Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
1. 概述 該論文的主要工做是，在只利用激光雷達的點雲數據做爲輸入，在點雲數據中進行類型爲」車輛」的目標進行檢測(在復現該算法過程當中，存在一個比較顯然的現象就是：目標所在的的位置，實際上垂直角通常都很小，在進行映射映射先後容易產生偏差，這種偏差對於遠的目標的定位會有很是明顯的影響；或者是是我理解有問題嗎？ -2018-04-25)。算法 2. 方法 2.1 數據預處理 將激光雷達掃描獲取的3維點
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相關文章
- 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. 論文閱讀《Edge Detection Using Convolutional Neural Network》