Inspiration
By harnessing the potential of 3D reconstruction, we can minimize our environmental impact by digitally preserving and exploring spaces, reducing the need for physical travel and resource consumption, while still immersing ourselves in rich, realistic experiences
What it does
*3D point-cloud Visualization from 2D Depth Image. *3D scene reconstruction using different Algorithms. *Noise removal, outlier reduction. *In-Depth Analysis of Depth Image, 3D mesh, and Point Cloud.
How we built it
*Programming, 3D Mathematics, Python, Help from Infineon, Open3D.
Challenges we ran into
*Challenges Octuples (2^8) from 2D to 3D!! *Maths is tough, debugging the error is hard.
Accomplishments that we're proud of
*Converting a depth Image into 3D Point Cloud *3D reconstruction using triangular mesh, Poisson surface reconstruction, and Laplace smoothing. *Outlier removal, Noise reduction from depth images in 3D. *Scene Understanding and salient object detection.
What we learned
*Working of 3D Depth sensor. *Point-cloud, Open3D, cloud compare. *3D data visualization techniques.
What's next for Infineon - Unlocking the Third Dimension
*A tool to get the point-cloud directly from the sensor. *Various noise and outlier removal techniques in 3D. *Visualization tools.
Built With
- 3dmesh
- cloudcompare
- depthimages
- open3d
- pointcloud
- python
- raspberry-pi
- visualization
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