3 repositorios
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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mmcv 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.
Este proyecto es un framework basado en PyTorch de modelos de deep learning diseñado para la clasificación y segmentación semántica de datos de nubes de puntos 3D. Proporciona implementaciones de la arquitectura PointNet para realizar el etiquetado de categorías globales de objetos completos y la partición detallada de entornos 3D a gran escala. El sistema maneja la segmentación semántica a través de múltiples escalas, desde la identificación de componentes individuales dentro de un solo objeto hasta el etiquetado de tipos de categorías distintos dentro de escenas a gran escala. El framework incluye componentes estructurales para procesar conjuntos de puntos desordenados, como el muestreo de puntos más lejanos (farthest-point sampling), agrupamiento jerárquico de conjuntos de puntos y redes de agregación de conjuntos simétricos. También proporciona utilidades de preprocesamiento de datos offline y herramientas para generar archivos de objetos 3D para visualizar los resultados de la segmentación en software externo.
Implements furthest point sampling to select representative subsets of 3D point clouds.
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
Implements furthest point sampling to maintain uniform coverage when selecting a subset of 3D point cloud data.