  The proposal of Pseudo-Lidar representation has significantly narrowed the
gap between visual-based and active Lidar-based 3D object detection. However,
current researches exclusively focus on pushing the accuracy improvement of
Pseudo-Lidar by taking the advantage of complex and time-consuming neural
networks. Seldom explore the profound characteristics of Pseudo-Lidar
representation to obtain the promoting opportunities. In this paper, we dive
deep into the pseudo Lidar representation and argue that the performance of 3D
object detection is not fully dependent on the high precision stereo depth
estimation. We demonstrate that even for the unreliable depth estimation, with
proper data processing and refining, it can achieve comparable 3D object
detection accuracy. With this finding, we further show the possibility that
utilizing fast but inaccurate stereo matching algorithms in the Pseudo-Lidar
system to achieve low latency responsiveness. In the experiments, we develop a
system with a less powerful stereo matching predictor and adopt the proposed
refinement schemes to improve the accuracy. The evaluation on the KITTI
benchmark shows that the presented system achieves competitive accuracy to the
state-of-the-art approaches with only 23 ms computing, showing it is a suitable
candidate for deploying to real car-hold applications.
