  Perception and localization are essential for autonomous delivery vehicles,
mostly estimated from 3D LiDAR sensors due to their precise distance
measurement capability. This paper presents a strategy to obtain the real-time
pseudo point cloud instead of the laser sensor from the image sensor. We
propose an approach to use different depth estimators to obtain pseudo point
clouds like LiDAR to obtain better performance. Moreover, the training and
validating strategy of the depth estimator has adopted stereo imagery data to
estimate more accurate depth estimation as well as point cloud results. Our
approach to generating depth maps outperforms on KITTI benchmark while yielding
point clouds significantly faster than other approaches.
