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CamViz

Overview // Installation // Demos // License

<img align="center" width="400" height="250" src="media/gifs/packnet-san.gif" /> <img align="center" width="400" height="250" src="media/gifs/packnet-ddad.gif" /> <img align="center" width="400" height="200" src="media/gifs/guda.gif" /> <img align="center" width="400" height="200" src="media/gifs/fsm.gif" width="40%" />

Overview

CamViz is a visualization library developed by the TRI-ML team with the goal of providing an interface for the visualization of monocular depth estimation results, both as depth maps and reconstructed pointclouds. It uses PyGame for window display and input management (mouse and keyboard), and OpenGL for 2D and 3D drawing and rendering. It provides an easy and intuitive way to:

Although CamViz works as a standalone library, it was designed specifically to be used in conjunction with other TRI-ML's repositories, in particular PackNet-SFM and DDAD. To facilitate integration, it is also provided as a submodule in those repositories.

Installation

We provide a requirements.txt file with all the required libraries (tested on Ubuntu 18.04). To start using CamViz all you need to do is:

git clone git@github.com:TRI-ML/camviz.git
cd camviz
pip install -r requirements.txt
PYTHONPATH=$PYTHONPATH:/path/to/camviz

Demos

The CamViz repository comes with a demo that visualizes a predicted monocular pointcloud (already calculated, and provided as part of the repository). We plan to include more demos as more functionalities are added, usually tied to scientific publications. To run it, type the following command from the root folder:

python demos/pointcloud.py

The output should look like this:

<a href="https://www.tri.global/" target="_blank"> <img align="center" src="media/figs/demo_pointcloud.png" width="70%"/> </a>

From this initial display you can:

License

The source code is released under the MIT license.