Neural radiance field (NeRF) attracts attention as a promising approach to reconstructing the 3D scene. As NeRF emerges, subsequent studies have been conducted to model dynamic scenes, which include motions or topological changes. However, most of them use an additional deformation network, slowing down the training and rendering speed. Tensorial radiance field (TensoRF) recently shows its potential for fast, high-quality reconstruction of static scenes with compact model size. In this paper, we present D-TensoRF, a tensorial radiance field for dynamic scenes, enabling novel view synthesis at a specific time. We consider the radiance field of a dynamic scene as a 5D tensor. The 5D tensor represents a 4D grid in which each axis corresponds to X, Y, Z, and time and has 1D multi-channel features per element. Similar to TensoRF, we decompose the grid either into rank-one vector components (CP decomposition) or low-rank matrix components (newly proposed MM decomposition). We also use smoothing regularization to reflect the relationship between features at different times (temporal dependency). We conduct extensive evaluations to analyze our models. We show that D-TensoRF with CP decomposition and MM decomposition both have short training times and significantly low memory footprints with quantitatively and qualitatively competitive rendering results in comparison to the state-of-the-art methods in 3D dynamic scene modeling.
翻译:作为重建 3D 场景的一个很有希望的方法,NeRF 吸引了人们的注意。 随着 NeRF 的出现,随后进行了一些研究,以模拟动态场景,其中包括运动或地形变化。然而,大多数研究都使用额外的变形网络,减缓了培训速度和转换速度。TensoRF 最近显示,它有可能以紧凑的模型大小快速、高质量地重建静态场景。在本文中,我们介绍了D-TensoRF, 一种动态场景的抗拉亮场, 一种抗拉亮场,在特定时间进行新的视图合成。我们还将动态场景的亮光场视为5D 10 。 5D 10 代表一个4D的网格,每个轴轴与X、Y、Z和时间相匹配,每个元素有1D多声道特征。 类似于TensorF, 我们将电网分解成一个级矢量控模型(CP ) 或低级矩阵组件(新提议的MM 解配置 ) 。我们还使用平滑式的组合来反映动态场景的动态场景场景与不同时段之间的特征关系。 和阵势分析。