Deep 3D Matching

List of Operators ↓

This chapter explains how to use Deep 3D Matching.

Deep 3D Matching is used to accurately detect objects in a scene and compute their 3D pose. This approach is particularly effective for complex scenarios where traditional 3D matching techniques (like shape-based 3D matching) may struggle due to variations in object appearance, occlusions, or noisy data. Compared to surface-based matching, Deep 3D Matching works with a calibrated multi-view setup and does not require data from a 3D sensor.

The Deep 3D Matching model consists of two components, which are dedicated to two distinct tasks, the detection, which localizes objects, and the estimation of object poses. For a Deep 3D Matching application, both components need to be trained on the 3D CAD model of the object to be found in the application scenes.

HALCON provides the functionalities to train both components of a Deep 3D Matching model. However, if you need assistance for training the networks, you can contact your HALCON sales partner for further information.

Once trained, the deep learning model can be used to infer the pose of the object in new application scenes. During the inference process, images from different angles are used as input.

General Inference Workflow

This paragraph describes how to determine a 3D pose using the Deep 3D Matching method. An application scenario can be seen in the HDevelop example deep_3d_matching_workflow.hdev.

1. Read the trained Deep 3D Matching model by using

   • read_deep_matching_3dread_deep_matching_3dReadDeepMatching3dReadDeepMatching3dread_deep_matching_3d.

2. Optimize the deep learning network for the use with AI ²-interfaces

   1. Extract the detection network from the deep 3d matching model using

      • get_deep_matching_3d_paramget_deep_matching_3d_paramGetDeepMatching3dParamGetDeepMatching3dParamget_deep_matching_3d_param.

   2. Optimize the parameter for inference with

      • optimize_dl_model_for_inferenceoptimize_dl_model_for_inferenceOptimizeDlModelForInferenceOptimizeDlModelForInferenceoptimize_dl_model_for_inference.

   3. Set the optimized detection network using

      • set_deep_matching_3d_paramset_deep_matching_3d_paramSetDeepMatching3dParamSetDeepMatching3dParamset_deep_matching_3d_param.

   4. Repeat these steps for the 3D pose estimation network.

   5. Save the optimized model using

      • write_deep_matching_3dwrite_deep_matching_3dWriteDeepMatching3dWriteDeepMatching3dwrite_deep_matching_3d.

      Note that the optimization of the model has significant impact on the runtime, if it is done with every inference run. So writing the optimized model saves time in the inference.

3. Set the camera parameters using

   • set_deep_matching_3d_paramset_deep_matching_3d_paramSetDeepMatching3dParamSetDeepMatching3dParamset_deep_matching_3d_param.

4. Apply the model using the operator

   • apply_deep_matching_3dapply_deep_matching_3dApplyDeepMatching3dApplyDeepMatching3dapply_deep_matching_3d.

5. Visualize the resulting 3D poses.

Creation of synthetic training data

The creation of realistic synthetic datasets for training Deep 3D Matching models in HALCON involves an integrated workflow with the Scene Engine. This enables realistic object placement, variable object and environmental properties, flexible camera perspectives, and photorealistic rendering.

A physics engine simulates realistic object placements by dropping them into the scene.

The Asset Manager defines material properties like texture, reflection, and transparency to create realistic surfaces. Various backgrounds and lighting scenarios add variability.

Strategic camera positioning enables capturing images from different angles and distances to simulate real observation conditions.

Photorealistic rendering accurately depicts light, shadows, and reflections, providing high-quality training data for Deep 3D Matching models.

The datasets generated in this way provide the extensive and diverse data needed for effectively training Deep 3D Matching Models in HALCON.

An application scenario using the Scene Engine can be seen in the HDevelop example deep_3d_matching_data_generation.hdev.

Creation of the dataset

1. Read the CAD object model using

   • read_object_model_3dread_object_model_3dReadObjectModel3dReadObjectModel3dread_object_model_3d.

2. Create a dictionary to collect the generated data using the procedure

   • create_dataset_deep_3d_matching.

3. Save the dictionary using

   • write_dictwrite_dictWriteDictWriteDictwrite_dict.

Setup of the Scene Engine environment

1. Start Scene Engine using the operator

   • open_scene_engineopen_scene_engineOpenSceneEngineOpenSceneEngineopen_scene_engine.

2. Get the default parameters for the Scene Engine environment using the procedure

   • create_scene_engine_run_params.

3. Set the parameters for material, surface finish, and color using the procedure

   • set_scene_engine_run_param.

4. Set the camera setup using the procedure

   • set_scene_engine_run_param.

Generating the data

1. Start the rendering process using the operator

   • run_scene_enginerun_scene_engineRunSceneEngineRunSceneEnginerun_scene_engine.

2. Get the ground truth data using the procedure

   • get_data_generation_gt.

3. Save the dictionary with the generated data using

   • write_dictwrite_dictWriteDictWriteDictwrite_dict.

Training of the Model

This section describes the training of the Deep 3D Matching model using synthetic data. For an application scenario, see also the HDevelop example deep_3d_matching_training_workflow.hdev

Creation of the Deep 3D Matching model

1. Read the rendered dataset using

   • read_dictread_dictReadDictReadDictread_dict.

2. Retrieve the CAD object model from the read dataset using the key 'orig_3d_model'.

3. Create the Deep 3D Matching model containing both of the two model components

   • dl_model_detection and

   • dl_model_pose_estimation

   using the operator create_deep_matching_3dcreate_deep_matching_3dCreateDeepMatching3dCreateDeepMatching3dcreate_deep_matching_3d.

Prepare for training

Before preprocessing, the dataset and the model need to be adapted, so that they can be used later on for training.

1. Before training the components of the Deep 3D Matching model separately, the components need to be extracted from the model. This can be done using

   • get_deep_matching_3d_paramget_deep_matching_3d_paramGetDeepMatching3dParamGetDeepMatching3dParamget_deep_matching_3d_param.

2. For the pose estimation component the dataset needs to be converted into a format that can be processed by the model using

   • convert_dl_dataset_detection_to_pose_estimation.

   This creates a dictionary DLDataset, serving as a database that stores all necessary information about your data. For more details on datasets, see the chapter Deep Learning / Model.

Preprocess the data

These steps need to be done separately for the detection and the pose estimation component of the model. See the section “Data” below for details on what data is required at each stage of the Deep 3D Matching workflow.

1. Split the dataset represented by the dictionary DLDataset. This can be done using

   • split_dl_dataset.

2. Specify preprocessing parameters, such as image size, and store them in a dictionary DLPreprocessParam, for which you can use

   • create_dl_preprocess_param_from_model.

3. Now you can preprocess your dataset. For this, you can use the procedure

   • preprocess_dl_dataset.

Training of the model

This section explains how to train the pose estimation or detection component of a Deep 3D Matching model.

1. Set training parameters and store them in the dictionary TrainParam using

   • create_dl_train_param.

2. Train the model. This can be done using

   • train_dl_model.

   The procedure expects:

   • the model handle DLModelHandleDLModelHandleDLModelHandleDLModelHandledlmodel_handle,

   • the dictionary DLDataset containing data information,

   • the dictionary TrainParam containing training parameters.

After a successful training of both the detection network and the pose estimation network, the combined Deep 3D Matching model can be used for inference (see section “General Workflow for Deep 3D Matching Inference” above).

Data

This section gives information on the camera setup and data that needs to be provided for the model inference or training of a Deep 3D Matching model.

More information on the data handling can be found in the chapter Deep Learning / Model.

Multi-View Camera Setup

In order to use Deep 3D Matching with high accuracy you need a calibrated stereo or multi-view camera setup. In comparison to stereo reconstruction, Deep 3D Matching can deal with more strongly varying camera constellations and distances. Also there is no need to use 3D sensors in the setup. For information how to calibrate the used setup, please refer to the chapter Calibration / Multi-View.

The objects to be detected must be captured from two or more different perspectives in order to calculate the 3D poses.

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Example setups for Deep 3D Matching: Scenes are recorded by several cameras, the objects to be detected do not have to be seen by every single camera (but by at least two cameras).

Data for Training

The training data is used to train and validate the two components of a Deep 3D Matching model specifically for your application.

The required training data is generated using CAD models. Synthetic images of the object are created from various angles, lighting conditions, and backgrounds. Note that there are no real images required, the required data is generated based on the CAD model.

The data needed for this is a CAD model and corresponding information on material, surface finish and color. Information about possible axial and radial symmetries can significantly improve the generated training data.

Requirements for DLDataset

For training the Deep 3D Matching model, the dataset needs to provide images with objects labeled using axis-aligned bounding boxes. The information is created during the creation of the synthetic data. This is

• 'class_ids' : class IDs

• 'class_names': class names

• 'image_dir' : base path to the images

• 'orig_3d_model' : 3D CAD object model

• 'samples': tuple of dictionaries, one for each sample

  • 'image_id' : ID of the image

  • 'image_file_name' : relative path and file name of the image

  • 'bbox_row1' : Row coordinate of the upper left corner of the bounding box

  • 'bbox_col1' : Column coordinate of the top left corner of the bounding box

  • 'bbox_row2' : Row coordinate of the bottom right corner of the bounding box

  • 'bbox_col2' : Column coordinate of the bottom right corner of the bounding box

  • 'bbox_label_id' : class ids of bounding boxes

  • 'camera_parameter': camera parameter for the image

  • 'mask' : masks of the object instances

  • 'pose' : poses of the objects in each bounding box (tuple of HALCON poses)

  • 'visibility' : fractional visibility of bounding boxes

Images

The model imposes requirements on images, such as dimensions, gray value range, and type. Refer to create_deep_matching_3dcreate_deep_matching_3dCreateDeepMatching3dCreateDeepMatching3dcreate_deep_matching_3d for specific values for the trainable components of the Deep 3D Matching model. For a read model, these can be queried with get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamget_dl_model_param. To meet these requirements, you may need to preprocess your images. The standard preprocessing for the entire sample and therefore also for the image is carried out using the procedure preprocess_dl_samples.

Model output of the Training

The operator will return a dictionary DLTrainResultDLTrainResultDLTrainResultDLTrainResultdltrain_result with the current value of total loss and values for all other losses included in your model.

List of Operators

apply_deep_matching_3dApplyDeepMatching3dapply_deep_matching_3dApplyDeepMatching3dapply_deep_matching_3d

Find the pose of objects using Deep 3D Matching.

create_deep_matching_3dCreateDeepMatching3dcreate_deep_matching_3dCreateDeepMatching3dcreate_deep_matching_3d

Create a Deep 3D Matching model.

get_deep_matching_3d_paramGetDeepMatching3dParamget_deep_matching_3d_paramGetDeepMatching3dParamget_deep_matching_3d_param

Read a parameter from a Deep 3D Matching model.

get_scene_engine_paramGetSceneEngineParamget_scene_engine_paramGetSceneEngineParamget_scene_engine_param

Read a parameter from a running Scene Engine instance.

open_scene_engineOpenSceneEngineopen_scene_engineOpenSceneEngineopen_scene_engine

Start and connect to the scene engine synthetic data generator.

read_deep_matching_3dReadDeepMatching3dread_deep_matching_3dReadDeepMatching3dread_deep_matching_3d

Read a Deep 3D Matching model from a file.

run_scene_engineRunSceneEnginerun_scene_engineRunSceneEnginerun_scene_engine

Generate synthetic data using a running Scene Engine instance.

set_deep_matching_3d_paramSetDeepMatching3dParamset_deep_matching_3d_paramSetDeepMatching3dParamset_deep_matching_3d_param

Set a parameter of a Deep 3D Matching model.

set_scene_engine_paramSetSceneEngineParamset_scene_engine_paramSetSceneEngineParamset_scene_engine_param

Sets a parameter of a running Scene Engine instance.

write_deep_matching_3dWriteDeepMatching3dwrite_deep_matching_3dWriteDeepMatching3dwrite_deep_matching_3d

Write a Deep 3D Matching model in a file.