Principle of multi-view camera image stitching technology

The multi-camera image stitching technology aims to merge images captured from different perspectives into a complete, coherent wide-field. The core principle involves the following key steps:

The cameras in the multi-camera system are triggered synchronously to capture images of different parts of the at the same moment. This requires precise clock synchronization among the cameras to ensure the temporal consistency of the captured images, preventing misalignment or blurring of dynamic objects the scene due to time differences in shooting.

For each image captured by the cameras, feature extraction algorithms are used to identify prominent feature points in the. Common feature extraction algorithms include SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Up Robust Features). These algorithms can accurately identify feature such as corners and edges in the image under different lighting, scale, and rotation changes, providing a basis for subsequent image matching. For example, the SIFT builds a Gaussian difference pyramid to detect extremum points in multi-scale space, assigns direction and descriptors to these extremum points, making them invariant to scale and rotation.

The feature points in the images from different cameras are matched pairwise to determine their spatial correspondence. This step typically uses feature descriptor-based matching methods, such measuring the similarity of two feature point descriptors using Euclidean distance or cosine similarity. If the similarity exceeds a set threshold, they are considered a match. During the process, it is also necessary to consider the possibility of false matches and remove erroneous pairs using algorithms like RANSAC (Random Sample Consensus) to ensure the of the matches. For instance, using Euclidean distance, the straight-line distance between two feature point descriptor vectors in vector space is calculated, with a smaller indicating higher similarity.

After completing the feature point matching, the geometric transformation relationship between the images is calculated based on the matched point pairs. Common models include affine transformation and perspective transformation. If the scene is approximately planar, affine transformation can describe the mapping relationship between the images; if the scene has depth, perspective transformation is more appropriate. The parameters of the transformation model are solved using optimization algorithms such as least squares, minimizing the position error of the matched points after. For example, in perspective transformation, an equation system is constructed using the known matched point pairs to solve for the 8 parameters representing the perspective transformation, thereby the precise mapping relationship between the images.

Based on the computed transformation model, the images from various cameras are fused together. During the fusion process, factors such as image and contrast are considered, and appropriate fusion algorithms, such as weighted averaging and Laplacian pyramid fusion, are employed to ensure the transition between images is natural without noticeable seams. The weighted averaging method assigns different weights to each pixel based on the overlapping area and pixel position, and then sums the pixel values in the overlapping with weights to achieve smooth transitions. The Laplacian pyramid fusion method first decomposes the image into different resolution pyramid layers, then fuses each layer separately, finally reconstructs the complete fused image.

Through the above series of precise and complex steps, multi-view camera image stitching technology can convert multi-perspective images into panoramic images, providing powerful visual support for various fields such as security surveillance, virtual reality, and autonomous driving.