A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3-D Reconstructions
文章目录 系统流程介绍 相机观测 激光观测 优化框架 感想 相机能获得稠密的彩色像素而深度估计不够精确，激光能获得精确的深度但是值较为稀疏，融合激光和相机是一种常见的思路。 目前主流的方法是将稀疏的激光测距融合到视觉中，然后对深度进行上采样。但这种方法存在两个问题： 相机和激光的外参标定误差较大。 上采样得到的深度由于光滑性假设，在一些区域不够准备。 本文提出的方法，在概率模型框架下，优化外
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相关文章
- 1. 论文笔记_2010-BMVC-Joint optimization for object class segmentation and dense stereo reconstruction
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- 4. A review of gradient descent optimization methods
- 5. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
- 6. 【点云识别】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》论文笔记
- 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