Graph neural networkCombinatorial relational reasoning in neural networks used for object detection is usually static; therefore, it cannot selectively fuse visual information and semantic relations, which limit
《论文阅读》Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud,程序员大本营,技术文章内容聚合第一站。
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud Motivation CNN需要regular grids作为输入,但点云通常是sparse and irregular,因此采用规则的网络表示点云grid-based,会导致每个grid cell中点的分布不均,用CNN处理crowded cells会有潜在的信息损失,处理empty cells会浪费计算资源 Point-based...
This repository contains a reference implementation of ourPoint-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud, CVPR 2020. If you find this code useful in your research, please consider citing our work: @InProceedings{Point-GNN, author = {Shi, Weijing and Rajkumar, Ragunath...
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning 摘要 标准MOT流程中的一个重要过程是从不同目标中学习具有区分度的特征来减少错误关联。本文提出两项技术来提升学习具有区分度的特征: 传统方法从每个目标独立地获取特征,本文提出一种引入图神经网络(GNN)的新型特征...
Point-GNN: graph neural network for 3D object detection in a point cloud. Preprint at https://arxiv.org/abs/2003.01251 (2020). Albertsson, K. & Meloni, F. Displaced event classification using graph networks. In Connecting the Dots Workshop 2020 (CTD2020) (Zenodo, 2020); https://doi....
In the field of object detection, due to the complexity of realistic scenarios, the objects are mostly obscured and semantic-confusable. The existing CNNs-
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud, CVPR 2020. - WeijingShi/Point-GNN
This repository contains an implementation of a graph neural network for the segmentation and object detection in radar point clouds. As shown in the figure below, the model architecture consists of three major components: Graph constructor, GNN, and Post-Processor. ...
原文标题：GraphFPN: Graph Feature Pyramid Network for Object Detection原文链接：https://arxiv.org/abs/2108.00580 一、摘要 图像语义理解往往需要多尺度特征，而特征金字塔在目标检测和语义分割等等问题中已经被证明十分有效。当前性能领先的多尺度特征学习方法主要是通过固定拓扑结构的神经网络去进行跨空间和跨尺度...