Pointnet

PointNet هو بنية تعلم عميق مصممة لمعالجة وتصنيف سحب النقاط ثلاثية الأبعاد الخام مباشرة دون الحاجة إلى تحويلها إلى فوكسل (voxelization). يوفر نظاماً لتصنيف الكائنات ثلاثية الأبعاد، وأطر عمل للتقسيم الدلالي (semantic segmentation) لتقسيم السحب إلى فئات، وأدوات لتصور الأشكال ثلاثية الأبعاد.

يستخدم المشروع شبكة تحويل لمحاذاة سحب النقاط في مساحة إحداثيات قانونية، ويستخدم تجميعاً قائماً على الدوال المتماثلة لتكثيف الميزات النقطية في متجهات عالمية بغض النظر عن ترتيب النقاط. كما يتميز ببنية تجميع متعددة المقاييس لاستخراج ميزات هندسية هرمية عبر مقاييس مكانية مختلفة.

يتضمن النظام خطوط أنابيب بيانات لتحويل بيانات تحليل الأشكال والأماكن المغلقة ثلاثية الأبعاد الخام إلى ملفات HDF5 للتدريب. تغطي قدرات التقييم مقاييس دقة التقسيم وعرض سحب النقاط ثلاثية الأبعاد المصنفة بشكل خاطئ في صور ثلاثية الزوايا لتحليل الأخطاء.

Features

Point Cloud Grouping - Employs multi-scale grouping techniques to extract hierarchical geometric features from raw 3D point clouds.
Point Cloud Neural Architectures - Provides a neural network architecture designed to process and classify raw 3D point clouds directly without voxelization.
Symmetric Aggregation Functions - Uses a max-pooling symmetric function to condense point-wise features into a single global vector regardless of point order.
Point Cloud and 3D Processing - Processes point sets sampled from a surface to assign a single categorical label to the entire 3D shape.
Point-Wise Label Prediction - Assigns semantic categories to every single point in a point cloud using shared weights.
Semantic Segmentations - Provides a framework for assigning semantic labels to individual points within 3D indoor scenes.
Semantic Part Segmentation - Partitions 3D point clouds into constituent parts using trained models to predict point-wise labels.
Neural Spatial Transformers - Features a T-Net transform network that learns a spatial transformation matrix to align point clouds into a canonical space.
3D Point Cloud Custom Training - Converts raw 3D point cloud data into structured HDF5 formats compatible with deep learning training pipelines.
Global-Local Feature Fusion - Combines global shape descriptors with local point features to enable precise semantic labeling of individual coordinates.
3D Data Pipeline Workflows - Implements a data processing workflow that converts raw 3D indoor parsing and shape data into HDF5 files.
3D Indoor Scene Dataset Exporters - Provides workflows for converting raw 3D indoor scene data into structured HDF5 files for model training.
Training Data Serialization - Implements an HDF5-based serialization system for efficient disk access to large-scale 3D point clouds during training.
Point Cloud Visualizers - Includes utilities for rendering raw 3D shapes and generating three-view images for error analysis.
3D Computer Vision - Deep learning architecture for 3D classification and segmentation.
3D Object Classification - Deep learning on point sets for classification and segmentation.
Deep Learning Architectures - Foundational deep learning architecture for point set classification and segmentation.
Deep Learning Frameworks - Pioneering deep learning architecture for point cloud classification and segmentation.
Semantic Segmentation - Deep learning framework for classification and segmentation on point sets.

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charlesq34pointnet

Name: charlesq34/pointnet
Author: charlesq34

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5,433 نجوم1,489 تفرعاتPython5 مشاهدات

Pointnet

Features

Point Cloud Grouping - Employs multi-scale grouping techniques to extract hierarchical geometric features from raw 3D point clouds.
Point Cloud Neural Architectures - Provides a neural network architecture designed to process and classify raw 3D point clouds directly without voxelization.
Symmetric Aggregation Functions - Uses a max-pooling symmetric function to condense point-wise features into a single global vector regardless of point order.
Point Cloud and 3D Processing - Processes point sets sampled from a surface to assign a single categorical label to the entire 3D shape.
Point-Wise Label Prediction - Assigns semantic categories to every single point in a point cloud using shared weights.
Semantic Segmentations - Provides a framework for assigning semantic labels to individual points within 3D indoor scenes.
Semantic Part Segmentation - Partitions 3D point clouds into constituent parts using trained models to predict point-wise labels.
Neural Spatial Transformers - Features a T-Net transform network that learns a spatial transformation matrix to align point clouds into a canonical space.
3D Point Cloud Custom Training - Converts raw 3D point cloud data into structured HDF5 formats compatible with deep learning training pipelines.
Global-Local Feature Fusion - Combines global shape descriptors with local point features to enable precise semantic labeling of individual coordinates.
3D Data Pipeline Workflows - Implements a data processing workflow that converts raw 3D indoor parsing and shape data into HDF5 files.
3D Indoor Scene Dataset Exporters - Provides workflows for converting raw 3D indoor scene data into structured HDF5 files for model training.
Training Data Serialization - Implements an HDF5-based serialization system for efficient disk access to large-scale 3D point clouds during training.
Point Cloud Visualizers - Includes utilities for rendering raw 3D shapes and generating three-view images for error analysis.
3D Computer Vision - Deep learning architecture for 3D classification and segmentation.
3D Object Classification - Deep learning on point sets for classification and segmentation.
Deep Learning Architectures - Foundational deep learning architecture for point set classification and segmentation.
Deep Learning Frameworks - Pioneering deep learning architecture for point cloud classification and segmentation.
Semantic Segmentation - Deep learning framework for classification and segmentation on point sets.

بدائل مفتوحة المصدر لـ Pointnet

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charlesq34/pointnet2
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PointNet++ 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
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ما هي وظيفة charlesq34/pointnet؟

ما هي الميزات الرئيسية لـ charlesq34/pointnet؟

الميزات الرئيسية لـ charlesq34/pointnet هي: Point Cloud Grouping, Point Cloud Neural Architectures, Symmetric Aggregation Functions, Point Cloud and 3D Processing, Point-Wise Label Prediction, Semantic Segmentations, Semantic Part Segmentation, Neural Spatial Transformers.

ما هي البدائل مفتوحة المصدر لـ charlesq34/pointnet؟

تشمل البدائل مفتوحة المصدر لـ charlesq34/pointnet: charlesq34/pointnet2 — PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a… yanx27/pointnet_pointnet2_pytorch — This project is a PyTorch-based framework of deep learning models designed for the classification and semantic… open-mmlab/mmdetection3d — MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting… xyf513/spidercnn — SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. ECCV 2018 Yifan Xu, Tianqi Fan,… wangyueft/dgcnn — We propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds… griegler/octnet — OctNet uses efficient space partitioning structures (i.e. octrees) to reduce memory and compute requirements of 3D…

Pointnet

Features

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Pointnet

Features

بدائل مفتوحة المصدر لـ Pointnet