2D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloud
整體網絡架構是這樣： 基本上是堆積起來的網絡，一共三條分支 1:image 2d feature 提取 2:正樣本point cloud pointnet feature提取，3：負樣本point cloud pointnet feature 提取 ，最後直接soft-margin triplet loss。 2d輸入的是image 的patches， 3d輸入的是volume。 2d使用
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相關文章
- 1. 【點雲識別】Learning to Segment 3D Point Clouds in 2D Image Space (CVPR 2020)
- 2. Point Cloud 2019
- 3. Learning 2D–3D Correspondences To Solve The Blind Perspective-n-Point Problem
- 4. (3D-HPE)Learning to Estimate 3D Human Pose and Shape from a Single Color Image
- 5. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 6. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 7. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 8. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 9. 三維重建工具補充---2D匹配3D補充
- 10. Deep Learning on Point Sets for 3D Classification and Segmentation