open-mmlab/OpenPCDet
OpenPCDet
OpenPCDet este o bibliotecă de deep learning PyTorch și un set de instrumente pentru detecția obiectelor 3D din date LiDAR. Acesta funcționează ca un framework de procesare a norilor de puncte, conceput pentru a dezvolta, antrena și evalua modele de machine learning care identifică și localizează obiecte în spațiul tridimensional.
Proiectul include un motor geometric accelerat pe GPU pentru implementarea de înaltă performanță a intersecției peste reuniune (IoU) 3D și a suprimării non-maxime rotite. De asemenea, oferă un instrument de antrenare distribuită a modelelor pentru a scala antrenarea și testarea modelelor de detecție pe mai multe GPU-uri și noduri de calcul.
Framework-ul acoperă procesarea datelor de tip nor de puncte prin standardizarea reprezentărilor scenelor 3D și gestionarea numărului variabil de puncte în diverse seturi de date LiDAR.
Features
- Point Cloud Detections - Provides a comprehensive framework for training and evaluating 3D object detectors using raw LiDAR point clouds.
- Data-Parallel Training - Provides distributed training capabilities to synchronize gradients and parameters across multiple GPU nodes.
- GPU Accelerated Computer Vision - Leverages GPU acceleration for high-performance geometric computations like 3D intersection and non-maximum suppression.
- Distributed Training - Enables scaling of model training and testing workloads across multiple GPUs and computing nodes.
- PyTorch Spatial Detection Libraries - Provides a set of PyTorch-based tools for building and training neural networks for spatial LiDAR data.
- 3D Object Detection - Implements algorithms for identifying and localizing objects in 3D space using LiDAR data.
- Distributed Training Tools - Includes tools for scaling the training and communication of 3D detection models across multiple nodes.
- Point Cloud and 3D Processing - Acts as a processing framework for analyzing 3D point clouds and standardizing scene representations.
- Coordinate System Mappings - Standardizes diverse LiDAR datasets into a unified 3D coordinate system to maintain consistency across detection models.
- 3D Intersection Over Union Calculators - Calculates 3D intersection over union and rotated non-maximum suppression to refine detected object precision.
- Detection Pipelines - Implements a two-stage detection pipeline combining region proposals with a refined bounding box regression head.
- 3D Geometry Engines - Includes a high-performance computational engine for processing 3D geometric data and transformations.
- Config-Driven Instantiation - Implements a system where model architectures and hyperparameters are defined in external YAML files for flexible instantiation.
- Data Model Decoupling - Decouples raw data storage from scene representations to efficiently handle variable point counts across datasets.
- 3D Detection and Segmentation - Point-voxel feature abstraction for 3D object detection.
- Procesarea norilor de puncte - Toolbox for LiDAR-based 3D object detection.
