Event-boosted Deformable 3D Gaussians for Fast Dynamic Scene Reconstruction

• URL: http://arxiv.org/abs/2411.16180v1
• Date: Mon, 25 Nov 2024 08:23:38 GMT
• Title: Event-boosted Deformable 3D Gaussians for Fast Dynamic Scene Reconstruction
• Authors: Wenhao Xu, Wenming Weng, Yueyi Zhang, Ruikang Xu, Zhiwei Xiong,
• Abstract summary: 3D Gaussian Splatting (3D-GS) enables real-time rendering but struggles with fast motion due to low temporal resolution of RGB cameras. We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for fast dynamic scene reconstruction.
• Score: 50.873820265165975
• License:
• Abstract: 3D Gaussian Splatting (3D-GS) enables real-time rendering but struggles with fast motion due to low temporal resolution of RGB cameras. To address this, we introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for fast dynamic scene reconstruction. We observe that threshold modeling for events plays a crucial role in achieving high-quality reconstruction. Therefore, we propose a GS-Threshold Joint Modeling (GTJM) strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling. Moreover, we introduce a Dynamic-Static Decomposition (DSD) strategy that first identifies dynamic areas by exploiting the inability of static Gaussians to represent motions, then applies a buffer-based soft decomposition to separate dynamic and static areas. This strategy accelerates rendering by avoiding unnecessary deformation in static areas, and focuses on dynamic areas to enhance fidelity. Our approach achieves high-fidelity dynamic reconstruction at 156 FPS with a 400$\times$400 resolution on an RTX 3090 GPU.

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