Fix main (typos) (#7765)

examples/python/instant_splat/README.md

@@ -3,14 +3,14 @@ title = "InstantSplat"
 tags = ["2D", "3D", "HuggingFace", "Pinhole camera", "Point cloud"]
 source = "https://github.com/pablovela5620/InstantSplat"
 thumbnail = "https://static.rerun.io/final_instantsplat/e488f427179b0f439e60b6a0c29440fc836860dd/480w.png"
-thumbnail_dimensions = [480, 268]
+thumbnail_dimensions = [480, 275]
 -->
 
 
 https://vimeo.com/1019845514?loop=1&autopause=0&background=1&muted=1&ratio=2802:1790
 
 ## Background
-InstantSplat is a sparse-view, SfM-free framework for large-scale scene reconstruction method using Gaussian Splatting. It allows for extremely fast reconstruction by using Dust3r, a multiview stereo network, to initialize camera poses and dense point cloud for all training views. To enhance pose accuracy and tune scene parameters a gradient-based joint optimization framework from self-supervision is used.  By employing this simplified framework, InstantSplat achieves a substantial reduction in training time, from hours to mere seconds, and demonstrates robust performance across various numbers of views in diverse datasets
+InstantSplat is a sparse-view, SfM-free framework for large-scale scene reconstruction method using Gaussian Splatting. It allows for extremely fast reconstruction by using Dust3r, a multiview stereo network, to initialize camera poses and dense point cloud for all training views. To enhance pose accuracy and tune scene parameters a gradient-based joint optimization framework from self-supervision is used. By employing this simplified framework, InstantSplat achieves a substantial reduction in training time, from hours to mere seconds, and demonstrates robust performance across various numbers of views in diverse datasets
 
 ## Run the code
 This is an external example. Check the [repository](https://github.com/pablovela5620/InstantSplat) for more information.