Train 3D segmentation model using labeled 2D images and raw 3D data

Train 3D segmentation model using labeled 2D images and raw 3D data

Learning 3D Semantic Segmentation with only 2D Image Supervision
arXiv paper abstract https://arxiv.org/abs/2110.11325
arXiv PDF paper https://arxiv.org/pdf/2110.11325.pdf

… there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.

However, due to high labeling costs, ground-truth 3D semantic segmentation annotations are limited …

… investigate how to use only those labeled 2D image collections to supervise training 3D semantic segmentation models.

Our approach is to train a 3D model from pseudo-labels derived from 2D semantic image segmentations using multiview fusion.

… proposed network architecture, 2D3DNet, achieves significantly better performance (+6.2–11.4 mIoU) than baselines …

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.