Open mmatiaschek opened 6 years ago
Interesting work on point clouds
List of Links: https://github.com/bluebox42/3D-deep-learning
After a couple of weeks working in the project I would love to share some ideas.
See for example: http://vis-www.cs.umass.edu/mvcnn/
Or in a speaking picture:
What does it do? It takes into account an ordered sequence of pictures. It is the 360deg view we have been talking about a couple of times.
How to turn this into a process? What are the alternatives for capturing images?
As a start we could comb through the already existing data and see if a fitting dataset could be derived from that.
I believe this can be generalized to use also pointclouds instead of pictures.
You can get some inspiration from the following, excellent Apple-article: https://machinelearning.apple.com/2017/07/07/GAN.html
Bottom line: Usually you need way too many images to solve problems that are similar to ours. A solution would be to use rendered data. Apple found out that this works. And they had to add a refining step that makes the artificial data more realistic.
I did some quick research and experiments. There is already some research done that is in our domain: http://grail.cs.washington.edu/projects/digital-human/pub/allen03space-submit.pdf Looks like parametrized models. You can change body metrics (e.g. weight, height) and body features (e.g. skin tone) of 3D models.
All you need to to is to create a pipeline that automatically generates images/pointclouds from some parametrized models. I tried Blender for a couple of minutes. It can be called from the command-line, this making it perfect for automatic rendering of 3D data. Note that I did not do an extensive research. I just used Blender as an example. Here are some simple images rendered from a simple model:
Look first:
See this picture animated in this great article: https://medium.com/@mohams3ios01/an-introduction-to-arkit-2-world-mapping-5b38827f8ec0
ARKit2 is currently beta. It will be available in autumn. Amongst other things it makes the World-Map available to developers. This is a data-structure that maps reality into a point-cloud of some sorts based on data from the motion sensors and 2d-camera data.
Questions:
The Learning/Prediction Pipeline
Objective:
3 million children are dying of malnutrition every year. We need a game-changer to identify malnutrition of children, to replace the manual measures of weight and height, which are costly, slow and often inaccurate. Our mobile app scans collects 3D point clouds and video data from children to extract anthropometric measurements like
Also, for rapid assessment especially in offline regions, and for cheap mass-market smartphones, classification of severely or moderate acute malnourished, normal or overweight children from video would be valuable.
Special attention is given to the fact that uncooperative children and imperfect lighting and internet connectivity need to be addressed for a useful general approach.
Our goal is to do online learning, so we can gradually improve the quality of our measurements.
Available data
Our current Dataset consist of
which amounts to 10 GB of zip compressed point cloud data
Throughout the next 6 month we will collect data of
more than 250 GB of data
Concepts to explore
Absolute millimeters from Point Clouds
SAM/MAM/Normal/Overweight Classification