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Intelligent selection tools turn
3D point clouds into a success
factor!
What’s the current situation for 3D point cloud tools?
Conventional point cloud tools are limited.
It takes a lot of patience to find the right
views to apply the right selection and a lot of
time to remove accidentally selected points.
The autonomous driving sector is a
notable exception. Teaching self-driving
cars how to navigate the world safely
requires the automatic detection of
objects such as cars, pedestrians, street
signs and other obstacles.
Training data for self-driving cars is usually generated in the form of
bounding boxes. That might work for single-standing objects and is
enough for many street scenes but let’s assume you want to
accurately detect parts of a building.
Standard classifiers are not fit to such
complex tasks.
Advanced AI
is required to
extract this
information
from point
clouds.
Think about trying to draw a
bounding box around the
complex shape of a roof
without including walls and
other objects that you would
like to separately detect
it is impossible.
What if you could not only detect any real-world
object automatically in point clouds but also drill it
down to its components?
And turn all that into better and more detailed 3D
models as well as maps?
That is the untapped potential of point
clouds and the promise of 3D AI.
Complex objects with difficult outlines can be
quickly selected and assigned with an object class.
Work efficiently with Pointly’s intelligent selection tools
To train classifiers in order to detect custom objects from point
clouds we need
training data
to teach the AI by example.
The only limit here is the quality and the amount of training
data one can generate.
Pointly’s segment selector allows you
to select coherent segments with
only one click.
No need to
• zoom around,
• change views and
• draw a perfect border.
A polygon lasso tool provides an alternative wherever you would
wish to adapt the segments manually. But the accuracy of Pointly’s
segment finder already satisfies most of all objects and does the
heavy lifting for you. Our labeling service provider said to that:
What’s next?
The segment tool is only the beginning of
many more intelligent selection tools that will
speed up working with point clouds: Next in
the pipeline is a dynamic Magic Wand selector
as well as a tool for selecting similar objects to
already selected ones.
“This would have taken
one of my guys a day, now
you can do it with one
click.”

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Intelligent selection tools turn 3D point clouds into a sucess factor

  • 1. Intelligent selection tools turn 3D point clouds into a success factor!
  • 2. What’s the current situation for 3D point cloud tools? Conventional point cloud tools are limited. It takes a lot of patience to find the right views to apply the right selection and a lot of time to remove accidentally selected points. The autonomous driving sector is a notable exception. Teaching self-driving cars how to navigate the world safely requires the automatic detection of objects such as cars, pedestrians, street signs and other obstacles. Training data for self-driving cars is usually generated in the form of bounding boxes. That might work for single-standing objects and is enough for many street scenes but let’s assume you want to accurately detect parts of a building. Standard classifiers are not fit to such complex tasks. Advanced AI is required to extract this information from point clouds. Think about trying to draw a bounding box around the complex shape of a roof without including walls and other objects that you would like to separately detect it is impossible. What if you could not only detect any real-world object automatically in point clouds but also drill it down to its components? And turn all that into better and more detailed 3D models as well as maps? That is the untapped potential of point clouds and the promise of 3D AI.
  • 3. Complex objects with difficult outlines can be quickly selected and assigned with an object class. Work efficiently with Pointly’s intelligent selection tools To train classifiers in order to detect custom objects from point clouds we need training data to teach the AI by example. The only limit here is the quality and the amount of training data one can generate. Pointly’s segment selector allows you to select coherent segments with only one click. No need to • zoom around, • change views and • draw a perfect border. A polygon lasso tool provides an alternative wherever you would wish to adapt the segments manually. But the accuracy of Pointly’s segment finder already satisfies most of all objects and does the heavy lifting for you. Our labeling service provider said to that: What’s next? The segment tool is only the beginning of many more intelligent selection tools that will speed up working with point clouds: Next in the pipeline is a dynamic Magic Wand selector as well as a tool for selecting similar objects to already selected ones. “This would have taken one of my guys a day, now you can do it with one click.”