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Found 5 papers, 1 papers with code
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies
The paper proposes a new defect detection pipeline called InspectNet (IPT-enhanced UNET) that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset and a Unet model tuned for window frame defect detection and segmentation.
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks
We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings.
MuGNet: Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds.
Ranked #23 on
Semantic Segmentation
on S3DIS
Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.
