3D Multi-frame Fusion for Video Stabilization

• URL: http://arxiv.org/abs/2404.12887v1
• Date: Fri, 19 Apr 2024 13:43:14 GMT
• Title: 3D Multi-frame Fusion for Video Stabilization
• Authors: Zhan Peng, Xinyi Ye, Weiyue Zhao, Tianqi Liu, Huiqiang Sun, Baopu Li, Zhiguo Cao,
• Abstract summary: We present RStab, a novel framework for video stabilization that integrates 3D multi-frame fusion through volume rendering. The core of our approach lies in Stabilized Rendering (SR), a volume rendering module, fusing multi-frame information in 3D space. SR involves warping features and colors from multiple frames by projection, fusing them into descriptors to render the stabilized image. In response, we introduce the Adaptive Ray Range (ARR) module to integrate depth priors, adaptively defining the sampling range for the projection process.
• Score: 32.42910053491574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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