BirdNet: a 3D Object Detection Framework from LiDAR information

时间 2020-12-30

标签 BirdNet lidar object detection 繁體版

原文原文链接

BirdNet 论文解读核心思想： Detection Framework: 第一步：将三维点云信息处理成2D的鸟瞰图。第二步：利用处理图像的卷积神经网络鸟瞰图，得到位置，朝向，类别等2D信息。第三步：进行3D Object Oriented Detection，对高度进行计算。网络框架：第一步：将３维点云信息投影成3-channel BEV map. 第二步：利用faster-rcnn

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1. 【论文阅读】【3D目标检测】BirdNet: a 3D Object Detection Framework from LiDAR Information
2. 论文笔记：Reinforcing LiDAR-Based 3D Object Detection with RGB and 3D Information
3. LiDAR 3D Object Detetcion
4. 【3D 目标检测】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
5. CVPR2018_Frustum PointNets for 3D Object Detection from RGB-D Data
6. Viola–Jones object detection framework--Rapid Object Detection using a Boosted Cascade of Simple Fea
7. Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
8. PIXOR Real-time 3D Object Detection from Point Clouds
9. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
10. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
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1. 【论文阅读】【3D目标检测】BirdNet: a 3D Object Detection Framework from LiDAR Information
2. 论文笔记：Reinforcing LiDAR-Based 3D Object Detection with RGB and 3D Information
3. LiDAR 3D Object Detetcion
4. 【3D 目标检测】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
5. CVPR2018_Frustum PointNets for 3D Object Detection from RGB-D Data
6. Viola–Jones object detection framework--Rapid Object Detection using a Boosted Cascade of Simple Fea
7. Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
8. PIXOR Real-time 3D Object Detection from Point Clouds
9. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
10. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection

>>更多相关文章<<