nbv_3d_cnn_missing_depth

Deep learning method to predict the missing measurements of a virtual depth camera, given a representation of the environment and the 3D pose of the depth camera.
That is, given a 3D pose of a virtual camera and a ternary voxel grid of the environment, this code predicts which pixels would contain missing measurements if the camera was actually placed at the given pose.
The result may be used to improve prediction of the information gain of candidate views in Next Best View planning.

Related publication:

• R. Monica, J. Aleotti, Prediction of Depth Camera Missing Measurements Using Deep Learning for Next Best View Planning, IEEE International Conference on Robotics and Automation (ICRA), May 23 - 27, 2022, Philadelphia (USA)

Installation

This repository contains ROS (Robot Operating System) packages. Download the repository into your workspace and compile it with catkin build or catkin_make.

System dependencies:

• ROS (Noetic)
• OpenCV
• Eigen3
• Point Cloud Library
• OpenCL
• OpenGL
• TensorFlow

ROS dependencies:

• Message Serialization
• Init Fake OpenGL Context
• nbv_3d_prob_cnn
• rmonica_voxelgrid_common

Note
The package render_robot_urdf requires the CAD models of the COMAU Smart Six robot manipulator. We are not allowed to distribute these CAD models. Simplified meshes have been provided in render_robot_urdf/meshes/stl/convex_hull. To use them, please change references from comau_nolimits_nobase.urdf into comau_nolimits_nobase_chull.urdf in the launch files.

Note
By default, ROS compiles without optimizations and produces very slow executables. Please activate optimizations. Example commands:

  catkin_make -DCMAKE_BUILD_TYPE=RelWithDebInfo
  catkin build --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo

Usage

Dataset

The dataset is composed of multiple complete 3D reconstructions of (tabletop) scenes, and the corresponding ground truth depth images acquired from various poses using the real depth camera.

The dataset must be placed into folder nbv_3d_cnn_real_image/data/training_images. One sub-folder for each scene, numbered incrementally: scenario_1, scenario_2, etc.
Each scene contains:

• kinfu_voxelgrid.voxelgrid: the raw 3D voxel grid of the environment, in the format supported by rmonica_voxelgrid_common.
• voxelgrid_metadata.txt: a text file defining 3D position and voxel size of the voxelgrid.
• poi.txt: a text file containing the definition of a sphere in 3D space, as CenterX CenterY CenterZ Radius. The sphere should be centered on the interesting part of the scene, because parts of this sphere will be set to unknown during partial environment generation.
• images: a sub-folder, containing, for each ground truth image:
  • camera_info_X.txt: camera intrinsic parameters (serialized camera info message).
  • depth_image_X_Y.png: 16-bit depth image.
  • pose_X.matrix: text file containing the 3D camera pose, as a 4x4 matrix.
  • rgb_image_X_Y.png: corresponding color image (unused).
  • joint_state_X.txt: robot joint state.
    where X is the pose number, 0-based, and Y is the image number (multiple images can be taken from the same pose).
• partial_environments: empty folder, which will be filled with the generated partial environments.
• virtual_images: empty folder, which will be filled with the generated ground truth images and input images.

Some sample scenes may be downloaded from here: scenario_1 scenario_2 scenario_3 scenario_4 (650-700 MB each).

Dataset pre-processing

For each scene:

• Edit nbv_3d_cnn_real_image/launch/generate_partial_environments.launch and nbv_3d_cnn_real_image/launch/generate_virtual_views.launch, set arg scenario to the scene number.

• Launch:

  roslaunch nbv_3d_cnn_real_image generate_partial_environments.launch

• Launch:

  roslaunch nbv_3d_cnn_real_image generate_virtual_views.launch

Training the auto-completion network

First, the auto-completion network in the nbv_3d_prob_cnn repository must be trained, as its weights are used by the main network for transfer learning. The auto-completion network should be re-trained from this repository (it uses slightly different parameters).

The dataset must be placed into folder nbv_3d_cnn_real_image/data/environments_3d_realistic. The dataset is composed of partially-known scenes. For each scene, four voxelgrids must be provided:

• X_empty.binvoxelgrid: voxelgrid with value 1 where the voxel is known and empty, 0 otherwise.
• X_occupied.binvoxelgrid: voxelgrid with value 1 where the voxel is known and occupied, 0 otherwise.
• X_unknown.binvoxelgrid: voxelgrid with value 1 where the voxel is unknown, 0 otherwise.
• X_environment.binvoxelgrid: ground truth voxelgrid, with value 1 where the environment is actually occupied and 0 otherwise (no unknown values).

where X is the scene number.

All voxelgrids are in the format loadable by the rmonica_voxelgrid_common package. A suitable dataset may be downloaded from here. It also contains .bt OctoMap files for visualization.
Warning: the download is less than 30 MB, but it is over 4 GB when uncompressed.

In the launch file nbv_3d_cnn_real_image/launch/cnn_real_image_autocomplete_train.launch, these parameters can be used to split the dataset into training set and test set:

• training_dataset_first_element: first scenario used for training.
• training_dataset_last_element: one past the last scenario used for training.
• validation_dataset_first_element: first scenario used for testing.
• validation_dataset_last_element: one past the last scenario used for testing.

For training, launch the file:

  roslaunch nbv_3d_cnn_real_image cnn_real_image_autocomplete_train.launch

Output is written into the folder nbv_3d_cnn_real_image/data/output_autocomplete. Create the folder beforehand if not existing.

Training the main network

The package supports training for the main network proposed in the paper, and the four other networks for the ablation study.

• projection: the method proposed in the paper.
• only2d: called "w/o 3D" in the paper.
• only3d: called "w/o imgs" in the paper.
• noattention: called "w/o attention" in the paper.
• unfrozen: called "w/o frozen 3D" in the paper.

Training of projection, only3d, noattention and unfrozen is done using the launch file cnn_real_image_projection_train.launch. Select the method by setting arg mode at the beginning of the launch file:

  roslaunch nbv_3d_cnn_real_image cnn_real_image_projection_train.launch

Training of only2d is done using the launch file cnn_real_image_model_train.launch:

  roslaunch nbv_3d_cnn_real_image cnn_real_image_model_train.launch

In each launch file, these parameters can be used to split the dataset into training set and test set:

• training_dataset_first_element: first scenario used for training.
• training_dataset_last_element: one past the last scenario used for training.
• validation_dataset_first_element: first scenario used for testing.
• validation_dataset_last_element: one past the last scenario used for testing.

At the end of the training, output images and trained model will be written into the folder:

  nbv_3d_cnn_real_image/data/output_METHOD

where METHOD is the network name (e.g., only2d).
Create the folder beforehand.

During training, the nbv_3d_cnn_real_image/data/tensorboard folder will be used for tensorboard.

Evaluation

To evaluate one of the methods, launch:

  nbv_3d_cnn_real_image/launch/cnn_real_image_evaluate.launch

Note that a trained auto-completion model (from nbv_3d_cnn) is required. Configure parameter checkpoint_file of node nbv_3d_cnn_predict.py to load the proper file.

Set the arg mode to the method to be evaluated. In addition to the networks (projection, only2d, only3d, noattention, unfrozen), these other methods can be evaluated:

• fixed: fixed valid measurement probability for the whole depth image. The value can be set by changing the parameter fixed_mode_fixed_probability.
• none: valid measurement probability set to 1 for the whole depth image.
• gt: ground truth generation. Predicts the correct information gain by accessing the ground truth valid measurement masks.
• adhoc: currently broken, do not use.

Evaluation output is written into the folder configured by the evaluation_file_prefix parameter. All valid measurement probability maps are saved.
Moreover, these files are generated:

• aaa_log.csv: for each view, the information gain predicted by the method.
• aaa_timers.csv: for each view, the method computation time.

2022-05-26