A Multi-layer Feed-forward Network for Model Estimation from Range Data
نویسنده
چکیده
A novel neural architecture aimed to estimate superquadrics parameters form range data is presented. The network topology is designed to model and compute the inside-outside function of an undeformed superquadric in whatever attitude, starting form the (x, y, z) data triples. The network has been trained using backpropagation, and the weights arrangement, after training, represents a robust estimate of the superquadric parameters. The architectural approach is general, it can be extended to other geometric primitives for part-based object recognition, and performs faster than classical model fitting techniques. Detailed explanation of the theoretical approach, along with some experiments with real data are reported. Keywords— Model estimation, Neural modeling of functions, Superquadrics.
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