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Found 6 papers, 4 papers with code
PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification
A typical way to assess a model's robustness is through adversarial attacks, where test-time examples are generated based on gradients to deceive the model.
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition
Due to the wide application area of point clouds and the recent advancements in deep neural networks, studies focusing on robust classification of the 3D point cloud data emerged.
Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns
This study integrates artificial intelligence and computational design tools to extract information from architectural heritage.
Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation
However, there are two drawbacks of the approach: most of the edges in the graph are assigned randomly and the GCN is trained independently from the segmentation network.
ODFNet: Using orientation distribution functions to characterize 3D point clouds
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures.
Ranked #18 on
3D Part Segmentation
on ShapeNet-Part
EfficientSeg: An Efficient Semantic Segmentation Network
Deep neural network training without pre-trained weights and few data is shown to need more training iterations.
Ranked #1 on
Semantic Segmentation
on Cityscapes VIPriors subset
