At present, the LiDAR ground filtering technology is very mature. There are fewer applications in 3D-object detection due to the limitations of filtering accuracy and efficiency. If the ground can be removed quickly and accurately, the 3D-object detection algorithm can detect objects more accurately and quickly. In order to meet the application requirements of 3D-object detection, inspired by Universal-RANSAC, we analyze the detailed steps of RANSAC and propose a precise and efficient RANSAC-based ground filtering method. The principle of GroupSAC is analyzed, and the sampled points are grouped by attributes to make it easier to sample the correct point. Based on this principle, we devise a method for limiting sampled points that is applicable to point clouds. We describe preemptive RANSAC in detail. Its breadth-first strategy is adopted to obtain the optimal plane without complex iterations. We use the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and the KITTI dataset for testing. Experiments show that our method has higher filtering accuracy and efficiency compared with the currently widely used methods. We explore the application of ground filtering methods in 3D-object detection, and the experimental results show that our method can improve the object detection accuracy without affecting the efficiency.

The generative adversarial network (GAN) has recently emerged as a promising generative model. Its application in the image field has been extensive, but there has been little research concerning point clouds.The combination of a GAN and a graph convolutional network has been the state-of-the-art method for generating point clouds. However, there is a significant gap between the generated point cloud and the point cloud used for training. In order to improve the quality of the generated point cloud, this study proposed multi-scale gradient point GAN (MSG-Point-GAN). The training of the GAN is a dynamic game process, and we expected the generation and discrimination capabilities to be symmetric, so that the network training would be more stable. Based on the concept of progressive growth, this method used the network structure of a multi-scale gradient GAN (MSG-GAN) to stabilize the training process. The discriminator of this method used part of the PointNet structure to resolve the problem of the disorder and rotation of the point cloud. The discriminator could effectively determine the authenticity of the generated point cloud. This study also analyzed the optimization process of the objective function of the MSG-Point-GAN. The experimental results showed that the training process of the MSG-Point-GAN was stable, and the point cloud quality was superior to other methods in subjective vision. From the perspective of performance metrics, the gap between the point cloud generated by the proposed method and the real point cloud was significantly smaller than that generated by other methods. Based on the practical analysis, the point cloud generated by the proposed method for training the point-cloud classification network was improved by about 0.2%, as compared to the original network. The proposed method provided a stable training framework for point cloud generation. It can effectively promote the development of point-cloud-generation technology.

The current generative adversarial network (GAN) is limited in the application of data augmentation in object recognition. The training of the GAN is unstable, and the generated image quality is poor. Methods such as progressive growing of GANs and multi-scale gradient GAN solve these problems. The packed GAN (PacGAN) solves the problem of mode collapse during training. However, these methods can generate only one type of image at a time, and the training time is long. To solve the above problems, this paper proposes the multi-class GAN (Mc-GAN). It uses an augmented discriminator to train multiple generators at the same time. Through iterative training, the discriminator can accurately judge the output of each generator and guide it to generate the corresponding image. This paper analyzes the optimization process of the objective function of Mc-GAN. Experiments show that the method can generate high-quality images and reduce training time, and it can be used for data augmentation in object recognition. It effectively improves the practicality of GAN.