The task of capturing and rendering 3D dynamic scenes from 2D images has become increasingly popular in recent years. However, most conventional cameras are bandwidth-limited to 30-60 FPS, restricting these methods to static or slowly evolving scenes. While overcoming bandwidth limitations is difficult for general scenes, recent years have seen a flurry of computational imaging methods that yield high-speed videos using conventional cameras for specific applications (e.g., motion capture and particle image velocimetry). However, most of these methods require modifications to a camera's optics or the addition of mechanically moving components, limiting them to a single-view high-speed capture. Consequently, these methods cannot be readily used to capture a 3D representation of rapid scene motion. In this paper, we propose a novel method to capture and reconstruct a volumetric representation of a high-speed scene using only unaugmented low-speed cameras. Instead of modifying the hardware or optics of each individual camera, we encode high-speed scene dynamics by illuminating the scene with a rapid, sequential color-coded sequence. This results in simultaneous multi-view capture of the scene, where high-speed temporal information is encoded in the spatial intensity and color variations of the captured images. To construct a high-speed volumetric representation of the dynamic scene, we develop a novel dynamic Gaussian Splatting-based approach that decodes the temporal information from the images. We evaluate our approach on simulated scenes and real-world experiments using a multi-camera imaging setup, showing first-of-a-kind high-speed volumetric scene reconstructions.

翻译：近年来，从二维图像捕捉和渲染三维动态场景的任务日益流行。然而，大多数传统相机的带宽限制在30-60帧/秒，使得这些方法仅适用于静态或缓慢演变的场景。虽然克服通用场景的带宽限制十分困难，但近年来涌现了大量计算成像方法，能够利用传统相机为特定应用（如动作捕捉和粒子图像测速）生成高速视频。然而，这些方法大多需要修改相机光学系统或增加机械运动部件，仅局限于单视角高速捕捉。因此，这些方法无法直接用于捕捉快速运动场景的三维表示。本文提出一种新颖方法，仅使用未增强的低速相机即可捕捉并重建高速场景的体积表示。我们并非修改各相机硬件或光学系统，而是通过快速、连续的彩色编码序列对场景进行照明，从而编码高速场景动态。这将实现场景的多视角同步捕捉，其中高速时间信息被编码在捕捉图像的空间强度与颜色变化中。为构建动态场景的高速体积表示，我们开发了一种基于动态高斯溅射的新方法，可从图像中解码时间信息。我们在模拟场景和基于多相机成像系统的真实实验中评估了该方法，展示了首次实现的高速体积场景重建。