Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
1. 概述 该论文的主要工做是，在只利用激光雷达的点云数据做为输入，在点云数据中进行类型为”车辆”的目标进行检测(在复现该算法过程当中，存在一个比较显然的现象就是：目标所在的的位置，实际上垂直角通常都很小，在进行映射映射先后容易产生偏差，这种偏差对于远的目标的定位会有很是明显的影响；或者是是我理解有问题吗？ -2018-04-25)。算法 2. 方法 2.1 数据预处理 将激光雷达扫描获取的3维点
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相关文章
- 1. Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
- 2. 3D Fully Convolutional Network for Vehicle Detection in Point Cloud解析(3D-CNN模型）
- 3. 3d fully convolutional network for vehicle detection in point cloud
- 4. Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
- 5. Convolutional Neural Network - Object Detection
- 6. BirdNet: a 3D Object Detection Framework from LiDAR information
- 7. View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
- 8. Transposed convolution & Fully Convolutional Neural Network
- 9. FCOS: Fully Convolutional One-Stage Object Detection
- 10. 论文阅读《Edge Detection Using Convolutional Neural Network》