update project page

Author: qq456cvb

README.md

@@ -17,17 +17,17 @@ CVPR 2022
 <a href='https://qq456cvb.github.io/files/cppf.pdf'>
   <img src='https://img.shields.io/badge/Paper-PDF-orange?style=flat&logo=arxiv&logoColor=orange' alt='Paper PDF'>
 </a>
-<a href='https://qq456cvb.github.io/cppf'>
+<a href='https://qq456cvb.github.io/projects/cppf'>
   <img src='https://img.shields.io/badge/Project-Page-green?style=flat&logo=googlechrome&logoColor=green' alt='Project Page'>
 </a>
   <a href='https://colab.research.google.com/'>
-      <img src='https://colab.research.google.com/assets/colab-badge.svg' alt='Google Colab'>
-    </a>
+    <img src='https://colab.research.google.com/assets/colab-badge.svg' alt='Google Colab'>
+  </a>
 <br>
-  </div>
+</h3>
+</div>
 
- <img>
-  CPPF is a pure sim-to-real method that achieves 9D pose estimation in the wild. Our model is trained solely on ShapeNet synthetic models (without any real-world background pasting), and could be directly applied to real-world scenarios (i.e., NOCS REAL275, SUN RGB-D, etc.). CPPF achieves the goal by using only local *SE3*-invariant geometric features, and leverages a bottom-up voting scheme, which is quite different from previous end-to-end learning methods. Our model is robust to noise, and can obtain decent predictions even if only bounding box masks are provided.
+  CPPF is a pure sim-to-real method that achieves 9D pose estimation in the wild. Our model is trained solely on ShapeNet synthetic models (without any real-world background pasting), and could be directly applied to real-world scenarios (i.e., NOCS REAL275, SUN RGB-D, etc.). CPPF achieves the goal by using only local $SE3$-invariant geometric features, and leverages a bottom-up voting scheme, which is quite different from previous end-to-end learning methods. Our model is robust to noise, and can obtain decent predictions even if only bounding box masks are provided.
 
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