charlesq34/pointnet
Pointnet
PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes.
The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric features across different spatial scales.
The system includes data pipelines for converting raw 3D indoor parsing and shape data into HDF5 files for training. Evaluation capabilities cover segmentation accuracy metrics and the rendering of misclassified 3D point clouds into three-view images for error analysis.
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.
Star history
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