Slicing-Tracking-Detection: Simultaneous Multi-Cylinder Detection From Large-Scale and Complex Point Clouds
Zhuheng Lu, Weiwei Mao, Yuewei Dai, Weiqing Li, Zhiyong Su
Abstract
Multiple cylinders detection from large-scale and complex point clouds is a historical but challenging problem, considering the efficiency and accuracy. We propose a novel framework, named slicing-tracking-detection (STD), that detects multiple cylinders accurately and simultaneously from point clouds of large-scale and complex process plants. In this framework, the 3D cylinder detection problem is reformulated as a cylinder ingredients tracking task based on multi-object tracking (MOT). First, we generate slices from the input point cloud, and render them to slice sequence. Then, the cycle of a cylinder is modeled with a Markov Decision Process (MDP), where the ingredient is tracked with a template and the miss tracking is associated with ingredient proposals through reinforcement learning. Finally, by applying MDP for each cylinder, multiple cylinders can be detected simultaneously and accurately. Extensive experiments show that the proposed STD framework can significantly outperform the state-of-the-art approaches in efficiency, accuracy, and robustness. The source code is available at http://zhiyongsu.github.io.