This paper presents a novel point-cloud descriptor for robust and real-time tracking of multiple objects without any object knowledge. Following with the framework of incremental model-free multiple object tracking from our previous work [5] [7][6], 6 DoF pose of each object is firstly estimated with input point-cloud data which is then segmented according to the estimated objects, and incremental model of each object is updated from the segmented point-clouds. Here, we propose Joint

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... al Descriptor (JCSD) to enhance the robustness of the pose hypothesis evaluation to the pointcloud scene in the particle filtering framework. The method outperforms widely used point-to-point comparison methods, especially in the partially occluded scene, which is frequently happened in the dynamic object manipulation cases. By means of the robust descriptor, we achieved unsupervised multiple object segmentation accuracy higher than 99%. The modelfree multiple object tracking was implemented by using a particle filtering with JCSD as a likelihood function. The robust likelihood function is implemented with GPU, thus facilitating real-time tracking of multiple objects. Pose estimation Segmentation Model update Sec. II-C, Sec III Sec. II-D Sec. II-D