OptimizeAT - Control Point Constrained AT Optimization

Overview

The OptimizeAT interface is used to optimize aerial triangulation results using control points. This interface is called after completing tie point measurement, improving the absolute accuracy of AT by introducing ground control points (GCP).

Use Cases

Surveying projects requiring high absolute accuracy
Projects with ground control point data
Need to verify and improve AT accuracy
Engineering surveying and professional mapping

Workflow

Interface Call

Command Line Call

reconstruct_full_engine.exe -reconstruct_type 3 -task_json optimize_config.json

Parameter Description

reconstruct_type: Fixed as 3 (indicates OptimizeAT)
task_json: Configuration file path

Configuration Parameters

OptimizeAT uses the same parameters as ReconstructAT, with additional control point parameters:

Required Parameters

All required parameters from ReconstructAT, plus:

Parameter	Type	Description
`control_point`	JSON	Control point group information

Control Point Data Structure

ControlPointGroup

{
  "coordinate_system": {      // Control point coordinate system
    "type": 3,               // Usually projected coordinate system
    "epsg_code": 32650       // e.g., UTM Zone 50N
  },
  "points": [                // Control point list
    // Array of ControlPoint objects
  ]
}

ControlPoint

{
  "id": "GCP001",           // Control point name
  "coordinate": [x, y, z],  // Control point coordinates
  "usage": 0,               // 0=control point, 1=check point, 2=disabled
  "observations": [         // Image observations
    {
      "id": 1,             // Image ID
      "uv": [1234.5, 2345.6]  // Pixel coordinates
    }
  ]
}

Complete Configuration Examples

Basic Control Point Optimization

{
  "license_id": 9200,
  "working_dir": "C:/Projects/AT_Optimize",
  "gdal_folder": "C:/MipMap/SDK/data",
  "coordinate_system": {
    "type": 2,
    "epsg_code": 4326
  },
  "camera_meta_data": [...],  // Same as ReconstructAT
  "image_meta_data": [...],   // Same as ReconstructAT
  "control_point": {
    "coordinate_system": {
      "type": 3,
      "epsg_code": 32650      // UTM Zone 50N
    },
    "points": [
      {
        "id": "GCP001",
        "coordinate": [500123.456, 2500123.456, 123.456],
        "usage": 0,
        "observations": [
          {
            "id": 1,
            "uv": [2736.5, 1824.3]
          },
          {
            "id": 5,
            "uv": [1892.7, 2104.8]
          }
        ]
      },
      {
        "id": "GCP002",
        "coordinate": [500223.456, 2500223.456, 125.678],
        "usage": 0,
        "observations": [
          {
            "id": 3,
            "uv": [3104.2, 1567.9]
          },
          {
            "id": 7,
            "uv": [2345.6, 1890.2]
          }
        ]
      },
      {
        "id": "CHECK001",
        "coordinate": [500323.456, 2500323.456, 127.890],
        "usage": 1,           // Check point
        "observations": [
          {
            "id": 2,
            "uv": [1567.8, 2345.6]
          }
        ]
      }
    ]
  }
}

High-Precision Surveying Project Configuration

{
  "license_id": 9200,
  "working_dir": "C:/Projects/HighPrecision_AT",
  "gdal_folder": "C:/MipMap/SDK/data",
  "coordinate_system": {
    "type": 2,
    "epsg_code": 4326
  },
  "camera_meta_data": [...],
  "image_meta_data": [
    {
      "id": 1,
      "path": "IMG_0001.JPG",
      "meta_data": {
        "pos": [114.123, 22.123, 150.0],
        "pos_sigma": [0.05, 0.05, 0.10],  // RTK high precision
        "position_constant": false          // Allow optimization
      }
    }
  ],
  "control_point": {
    "coordinate_system": {
      "type": 3,
      "epsg_code": 4978       // ECEF for high precision
    },
    "points": [
      // Multiple evenly distributed control points
      {
        "id": "GCP_NW",
        "coordinate": [...],
        "usage": 0,
        "observations": [...]
      },
      {
        "id": "GCP_NE",
        "coordinate": [...],
        "usage": 0,
        "observations": [...]
      },
      {
        "id": "GCP_SW",
        "coordinate": [...],
        "usage": 0,
        "observations": [...]
      },
      {
        "id": "GCP_SE",
        "coordinate": [...],
        "usage": 0,
        "observations": [...]
      },
      {
        "id": "GCP_CENTER",
        "coordinate": [...],
        "usage": 0,
        "observations": [...]
      }
    ]
  }
}

Control Point Placement Principles

1. Quantity Requirements

Minimum: 3 control points (required for solution)
Recommended: 5-8 control points + 2-3 check points
Large area: At least 1 control point per 50-100 images

2. Distribution Requirements

Ideal control point distribution:

+-----+-----+-----+
| GCP | GCP | GCP |
+-----+-----+-----+
| GCP | CHK | GCP |  GCP: Control Point
+-----+-----+-----+  CHK: Check Point
| GCP | GCP | GCP |
+-----+-----+-----+

3. Elevation Distribution

Place control points at different elevation levels
Avoid all control points on the same plane
Pay special attention to elevation changes in mountainous projects

Measurement Accuracy Requirements

Pixel Accuracy

Ideal: < 1 pixel
Acceptable: < 2 pixels
Needs improvement: > 3 pixels

Multi-View Observations

Each control point visible in at least 2 images
Ideally 3-5 images
Viewing angle difference > 15°

Coordinate System Considerations

1. Coordinate System Consistency

{
  // Image positions usually in WGS84
  "coordinate_system": {
    "type": 2,
    "epsg_code": 4326
  },
  
  // Control points usually in local projected coordinate system
  "control_point": {
    "coordinate_system": {
      "type": 3,
      "epsg_code": 32650  // Choose based on project location
    }
  }
}

2. Common Coordinate Systems

Region	EPSG	Description
China	4490	CGCS2000 Geographic
China	4547-4554	CGCS2000 Gauss Projection
Global	32601-32660	UTM Northern Hemisphere
Global	32701-32760	UTM Southern Hemisphere

Quality Assessment

1. Optimization Report Interpretation

After optimization, check the accuracy report in logs:

[INFO] Control Point Residuals:
  GCP001: 0.023m (X), 0.015m (Y), 0.041m (Z)
  GCP002: 0.019m (X), 0.022m (Y), 0.038m (Z)
  
[INFO] Check Point Errors:
  CHECK001: 0.045m (X), 0.052m (Y), 0.068m (Z)
  
[INFO] RMS Error: 0.048m

2. Accuracy Standards

Project Type	Horizontal Accuracy	Vertical Accuracy
Topographic Mapping	< 0.05m	< 0.10m
Engineering Survey	< 0.10m	< 0.15m
General Applications	< 0.30m	< 0.50m

Best Practices

1. Control Point Collection

Use high-precision equipment like RTK/Total Station
Choose stable, easily identifiable features
Record detailed point descriptions and photos

2. Measurement Process

Load images in AT result viewer
Find corresponding features for control points
Precisely mark pixel positions
Check multi-view consistency

3. Iterative Optimization

# First optimization
reconstruct_full_engine.exe -reconstruct_type 3 -task_json optimize_v1.json

# Check results, adjust anomalous points

# Second optimization
reconstruct_full_engine.exe -reconstruct_type 3 -task_json optimize_v2.json

4. Common Issue Handling

Large Residuals

Check measurement accuracy
Verify control point coordinates
Consider setting the point as check point

Systematic Bias

Check coordinate system settings
Verify control point coordinate system
Consider camera calibration issues

Output Results

Optimized AT results are saved in the same location:

milestones/mvs_optimized.xml - Optimized internal format
products/AT/block_exchange_optimized.xml - Optimized exchange format
log/optimization_report.txt - Optimization report

Example: Complete Workflow

import subprocess
import json

# 1. Execute initial AT
at_config = {
    "license_id": 9200,
    "working_dir": "C:/Project",
    # ... other parameters
}
with open("at_config.json", "w") as f:
    json.dump(at_config, f)
    
subprocess.run(["reconstruct_full_engine.exe", "-reconstruct_type", "1", "-task_json", "at_config.json"])

# 2. Perform control point measurement (usually done in external software)

# 3. Prepare optimization configuration
optimize_config = at_config.copy()
optimize_config["control_point"] = {
    "coordinate_system": {"type": 3, "epsg_code": 32650},
    "points": [
        # Control point data
    ]
}
with open("optimize_config.json", "w") as f:
    json.dump(optimize_config, f)

# 4. Execute optimization
subprocess.run(["reconstruct_full_engine.exe", "-reconstruct_type", "3", "-task_json", "optimize_config.json"])

# 5. Use optimized results for 3D reconstruction
# ...

Next Steps

Use optimized AT results for Reconstruct3D
Learn about large-scale data tiling processing
Check Basic Concepts to understand control point usage

Tip: Control points are key to ensuring mapping accuracy. Proper control point placement and precise measurement are the foundation for obtaining high-accuracy results.

Overview​

Workflow​

Interface Call​

Command Line Call​

Parameter Description​

Configuration Parameters​

Required Parameters​

Control Point Data Structure​

ControlPointGroup​

ControlPoint​

Complete Configuration Examples​

Basic Control Point Optimization​

High-Precision Surveying Project Configuration​

Control Point Placement Principles​

1. Quantity Requirements​

2. Distribution Requirements​

3. Elevation Distribution​

Measurement Accuracy Requirements​

Pixel Accuracy​

Multi-View Observations​

Coordinate System Considerations​

1. Coordinate System Consistency​

2. Common Coordinate Systems​

Quality Assessment​

1. Optimization Report Interpretation​

2. Accuracy Standards​

Best Practices​

1. Control Point Collection​

2. Measurement Process​

3. Iterative Optimization​

4. Common Issue Handling​

Large Residuals​

Systematic Bias​

Output Results​

Example: Complete Workflow​

Next Steps​