PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
PointPillars: Fast Encoders for 3D Object Detection from Point Clouds 簡介 3D目標檢測整體框架 數據增強 生成僞圖像 基礎網絡 檢測頭 Loss 簡介 考慮到點雲數據的不規則，將點雲編碼爲適合下游檢測流程的格式，是一個首先需要處理的問題。一般有兩種處理方式： 固定編碼器，這種編碼器往往速度快但是以犧牲精度爲前提 在數據中學習的
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相關文章
- 1. PIXOR Real-time 3D Object Detection from Point Clouds
- 2. 【3D計算機視覺】Deep Hough Voting for 3D Object Detection in Point Clouds
- 3. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient CNN論文綜述（2016）
- 4. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
- 5. 【論文閱讀】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 6. CVPR2018_Frustum PointNets for 3D Object Detection from RGB-D Data
- 7. 3D Point Clouds-1
- 8. Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds
- 9. Deep Hough Voting for 3D Object Detection in Point Clouds 論文筆記
- 10. 【論文閱讀】【3D目標檢測】StarNet: Targeted Computation for Object Detection in Point Clouds