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

Installation Requirements

pip install gdal numpy

Recommended environment: Anaconda with GDAL installed via conda install gdal

Command Syntax

python gdal2PLY.py input.tif output.ply

Parameters

input.tif · string · required

Input DEM/DTM file (any GDAL-supported raster format)

output.ply · string · required

Output PLY mesh file (binary format)

Usage Examples

Basic DEM to PLY Conversion

python gdal2PLY.py elevation_dem.tif terrain_mesh.ply

Complete Workflow with NoData Handling

# 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

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

NoData Work-around Process

1. Identify Minimum Elevation

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:

gdalwarp -dstnodata -2791 -tr 10 10 -r bilinear input_DEM.tif prepared_DEM.tif

3. Generate PLY

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):

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):

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

Large DEMs: For very large DEMs, consider resampling before conversion to reduce mesh complexity and file size.

Optimization Tips

1. Resample Large Datasets

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

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

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

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:

# 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

• 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)

See Also

• PLY File Format Specification
• GDAL Raster Data Model
• NumPy Meshgrid