  In this paper, we present RStab, a novel framework for video stabilization
that integrates 3D multi-frame fusion through volume rendering. Departing from
conventional methods, we introduce a 3D multi-frame perspective to generate
stabilized images, addressing the challenge of full-frame generation while
preserving structure. The core of our approach lies in Stabilized Rendering
(SR), a volume rendering module, which extends beyond the image fusion by
incorporating feature fusion. The core of our RStab framework lies in
Stabilized Rendering (SR), a volume rendering module, fusing multi-frame
information in 3D space. Specifically, SR involves warping features and colors
from multiple frames by projection, fusing them into descriptors to render the
stabilized image. However, the precision of warped information depends on the
projection accuracy, a factor significantly influenced by dynamic regions. In
response, we introduce the Adaptive Ray Range (ARR) module to integrate depth
priors, adaptively defining the sampling range for the projection process.
Additionally, we propose Color Correction (CC) assisting geometric constraints
with optical flow for accurate color aggregation. Thanks to the three modules,
our RStab demonstrates superior performance compared with previous stabilizers
in the field of view (FOV), image quality, and video stability across various
datasets.
