Point Cloud Classification
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Benchmarks
PointCloud-C
ISPRS
Most implemented
Dynamic Graph CNN for Learning on Point Clouds
PCT: Point cloud transformer
Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
Deep Sets
Benchmarking and Analyzing Point Cloud Classification under Corruptions
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Papers
Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification
Noisy and corrupted points can substantially degrade point cloud recognition performance, especially under challenging corruption settings. In particular, full fine-tuning of 3D pre-trained models may amplify the influen…
Point Cloud ClassificationAn Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification
Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classification. However, the representation power of classification models is limit…
Point Cloud ClassificationA Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity. However, its inherent unordered and irregular nature, exacerbated by sensor noise a…
Point Cloud ClassificationSemantic SegmentationA Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-FCW has shown promising results for point cloud classification, the r…
Point Cloud ClassificationSemantic SegmentationPoint CloudsLearning Significant Persistent Homology Features for 3D Shape Understanding
Geometry and topology constitute complementary descriptors of three-dimensional shape, yet existing benchmark datasets primarily capture geometric information while neglecting topological structure. This work addresses t…
Point Cloud ClassificationCASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection
Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly detection tasks, which limits their gen…
Point Cloud ClassificationSelf-Supervised LearningRepresentation LearningAnomaly Classification