A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3-D Reconstructions
文章目錄 系統流程介紹 相機觀測 激光觀測 優化框架 感想 相機能獲得稠密的彩色像素而深度估計不夠精確，激光能獲得精確的深度但是值較爲稀疏，融合激光和相機是一種常見的思路。 目前主流的方法是將稀疏的激光測距融合到視覺中，然後對深度進行上採樣。但這種方法存在兩個問題： 相機和激光的外參標定誤差較大。 上採樣得到的深度由於光滑性假設，在一些區域不夠準備。 本文提出的方法，在概率模型框架下，優化外
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相關文章
- 1. 論文筆記_2010-BMVC-Joint optimization for object class segmentation and dense stereo reconstruction
- 2. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
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- 4. A review of gradient descent optimization methods
- 5. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
- 6. 【點雲識別】D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features(CVPR 2020 Oral)
- 7. 《3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction》論文筆記
- 8. Integration of image quality and motion cues for face anti-spoofing A neural network approach
- 9. TensorMask: A Foundation for Dense Object Segmentation
- 10. 一、 Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach