We propose DynamicSurf, a model-free neural implicit surface reconstruction method for high-fidelity 3D modelling of non-rigid surfaces from monocular RGB-D video. To cope with the lack of multi-view cues in monocular sequences of deforming surfaces, one of the most challenging settings for 3D reconstruction, DynamicSurf exploits depth, surface normals, and RGB losses to improve reconstruction fidelity and optimisation time. DynamicSurf learns a neural deformation field that maps a canonical representation of the surface geometry to the current frame. We depart from current neural non-rigid surface reconstruction models by designing the canonical representation as a learned feature grid which leads to faster and more accurate surface reconstruction than competing approaches that use a single MLP. We demonstrate DynamicSurf on public datasets and show that it can optimize sequences of varying frames with $6\times$ speedup over pure MLP-based approaches while achieving comparable results to the state-of-the-art methods. Project is available at https://mirgahney.github.io//DynamicSurf.io/.

翻译：我们提出DynamicSurf，一种无需先验模型的神经隐式表面重建方法，用于从单目RGB-D视频中对非刚性表面进行高保真三维建模。为应对单目图像序列中变形表面缺乏多视角线索这一三维重建领域最具挑战性的场景之一，DynamicSurf利用深度、表面法向和RGB损失函数提升重建保真度与优化效率。该方法学习一个神经变形场，将表面几何的规范表示映射至当前帧。相较于当前采用单一MLP的非刚性表面重建模型，我们通过设计基于可学习特征网格的规范表示，实现了更快更精准的表面重建。在公开数据集上的实验表明，DynamicSurf可优化不同帧长序列，在取得与最先进方法可比结果的同时，相比纯MLP方法实现6倍加速。项目地址：https://mirgahney.github.io//DynamicSurf.io/。