fix : typo

index.html

@@ -155,17 +155,17 @@ <h2>
             </div>
             <div style="text-align:justify;  margin-left:100px; margin-right:100px">
             3D Gaussian splatting (3DGS) has recently demonstrated
-            impressive capabilities in real-time novel view synthesis and 3D recon-
-            struction. However, 3DGS heavily depends on the accurate initialization
+            impressive capabilities in real-time novel view synthesis and 3D reconstruction.
+            However, 3DGS heavily depends on the accurate initialization
             derived from Structure-from-Motion (SfM) methods. When trained with
             randomly initialized point clouds, 3DGS often fails to maintain its ability
             to produce high-quality images, undergoing large performance drops
             of 4-5 dB in PSNR in general. Through extensive analysis of SfM initialization 
             in the frequency domain and analysis of a 1D regression task
             with multiple 1D Gaussians, we propose a novel optimization strategy
             dubbed <strong>RAIN-GS</strong> (<strong>R</strong>elaxing <strong>A</strong>ccurate <strong>IN</strong>itialization Constraint for
-            3D Gaussian Splatting) that successfully trains 3D Gaussians from ran-
-            domly initialized point clouds. We show the effectiveness of our strategy
+            3D Gaussian Splatting) that successfully trains 3D Gaussians from randomly
+            initialized point clouds. We show the effectiveness of our strategy
             through quantitative and qualitative comparisons on standard datasets,
             largely improving the performance in all settings.
             </div>