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Datasets:
haopt
/
prox-e-shapetalk-benchmark

Tasks:
Text-to-3D
Image-to-3D
Modalities:
3D
Image
Formats:
imagefolder
Size:
< 1K
Tags:
3d-editing
shape-editing
shape-generation
evaluation-benchmark
siggraph2026
Libraries:
Datasets
License:
Dataset card Data Studio Files
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add point_cloud and update README

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  1. README.md +6 -5
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README.md CHANGED
@@ -15,16 +15,17 @@ size_categories:
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  # Prox-E ShapeTalk Evaluation Benchmark Dataset
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- This is the official evaluation and benchmark dataset for **Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions (SIGGRAPH'26)**.
19
 
20
  ## 📄 Dataset Overview
21
- This benchmark consists of a curated subset of **600 samples** from the **ShapeTalk** dataset, specifically chosen for evaluating structural, fine-grained 3D shape editing models. It balances identity preservation, 3D quality, and instruction fidelity.
22
 
23
  ## 📂 Repository Structure
24
  The repository is structured to seamlessly plug into the `Prox-E` unified evaluator:
25
 
26
- - **`input_shapes/`**: Directory containing `600` Ground Truth 3D meshes in Wavefront OBJ format (`.obj`).
27
- - **`rendered_images/`**: Directory containing `600` high-resolution upright 2D renders of the Ground Truth meshes in PNG format (`.png`).
 
28
  - **`instructions.json`**: Key-value metadata containing evaluation prompts/instructions for each assignment, including object classes and part-specific keywords.
29
  - **`shapetalk_600_set.csv`**: Full detailed metadata from the original ShapeTalk dataset mapping back to these evaluation samples.
30
 
@@ -40,7 +41,7 @@ git clone https://huggingface.co/datasets/haopt/prox-e-shapetalk-benchmark
40
  ```
41
 
42
  ### 2. Run the Unified Evaluator
43
- Once downloaded, plug this benchmark directly into the Prox-E unified evaluation pipeline to compute metric scores (e.g., Localized Chamfer Distance l-GD, LPIPS, DINO-I, FID, PFID, and CLIP edit direction):
44
 
45
  ```bash
46
  python -m evals.run_eval \
 
15
 
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  # Prox-E ShapeTalk Evaluation Benchmark Dataset
17
 
18
+ This is the official benchmark dataset for **Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions (SIGGRAPH'26)**.
19
 
20
  ## 📄 Dataset Overview
21
+ This benchmark consists of a curated subset of **600 samples** from the **ShapeTalk** dataset, specifically chosen for evaluating structural, fine-grained 3D shape editing models. It is used to evaluate identity preservation, 3D quality, and edit fidelity.
22
 
23
  ## 📂 Repository Structure
24
  The repository is structured to seamlessly plug into the `Prox-E` unified evaluator:
25
 
26
+ - **`input_shapes/`**: Directory containing `600` input 3D meshes in OBJ format (`.obj`).
27
+ - **`rendered_images/`**: Directory containing `600` high-resolution upright 2D renders of the input meshes in PNG format (`.png`).
28
+ - **`point_cloud/`**: Directory containing `600` dense point cloud of the Ground Truth meshes (`.npz`).
29
  - **`instructions.json`**: Key-value metadata containing evaluation prompts/instructions for each assignment, including object classes and part-specific keywords.
30
  - **`shapetalk_600_set.csv`**: Full detailed metadata from the original ShapeTalk dataset mapping back to these evaluation samples.
31
 
 
41
  ```
42
 
43
  ### 2. Run the Unified Evaluator
44
+ Once downloaded, plug this benchmark directly into the Prox-E unified evaluation pipeline to compute metric scores (e.g., l-GD, LPIPS, DINO-I, FID, PFD, CLIP, and VQA):
45
 
46
  ```bash
47
  python -m evals.run_eval \
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