3D object detection in autonomous driving: matching point cloud clustering with neural network classification

This paper proposes a novel 3D object detection method based on object matching for autonomous driving, which combines point cloud data and image processing. The method utilizes point cloud clustering to determine the location and size of objects, while a neural network classifies objects based on image data. Object detection is then achieved through the matching of these results. Experimental testing of the algorithm demonstrates the effectiveness of this fusion approach.