Rethinking network design and local geometry in point cloud: A simple residual MLP framework
Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …
Dual-graph attention convolution network for 3-D point cloud classification
Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …
Improving graph representation for point cloud segmentation via attentive filtering
Recently, self-attention networks achieve impressive performance in point cloud
segmentation due to their superiority in modeling long-range dependencies. However …
segmentation due to their superiority in modeling long-range dependencies. However …
[HTML][HTML] Graph Neural Networks in Point Clouds: A Survey
D Li, C Lu, Z Chen, J Guan, J Zhao, J Du - Remote Sensing, 2024 - mdpi.com
With the advancement of 3D sensing technologies, point clouds are gradually becoming the
main type of data representation in applications such as autonomous driving, robotics, and …
main type of data representation in applications such as autonomous driving, robotics, and …
Interpretable chirality-aware graph neural network for quantitative structure activity relationship modeling in drug discovery
In computer-aided drug discovery, quantitative structure activity relation models are trained
to predict biological activity from chemical structure. Despite the recent success of applying …
to predict biological activity from chemical structure. Despite the recent success of applying …
Uncertainty-guided contrastive learning for weakly supervised point cloud segmentation
Three-dimensional point cloud data are widely used in many fields, as they can be easily
obtained and contain rich semantic information. Recently, weakly supervised segmentation …
obtained and contain rich semantic information. Recently, weakly supervised segmentation …
Classification of urban interchange patterns using a model combining shape context descriptor and graph convolutional neural network
M Yang, M Cao, L Cheng, H Jiang, T Ai… - Geo-Spatial Information …, 2024 - Taylor & Francis
Pattern recognition is critical to map data handling and their applications. This study
presents a model that combines the Shape Context (SC) descriptor and Graph …
presents a model that combines the Shape Context (SC) descriptor and Graph …
RailSeg: Learning Local-Global Feature Aggregation with Contextual Information for Railway Point Cloud Semantic Segmentation
Incomplete or outdated inventories of railway infrastructures may disrupt the railway sector's
administration and maintenance of transportation infrastructure, thus posing potential threats …
administration and maintenance of transportation infrastructure, thus posing potential threats …
Kernel-based feature aggregation framework in point cloud networks
Various effective deep networks have been developed for analysis of 3D point clouds. One
key step in these networks is to aggregate the features of orderless points into a compact …
key step in these networks is to aggregate the features of orderless points into a compact …
Vote2cap-detr++: Decoupling localization and describing for end-to-end 3d dense captioning
3D dense captioning requires a model to translate its understanding of an input 3D scene
into several captions associated with different object regions. Existing methods adopt a …
into several captions associated with different object regions. Existing methods adopt a …