Merge pull request #7 from clinplayer/patch-1

Update README.md

README.md

@@ -60,7 +60,7 @@ where `world_mat_xx` denotes the world to image projection matrix, and `scale_ma
 - **On objects with closed surfaces (DTU)**
 
 The training has two stages. 
-We apply blending-based patch loss (used in SparseNeuS) to further improve the reconstruction.
+We apply blending-based patch loss (used in SparseNeuS) to further improve the reconstruction quality.
 
 ```shell
 bash bashs/bash_dtu_blending.sh --gpu 0 --case scan118
@@ -94,13 +94,13 @@ You can download the results of the methods mentioned in the paper here:
 
 ### Discussions and future work
 As we stated in the paper, it's more difficult to train a UDF field than a SDF field, 
-since UDF doesn't adopt any geometric assumption(like the surfaces are closed) and UDF is not differentiable at zero-level sets.
+since UDF doesn't enforce any topological assumption (like the surfaces are closed) and UDF is not differentiable at zero-level sets.
 Although we propose a series of strategies to alleviate the problem,
-there are still some limitations, and hope that they can be improved in the future.
+there are still some limitations, and hope that they can be addressed in the future.
 -  The weight of the geometric regularization sometimes is sensitive to some cases, and need to be tuned for better results.
-Maybe a more robust regularization can handle this.
+Maybe a more robust regularization stragtegy can handle this.
 - How to initialize the UDF field for open surfaces ? In the work, we still adopt sphere initialization.
-- How to extract mesh from the optimized UDF in a more robust way ? MeshUDF is a great work, 
+- How to extract mesh from the optimized UDF in a more robust way ? MeshUDF provides an inspiring and effective solution, 
 but it's sensitive to the gradients near zero-level sets, and cannot handle non-manifold surfaces.
 
 ## Citation