From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks && 2020論文筆記
通訊作者：R. Venkatesh Babu 第一作者：K L Navaneet 研究機構：Indian Institute of Science，加利福尼亞大學，Google Research 代碼： 代碼鏈接 自己的想法： 將一個從2D圖像生成的點雲模型從不同視角進行觀察，各個視角的觀察都應該符合該模型在對應視角下的投影，這就是循環一致性損失函數的核心思想 不需要真值進行監督的代價就是需要複雜
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相關文章
- 1. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds && NIPS 2018 論文筆記
- 2. 【論文閱讀】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 3. 論文閱讀筆記：From Image-level to Pixel-level Labeling with Convolutional Networks
- 4. 論文學習筆記：《DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image》
- 5. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 論文筆記
- 6. 論文解讀：3D Hand Shape and Pose Estimation from a Singl RGB Image
- 7. Image-to-Image Translation with Conditional Adversarial Networks筆記
- 8. (3D-HPE)Learning to Estimate 3D Human Pose and Shape from a Single Color Image
- 9. 《Image-to-Image Translation with Conditional Adversarial Networks》論文筆記
- 10. 《論文閱讀》ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes