fix : typo

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@@ -165,7 +165,7 @@ <h2>
             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 randomly
+            3D <strong>G</strong>aussian <strong>S</strong>platting) 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.