The fisheye-type camera is widely used since it has a larger view than that of a pinhole camera. But its applications have frequently been tortured by its higher distortion in imaging procedure. We propose a strategy for fisheye camera modeling based on the receptive field weighted regression algorithm. The camera’s field of view is adaptively divided into multiple grids, each of which is described by an actively learned receptive field affiliated by a regression model to denote specific imaging relation in this grid with prescribed precision. The whole model of the camera is thus achieved by weighted regressions overall receptive fields on grids. Moreover, the proposed strategy could also be applied to the simultaneous calibration of a visual system of multiple cameras since the final sensing model stems from all receptive fields over any of the camera’s field of view with the unified weighted regression scheme. Experiments on monocular and multicamera systems validate the feasibility of the proposed strategy, with performance comparisons with a state-of-the-art method.