4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

• URL: http://arxiv.org/abs/2310.08528v3
• Date: Mon, 15 Jul 2024 12:34:51 GMT
• Title: 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
• Authors: Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, Xinggang Wang,
• Abstract summary: We propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes. A neuralvoxel encoding algorithm inspired by HexPlane is proposed to efficiently build features from 4D neural voxels. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$times$800 resolution on an 3090 GPU.
• Score: 103.32717396287751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Representing and rendering dynamic scenes has been an important but challenging task. Especially, to accurately model complex motions, high efficiency is usually hard to guarantee. To achieve real-time dynamic scene rendering while also enjoying high training and storage efficiency, we propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame. In 4D-GS, a novel explicit representation containing both 3D Gaussians and 4D neural voxels is proposed. A decomposed neural voxel encoding algorithm inspired by HexPlane is proposed to efficiently build Gaussian features from 4D neural voxels and then a lightweight MLP is applied to predict Gaussian deformations at novel timestamps. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$\times$800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state-of-the-art methods. More demos and code are available at https://guanjunwu.github.io/4dgs/.

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