3D Mobile Mapping Artificial Intelligence (3DMMAI), is supported by the NSERC (Natural Sciences and Engineering Research Council of Canada) Collaborative Research Development (CRD) program and Teledyne Optech in Toronto. Research grant worth $1,024,000, from NSERC, for this project. Additional cash and in-kind contributions made from Teledyne Optech were also significant: the total cash contribution is approximately about $1.5 million ($1,536,000 total; $512,000 from Teledyne Optech) and $1 million in-kind contribution ($1,048,146). Total funding is $2.5 million over four years. This research project seeks to update Canada’s critical infrastructure – the independent network of utilities, transportation, and facilities. Although Canada is the second-largest country in the world (in terms of area), with the world’s 10th largest economy, one-third of its infrastructure needs a significant update. In collaboration with Teledyne Optech, this project will develop an advanced data processing system using a deep neural network pipeline, which has recently achieved remarkable success in a computer and robotic vision and machine learning. This work will allow for the autonomous recognition of infrastructure assets using high-quality 3D models of critical networks, thus contributing to the field of infrastructure management and improving urban sustainability as a whole.

Overview: Our lab will lead the development of deep learning-based 3D scene classification pipelines including noise filtering, terrain filtering, and semantic segmentation for the point cloud. We will also work on high perception pipelines for LIDAR and visual SLAM and development of the co-alignment network. It will also focus on transmission network modeling.

3D Scene Classification, Noise Filtering

Contributors: Maryam Jameela

3D Scene Classification, Semantic Segmentation:

Contributors: Jacob Yoo