Examples Walkthrough

All examples take an image path as argument:

cargo run --release --example <name> -- path/to/image.png

fast_demo

Detects FAST corners, scores with Harris, suppresses with NMS. Prints corner count and timing.

Demonstrates: Feature detection pipeline, FastDetector, HarrisScorer, NmsSuppressor.

edge_detection_demo

Runs Gaussian blur → Sobel gradients → Canny edges. Reports timing for each stage and pixel statistics.

Demonstrates: Image processing chain, GaussianBlur, SobelFilter, CannyDetector.

threshold_demo

Compares thresholding methods: histogram analysis, Otsu’s automatic threshold, fixed binary, integral image computation, and adaptive threshold. Prints timing and foreground percentages.

Demonstrates: Histogram, Threshold (all modes), IntegralImage.

advanced_cv_demo

Runs five algorithms on one image: bilateral filter, Canny + Hough line detection, Otsu + distance transform, connected components, and template matching (self-patch). Prints detailed results for each.

Demonstrates: Full range of analysis kernels.

feature_matching_demo

Detects ORB features in two images, matches with brute-force Hamming distance, and reports match statistics. Also runs SIFT detection for comparison.

Demonstrates: OrbDescriptor, BruteMatcher, SiftPipeline.

klt_benchmark

Loads a sequence of PNG frames (e.g., from EuRoC dataset), detects FAST corners on the first frame, then tracks them through subsequent frames using KLT optical flow. Reports per-frame timing and track survival rate.

Demonstrates: KltTracker, multi-frame processing, re-detection strategy.

orb_stereo_benchmark

Runs the full stereo pipeline on synthetic or real stereo pairs: FAST detection, Harris scoring, NMS, ORB descriptors, stereo matching with epipolar constraints. Reports 3D point triangulation results.

Demonstrates: StereoMatcher, full detection-to-3D pipeline.

pipeline_pool_demo

Benchmarks three approaches to multi-frame processing: individual dispatches, pipeline batching, and pipeline + TexturePool. Reports timing comparison and pool hit rates.

Demonstrates: Pipeline, TexturePool, performance comparison.

3D Reconstruction Examples

These require the reconstruction feature: cargo run --release --features reconstruction --example <name>

depth_to_cloud_demo

Takes a grayscale image, creates a synthetic stereo pair, runs SGM stereo matching, colorizes the depth map, unprojects to a 3D point cloud, and exports to PLY.

cargo run --release --features reconstruction --example depth_to_cloud_demo -- path/to/image.png

Demonstrates: SGMStereo, DepthColorize, DepthToCloud, PointCloud, PLY export.

point_cloud_processing_demo

Generates a synthetic sphere point cloud with noise and outliers, then demonstrates the full processing pipeline: normal estimation, outlier removal, voxel downsampling, and PLY export. No image input needed.

cargo run --release --features reconstruction --example point_cloud_processing_demo

Demonstrates: NormalEstimator, OutlierFilter, VoxelDownsample, PointCloud.

tsdf_fusion_demo

Creates a TSDF volume, generates synthetic depth frames of a sphere from multiple views, integrates them into the volume, extracts surface points and a triangle mesh via Marching Cubes, and exports to OBJ and PLY. No image input needed.

cargo run --release --features reconstruction --example tsdf_fusion_demo

Demonstrates: TSDFVolume, MarchingCubes, Mesh, OBJ/PLY export, the complete depth→volume→mesh pipeline.