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.. _t_reconstruction_system:
Reconstruction system (Tensor)
===================================================================
This tutorial demonstrates volumetric RGB-D reconstruction and dense RGB-D SLAM with the Open3D :ref:`/tutorial/core/tensor.ipynb` interface and the Open3D :ref:`/tutorial/core/hashmap.ipynb` backend.
It is possible to run the tutorial with the minimalistic dataset ``SampleRedwoodRGBDImages``, but it is recommended to run the tutorial with real-world datasets with longer sequences to demonstrate its capability. Please refer to :ref:`/tutorial/geometry/rgbd_image.ipynb` for more available datasets. The ``Redwood`` dataset can be a good starting point.
If you use any part of the tensor-based reconstruction system or the hash map backend in Open3D, please cite [Dong2021]_::
@article{Dong2021,
author = {Wei Dong, Yixing Lao, Michael Kaess, and Vladlen Koltun}
title = {{ASH}: A Modern Framework for Parallel Spatial Hashing in {3D} Perception},
journal = {arXiv:2110.00511},
year = {2021},
}
.. note::
As of now the tutorial is only for **online** dense SLAM, and **offline** integration **with** provided poses. The tutorials for tensor-based **offline** reconstruction system, Simultaneous localization and calibration (SLAC), and shape from shading (SfS) tutorials as mentioned in [Dong2021]_ are still under construction. At current, please refer to :ref:`reconstruction_system` for the legacy versions.
Quick start
``````````````````````````````````````
Getting the example code
.. code-block:: sh
# Activate your conda environment, where you have installed open3d pip package.
# Clone the Open3D github repository and go to the example.
cd examples/python/t_reconstruction_system/
# Show CLI help for ``dense_slam_gui.py``
python dense_slam_gui.py --help
Running the example with default dataset.
.. code-block:: sh
# The following command, will download and use the default dataset,
# which is ``lounge`` dataset from stanford.
python dense_slam_gui.py
It is recommended to use CUDA if available.
.. code-block:: sh
# The following command, will download and use the default dataset,
# which is ``lounge`` dataset from stanford.
python dense_slam_gui.py --device 'cuda:0'
Changing the default dataset.
One may change the default dataset to other available datasets.
Currently the following datasets are available:
1. Lounge (keyword: ``lounge``) (Default)
2. Bedroom (keyword: ``bedroom``)
3. Jack Jack (keyword: ``jack_jack``)
.. code-block:: sh
# Using jack_jack as the default dataset.
python dense_slam_gui.py --default_dataset 'bedroom'
Running the example with custom dataset using config file.
Manually download or store the data in a folder and store all the color images
in the ``image`` sub-folder, and all the depth images in the ``depth`` sub-folder.
Create a ``config.yml`` file and set the ``path_dataset`` to the data directory.
Override the parameters for which you want to change the default values.
Example config file for online reconstruction system has been provided in
``examples/python/t_reconstruction_system/default_config.yml``, which looks like the following:
.. literalinclude:: ../../../examples/python/t_reconstruction_system/default_config.yml
:language: yaml
:lineno-start: 1
:lines: 1-
:linenos:
Capture your own dataset
``````````````````````````````````````
This tutorial provides an example that can record synchronized and aligned RGBD
images using the Intel RealSense camera. For more details, please see
:ref:`capture_your_own_dataset`.
Getting started with online reconstruction system
`````````````````````````````````````````````````
.. toctree::
voxel_block_grid
integration
customized_integration
ray_casting
dense_slam
|
