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。

用于少样本全头重建的隐式形状和外观先验

Implicit Shape and Appearance Priors for Few-Shot Full Head  Reconstruction

Recent learning approaches that implicitly represent surface geometry using
coordinate-based neural representations have shown impressive results in the
problem of multi-view 3D reconstruction. The effectiveness of these techniques
is, however, subject to the availability of a large number (several tens) of
input views of the scene, and computationally demanding optimizations. In this
paper, we tackle these limitations for the specific problem of few-shot full 3D
head reconstruction, by endowing coordinate-based representations with a
probabilistic shape prior that enables faster convergence and better
generalization when using few input images (down to three). First, we learn a
shape model of 3D heads from thousands of incomplete raw scans using implicit
representations. At test time, we jointly overfit two coordinate-based neural
networks to the scene, one modeling the geometry and another estimating the
surface radiance, using implicit differentiable rendering. We devise a
two-stage optimization strategy in which the learned prior is used to
initialize and constrain the geometry during an initial optimization phase.
Then, the prior is unfrozen and fine-tuned to the scene. By doing this, we
achieve high-fidelity head reconstructions, including hair and shoulders, and
with a high level of detail that consistently outperforms both state-of-the-art
3D Morphable Models methods in the few-shot scenario, and non-parametric
methods when large sets of views are available.

本研究介绍了一种利用概率形状先验的坐标神经表示方法，结合隐式可微渲染，通过两阶段优化过程，成功实现了多视角三维重建中几乎与 3D 可塑性模型方法、视角较多时的非参数方法相当的高保真度的少视角全三维头部重建。

H3D-Net：小样本高保真 3D 头部重建

H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

Coordinate-based neural representations have shown significant promise as an
alternative to discrete, array-based representations for complex low
dimensional signals. However, optimizing a coordinate-based network from
randomly initialized weights for each new signal is inefficient. We propose
applying standard meta-learning algorithms to learn the initial weight
parameters for these fully-connected networks based on the underlying class of
signals being represented (e.g., images of faces or 3D models of chairs).
Despite requiring only a minor change in implementation, using these learned
initial weights enables faster convergence during optimization and can serve as
a strong prior over the signal class being modeled, resulting in better
generalization when only partial observations of a given signal are available.
We explore these benefits across a variety of tasks, including representing 2D
images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D
image observations.

通过元学习算法学习基于底层信号类别的全连接网络的初始权重参数以进行优化，此方法可以加速收敛且在只有给定信号的部分观测时产生更好的泛化，已在表示 2D 图像、重建 CT 扫描和从 2D 图像观测中恢复 3D 形状和场景等各种任务中获得了探究。