LIDAR3DNETV2: Smart Approach to Classify 3D Objects Based on Real Point Clouds

Abstract

Point clouds generated in simulators avoid collection time costs and provide a high and organized amount of point clouds, an ideal scenario for deep learning networks. However, these networks have limitations when applied to real point clouds. This work proposes a multilayer perceptron-based method to classify 3D objects based on real point clouds obtained using LiDAR sensors. The method includes a pre-processing step that normalizes and adjusts the point clouds in the 3D Cartesian plane to overcome discrepancies in the point distribution. Furthermore, we created a dataset and used the ModelNet dataset for comparison purposes. The proposed neural network, Lidar3DNetV2, achieved 98.47% and 125 μs in accuracy and test time with real data, respectively. The pre-processing step provided a significant increase in the classifier’s performance. Finally, the proposed method performs better than other state-of-the-art networks considering real point clouds.