In the Rerun GitHub repository we maintain a list of examples that demonstrate using the Rerun logging APIs. Generally the examples are individually self-contained, and can be run directly from a Git clone of the repository. Many of the Python examples need additional dependencies set up in a requirements.txt next to the example. These are noted in the individual example sections below.


Make sure you have the Rerun repository checked out and the latest SDK installed.

pip install --upgrade rerun-sdk # install the latest Rerun SDK git clone git@github.com:rerun-io/rerun.git # Clone the repository cd rerun git checkout latest # Check out the commit matching the latest SDK release

Note: Make sure your SDK version matches the examples. For example, if your SDK version is 0.3.1, check out the matching tag in the Rerun repository by running git checkout v0.3.1.

Minimal example

Python | Rust

The simplest example of how to use Rerun, showing how to log a point cloud.

python examples/python/minimal/main.py

minimal example>

Examples with Real Data

The following examples illustrate using the Rerun logging SDK with potential real-world (if toy) use cases. They all require additional data to be downloaded, so an internet connection is needed at least once. The dataset fetching logic is all built into the examples, so no additional steps are needed. In some of the examples such as Stable Diffusion, the algorithm is run on-line, and may benefit from a GPU-enabled PyTorch machine.



colmap example>

An example using Rerun to log and visualize the output of COLMAP's sparse reconstruction.

COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface.

In this example a short video clip has been processed offline by the COLMAP pipeline, and we use Rerun to visualize the individual camera frames, estimated camera poses, and resulting point clouds over time.

pip install -r examples/python/colmap/requirements.txt python examples/python/colmap/main.py

Deep SDF


deep_sdf example>

Generate Signed Distance Fields for arbitrary meshes using both traditional methods as well as the one described in the DeepSDF paper, and visualize the results using the Rerun SDK.

pip install -r examples/python/deep_sdf/requirements.txt python examples/python/deep_sdf/main.py



dicom example>

Example using a DICOM MRI scan. This demonstrates the flexible tensor slicing capabilities of the Rerun viewer.

pip install -r examples/python/dicom/requirements.txt python examples/python/dicom/main.py

MP Pose


mp_pose example>

Use the MediaPipe Pose solution to detect and track a human pose in video.

pip install -r examples/python/mp_pose/requirements.txt python examples/python/mp_pose/main.py



nyud example>

Example using an example dataset from New York University with RGB and Depth channels.

pip install -r examples/python/nyud/requirements.txt python examples/python/nyud/main.py


Python | Rust

objectron example>

Example of using the Rerun SDK to log the Objectron dataset.

The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment.

pip install -r examples/python/objectron/requirements.txt python examples/python/objectron/main.py

Raw Mesh

Python | Rust

raw_mesh example>

This example demonstrates how to use the Rerun SDK to log raw 3D meshes (so-called "triangle soups") and their transform hierarchy. Simple material properties are supported.

pip install -r examples/python/raw_mesh/requirements.txt python examples/python/raw_mesh/main.py

Stable Diffusion


stable_diffusion example>

A more elaborate example running Depth Guided Stable Diffusion 2.0.

For more info see here.

pip install -r examples/python/stable_diffusion/requirements.txt python examples/python/stable_diffusion/main.py

Tracking HF OpenCV


tracking_hf_opencv example>

Another more elaborate example applying simple object detection and segmentation on a video using the Huggingface transformers library. Tracking across frames is performed using CSRT from OpenCV.

For more info see: https://huggingface.co/docs/transformers/index

pip install -r examples/python/tracking_hf_opencv/requirements.txt python examples/python/tracking_hf_opencv/main.py

Examples with Artificial Data

The following examples serve to illustrate various uses of the Rerun logging SDK. They should not require any additional data downloads, and should run offline.

API Demo

Python | Rust

api_demo example>

This is a swiss-army-knife example showing the usage of most of the Rerun SDK APIs. The data logged is static and meaningless.

Multiple sub-examples are available (See the instructions by running with the --help flag).

python examples/python/api_demo/main.py



car example>

A very simple 2D car is drawn using OpenCV, and a depth image is simulated and logged as a point cloud.

pip install -r examples/python/car/requirements.txt python examples/python/car/main.py



clock example>

An example visualizing an analog clock with hour, minute and seconds hands using Rerun Arrow3D primitives.

python examples/python/clock/main.py



Demonstrates how rerun can work with the python multiprocessing library.

python examples/python/multiprocessing/main.py



Demonstration of logging to Rerun from multiple threads.

python examples/python/multithreading/main.py



plots example>

This example demonstrates how to log simple plots with the Rerun SDK. Charts can be created from 1-dimensional tensors, or from time-varying scalars.

python examples/python/plots/main.py

Text Logging


text_logging example>

This example demonstrates how to integrate python's native logging with the Rerun SDK.

Rerun is able to act as a Python logging handler, and can show all your Python log messages in the viewer next to your other data.

python examples/python/text_logging/main.py