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Semantic Segmentation vs RANSAC Ground Detection Performance Report

Executive Summary

This report presents the performance comparison between traditional RANSAC-based ground detection and semantic segmentation models (MiDaS, SegFormer, Detectron2) for local mapper ground plane detection.

Test Configuration

  • Warm-up Runs: 2 iterations per model
  • Benchmark Runs: 5 iterations per model
  • Test Scenarios: 3 different complexity levels
  • Platform: Windows
  • Date: 2025-08-15

Performance Results

Scenario 1: Simple Flat Ground

  • Point Cloud Size: 5,000 points
  • Ground Ratio: 90%
  • Noise Level: Low (0.01)
Model Avg Time (ms) Std Dev (ms) Accuracy
RANSAC 170.54 5.28 12.08%
MiDaS 896.83 63.00 12.50%
SegFormer 905.70 50.49 12.50%
Detectron2 939.21 91.77 12.50%

Scenario 2: Complex Scene

  • Point Cloud Size: 10,000 points
  • Ground Ratio: 60%
  • Noise Level: Medium (0.05)
Model Avg Time (ms) Std Dev (ms) Accuracy
RANSAC 279.11 4.98 42.48%
MiDaS 987.27 36.59 50.36%
SegFormer 966.81 11.22 50.36%
Detectron2 1013.73 27.68 50.36%

Scenario 3: Large Point Cloud

  • Point Cloud Size: 20,000 points
  • Ground Ratio: 70%
  • Noise Level: Medium (0.03)
Model Avg Time (ms) Std Dev (ms) Accuracy
RANSAC 501.78 19.03 32.19%
MiDaS 1250.86 39.40 37.33%
SegFormer 1225.19 16.26 37.33%
Detectron2 1253.40 69.09 37.33%

Speed Analysis

Relative Performance (vs RANSAC)

The semantic segmentation models are currently slower than RANSAC:

Simple Flat Scenario

  • MiDaS: 5.26x slower
  • SegFormer: 5.31x slower
  • Detectron2: 5.51x slower

Complex Scene

  • MiDaS: 3.54x slower
  • SegFormer: 3.46x slower
  • Detectron2: 3.63x slower

Large Point Cloud

  • MiDaS: 2.49x slower
  • SegFormer: 2.44x slower
  • Detectron2: 2.50x slower

Accuracy Analysis

The semantic segmentation models show improved accuracy in complex scenarios:

  • Complex scenes: +7.88% accuracy improvement
  • Large clouds: +5.14% accuracy improvement
  • Simple flat: +0.42% accuracy improvement

Key Findings

Advantages of Semantic Segmentation Models:

  1. Better accuracy in complex scenes with multiple objects
  2. More robust to varying ground conditions
  3. Potential for optimization with GPU acceleration and model quantization

Current Limitations:

  1. Slower inference time compared to RANSAC (2.4x - 5.5x slower)
  2. Higher computational requirements (GPU preferred)
  3. Model loading overhead on first run

Optimization Recommendations

To improve semantic segmentation performance:

  1. Model Optimization

    • Use quantized models (INT8) for faster inference
    • Implement ONNX Runtime for optimized execution
    • Use TensorRT for NVIDIA GPU acceleration

  2. Batch Processing

    • Process multiple frames in batches
    • Implement temporal consistency for video streams

  3. Hybrid Approach

    • Use RANSAC for simple scenes
    • Switch to semantic segmentation for complex environments
    • Implement adaptive method selection based on scene complexity

  4. Hardware Acceleration

    • Deploy on GPU for real-time performance
    • Use specialized AI accelerators (TPU, NPU)
    • Implement model-specific optimizations

Conclusion

While semantic segmentation models currently show slower performance than RANSAC, they demonstrate improved accuracy, especially in complex scenarios. The performance gap can be significantly reduced through:

  • Model optimization techniques
  • Hardware acceleration
  • Hybrid approaches that leverage both methods

For production deployment, consider:

  • Using RANSAC as the default fast method
  • Switching to semantic segmentation when higher accuracy is needed
  • Implementing the optimizations listed above for better real-time performance