A Comparative Study of VoxelNet and PointNet for 3D Object Detection in Car by Using KITTI Benchmark

In today's world, 2D object recognition is a normal course of study in research. 3D objection recognition is more in demand and important in the present scenario. 3D object recognition has gained importance in areas such as navigation of vehicles, robotic vision, HoME, virtual reality, etc. This work reveals the two important methods, Voxelnet and PointNet, useful in 3D object recognition. In case of NetPoint, the recognition is good when used with segmentation of point clouds which are in small-scale. Whereas, in case of Voxelnet, scans are used directly on raw points of clouds which are directly operated on patterns. The above conclusion is arrived on KITTI car detection. The KITTI uses detection by using bird's eye view. In this method of KITTI we compare two different methods called LiDAR and RGB-D. We arrive at a conclusion that pointNet is useful and has high performance when we are using small scenarios and Voxelnet is useful and has high performance when we are using large scenarios.