OPTIMIZATION-BASED PARAMETRIC MODEL FITTING VIA DEEP LEARNING

A neural optimizer is disclosed that is easily applicable to different fitting problems, can run at interactive rates without requiring significant efforts, does not require hand crafted priors, carries over information about previous iterations of the solve, controls the learning rate of each parameter independently for robustness and convergence speed, and combines updates from gradient descent and from a method capable of very quickly reducing the fitting energy. A neural fitter estimates the values of the parameters Θ by iteratively updating an initial estimate Θ0.