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Using a depth camera, the KinectFusion with Moving Objects Tracking (KinFu MOT) algorithm permits tracking the camera poses and building a dense 3D reconstruction of the environment which can also contain moving objects. The GPU processing pipeline allows this simultaneously and in real-time. During the reconstruction, yet untraced moving objects are detected and new models are initialized. The original approach to detect unknown moving objects is not very precise and may include wrong vertices. This paper describes an improvement of the detection based on connected component labeling (CCL) on the GPU. To achieve this, three CCL algorithms are compared. Afterwards, the migration into KinFu MOT is described. It incorporates the 3D structure of the scene and three plausibility criteria refine the detection. In addition, potential benefits on the CCL runtime of CUDA Dynamic Parallelism and of skipping termination condition checks are investigated. Finally, the enhancement of the detection performance and the reduction of response time and computational effort is shown.

Moving Object Detection by Connected Component Labeling of Point Cloud Registration Outliers on the GPU