charlesq34/pointnet
Pointnet
PointNet 是一种深度学习架构，旨在直接处理和分类原始 3D 点云，无需体素化。它提供了一个 3D 对象分类系统、用于将点云划分为类别的语义分割框架，以及可视化 3D 形状的工具。
该项目利用变换网络将点云对齐到规范坐标空间，并采用基于对称函数的聚合方法，无论点序如何，都能将点级特征压缩为全局向量。它还具有多尺度分组架构，用于提取不同空间尺度的层级几何特征。
该系统包含用于将原始 3D 室内解析和形状数据转换为 HDF5 文件以进行训练的数据流水线。评估功能涵盖分割准确度指标，以及将分类错误的 3D 点云渲染为三视图图像以进行误差分析。
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.
