2D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloud
整体网络架构是这样： 基本上是堆积起来的网络，一共三条分支 1:image 2d feature 提取 2:正样本point cloud pointnet feature提取，3：负样本point cloud pointnet feature 提取 ，最后直接soft-margin triplet loss。 2d输入的是image 的patches， 3d输入的是volume。 2d使用
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相关文章
- 1. 【点云识别】Learning to Segment 3D Point Clouds in 2D Image Space (CVPR 2020)
- 2. Point Cloud 2019
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- 5. 【3D 目标检测】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
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