Both light-field and polarization information contain lots of clues about scenes, and they can be widely used in variety of computer vision tasks. However, existing imaging systems cannot simultaneously capture the light-field and polarization information. In this paper, we present a low-cost and high-performance miniaturized polarimetric light-field camera, which is based on the six heterogeneous sensors array. The main challenge for the proposed strategy is to align the multi-view images with different polarization characteristics, especially for regions with high degree of polarization -- in which the intensity correlations are commonly weak. To solve this problem, we propose to use Convolutional Neural Network (CNN) based stereo matching method for aligning the heterogeneously polarized images accurately. After stereo matching, both the light field and the Stokes vectors of scene are estimated, and the polarimetry conventions, e.g., the polarization angle, the linear polarization degree and the circular polarization degree, are given. We implement the prototype of the multisensor polarization light-field camera and perform extensive experiments on it. The polarimetric light-field camera achieves six live streaming on time and the heterogeneous processor of NVIDIA Jetson TX2 is exploited for image processing. Benefiting from the multi-sensor parallel polarization imaging and efficient parallel processing, the proposed system achieves promising performance on time resolution, signal-to-noise ratio. Besides, we develop the object recognition applications to show the superiorities of proposed system.