[論文解讀] 3DOP: 3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
基本介紹 3DOP這篇文章是當下使用雙目相機進行3D bounding-box效果做好的方法，其是Fast RCNN方法在3D領域之內的拓展。由於原論文發表於NIPS15，出於Fast RCNN的效果並沒有Faster RCNN和基於迴歸的方法好，且遠遠達不到實時性，因此其處理一張圖片的時間達到了4.0s。 以下文章的介紹將主要依據資料更爲完成的PAMI17來分析。 總體結構 如下則是該方法的總體
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相關文章
- 1. 論文閱讀CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 2. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 3. Object Detection · RCNN論文解讀
- 4. Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 5. 【論文閱讀】Rich feature hierarchies for accurate object detection and semantic segmentation
- 6. 【論文閱讀筆記】Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
- 8. Object Detection[論文合集]
- 9. [論文解讀]Multi-View 3D Object Detection Network for Autonomous Driving
- 10. Acquistion Location Confidence for accurate object detection