Update README.md

README.md

@@ -40,9 +40,12 @@ The dataset is built through a structured four-stage pipeline:
 
 ### 📈 Class Distribution
 
-![Class Frequency](figures/fashion_class_distribution.png)
+![Used Fasion Item Count for 3D Dataset](figures/fashion_class_distribution.png)
+**Pants** and **sweatshirts** are used more than other fashion items.
 
-The most frequently appearing classes include **pants**, **sweatshirts**, and **jeans**.
+
+![Fashion Item Count in Mannequin-wear Combinations](figures/Count appears in Combination.png)
+**Sneakers** and **Pants** are the most frequent fashion items in Mannequin-wear combinations.
 
 ### 👚 Combination Metadata
 
@@ -125,10 +128,28 @@ Each mata contains this detailed information:
 
 ---
 
+## 🧪 Benchmarks
+
+### 3D object segmentation
+The Baseline model using [**SAMPart3D**](https://yhyang-myron.github.io/SAMPart3D-website/) demonstrates high segmentation quality (mIoU: 0.9930) but shows varying average precision (AP) across classes.
+
+![3D object segmentation with SAMPart3D](figures/seg_tuning.png)
+
+
+### 3D data reconstruction
+The baseline models are [**DDPM-A**](https://github.com/lucidrains/denoising-diffusion-pytorch) diffusion-based probabilistic model for the generation task, and [**SVD-SVDFormer**](https://github.com/czvvd/SVDFormer_PointSea) for the completion task.
+Performance is measured using Chamfer Distance (CD), Density-aware Chamfer Distance (DCD), and F1-Score (F1). For DDPM, sampled point clouds are shuffled without considering the sampling ratio 𝑛, and the performance of DDPM is measured with CD. DDPM shows 0.628±0.887 of the average CD. 
+
+![3D data re onstruction example](figures/sampledPC.png)
+
+---
+
+## 💻 Use Cases
 
-- 3D data rendering from 2D video
-- 3D semantic segmentation
-- 3D data reconstruction
+- **Virtual try-on**
+- **Metaverse asset creation**
+- **Pose-aware segmentation**
+- **Avatar rigging & deformation simulation**
 
 
 ## 📃 License