Evolutionary Multitasking with Compatibility Graph for Point Cloud Registration

3D point cloud registration is a fundamental task in computer vision, aimed at estimating a transformation to align a pair of point clouds. For point cloud registration, where the popular methods are used to build a compatibility graph. Different methods of constructing compatibility graphs can result in different information regions to be searched in the graph, due to varying constraints. Evolutionary multitask optimization has gained attention in the field of evolutionary computation, as it enables knowledge transfer among multitasks to enhance the exploration of information. Inspired by this theory, this paper proposes a method to utilize compatibility graphs as tasks through evolutionary multitasking for solving the problem of point cloud registration. We first construct two compatibility graph tasks with different tightness constraints for point cloud registration. Due to the different constraints of the two proposed tasks, the local information of the graph of interest might be biased. This bias can be effectively utilized in the evolutionary multitasking framework to enhance the ability to discover meaningful consensus relationships in the search space of the graph. Then we map the two tasks to a unified search space by designing clique selection strategies, and carrying out knowledge transfer between the two tasks, aiming to emphasize more on the local consensus information in the graphs. Lastly, the efficacy of our proposed method is further validated on multiple registration datasets.