4K4D: Real-Time 4D View Synthesis at 4K Resolution
Authors: name: Zhen Xu, name: Sida Peng, name: Haotong Lin, name: Guangzhao He, name: Jiaming Sun, name: Yujun Shen, name: Hujun Bao, name: Xiaowei Zhou

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.