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# 数据处理
这是用于3D形状和纹理生成的数据处理流程。
**注意事项**
1. 该实现是我们工业流程的简化版本。
2. 渲染脚本基于[TRELLIS](https://github.com/microsoft/TRELLIS/blob/main/dataset_toolkits/blender_script/render.py)。
## 渲染
### 动机
渲染脚本`render/render.py`主要有三个目的:
1. 使用Blender将复杂的3D格式转换为PLY文件以便进行进一步处理。
2. 为DiT训练渲染条件图像。
3. 渲染正交图像、PBR材质以及用于纹理生成的条件信号世界空间法线和位置
### 需求
渲染脚本使用Blender 4.1执行。你需要使用Blender的Python安装`opencv``OpenEXR``Imath`。以下是Macbook上的示例
```bash
/Applications/Blender.app/Contents/Resources/4.1/python/bin/python3.11 -m pip install OpenEXR Imath opencv-python
```
### 执行
前两个目的可以通过以下单一命令执行:
```bash
$BLENDER_PATH -b -P render/render.py -- \
--object ${INPUT_FILE} --geo_mode --resolution 512 \
--output_folder $OUTPUT_FOLDER
```
对于第三个目的,只需移除`--geo_mode`标志。
## 水密网格处理和采样
### 动机
为了学习3DShape2VecSets的SDF表示我们需要一个水密输入网格。该流程处理原始三角网格生成三种必要的数据类型
1. **表面采样** - 编码器的输入点。
2. **体积采样** - 解码器中SDF评估的查询点。
3. **体积SDFs** - VAE训练的地面真实有符号距离值。
### 执行
处理三角网格OBJ/OFF格式生成以下内容
1. 水密网格(`${OUTPUT_NAME}_watertight.obj`)。
2. 表面点采样(`${OUTPUT_NAME}_surface.npz`)。
3. 带有SDF的体积采样`${OUTPUT_NAME}_sdf.npz`)。
**命令:**
```bash
python3 watertight/watertight_and_sample.py \
--input_obj ${INPUT_MESH} \
--output_prefix ${OUTPUT_NAME}
```
### 输出数据格式
#### 1. 表面采样(`${OUTPUT_NAME}_surface.npz`
包含两个点云数组以numpy NPZ格式存储
| 键 | 形状 | 格式 | 描述 |
|-----------------|----------|----------|---------------------------------|
| `random_surface` | `(N, 6)` | `float16`| 表面上的均匀点采样 |
| `sharp_surface` | `(M, 6)` | `float16`| 靠近网格锐边的采样 |
#### 2. 体积SDF采样`${OUTPUT_NAME}_sdf.npz`
包含三种采样类型,以数组对的形式存储。对于每种类型`${type}`
| 采样类型 | 点数组 | SDF标签数组 | 形状 | 格式 | 描述 |
|-----------------|----------------------|----------------------|----------|----------|-------------------------|
| `vol` | `vol_points` | `vol_label` | `(P, 3)/(P,)` | `float16`| 随机空间采样 |
| `random_near` | `random_near_points` | `random_near_label` | `(Q, 3)/(Q,)` | `float16`| 靠近表面的采样 |
| `sharp_near` | `sharp_near_points` | `sharp_near_label` | `(R, 3)/(R,)` | `float16`| 靠近锐边的采样 |
**数据规格**
- 所有点坐标(`*_points`数组)包含以`float16`值存储的3D位置。
- 所有SDF值`*_label`数组)是表示以下内容的`float16`标量:
- **正值**:在表面外。
- **负值**:在表面内。
- **零值**:在表面上。
- 数组维度:
- `N``M``P``Q``R`表示采样数量(因形状而异)。
- `3`表示XYZ坐标。
- `6`表示XYZ/法线坐标。
- 所有数组均以未压缩形式存储在numpy的NPZ格式中。
## 整体脚本
修改pipeline.sh里面这4个变量
1. **INPUT_FILE** 每个3D数据的路径。
2. **OUTPUT_FOLDER** 输出数据集的总路径。
3. **NAME** 每个数据的输出路径命名。
4. **BLENDER_PATH** Blender可执行路径。
然后运行以下脚本:
```bash
bash pipeline.sh
```

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# Data Processing
This is the data processing pipeline for 3D shape and texture generation.
**Notes**:
1. This implementation is a simplified version of our industrial pipeline.
2. The rendering script is based on [TRELLIS](https://github.com/microsoft/TRELLIS/blob/main/dataset_toolkits/blender_script/render.py).
## Rendering
### Motivation
The rendering script `render/render.py` serves three main purposes:
1. Converting complex 3D formats to PLY files using Blender for further processing.
2. Rendering condition images for DiT training.
3. Rendering orthogonal images, PBR materials, and conditional signals (world-space normals and positions) for texture generation.
### Requirements
The rendering scripts are executed with Blender 4.1. You need to install `opencv`, `OpenEXR`, and `Imath` using Blender's Python. Here is an example for a Macbook:
```bash
/Applications/Blender.app/Contents/Resources/4.1/python/bin/python3.11 -m pip install OpenEXR Imath opencv-python
```
### Execution
The first two purposes can be executed with a single command:
```bash
$BLENDER_PATH -b -P render/render.py -- \
--object ${INPUT_FILE} --geo_mode --resolution 512 \
--output_folder $OUTPUT_FOLDER
```
For the third purpose, simply remove the `--geo_mode` flag.
## Watertight Mesh Processing and Sampling
### Motivation
To learn an SDF representation for 3DShape2VecSets, we require a watertight input mesh. This pipeline processes raw triangle meshes to generate three essential data types:
1. **Surface samples** - Input points for the encoder.
2. **Volume samples** - Query points for SDF evaluation in the decoder.
3. **Volume SDFs** - Ground-truth signed distance values for VAE training.
### Execution
Process a triangle mesh (OBJ/OFF format) to generate:
1. Watertight mesh (`${OUTPUT_NAME}_watertight.obj`).
2. Surface point samples (`${OUTPUT_NAME}_surface.npz`).
3. Volume samples with SDFs (`${OUTPUT_NAME}_sdf.npz`).
**Command:**
```bash
python3 watertight/watertight_and_sample.py \
--input_obj ${INPUT_MESH} \
--output_prefix ${OUTPUT_NAME}
```
### Output Data Format
#### 1. Surface Samples (`${OUTPUT_NAME}_surface.npz`)
Contains two point cloud arrays in numpy NPZ format:
| Key | Shape | Format | Description |
|-----------------|----------|----------|---------------------------------|
| `random_surface` | `(N, 6)` | `float16`| Uniform point samples on surface |
| `sharp_surface` | `(M, 6)` | `float16`| Samples near sharp mesh edges |
#### 2. Volume SDF Samples (`${OUTPUT_NAME}_sdf.npz`)
Contains three sample types stored as array pairs. For each type `${type}`:
| Sample Type | Points Array | SDF Labels Array | Shape | Format | Description |
|-----------------|----------------------|----------------------|----------|----------|-------------------------|
| `vol` | `vol_points` | `vol_label` | `(P, 3)/(P,)` | `float16`| Random spatial samples |
| `random_near` | `random_near_points` | `random_near_label` | `(Q, 3)/(Q,)` | `float16`| Samples near surface |
| `sharp_near` | `sharp_near_points` | `sharp_near_label` | `(R, 3)/(R,)` | `float16`| Samples near sharp edges |
**Data Specifications**:
- All point coordinates (`*_points` arrays) contain 3D positions stored as `float16` values.
- All SDF values (`*_label` arrays) are `float16` scalars representing:
- **Positive values**: Outside the surface.
- **Negative values**: Inside the surface.
- **Zero values**: On the surface.
- Array dimensions:
- `N`, `M`, `P`, `Q`, `R` represent sample counts (vary per shape).
- `3` indicates XYZ coordinates.
- `6` indicates XYZ/Normal coordinates.
- All arrays are stored uncompressed in numpy's NPZ format.
## Overall Script
Modify the first four variables in `pipeline.sh`:
1. **INPUT_FILE** The path to each 3D data file.
2. **OUTPUT_FOLDER** The overall path for the output dataset.
3. **NAME** The naming for the output path of each data.
4. **BLENDER_PATH** The executable path for Blender.
Then run the following script:
```bash
bash pipeline.sh
```

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"file_list": [
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"tools/mini_testset/images/015.png",
"tools/mini_testset/images/023.png"
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}
]
}

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