Processing point cloud sequences with Growing Neural Gas

We consider the problem of processing point cloud sequences. In particular, we represent and track objects in dynamic scenes acquired using low-cost sensors such as the Kinect. A neural network based approach is proposed to represent and estimate 3D objects motion. This system addresses multiple computer vision tasks such as object segmentation, representation, motion analysis and tracking. The use of a neural network allows the unsupervised estimation of motion and the representation of objects in the scene. This proposal avoids the problem of finding corresponding features while tracking moving objects. A set of experiments are presented that demonstrate the validity of our method to track 3D objects. Favorable results are presented demonstrating the capabilities of the GNG algorithm for this task.