4K4D: Real-Time 4D View Synthesis at 4K Resolution

• URL: http://arxiv.org/abs/2310.11448v3
• Date: Sat, 28 Oct 2023 06:41:48 GMT
• Title: 4K4D: Real-Time 4D View Synthesis at 4K Resolution
• Authors: Zhen Xu, Sida Peng, Haotong Lin, Guangzhao He, Jiaming Sun, Yujun Shen, Hujun Bao, Xiaowei Zhou
• Abstract summary: This paper targets high-fidelity and real-time view of dynamic 3D scenes at 4K resolution. We propose a 4D point cloud representation that supports hardwareization and enables unprecedented rendering speed. Our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an 4090 GPU.
• Score: 86.6582179227016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-resolution images. To overcome this problem, we propose 4K4D, a 4D point cloud representation that supports hardware rasterization and enables unprecedented rendering speed. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition, we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover, we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality. Our project page is available at https://zju3dv.github.io/4k4d/.

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