Workflow:Kornia Kornia Image Stitching Panorama
| Knowledge Sources | |
|---|---|
| Domains | Computer_Vision, Image_Stitching, Geometry |
| Last Updated | 2026-02-09 15:00 GMT |
Overview
End-to-end process for combining multiple overlapping images into a single panoramic image using Kornia's ImageStitcher with feature matching and homography estimation.
Description
This workflow demonstrates how to create panoramic images by stitching together multiple photographs with overlapping fields of view. The process uses Kornia's high-level ImageStitcher API, which internally chains feature matching (via LoFTR or LocalFeatureMatcher), homography estimation (via RANSAC or DLT), perspective warping, and image blending. The pipeline processes image pairs sequentially from left to right, estimating the geometric transformation between each pair and warping one image into the coordinate frame of the other. The result is a single wide-field image that combines the content of all input images.
Usage
Execute this workflow when you have multiple photographs taken from approximately the same location but with different viewing directions (panning), and want to combine them into a single panoramic image. This is commonly used in landscape photography, virtual tours, satellite imagery mosaicking, and scene documentation.
Execution Steps
Step 1: Load and Prepare Input Images
Load the input images that will be stitched together. Images should be ordered from left to right as the current implementation requires strict spatial ordering. Convert images to float tensors and ensure they share the same height for optimal stitching quality.
Key considerations:
- Images must be ordered left-to-right (strict requirement)
- All images should have the same height for best results
- Convert to float tensors in [0, 1] range
- Transfer to GPU for faster processing
Step 2: Initialize the Feature Matcher
Create a feature matching module that will find correspondences between overlapping image pairs. LoFTR with outdoor pretrained weights is the recommended matcher for general panorama stitching. The matcher operates on grayscale versions of the images, which the ImageStitcher handles internally.
Key considerations:
- LoFTR pretrained with outdoor weights works best for general scenes
- LocalFeatureMatcher is also supported as an alternative
- The matcher is passed to ImageStitcher as a constructor argument
- GPU execution is strongly recommended for LoFTR inference
Step 3: Configure the ImageStitcher
Initialize the ImageStitcher with the chosen matcher and homography estimation method. The estimator parameter controls how the homography is computed from matched keypoints: RANSAC provides robust estimation with outlier rejection, while vanilla DLT is faster but sensitive to outliers.
Key considerations:
- Use estimator='ransac' for robust results with real-world images
- Use estimator='vanilla' only for clean data with minimal outliers
- Blending method 'naive' performs binary selection between source and destination pixels
- Move the ImageStitcher to GPU with .cuda() for accelerated computation
Step 4: Execute Stitching Under Inference Mode
Pass the input images to the ImageStitcher's forward method. Use torch.inference_mode() or torch.no_grad() to reduce memory consumption since gradients are not needed for the stitching output. The stitcher processes image pairs sequentially, accumulating the panorama from left to right.
Key considerations:
- Use torch.inference_mode() to save GPU memory
- The stitcher requires significant memory for large images
- Processing is sequential: each pair is matched and warped in order
- The output canvas width is the sum of all input image widths (before cropping)
Step 5: Post-process and Crop Result
The ImageStitcher automatically trims unused canvas area on the right side of the result. Convert the output tensor back to a displayable image format. Additional post-processing such as exposure compensation or advanced blending can be applied if needed.
Key considerations:
- The postprocess method removes empty columns from the right edge
- Use kornia.tensor_to_image to convert the result for display
- The output may have visible seams with naive blending
- For better quality, consider additional blending techniques externally