PanoStitch

A modular panoramic image stitching pipeline featuring both classic algorithms and modern Deep Learning integrations.

Overview

PanoStitch is an intelligent panoramic image stitching system that seamlessly combines multiple overlapping images into a single high-resolution panorama. Our modular architecture supports both traditional computer vision techniques and cutting-edge deep learning approaches, offering flexibility and robustness for various stitching scenarios.

Input Images

Image 1	Image 2

Result Panorama

Pipeline Architecture

flowchart TD
    A[Input Images] --> B[Feature Detection]
    B --> C[Feature Description]
    C --> D[Feature Matching]
    D --> E[Homography Estimation]
    E --> F[Gain Compensation]
    F --> G[Image Blending]
    G --> H[Output Panorama]
    
    style A fill:#e3f2fd,stroke:#1976d2
    style B fill:#f3e5f5,stroke:#7b1fa2
    style C fill:#fce4ec,stroke:#c2185b
    style D fill:#fff3e0,stroke:#f57c00
    style E fill:#e8f5e9,stroke:#388e3c
    style F fill:#e0f7fa,stroke:#00838f
    style G fill:#fff8e1,stroke:#ffa000
    style H fill:#c8e6c9,stroke:#2e7d32

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Test Cases: You can find 8 comprehensive test cases with their stitched panorama results in the imgs/ folder (bicycle, boat, clock, dam, flower, mountain, river, tree).

Features

• Feature Detection: Custom Harris Corner Detector or SIFT.
• Feature Description: Custom 128D HOG-based descriptors.
• Matching: Vectorized Brute-force matcher with Lowe's ratio test.
• Deep Learning: Integration with DISK + LightGlue for robust matching.
• Homography: RANSAC with Direct Linear Transform (DLT).
• Processing: Gain compensation for exposure correction and weighted blending.

Installation

pip install -r requirements.txt

Usage

Basic Usage (Recommended)

Stitch all images in a directory using the standard SIFT pipeline:

python panostitch.py imgs/boat/

Comparing Methods

1. Custom "From Scratch" Implementation (Harris + HOG Descriptors)

python panostitch.py imgs/boat/ --harris

2. Deep Learning Pipeline (DISK + LightGlue)

python panostitch.py imgs/boat/ --dnn

Benchmark Comparison

We benchmarked three matching methods across 8 scenes: traditional SIFT, and deep learning approaches LoFTR and DISK+LightGlue.

Feature Matching Visualization

SIFT (Traditional)	DISK+LightGlue (Deep Learning)	LoFTR (Deep Learning)

Speed Comparison

Method	Avg Time	Min	Max
SIFT	2.0s	0.46s	7.92s
DISK+LightGlue	14.8s	12.9s	16.3s
LoFTR	30.1s	25.2s	32.5s

Match Quality by Scene

Scene	Method	Matches	Inliers	Inlier Ratio
boat	SIFT	3836	3227	84.1%
	DISK+LightGlue	1238	1209	97.7%
	LoFTR	4503	1963	43.6%
dam	SIFT	3883	3856	99.3%
	DISK+LightGlue	1319	1272	96.4%
	LoFTR	4218	4213	99.9%
mountain	SIFT	1277	1267	99.2%
	DISK+LightGlue	1190	1174	98.7%
	LoFTR	4994	4495	90.0%
clock	SIFT	1626	1389	85.4%
	DISK+LightGlue	592	517	87.3%
	LoFTR	4214	1541	36.6%
bicycle	SIFT	774	558	72.1%
	DISK+LightGlue	824	707	85.8%
	LoFTR	4606	1826	39.6%
flower	SIFT	591	433	73.3%
	DISK+LightGlue	1097	893	81.4%
	LoFTR	4060	1013	25.0%
tree	SIFT	570	301	52.8%
	DISK+LightGlue	214	117	54.7%
	LoFTR	3995	664	16.6%
river	SIFT	2178	2139	98.2%
	DISK+LightGlue	898	893	99.4%
	LoFTR	4675	4657	99.6%

Key Findings

graph TB
    subgraph "Speed"
        A["SIFT: 2.0s avg<br/>7-15x Faster"]
    end
    subgraph "Quality"
        B["DISK+LightGlue<br/>85-99% Inlier Ratio"]
    end
    subgraph "Coverage"
        C["LoFTR<br/>4000-5000 matches"]
    end
    
    style A fill:#e8f5e9
    style B fill:#fff3e0
    style C fill:#e3f2fd

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Summary:

• SIFT is 7-15x faster than deep learning methods, ideal for real-time applications
• DISK+LightGlue achieves highest inlier ratios (85-99%) with moderate match counts
• LoFTR produces the most matches but lower inlier ratios in challenging scenes
• On well-textured scenes (dam, river), all methods achieve >96% inlier ratio

Contributors

Amira Khalid	Youssef Noser	Hussein Mohammed	Abdulrahman Medhat