## 2 Related Works
The dataset used to evaluate object detection and 3D orientation estimation methods comprises ground-truth data including accurate 3D bounding boxes for object classes such as cars, vans, trucks, pedestrians, cyclists, and trans. Original data of 3D objects in point cloud data were manually labeled to evaluate algorithms for 3D orientation estimation and 3D tracking. [PERSON] et al. [10] provided a raw dataset for evaluating stereo, optical flow, and object detection models. The data collection system was built based on the following sources:camera images, laser scans, high-precision GPS measurements, and IMU accelerations. The data collection context is very diverse; the data collection system captures real-world traffic situations and ranges from highways through rural areas to inner-city scenes with many static objects and dynamic objects. The raw dataset includes color and gray-scale image data, saved as 8-bit \".png\" files. The second type of data are OXTS (GPS/IMU), with each frame having 30 different values that are stored as text files, including the following information: the geographic coordinates including altitude, global orientation, velocities, accelerations, angular rates, accuracies, and satellite information. The third type of data comes from Velodyne scans; these data are stored as floating point binaries. This dataset also provides ground-truth data including 3D bounding box trackless annotation that represents Velodyne coordinates and labels of object classes, including \"Car\", \"Van\", \"Truck\", \"Pedestrian\", \"Person (sitting)\", \"Cyclist\", \"Tram\", and \"Misc\". [PERSON] et al. [11] published the KITTI 2015 dataset for evaluating optical flow algorithms. The annotation data provided are the annotation data of 3D objects in the scene, where the construction of the original data is based on the process of recovering the static elements of the scene and inserting them as moving objects using the 3D CAD model into the scene with Google 3D Warehouse. NYUDepth dataset [13]: This dataset consists of 1449 RGB-D images collected from MS Kinect from multiple buildings in three US cities, with 464 different indoor scenes belonging to 26 scene classes. The dataset contains 35,064 distinct objects spread across 894 different classes. For each of the 1449 images, supporting captions were added manually. Make3D dataset [44]: This dataset includes 534 images and depth maps; the resolution of the images is \(2272\times 1704\), and the size of the depth maps is \(55\times 305\). Training data include 400 images, and testing data include 134 images collected from a 3D scanner. In addition, 588 photos were also collected from the internet. Algorithm evaluation data are based on a person, not part of the project, which collected data of the environment in images larger than \(800\times 600\) of scenes at a campus, garden, park, house, building, college, university, church, castle, court, square, lake, temple, and scene. Cityscapes dataset [45]: To evaluate object detection and classification models, especially when using DL models with outdoor environments, [PERSON] et al. [45] published the Cityscapes dataset. The data were collected from stereo cameras using 1/3 in CMOS 2 MP sensors (OnSemi AR0331) in 50 different cities. The original data consist of 5000 manually annotated images from 27 cities for a dense pixel-level. In addition, there are 20,000 raw pixel-level annotated images for evaluating object detection using object boundaries. TUM RGB-D SLAM dataset [12]: The authors collected the TUM RGB-D SLAM dataset using an MS Xbox Kinect sensor; the collected data consist of RGB-D frame sequences. The environment for data collection included two different indoor scenes: the first is a typical office environment called \"fr1\" with a size of \(6\times 6\) m\({}^{2}\), and second is a large industrial hall called \"fr2\" with a size of \(10\times 12\)m\({}^{2}\). The ground-truth trajectory from the motion capture system was provided by eight high-speed tracking cameras.