## 2 Methodology  
In this paper, we propose a framework capable of tackling the following two problems: generate 3D based auto-labeled datasets for AV tasks and verify the auto-labeling process by evaluating the performance of the datasets regardless of the point of view from which the sensors, such as cameras or LiDAR, are placed to generate the dataset.  
To achieve our goal and propose a methodology, we first used the KITTI dataset as our base workbench, as it is a dataset used in multiple previous AV papers such as [13; 14; 15] in which DL models applied to 3D object detection are presented on the point cloud generated by lasers. However, although the aforementioned works are recognized as the models that offer the best results with KITTI, in particular [9; 10], these works use radar sensors as input to carry out the detection work. There are other classification works that allow vehicle detection through techniques such as object segmentation and using transfer learning techniques [20; 25; 26]. Finally, other works, such as [8; 21], try to implement data augmentation techniques through GAN models applied to point cloud datasets.  
Furthermore, the KITTI dataset is published and can be publicly accessed. Thus, we will apply the 2D auto-labeling model to the raw KITTI dataset to project the bounding box of the images into point clouds, generating new automatic labels. Then the results of the auto-label dataset will be compared against the human KITTI annotated dataset. These results, measured with the F-Score, will show the difference between the human annotated labels and the auto-labels produced in our system.  
Furthermore, we will compare the performance of Artificial Neural Network (ANN) models by training some models from OpenMMLab's OpenPCDet open-source project [27] with the new auto-labeled images. OpenPCDet is a general codebase for 3D object detection from point cloud and the way in which the models have been designed to work is by projecting the labels of the 2D images onto their corresponding 3D point cloud. In addition, these models have been shown to work effectively with KITTI and these will be used as a comparison for the performance of the models. In order to evaluate the performance of the proposed auto-label method, we will train the models mentioned from scratch with the new datasets. Moreover, as the main purpose is to demonstrate the good performance of the models with the auto-generated labels rather than to propose improvements such as a greater capacity to detect more vehicle classes, all tests have been performed only attending to the car class because it is sufficient to obtain clear results of the performance of the methodology.  
Therefore, since the KITTI dataset is widely the most used in the field of AV object detection as it has been public since 2012 rather than Waymo, which has been available since 2020, KITTI will be used to develop a 3D auto-label methodology, which will then serve to label new datasets automatically. Thus, the presented framework demonstrates and tests an auto-labeling workflow using a well-documented dataset. This method, which demonstrates the possibility of exchanging annotator labels for auto-generated ones, can be applied to any dataset, including Waymo.