  Recent approaches for 3D object detection have made tremendous progresses due
to the development of deep learning. However, previous researches are mostly
based on individual frames, leading to limited exploitation of information
between frames. In this paper, we attempt to leverage the temporal information
in streaming data and explore 3D streaming based object detection as well as
tracking. Toward this goal, we set up a dual-way network for 3D object
detection based on keyframes, and then propagate predictions to non-key frames
through a motion based interpolation algorithm guided by temporal information.
Our framework is not only shown to have significant improvements on object
detection compared with frame-by-frame paradigm, but also proven to produce
competitive results on KITTI Object Tracking Benchmark, with 76.68% in MOTA and
81.65% in MOTP respectively.
