面向高速体积场景重建的颜色编码照明
Color-Encoded Illumination for High-Speed Volumetric Scene Reconstruction
用快速顺序彩色照明把高速时序信息编码进普通低帧率相机的图像颜色中,再借助动态高斯泼溅从多视角彩色编码图像中解码时序并重建高速体积场景;这为用低成本多相机阵列突破带宽限制、做动态场景重建提供了可复用的编码-解码思路。
原文依据 we encode high-speed scene dynamics by illuminating the scene with a rapid, sequential color sequence
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近年来,从二维图像中捕获并渲染三维动态场景这一任务日益流行。然而,大多数传统相机的带宽被限制在 30–60 FPS,使这些方法只能局限于静态或缓慢演化的场景。虽然在一般场景中克服带宽限制十分困难,但近年来涌现出大量计算成像方法,它们针对特定场景(例如动作捕捉和粒子图像测速)利用传统相机获得高速视频。然而,这些方法大多需要改造相机光学系统或增加机械运动部件,因而只能实现单视角高速捕获。因此,它们无法直接用于捕获快速场景运动的三维表示。本文提出一种新方法,仅使用未经改造的低速相机即可捕获并重建高速场景的体积表示。我们没有改造每台相机的硬件或光学系统,而是用快速、顺序的彩色序列照明场景,以此对高速场景动态进行编码。由此实现对场景的同步多视角捕获,其中高速时序信息被编码在图像的颜色中。为构建动态场景的高速体积表示,我们提出一种基于动态高斯泼溅的新方法,从图像中解码时序信息。我们在仿真场景以及使用多相机成像装置的真实世界实验中评估了该方法,展示了首创的高速体积场景重建结果。
英文原文摘要 1627 字符
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 in general scenes, recent years have seen a flurry of computational imaging methods that yield high-speed videos using conventional cameras for specific scenarios (e.g., motion capture and particle image velocimetry). However, most of these methods require modifications to camera optics or the addition of mechanically moving components, limiting them to a single-view high-speed capture. Consequently, these 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 sequence. This results in simultaneous multi-view capture of the scene, in which high-speed temporal information is encoded in the images' color. 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.