Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes论文解读
本文是将参照二维Harris角点检测改进而来，目的是在三维点云中检测出关键点。 1、计算Harris算子 为了计算导数，我们将二次曲面拟合到以下点上变换点的集合。利用最小平方法，我们找到一个抛物线的形式。 我们选择了一个只有6个项的二次曲面，因为它代表一个抛物线。也就是说，如果我们需要一个带有二次项的两个变量的函数，它是最佳选择。增加更多的项意味着可以拟合一个更复杂的曲面。然而，更复杂的曲面在域的
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相关文章
- 1. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 论文笔记
- 2. [ICCV 2019] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds
- 3. Deep Hough Voting for 3D Object Detection in Point Clouds 论文笔记
- 4. 【论文阅读】【综述】Deep Learning for 3D Point Clouds: A Survey
- 5. 论文阅读：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
- 6. Sliding Line Point Regression for Shape Robust Scene Text Detection 论文翻译解读
- 7. 【论文速览】PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
- 8. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文解读
- 9. 005 Harris Corner Detection
- 10. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （论文速读）