Depth Estimation Benchmark: Monocular vs RealSense

A production-grade benchmarking framework for comparing monocular depth estimation models against Intel RealSense depth sensing, with pose-aware analysis and temporal refinement.

Features

✓ 8 State-of-the-Art Depth Models

• MiDaS v3
• Depth Anything v1
• Depth Anything v2
• ZoeDepth
• DPT-Large
• Depth Pro
• LeReS
• BTS

✓ Advanced Processing

• DepthCrafter temporal refinement for all models
• Mixed precision (FP16) inference
• Tiled inference for 4K resolution
• CUDA acceleration with efficient memory handling

✓ RealSense Integration

• .bag file support with synchronized RGB-D extraction
• Depth alignment and scale matching
• Reference comparison metrics

✓ Comprehensive Metrics

• Depth: MAE, RMSE, Abs Rel, SILog, Delta Accuracy
• Temporal: Consistency, Flickering, Motion Stability
• Pose: Joint depth error, skeleton stability, temporal consistency

✓ Pose Estimation

• MediaPipe Pose detection
• Depth-aware skeleton visualization
• Body-part depth consistency analysis
• Joint-wise depth comparison

✓ Visualizations

• Side-by-side RGB/depth/error comparisons
• Depth heatmaps and error maps
• Pose overlays with depth coloring
• Temporal consistency graphs

Project Structure

depth_benchmark/
├── common/                          # Shared utilities
│   ├── config.py                   # Configuration management
│   ├── utils.py                    # General utilities
│   ├── video_loader.py             # Video/RealSense .bag loading
│   ├── frame_extractor.py          # Frame extraction
│   ├── depth_alignment.py          # Depth alignment
│   ├── metrics.py                  # Metric computation
│   ├── pose_estimation.py          # MediaPipe integration
│   ├── temporal_refinement.py      # DepthCrafter refinement
│   └── visualization.py            # Visualization utilities
│
├── models/                          # Depth model runners
│   ├── base_model.py               # Base model class
│   ├── midas_runner.py
│   ├── depth_anything_runner.py
│   ├── depth_anything_v2_runner.py
│   ├── zoedepth_runner.py
│   ├── dpt_large_runner.py
│   ├── depth_pro_runner.py
│   ├── leres_runner.py
│   └── bts_runner.py
│
├── benchmark/                      # Benchmarking pipeline
│   ├── benchmark_pipeline.py       # Main pipeline
│   ├── compare_models.py           # Model comparison utilities
│   ├── generate_csv.py             # CSV export
│   └── generate_report.py          # Report generation
│
├── notebooks/                      # Jupyter notebooks
│   ├── evaluation.ipynb            # Full evaluation pipeline
│   └── visualization.ipynb         # Results visualization
│
├── outputs/                        # Results (auto-created)
│   ├── raw_depth/
│   ├── refined_depth/
│   ├── metrics/
│   ├── visualizations/
│   └── pose_results/
│
├── config.yaml                     # Main configuration
├── main.py                         # Entry point
└── requirements.txt                # Dependencies

Installation

1. Clone Repository

git clone https://github.com/M0hammedAyan/Models-Check.git

2. Create Virtual Environment

python3.10 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

4. Install Model-Specific Dependencies

# Depth Anything
git clone https://github.com/LiheYoung/Depth-Anything
cd Depth-Anything
pip install -e .
cd ..

# Depth Anything V2
pip install git+https://github.com/DepthAnything/Depth-Anything-V2.git

# ZoeDepth
pip install git+https://github.com/isl-org/ZoeDepth.git

# Depth Pro (if available)
pip install git+https://github.com/apple/depth-pro.git

# RealSense (for .bag support)
pip install pyrealsense2

5. Optional: Install Optional Packages

# FLOPs calculation
pip install fvcore

# Development tools
pip install black pylint pytest

Quick Start

1. Prepare Data

Organize test data in the following structure:

test_data/
├── Ncam/
│   ├── video1.mp4
│   └── video2.mp4
└── Realsense/
    ├── recording1.bag
    └── recording2.bag

2. Update Configuration

Edit config.yaml:

data:
  video_path: ../test_data/Ncam/video.mp4  # or set via CLI
  bag_path: ../test_data/Realsense/recording.bag
  
output_dir: ./outputs
device: cuda

3. Run Benchmark

# Full benchmark with all models
python main.py --config config.yaml --input-video ../test_data/Ncam/video.mp4

# With RealSense .bag
python main.py --config config.yaml --input-bag ../test_data/Realsense/recording.bag

# Specific models only
python main.py --config config.yaml --models midas depth_anything dpt_large

# On CPU
python main.py --config config.yaml --device cpu

# Skip pose estimation
python main.py --config config.yaml --skip-pose

# Skip temporal refinement
python main.py --config config.yaml --skip-temporal-refinement

Raspberry Pi 5 Safe Mode

On Raspberry Pi 5, run one model at a time with CPU-only inference and optional pose/refinement disabled:

python main.py --config config.yaml \
  --input-video ../test_data/Ncam/video.mp4 \
  --device cpu \
  --model midas \
  --skip-pose \
  --skip-temporal-refinement

You can repeat the same command later with --model depth_anything, --model zoedepth, or any other single model you want to test.

4. View Results

# Check outputs
ls -la outputs/

# Open visualization notebook
jupyter notebook notebooks/visualization.ipynb

# View generated reports
cat outputs/reports/model_rankings.txt
cat outputs/metrics/model_comparison.csv

Usage Examples

Example 1: Benchmark Single Video Against RealSense

python main.py \
  --config config.yaml \
  --input-video test_data/Ncam/person.mp4 \
  --input-bag test_data/Realsense/person.bag \
  --output-dir outputs/person_benchmark

Example 2: Compare Refined vs Non-Refined

# With refinement (default)
python main.py --config config.yaml --input-video video.mp4 --output-dir outputs/refined

# Without refinement
python main.py --config config.yaml --input-video video.mp4 --output-dir outputs/non_refined --skip-temporal-refinement

Example 3: Specific Model Comparison

python main.py \
  --config config.yaml \
  --input-video test_data/Ncam/4k_video.mp4 \
  --models midas depth_anything depth_anything_v2 depth_pro \
  --device cuda

Example 4: Programmatic Usage (Python)

from common import BenchmarkConfig
from benchmark import BenchmarkPipeline
from models import ModelFactory

# Load config
config = BenchmarkConfig.from_yaml('config.yaml')

# Create pipeline
pipeline = BenchmarkPipeline(config)
pipeline.load_models()

# Process video
results, rgb_frames = pipeline.process_video('test_data/Ncam/video.mp4')

# Compute metrics
metrics = pipeline.compute_metrics(
    results['models']['midas']['depths'],
    reference_depths
)

print(metrics)

API Reference

Core Classes

BenchmarkConfig

Configuration management with YAML/JSON support.

from common import BenchmarkConfig

config = BenchmarkConfig.from_yaml('config.yaml')
config.models['midas'].use_cuda = True
config.to_yaml('config_modified.yaml')

BenchmarkPipeline

Main benchmarking pipeline.

from benchmark import BenchmarkPipeline

pipeline = BenchmarkPipeline(config)
pipeline.load_models()
results, rgb_frames = pipeline.process_video(video_path)

ModelFactory

Create depth models on-the-fly.

from models import ModelFactory

model = ModelFactory.create_model('midas', device='cuda', use_fp16=True)
depth = model.predict(rgb_image)

# List available models
print(ModelFactory.list_models())

DepthMetrics

Compute depth comparison metrics.

from common import DepthMetrics

metrics = DepthMetrics.compute_all_metrics(predicted, reference)
print(metrics)
# {'mae': ..., 'rmse': ..., 'abs_rel': ..., 'silog': ..., 'delta_1.25': ...}

MediaPipePose

Pose estimation and analysis.

from common import MediaPipePose, PoseProcessor

pose_detector = MediaPipePose()
pose_result = pose_detector.detect(rgb_image)

# Get joint positions
joints = PoseProcessor.extract_joint_positions(pose_result, [12, 14, 16])

Utilities

from common import (
    VideoLoader, RealSenseLoader,
    DepthAligner, DepthVisualizer,
    apply_temporal_refinement
)

# Load video
with VideoLoader(path) as loader:
    for frame, idx, timestamp in loader.get_frames():
        pass

# Load RealSense .bag
with RealSenseLoader(bag_path) as loader:
    for rgb, depth, idx, timestamp in loader.get_frames():
        pass

# Align depths
aligner = DepthAligner()
aligner.compute_alignment(predicted, reference)
aligned = aligner.align(predicted)

# Visualize
colored = DepthVisualizer.colorize_depth(depth, cmap='viridis')

# Temporal refinement
refined = apply_temporal_refinement(depth_sequence, method='gaussian')

Output Structure

outputs/
├── raw_depth/              # Unrefined depth maps
├── refined_depth/          # Temporally refined depth maps
├── metrics/
│   ├── benchmark_results.json
│   ├── model_comparison.csv
│   └── summary.json
├── visualizations/
│   ├── metrics_comparison.png
│   ├── rgb_depth_0000.png
│   ├── comparison_video.mp4
│   └── pose_overlay_0000.png
├── pose_results/
│   ├── pose_joints.json
│   ├── skeleton_depth_analysis.csv
│   └── consistency_scores.json
└── reports/
    ├── model_rankings.txt
    └── summary.json

Metrics Explained

Depth Metrics

• MAE: Mean Absolute Error (meters)
• RMSE: Root Mean Squared Error (meters)
• Abs Rel: Absolute Relative Error (unitless ratio)
• SILog: Scale-Invariant Logarithmic Error
• δ < 1.25: Percentage of pixels within 1.25x of ground truth

Temporal Metrics

• Consistency: Frame-to-frame depth stability
• Flickering Score: Temporal depth variation (lower = better)
• Motion Stability: Skeleton stability during motion

Pose Metrics

• Joint Error: Per-joint depth discrepancy (meters)
• Skeleton Stability: Temporal skeleton motion smoothness
• Temporal Consistency: Pose prediction consistency over time

Performance Notes

GPU Memory Requirements

• MiDaS/DPT: ~2GB
• Depth Anything: ~3GB
• Depth Pro: ~4GB
• All models with batch inference: Add 1GB per additional batch

Speed Benchmarks (RTX 3090, 1080p)

• MiDaS: ~45 FPS
• Depth Anything: ~30 FPS
• DPT-Large: ~25 FPS
• Depth Pro: ~15 FPS

Optimization Tips

1. Reduce Resolution: Set resolution: [960, 540]
2. Batch Processing: Set batch_size: 4 (if GPU memory allows)
3. CPU Inference: Use device: cpu for testing
4. Skip Optional: Use --skip-pose and --skip-temporal-refinement

Troubleshooting

CUDA Out of Memory

# Reduce batch size
python main.py --config config.yaml --models midas

# Use CPU
python main.py --config config.yaml --device cpu

# Reduce resolution in config.yaml

Missing Model Weights

Models download weights automatically on first run. Ensure internet connection.

# Pre-download weights
python -c "from models import ModelFactory; ModelFactory.create_model('midas')"

RealSense .bag Issues

# Install pyrealsense2
pip install pyrealsense2

# Test with sample .bag
python -c "from common import RealSenseLoader; loader = RealSenseLoader('test.bag')"

Citation

If you use this benchmark in your research, please cite:

@software{depth_benchmark_2024,
  title={Depth Estimation Benchmark: Monocular vs RealSense},
  author={Research Team},
  year={2024},
  howpublished={\url{https://github.com/...}}
}

License

MIT License - See LICENSE file for details

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit changes with clear messages
4. Submit a pull request

Support

For issues and questions:

• Check GitHub issues
• Review documentation
• Create detailed bug reports

References

• MiDaS
• Depth Anything
• ZoeDepth
• Depth Pro
• MediaPipe
• RealSense SDK

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Last Updated: January 2024
Maintainer: Research Team
Status: Production Ready