《3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder》讀書筆記
3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder 題目：基於深度神經網絡自編碼的3D激光點雲匹配定位方法 介紹： 本文將介紹一種3D點雲定位方法，該方法需要一個全局的3D點雲地圖，以及幾次連續掃描得到局部的點雲子集，而無需考慮先驗信息。 局部的點雲子集又稱爲超點集。超點集是連續
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相關文章
- 1. 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder閱讀筆記
- 2. 3DRegNet: A Deep Neural Network for 3D Point Registration 2020論文筆記
- 3. [CVPR 2020] 3DRegNet: A Deep Neural Network for 3D Point Registration
- 4. DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration 2019 論文筆記
- 5. [arXiv 2019] PCRNet: Point Cloud Registration Network using PointNet Encoding
- 6. 《3-D Deep Feature Construction for Mobile Laser Scanning Point Cloud Registration》閱讀筆記
- 7. Deep Closest Point: Learning Representations for Point Cloud Registration && 2019論文筆記
- 8. Deformable MRI-Ultrasound Registration Using 3D Convolutional Neural Network
- 9. 讀論文 3D Point Cloud Classification using 3D Modified Fisher Vector Representation for CNN
- 10. PCRNet: Point Cloud Registration Network using PointNet Encoding 2019 論文筆記