Panorama video for stitch software#

We propose a pushbroom stitching network based on deep convolutional neural networks (CNNs). Specifically, we take inspiration from the pushbroom camera, which concatenates vertical image slices that are captured while the camera translates With these three desiderata in mind, we formulate video stitching as a spatial view interpolation problem. (3) The stitched video needs to be temporally stable.

(2) Objects may be distorted, but should not be cut off or disappear in any frame. (1) Artifacts, such as ghosting, should not appear. We identify three desirable properties in the output video: We propose a video stitching solution for linear cameras arrays that produces a panoramic video. Moreover, seams can cause objects to be cut off or completely disappear from stitched images-a particularly dangerous outcome for use cases such as autonomous driving. These methods, however, often cause severe artifacts, such as ghosting or misalignment, as shown in Figure 6.

Panorama video for stitch software#

In fact, commercial video stitching software often adopts simple seam cutting and multi-band blending. On the other hand, algorithms that explicitly enforce temporal consistency, such as spatio-temporal mesh warping with a large-scale optimization, are computationally expensive. Īpplying these strategies to process a video frame-by-frame inevitably produces noticeable jittering or wobbling artifacts. To obtain better alignment, existing image stitching algorithms perform content-aware local warping or find optimal seams around objects to mitigate artifacts at the transition from one view to the other. In addition, occlusions, dis-occlusions, and limited overlap between the FOVs also cause a significant amount of stitching artifacts. Parallax varies with object depth, which makes it impossible to properly align objects without knowing dense 3D information. One main challenge for video stitching with wide baselines is parallax, i.ethe apparent displacement of an object in multiple input videos due to camera translation.