Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes論文解讀
本文是將參照二維Harris角點檢測改進而來，目的是在三維點雲中檢測出關鍵點。 1、計算Harris算子 爲了計算導數，我們將二次曲面擬合到以下點上變換點的集合。利用最小平方法，我們找到一個拋物線的形式。 我們選擇了一個只有6個項的二次曲面，因爲它代表一個拋物線。也就是說，如果我們需要一個帶有二次項的兩個變量的函數，它是最佳選擇。增加更多的項意味着可以擬合一個更復雜的曲面。然而，更復雜的曲面在域的
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相關文章
- 1. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 論文筆記
- 2. [ICCV 2019] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds
- 3. Deep Hough Voting for 3D Object Detection in Point Clouds 論文筆記
- 4. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 5. 論文閱讀：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
- 6. Sliding Line Point Regression for Shape Robust Scene Text Detection 論文翻譯解讀
- 7. 【論文速覽】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 論文解讀
- 9. 005 Harris Corner Detection
- 10. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）