A software to automate the process of constructing the synthetic dataset in COCO format, training state-of-the-art deep neural networks (DNNs) from the framework Detectron2, then doing depth estimation for the scene. The results are detected objects with corresponding [x, y, z] coordinates relative to the camera.

The whole pipeline could be seen in Figure 1 Figure 1: Data processing and training pipeline

An output is shown in Figure 2 Figure 2: An example output

torch>=1.8
detectron2

Change the main configurations in the file config.yaml.
Configurations for each process (preparing the dataset, training the models on a dataset, and reconstructing 3D positions) are in the yaml files in the directories dataset, dataset_model and reconstruct_3d.
After you are done with the configurations, run python main.py.