3 Repos
Awesome GitHub RepositoriesSampling
Techniques for selecting a subset of representative points from a 3D point cloud.
Distinct from Point Cloud: Distinct from general Point Cloud software by focusing specifically on sampling strategies like furthest point sampling.
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Awesome Sampling GitHub Repositories
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- open-mmlab/mmcv
open-mmlab/mmcv
6,446Auf GitHub ansehenmmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing convolutional neural networks, a toolkit for image and video preprocessing, and a collection of high-performance deep learning vision operators. The project is distinguished by its hardware-accelerated kernels for complex operations such as deformable convolutions and region pooling. It features a configuration-driven framework that allows for the dynamic instantiation of network layers and the registration of custom modules without modifying code. The library covers a broad
Selects representative points from 3D point clouds using furthest point sampling and ball queries.
Python - yanx27/pointnet_pointnet2_pytorch
yanx27/Pointnet_Pointnet2_pytorch
4,894Auf GitHub ansehenDieses Projekt ist ein PyTorch-basiertes Framework für Deep-Learning-Modelle, das für die Klassifizierung und semantische Segmentierung von 3D-Punktwolkendaten entwickelt wurde. Es bietet Implementierungen der PointNet-Architektur, um globales Category-Labeling ganzer Objekte sowie detaillierte Partitionierungen großflächiger 3D-Umgebungen durchzuführen. Das System handhabt semantische Segmentierung über mehrere Skalen hinweg, von der Identifizierung einzelner Komponenten innerhalb eines Objekts bis hin zur Kennzeichnung unterschiedlicher Kategorietypen innerhalb großflächiger Szenen. Das Framework enthält strukturelle Komponenten für die Verarbeitung ungeordneter Punktmengen, wie Farthest-Point-Sampling, hierarchisches Point-Set-Grouping und symmetrische Set-Aggregation-Netzwerke. Es bietet zudem Offline-Daten-Preprocessing-Utilities und Tools zur Generierung von 3D-Objektdateien, um Segmentierungsergebnisse in externer Software zu visualisieren.
Implements furthest point sampling to select representative subsets of 3D point clouds.
Pythonclassificationmodelnetpoint-cloud - charlesq34/pointnet2
charlesq34/pointnet2
3,678Auf GitHub ansehenPointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da
Implements furthest point sampling to maintain uniform coverage when selecting a subset of 3D point cloud data.
Python
