DLGC20
* *Deep Learning for Geometric Computing
* Capturing Cellular Topology in Multi-Gigapixel Pathology Images
* Novel Local Geometry Capture in Pointnet++ for 3D Classification, A
* Subpixel Dense Refinement Network for Skeletonization
DLGC21
* *Deep Learning for Geometric Computing
* 3D Shapes Local Geometry Codes Learning with SDF
* DISCO: U-Net based Autoencoder Architecture with Dual Input Streams for Skeleton Image Drawing
* Distance and Edge Transform for Skeleton Extraction
* Evaluation of Latent Space Learning with Procedurally-Generated Datasets of Shapes
* Investigating transformers in the decomposition of polygonal shapes as point collections
* Learning Laplacians in Chebyshev Graph Convolutional Networks
* PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification
* SkeletonNetV2: A Dense Channel Attention Blocks for Skeleton Extraction
* Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
* U-Net based skeletonization and bag of tricks
11 for DLGC21
DLGC22
* *Deep Learning for Geometric Computing
* CAMION: Cascade Multi-input Multi-output Network for Skeleton Extraction
* Concept Activation Vectors for Generating User-Defined 3D Shapes
* Context Attention Network for Skeleton Extraction
* GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation
* Multimodal Shape Completion via Implicit Maximum Likelihood Estimation
* Shape Enhanced Keypoints Learning with Geometric Prior for 6D Object Pose Tracking
* VG-VAE: A Venatus Geometry Point-Cloud Variational Auto-Encoder
8 for DLGC22