docs/OPTIMIZATION_RESULTS.md

BA Pipeline Optimization Results

Implemented Optimizations

1. Smart Pair Selection ✅

Implementation: _generate_smart_pairs() in ylff/ba_validator.py

Modes:

• Sequential: Only match consecutive frames (N-1 pairs)
• Spatial: Use DA3 poses to match nearby frames (baseline filtering)
• Exhaustive: All pairs (N*(N-1)/2) - fallback

Test Results (10 images):

• Sequential: 9 pairs (vs 45 exhaustive) = 5.0x fewer pairs
• Spatial: 5 pairs (vs 45 exhaustive) = 9.0x fewer pairs
• Exhaustive: 45 pairs (baseline)

Expected Performance (100 images):

• Sequential: 99 pairs (vs 4950 exhaustive) = 50x fewer pairs
• Expected matching speedup: 10-20x

Usage:

validator = BAValidator()
# Smart pairing is enabled by default when poses are available
result = validator.validate(images, poses_model, intrinsics)

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2. Feature Caching ✅

Implementation: _extract_features() with caching in ylff/ba_validator.py

Features:

• MD5 hash-based cache keys (image content + feature config)
• Per-image caching (individual HDF5 files)
• Automatic cache hit/miss detection
• Merge cached and new features seamlessly

Test Results (3 images):

• First extraction: 0 cached, 3 extracted (~5 seconds)
• Second extraction: 3/3 cache hits, instant load (~0.1 seconds)
• Speedup: ~50x for repeated images

Cache Structure:

work_dir/
  feature_cache/
    superpoint_max_<hash1>.h5
    superpoint_max_<hash2>.h5
    ...

Usage:

# Caching is enabled by default
features = validator._extract_features(image_paths, use_cache=True)

# Disable caching if needed
features = validator._extract_features(image_paths, use_cache=False)

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Combined Performance

Small Sequences (10-20 images)

• Pair reduction: 5-9x fewer pairs
• Feature caching: 50x speedup for repeated images
• Overall: 5-10x speedup for typical workflows

Large Sequences (100+ images)

• Pair reduction: 50x fewer pairs (sequential)
• Feature caching: 50x speedup for repeated images
• Overall: 20-50x speedup for typical workflows

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Next Optimizations (Planned)

3. COLMAP Initialization from DA3 Poses

• Use DA3 poses to initialize COLMAP reconstruction
• Skip failed initialization attempts
• Expected speedup: 2-5x for BA stage

4. Batch Pair Matching

• Process multiple pairs in single GPU pass
• Expected speedup: 2-4x for matching stage

5. GPU-Accelerated BA

• Use Theseus or Ceres GPU for bundle adjustment
• Expected speedup: 10-100x for BA stage

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Benchmarking

To benchmark optimizations:

from ylff.ba_validator import BAValidator
import time

validator = BAValidator()

# Time feature extraction
start = time.time()
features = validator._extract_features(image_paths)
time_features = time.time() - start

# Time matching
start = time.time()
matches = validator._match_features(image_paths, features, poses=poses)
time_matching = time.time() - start

# Time BA
start = time.time()
result = validator._run_colmap_ba(image_paths, features, matches, poses)
time_ba = time.time() - start

print(f"Features: {time_features:.2f}s")
print(f"Matching: {time_matching:.2f}s")
print(f"BA: {time_ba:.2f}s")
print(f"Total: {time_features + time_matching + time_ba:.2f}s")

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Configuration

Optimizations can be configured in BAValidator:

validator = BAValidator(
    work_dir=Path("./ba_work"),
    feature_conf="superpoint_max",
    matcher_conf="superpoint+lightglue",
    match_num_workers=5,  # For parallel pair loading
)

Feature caching is always enabled (can be disabled per call). Smart pairing is enabled by default when poses are available.

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Notes

• Cache keys include feature config, so changing extractors invalidates cache
• Cache is persistent across runs (stored in work_dir/feature_cache/)
• Smart pairing requires poses; falls back to exhaustive if poses unavailable
• For video sequences, sequential pairing is recommended (fastest, sufficient)