  We present a novel 3D object detection framework, named IPOD, based on raw
point cloud. It seeds object proposal for each point, which is the basic
element. This paradigm provides us with high recall and high fidelity of
information, leading to a suitable way to process point cloud data. We design
an end-to-end trainable architecture, where features of all points within a
proposal are extracted from the backbone network and achieve a proposal feature
for final bounding inference. These features with both context information and
precise point cloud coordinates yield improved performance. We conduct
experiments on KITTI dataset, evaluating our performance in terms of 3D object
detection, Bird's Eye View (BEV) detection and 2D object detection. Our method
accomplishes new state-of-the-art , showing great advantage on the hard set.
