The document details the process of converting 2D drawings into 3D models using DII's AI platform, emphasizing the importance of obtaining accurate information about object parts, materials, and components. It outlines key methods such as de-noising and vectorizing images, recognizing functional areas and symbols, and utilizing computer vision techniques to ensure precise transformations. The steps involve utilizing existing algorithms and automating the recognition process to create digital models in alignment with industry standards.

1. DII
Presentation 3
Reading drawings
and creating 3-D models out of 2-D drawings
The approach and key methods

2. 1. To get the exact information on an object’s parts and assemblies, its volumetric
model and composition
2. To get exact information about materials and components of the object
3. To be able to obtain all information from the drawings and convert it into a 3-D
model in a compatible software package.
The drawing must be converted to the selected format. Thus, DII’s AI platform has to
read the information from the available drawings in a similar way to a human.
Tasks

3. 0. Prepare the drawing for recognition. “De-noise” the drawing and “vectorise” the
image (converting from a raster to a vector image).
1. Define the drawing’s functional areas
2. Recognise signs and symbols
3. Distinguish service graphics from the object’s dimensions
4. Relate geometry with service signs and symbols
5. Turn 2-D views and the material library into a precise 3-D model
Steps

4. 0. De-noise and vectorise
The images are prepared for future work. In most
cases we can use available algorithms and
integrate them into our software.

5. Decrease ‘noise’
Paper drawings, especially older ones come with
lots of ‘noise’. This can be cleaned using available
methods.

6. Scanned drawings also need to be ‘vectorised’
meaning the image is converted into vector format.
Numerous automatic solutions are available.
(https://en.wikipedia.org/wiki/Image_tracing)

7. Examples from vectorisation services and software
providers;
(NB we do not own copyright for these images)

8. Examples from
vectorisation
services and
software
providers.
(NB we do not
own copyright for
these images).

9. Result: Clean vector drawing

10. Result: Clean vector drawing

11. 1. Define the drawing’s functional
areas
Using computer vision-(CV) trained software, we
will be able to determine key functional areas of
the drawings. Existing CV object and area
detection algorithms will be used. Training will be
required for the engineering drawings’ domain.

12. Image detection and segmentation are both mature technologies and need to be transferred to
CAD objects / labels

15. Object views
Bill of materials
Isometry
Notes
Title

16. 2. Recognise symbols and special
signs
Existing text and symbol detection algorithms will
be used. It needs knowledge transfer for the
engineering drawings’ domain.

17. OCR technologies allows one to recognize 100+ languages text and technical symbols with complicated structure
which have to be adapted to industry standards and graphical objects

23. 3. Distinguish service graphics from
objects
Combining computer vision and one’s own library
of service graphics

24. AA
B
B

25. AA
B
B

26. 4. Relate geometry with service
signs and symbols
Combine computer vision and standard rules of
automatic reading. R&D is still required with
software implementation according to ISO
standards.

32. Glass cube projection –
Orthographic projection method
used for a 2-D to 3-D transformation

33. 5. Turn 2-D views and bill of
materials into a 3-D model
Combine a digital view projection with a bill of
materials (BOM)
Requires BOM digitisation and
object assembly algorithms

48. Thank you
This was Presentation-3 of 3 outlining
DII business case.
www.dii.ai