3D object detection using LiDAR sensory point-cloud data is widely used for many applications, including autonomous driving and map building. Existing solutions mainly leverage deep learning models; nevertheless, one of the underlying challenges is reducing computational load, thus latency, while maintaining high accuracy, particularly for detecting objects in the long-range. Here, we introduce a novel streaming-style detector utilizing polar space feature representations to provide faster inference for 3D object detection. Our method improves detection performance using pseudo-image features and can support edge devices with limited memory requirements. Comparing with other state-of-art methods along with experimental validations, we show our methods corroborates superiority on Waymo, KITTI dataset. On KITTI validation, it achieves 94.7\% AP for cars in BEV detection.