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Found 7 papers, 2 papers with code
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream.
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Second, we experimentally observe and verify the edge enhancement and suppression behavior.
Ranked #3 on
3D Part Segmentation
on ShapeNet-Part
Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution
Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information.
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding
Learning point clouds is challenging due to the lack of connectivity information, i. e., edges.
Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention
We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds.
Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.
Ranked #8 on
Cloud Removal
on SEN12MS-CR
