spin image features and RGB colour information, leading to enhanced feature robustness. The performance evaluation shows that CSpin significantly outperforms the traditional spin descriptor by improving accuracy by 11%. Although the colour-signature of histograms of orientations (CSHOT) descriptor presented better accuracy, CSpin's lower computational time makes it more suitable for real-time applications. Additionally, the 3D bag of words (3D BoW) model was optimised using a support vector machine (SVM) with a nonlinear radial basis function (RBF) kernel and a codebook size of 250. Our approach achieved an overall recognition accuracy of 96.42% on the publicly available RGBD Washington dataset, outperforming other state-of-the-art methods. The proposed solution shows considerable promise for practical applications in 3D object recognition and detection. Inderscience Publishers - linking academia, business and industry through research

Title: Colour-spin: a novel PCL descriptor for 3D object recognition and detection

Authors: Mohamed Hannat; Nabila Zrira; Fatima Zahra Ouadiay; Mohammed Majid Himmi; El Houssine Bouyakhf

Addresses: Faculty of Sciences, Mohammed V University in Rabat, Morocco ' ADOS Team, LISTD Laboratory, Ecole Nationale Supérieure des Mines, Rabat, Morocco ' Faculty of Sciences, Mohammed V University in Rabat, Morocco ' Faculty of Sciences, Mohammed V University in Rabat, Morocco ' Faculty of Sciences, Mohammed V University in Rabat, Morocco

Abstract: This paper addresses the challenge of three-dimensional (3D) object recognition and detection in real-world scenes by introducing a new feature descriptor, the colour-spin (CSpin). CSpin combines spin image features and RGB colour information, leading to enhanced feature robustness. The performance evaluation shows that CSpin significantly outperforms the traditional spin descriptor by improving accuracy by 11%. Although the colour-signature of histograms of orientations (CSHOT) descriptor presented better accuracy, CSpin's lower computational time makes it more suitable for real-time applications. Additionally, the 3D bag of words (3D BoW) model was optimised using a support vector machine (SVM) with a nonlinear radial basis function (RBF) kernel and a codebook size of 250. Our approach achieved an overall recognition accuracy of 96.42% on the publicly available RGBD Washington dataset, outperforming other state-of-the-art methods. The proposed solution shows considerable promise for practical applications in 3D object recognition and detection.

Keywords: 3D object recognition; 3D object detection; colour-spin descriptor; CSpin; support vector machine; SVM; bag of words; BoW; point cloud library; PCL; RGBD Washington dataset.

DOI: 10.1504/IJCVR.2026.155535

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.2, pp.178 - 198

Received: 28 Jul 2022
Accepted: 15 Nov 2023
Published online: 05 Aug 2026 *

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Title: Colour-spin: a novel PCL descriptor for 3D object recognition and detection

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