Explain What You See: Open-Ended Segmentation and Recognition of Occluded 3D Objects

Hamed Ayoobi, Hamidreza Kasaei, Ming Cao, Rineke Verbrugge, Bart Verheij

Local-HDP (Local Hierarchical Dirichlet Process) is a hierarchical Bayesian method recently used for open-ended 3D object category recognition. It has been proven to be efficient in real-time robotic applications. However, the method is not robust to a high degree of occlusion. We address this limitation in two steps. First, we propose a novel semantic 3D object-parts segmentation method that has the flexibility of Local-HDP. This method is shown to be suitable for open-ended scenarios where the number of 3D objects or object parts are not fixed and can grow over time. We show that the proposed method has a higher percentage of mean intersection over union, using a smaller number of learning instances. Second, we integrate this technique with a recently introduced argumentation-based online incremental learning method, enabling the model to handle a high degree of occlusion. We show that the resulting model produces explicit explanations for the 3D object category recognition task.

Manuscript (in PDF-format)

A version of the paper is available at arXiv.

Presentation by Hamed Ayoobi (video at Youtube)

Ayoobi, H., Kasaei, H., Cao, M., Verbrugge, R., & Verheij, B. (2023). Explain What You See: Open-Ended Segmentation and Recognition of Occluded 3D Objects. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 4960-4966. https://doi.org/10.1109/ICRA48891.2023.10160927.

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