Sparse4DGS：面向稀疏帧动态场景重建的4D高斯泼溅
Sparse4DGS: 4D Gaussian Splatting for Sparse-Frame Dynamic Scene Reconstruction
AI总结：
Sparse4DGS提出首个稀疏帧动态场景重建方法，通过纹理感知形变正则化和纹理感知规范优化聚焦纹理丰富区域，在多个数据集上超越现有动态与少样本方法。
摘要：
动态高斯泼溅方法在4D场景重建中取得了显著性能，但这些方法依赖密集帧视频序列来实现逼真重建。在现实场景中，由于设备限制，有时只能获取稀疏帧。本文提出Sparse4DGS，这是首个面向稀疏帧动态场景重建的方法。我们观察到，在稀疏帧设置下，动态重建方法在规范空间和形变空间中都会失效，尤其是在纹理丰富度高的区域。Sparse4DGS通过聚焦纹理丰富区域来应对这一挑战。针对形变网络，我们提出纹理感知形变正则化，引入基于纹理的深度对齐损失来约束高斯形变。针对规范高斯场，我们引入纹理感知规范优化，将基于纹理的噪声融入规范高斯的梯度下降过程中。大量实验表明，以稀疏帧作为输入时，我们的方法在NeRF-Synthetic、HyperNeRF、NeRF-DS以及我们的iPhone-4D数据集上优于现有的动态或少样本技术。
英文摘要：
Dynamic Gaussian Splatting approaches have achieved remarkable performance for 4D scene reconstruction. However, these approaches rely on dense-frame video sequences for photorealistic reconstruction. In real-world scenarios, due to equipment constraints, sometimes only sparse frames are accessible. In this paper, we propose Sparse4DGS, the first method for sparse-frame dynamic scene reconstruction. We observe that dynamic reconstruction methods fail in both canonical and deformed spaces under sparse-frame settings, especially in areas with high texture richness. Sparse4DGS tackles this challenge by focusing on texture-rich areas. For the deformation network, we propose Texture-Aware Deformation Regularization, which introduces a texture-based depth alignment loss to regulate Gaussian deformation. For the canonical Gaussian field, we introduce Texture-Aware Canonical Optimization, which incorporates texture-based noise into the gradient descent process of canonical Gaussians. Extensive experiments show that when taking sparse frames as inputs, our method outperforms existing dynamic or few-shot techniques on NeRF-Synthetic, HyperNeRF, NeRF-DS, and our iPhone-4D datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
