> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/thareUSGS/GDAL_scripts/llms.txt
> Use this file to discover all available pages before exploring further.

# PLY Export

> Export GDAL rasters to PLY 3D mesh format

## Overview

`gdal2PLY.py` converts Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs) into 3D binary PLY (Polygon File Format) meshes suitable for 3D visualization and analysis.

## Purpose

Create a 3D binary PLY format file from a GDAL-supported DEM/DTM raster, generating a triangulated mesh representation of terrain data.

**Original Author:** Jake (posted on GIS StackExchange)\
**Modifications:** Trent Hare, USGS\
**Source:** [GIS StackExchange](http://gis.stackexchange.com/questions/121561/generating-a-mesh-from-dtm)

## Installation Requirements

```bash theme={null}
pip install gdal numpy
```

Recommended environment: Anaconda with GDAL installed via `conda install gdal`

## Command Syntax

```bash theme={null}
python gdal2PLY.py input.tif output.ply
```

### Parameters

<ParamField path="input.tif" type="string" required>
  Input DEM/DTM file (any GDAL-supported raster format)
</ParamField>

<ParamField path="output.ply" type="string" required>
  Output PLY mesh file (binary format)
</ParamField>

## Usage Examples

### Basic DEM to PLY Conversion

```bash theme={null}
python gdal2PLY.py elevation_dem.tif terrain_mesh.ply
```

### Complete Workflow with NoData Handling

```bash theme={null}
# Step 1: Find minimum Z value
gdalinfo -mm input_DEM.tif
# Output shows: min = -2790.594

# Step 2: Set NoData value and resample to 10m/pixel
gdalwarp -dstnodata -2791 -tr 10 10 -r bilinear input_DEM.tif input_DEM_10m_nodata.tif

# Step 3: Generate PLY mesh
python gdal2PLY.py input_DEM_10m_nodata.tif output_DEM_10m.ply
```

## NoData Handling

<Warning>
  The current version does not automatically handle NoData values. Use the workflow below to prepare your data.
</Warning>

### NoData Work-around Process

**1. Identify Minimum Elevation**

```bash theme={null}
gdalinfo -mm input_DEM.tif
```

Example output: `Computed Min/Max=-2790.594,5432.123`

**2. Set NoData to Safe Value**

Set GDAL NoData to the next round value below the minimum:

```bash theme={null}
gdalwarp -dstnodata -2791 -tr 10 10 -r bilinear input_DEM.tif prepared_DEM.tif
```

**3. Generate PLY**

```bash theme={null}
python gdal2PLY.py prepared_DEM.tif output_mesh.ply
```

## How It Works

### Vertex Array Creation

The script creates a vertex array from the raster (see `gdal2PLY.py:69-78`):

```python theme={null}
def createvertexarray(raster):
    transform = raster.GetGeoTransform()
    width = raster.RasterXSize
    height = raster.RasterYSize
    x = np.arange(0, width) * transform[1] + transform[0]
    y = np.arange(0, height) * transform[5] + transform[3]
    xx, yy = np.meshgrid(x, y)
    zz = raster.ReadAsArray()
    vertices = np.vstack((xx,yy,zz)).reshape([3, -1]).transpose()
    return vertices
```

### Triangle Index Generation

Creates triangulated faces from the grid (see `gdal2PLY.py:81-93`):

```python theme={null}
def createindexarray(raster):
    width = raster.RasterXSize
    height = raster.RasterYSize
    ai = np.arange(0, width - 1)
    aj = np.arange(0, height - 1)
    aii, ajj = np.meshgrid(ai, aj)
    a = aii + ajj * width
    a = a.flatten()
    tria = np.vstack((a, a + width, a + width + 1, a, a + width + 1, a + 1))
    tria = np.transpose(tria).reshape([-1, 3])
    return tria
```

### PLY File Structure

The output PLY file contains:

* **Header**: Binary little/big-endian format specification
* **Vertex Properties**: x, y, z coordinates (float32)
* **Face Definitions**: Triangle vertex indices (int32)
* **Binary Data**: Efficient storage of geometry

## PLY Format Specification

Example PLY header generated:

```
ply
format binary_little_endian 1.0
element vertex 1000000
property float x
property float y
property float z
element face 1998000
property list int int vertex_index
end_header
```

## Performance Considerations

<Info>
  **Large DEMs**: For very large DEMs, consider resampling before conversion to reduce mesh complexity and file size.
</Info>

### Optimization Tips

**1. Resample Large Datasets**

```bash theme={null}
gdalwarp -tr 50 50 -r average large_dem.tif resampled_dem.tif
python gdal2PLY.py resampled_dem.tif mesh.ply
```

**2. Clip to Area of Interest**

```bash theme={null}
gdal_translate -projwin ulx uly lrx lry input.tif clipped.tif
python gdal2PLY.py clipped.tif clipped_mesh.ply
```

**3. Memory Considerations**

The script loads the entire raster into memory. For a 10000x10000 pixel DEM:

* Vertices: \~1.2 GB
* Triangles: \~2.4 GB
* Total memory usage: \~3-4 GB

## Viewing PLY Files

### Recommended Software

* **MeshLab**: Open-source mesh processing tool
* **CloudCompare**: Point cloud and mesh viewer
* **Blender**: 3D modeling and visualization
* **ParaView**: Scientific visualization

### Load in MeshLab

```bash theme={null}
meshlab output_mesh.ply
```

## Related Tools

### PlateVertex2Obj

For converting PDS Shapemodel or Plate-Vertex formats to Wavefront OBJ format, use the companion tool `PlateVertex2Obj.py`.

#### Usage

```bash theme={null}
PlateVertex2Obj.py input.tab output.obj
```

#### Purpose

Convert simple 3D PDS Shapemodel or Plate-Vertex files to Alias WaveFront OBJ format. This assumes original vertices are listed in sequential order (1, 2, 3, 4, ...n).

#### Example: NEAR Eros Shape Model

Download from [SBN Tools](http://sbntools.psi.edu/ferret/datasetDetail.action?dataSetId=NEAR-A-MSI-5-EROSSHAPE-V1.0):

```bash theme={null}
# Find plate-vertex files under: NEAR_A_MSI_5_EROSSHAPE_V1_0/data/vertex/
PlateVertex2Obj.py ver512q.tab ver512q_3D.obj
```

#### File Format

Input file structure:

```
nVertices nFaces
index x y z
index x y z
...
index v1 v2 v3
index v1 v2 v3
...
```

Output OBJ format:

```
v x y z
v x y z
...
f v1 v2 v3
f v1 v2 v3
...
```

## Technical Details

### Coordinate System

The PLY mesh uses the same coordinate system as the input raster:

* X, Y: Derived from geotransform (map coordinates)
* Z: Elevation values from raster band

### Mesh Topology

Each grid cell creates two triangles:

```
(x,y) -------- (x+1,y)
  |  \            |
  |    \          |
  |      \        |
  |        \      |
(x,y+1) ---- (x+1,y+1)
```

Triangles: `(a, a+width, a+width+1)` and `(a, a+width+1, a+1)`

## Source Code Reference

* `gdal2PLY.py:28-62` - PLY file writing with binary format support
* `gdal2PLY.py:69-78` - Vertex array creation from raster
* `gdal2PLY.py:81-93` - Triangle index generation
* `PlateVertex2Obj.py:38-53` - PDS to OBJ conversion loop

## Limitations

<Warning>
  * Does not currently handle NoData values automatically (use workaround)
  * Loads entire raster into memory (may require significant RAM for large DEMs)
  * Output is binary format only (ASCII option exists but not default)
  * No color/texture mapping (geometry only)
</Warning>

## See Also

* [PLY File Format Specification](http://paulbourke.net/dataformats/ply/)
* [GDAL Raster Data Model](https://gdal.org/user/raster_data_model.html)
* [NumPy Meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html)