In this episode I’m joined by Lyne Tchapmi, PhD student in the Stanford Computational Vision and Geometry Lab, to discuss her paper, “SEGCloud: Semantic Segmentation of 3D Point Clouds.”
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SEGCloud is an end-to-end framework that performs 3D point-level segmentation combining the advantages of neural networks, trilinear interpolation and fully connected conditional random fields. In our conversation, Lyne and I cover the ins and outs of semantic segmentation, starting from the sensor data that we’re trying to segment, 2d vs 3d representations of that data, and how we go about automatically identifying classes. Along the way we dig into some of the details, including how she obtained a more fine grain labeling of points from sensor data and the transition from point clouds to voxels.
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Mentioned in the Interview
- Paper – SEGCloud: Semantic Segmentation of 3D Point Clouds
- MIT SuperUROP
- Sebastian Thrun
- Nearest Neighbors Algorithm
- 2D-3D-S Dataset
- Joan Bruna & Michael Bronstein – Geometric Deep Learning
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“More On That Later” by Lee Rosevere licensed under CC By 4.0