論文筆記：MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
曾經Kitti道路分割排名第一的方案， 現爲Kitti上UM-road 排名35的方案。 main contribution： 1.將分類、檢測、分割三個任務結合起來，共用encoder框架。 2.簡單，效果還好。 3.效率高，23 fps （沒說在什麼環境下）架構 architecture：框架 共享一個encoder，對不一樣的任務設計不一樣的decoder性能 encoder: 用於提取特徵
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相關文章
- 1. MultiNet: Real-time Joint Semantic Reasoning for AutonomousDriving
- 2. 論文筆記_CV_AD_Visual Perception for Autonomous Driving
- 3. LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving 論文筆記
- 4. 論文筆記_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 5. Pillar-based Object Detection for Autonomous Driving
- 6. Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
- 7. [論文筆記] Enhancements of V2X Communication in Support of Cooperative Autonomous Driving
- 8. 論文閱讀CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 9. 【論文閱讀】A survey of deep learning techniques for autonomous driving
- 10. [論文解讀]Multi-View 3D Object Detection Network for Autonomous Driving