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. We will release the code for reproducibility.

翻译：本文针对动态三维场景在4K分辨率下的高保真、实时视角合成问题。近年来，一些动态视角合成方法展现了令人印象深刻的渲染质量，但在渲染高分辨率图像时速度仍然受限。为解决这一问题，我们提出4K4D——一种支持硬件光栅化、可实现前所未有渲染速度的四维点云表示。该表示基于四维特征网格构建，使得点云自然正则化且能够稳健优化。此外，我们设计了一种新颖的混合外观模型，在保持渲染效率的同时显著提升了图像质量。同时，我们开发了一种可微分层深度剥离算法，以有效从RGB视频中学习所提模型。实验表明，在RTX 4090 GPU上，我们的表示方法在DNA-Rendering数据集1080p分辨率下渲染速度超过400 FPS，在ENeRF-Outdoor数据集4K分辨率下达到80 FPS，较此前方法提速30倍，并实现了最先进的渲染质量。我们将开源代码以保证可复现性。