3D Multi-frame Fusion for Video Stabilization

TL;DR

This paper introduces RStab, a novel 3D multi-frame fusion framework for video stabilization using volume rendering, which improves stability and image quality by integrating depth priors and color correction.

Contribution

The paper presents a new 3D multi-frame fusion approach with Stabilized Rendering, Adaptive Ray Range, and Color Correction modules for enhanced video stabilization.

Findings

Outperforms previous stabilizers in FOV and image quality

Demonstrates superior stability across various datasets

Integrates depth priors for improved projection accuracy

Abstract

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.