Recognizing 3D objects with multiple views is an important problem in computer vision. However, multi view object recognition can be challenging for networked embedded intelligent systems (IoT devices) as they have data transmission limitation as well as computational resource constraint. In this work, we design an enhanced multi-view distributed recognition system which deploys a view importance estimator to transmit data with different resolutions. Moreover, a multi-view learning-based super-resolution enhancer is used at the back-end to compensate for the performance degradation caused by information loss from resolution reduction. The extensive experiments on the benchmark dataset demonstrate that the designed resolution-aware multi-view system can decrease the endpoint's communication energy by a factor of 5× while sustaining accuracy. Further experiments on the enhanced multi-view recognition system show that accuracy increment can be achieved with minimum effect on the computational cost of back-end system.