172 lines
6.0 KiB
Markdown
172 lines
6.0 KiB
Markdown
# Dataset Preparation Toolkit
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This toolkit provides a comprehensive pipeline for preparing 3D datasets, including downloading, processing, voxelizing, and latent encoding for SC-VAE and Flow Model training.
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### Step 1: Install Dependencies
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Initialize the environment and install necessary dependencies:
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```bash
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. ./data_toolkit/setup.sh
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```
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### Step 2: Initialize Metadata
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Before processing, load the dataset metadata.
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```bash
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python data_toolkit/build_metadata.py <SUBSET> --root <ROOT> [--source <SOURCE>]
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```
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**Arguments:**
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- `SUBSET`: Target dataset subset. Options: `ObjaverseXL`, `ABO`, `HSSD`, `TexVerse` (Training sets); `SketchfabPicked`, `Toys4k` (Test sets).
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- `ROOT`: Root directory to save the data.
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- `SOURCE`: Data source (Required if `SUBSET` is `ObjaverseXL`). Options: `sketchfab`, `github`.
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**Example:**
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Load metadata for `ObjaverseXL` (sketchfab) and save to `datasets/ObjaverseXL_sketchfab`:
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```bash
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python data_toolkit/build_metadata.py ObjaverseXL --source sketchfab --root datasets/ObjaverseXL_sketchfab
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```
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### Step 3: Download Data
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Download the 3D assets to the local storage.
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```bash
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python data_toolkit/download.py <SUBSET> --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>]
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```
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**Arguments:**
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- `RANK` / `WORLD_SIZE`: Parameters for multi-node distributed downloading.
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**Example:**
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To download the `ObjaverseXL` subset:
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> **Note:** The example below sets a large `WORLD_SIZE` (160,000) for demonstration purposes, meaning only a tiny fraction of the dataset will be downloaded by this single process.
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```bash
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python data_toolkit/download.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab --world_size 160000
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```
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*Attention: Some datasets may require an interactive Hugging Face login or manual steps. Please follow any on-screen instructions.*
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**Update Metadata:**
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After downloading, update the metadata registry:
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```bash
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python data_toolkit/build_metadata.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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```
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### Step 4: Process Mesh and PBR Textures
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Standardize 3D assets by dumping mesh and PBR textures.
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*Note: This process utilizes the CPU.*
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```bash
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# Dump Meshes
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python data_toolkit/dump_mesh.py <SUBSET> --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>]
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# Dump PBR Textures
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python data_toolkit/dump_pbr.py <SUBSET> --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>]
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# Get statisitics of the asset
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python asset_stats.py --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>]
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```
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**Example:**
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```bash
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python data_toolkit/dump_mesh.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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python data_toolkit/dump_pbr.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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python asset_stats.py --root datasets/ObjaverseXL_sketchfab
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```
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**Update Metadata:**
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```bash
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python data_toolkit/build_metadata.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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```
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### Step 5: Convert to O-Voxels
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Convert the processed meshes and textures into O-Voxels format.
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*Note: This process utilizes the CPU.*
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```bash
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python data_toolkit/dual_grid.py <SUBSET> --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>] [--resolution <RESOLUTION>]
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python data_toolkit/voxelize_pbr.py <SUBSET> --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>] [--resolution <RESOLUTION>]
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```
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**Arguments:**
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- `RESOLUTION`: Target resolutions for O-Voxels, comma-separated (e.g., `256,512,1024`). Default is `256`.
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**Example:**
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Convert `ObjaverseXL` to resolutions 256, 512, and 1024:
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```bash
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python data_toolkit/dual_grid.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab --resolution 256,512,1024
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python data_toolkit/voxelize_pbr.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab --resolution 256,512,1024
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```
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### At this point, the dataset is ready for SC-VAE Training
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### Step 6: Encode Latents
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Encode sparse structures into latents to train the first-stage generator.
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```bash
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# 1. Encode Shape Latents
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python data_toolkit/encode_shape_latent.py --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>] [--resolution <RESOLUTION>]
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# 2. Encode PBR Latents
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python data_toolkit/encode_pbr_latent.py --root <ROOT> [--rank <RANK> --world_size <WORLD_SIZE>] [--resolution <RESOLUTION>]
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# 3. Update Metadata (Required before next step)
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python data_toolkit/build_metadata.py <SUBSET> --root <ROOT>
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# 4. Encode Sparse Structure (SS) Latents
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python data_toolkit/encode_ss_latent.py --root <ROOT> --shape_latent_name <SHAPE_LATENT_NAME> [--rank <RANK> --world_size <WORLD_SIZE>] [--resolution <SS_RESOLUTION>]
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```
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**Arguments:**
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- `RESOLUTION`: Input O-Voxel resolution. Default is `1024`.
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- `SS_RESOLUTION`: Resolution for sparse structures. Default is `64`.
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- `SHAPE_LATENT_NAME`: The specific version name of the shape latent.
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**Example:**
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```bash
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python data_toolkit/encode_shape_latent.py --root datasets/ObjaverseXL_sketchfab --resolution 512
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python data_toolkit/encode_pbr_latent.py --root datasets/ObjaverseXL_sketchfab --resolution 512
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python data_toolkit/encode_shape_latent.py --root datasets/ObjaverseXL_sketchfab --resolution 1024
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python data_toolkit/encode_pbr_latent.py --root datasets/ObjaverseXL_sketchfab --resolution 1024
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# Update metadata
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python data_toolkit/build_metadata.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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# Encode SS Latents
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python data_toolkit/encode_ss_latent.py --root datasets/ObjaverseXL_sketchfab --shape_latent_name shape_enc_next_dc_f16c32_fp16_1024 --resolution 64
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# Final Metadata Update
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python data_toolkit/build_metadata.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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```
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### Step 7: Render Image Conditions
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Render multi-view images to train the image-conditioned generator.
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*Note: This process may utilize the CPU.*
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```bash
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python data_toolkit/render_cond.py <SUBSET> --root <ROOT> [--num_views <NUM_VIEWS>] [--rank <RANK> --world_size <WORLD_SIZE>]
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```
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**Arguments:**
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- `NUM_VIEWS`: Number of views to render per asset. Default is `16`.
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**Example:**
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```bash
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python data_toolkit/render_cond.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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```
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**Final Metadata Update:**
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```bash
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python data_toolkit/build_metadata.py ObjaverseXL --root datasets/ObjaverseXL_sketchfab
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``` |