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Using Deep Learning on Point Clouds for Object Classification and Geometric Analysis

Course: 236754 (Project in intelligent systems) or 236874 (Project in computer vision) (3 points)
Instructors: Prof. Micha Lindenbaum and Elad Osherov
Number of students: 2
prerequisites: Computer Vision and/or Image Processing 

 

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and may cause issues processing the data for classification purposes.
In this project you will design a deep neural network, which operates over point clouds to provide object classification and possibly, object part segmentation.
The project can be implemented in several Deep learning environments such as MatConvNet, TensorFlow or Torch. We will provide the students an access to a computation server equipped with GPUs.
The students would preferably have at least basic knowledge in machine learning and computer vision/image processing.

 

Using Deep Learning on Point Clouds for Object Classification and Geometric AnalysisUsing Deep Learning on Point Clouds for Object Classification and Geometric Analysis

 


Contact information:

Elad Osherov
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.