Recent advancements in learning techniques that employ coordinate-based
neural representations have yielded remarkable results in multi-view 3D
reconstruction tasks. However, these approaches often require a substantial
number of input views (typically several tens) and computationally intensive
optimization procedures to achieve their effectiveness. In this paper, we
address these limitations specifically for the problem of few-shot full 3D head
reconstruction. We accomplish this by incorporating a probabilistic shape and
appearance prior into coordinate-based representations, enabling faster
convergence and improved generalization when working with only a few input
images (even as low as a single image). During testing, we leverage this prior
to guide the fitting process of a signed distance function using a
differentiable renderer. By incorporating the statistical prior alongside
parallelizable ray tracing and dynamic caching strategies, we achieve an
efficient and accurate approach to few-shot full 3D head reconstruction.
Moreover, we extend the H3DS dataset, which now comprises 60 high-resolution 3D
full head scans and their corresponding posed images and masks, which we use
for evaluation purposes. By leveraging this dataset, we demonstrate the
remarkable capabilities of our approach in achieving state-of-the-art results
in geometry reconstruction while being an order of magnitude faster than
previous approaches.

该研究利用坐标为基础的神经表示学习技术，在少样本条件下实现了全 3D 头部重建，并结合概率形状和外观先验，通过可微分渲染器引导符号距离函数的拟合过程，实现了高效准确的重建，同时利用 H3DS 数据集证明了该方法在几何重建方面具有出色的性能并比之前的方法快一个数量级。