BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting

• URL: http://arxiv.org/abs/2503.15835v1
• Date: Thu, 20 Mar 2025 04:23:52 GMT
• Title: BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting
• Authors: Yiren Lu, Yunlai Zhou, Disheng Liu, Tuo Liang, Yu Yin,
• Abstract summary: We introduce BARD-GS, a novel approach for robust dynamic scene reconstruction that effectively handles blurry inputs and imprecise camera poses.<n>By explicitly decomposing motion blur into camera motion blur and object motion blur, we achieve significantly improved rendering results in dynamic regions.
• Score: 8.480548767680894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has shown remarkable potential for static scene reconstruction, and recent advancements have extended its application to dynamic scenes. However, the quality of reconstructions depends heavily on high-quality input images and precise camera poses, which are not that trivial to fulfill in real-world scenarios. Capturing dynamic scenes with handheld monocular cameras, for instance, typically involves simultaneous movement of both the camera and objects within a single exposure. This combined motion frequently results in image blur that existing methods cannot adequately handle. To address these challenges, we introduce BARD-GS, a novel approach for robust dynamic scene reconstruction that effectively handles blurry inputs and imprecise camera poses. Our method comprises two main components: 1) camera motion deblurring and 2) object motion deblurring. By explicitly decomposing motion blur into camera motion blur and object motion blur and modeling them separately, we achieve significantly improved rendering results in dynamic regions. In addition, we collect a real-world motion blur dataset of dynamic scenes to evaluate our approach. Extensive experiments demonstrate that BARD-GS effectively reconstructs high-quality dynamic scenes under realistic conditions, significantly outperforming existing methods.

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