Point cloud upsampling aims to generate dense and uniformly distributed point
sets from a sparse point cloud, which plays a critical role in 3D computer
vision. Previous methods typically split a sparse point cloud into several
local patches, upsample patch points, and merge all upsampled patches. However,
these methods often produce holes, outliers or nonuniformity due to the
splitting and merging process which does not maintain consistency among local
patches. To address these issues, we propose a novel approach that learns an
unsigned distance field guided by local priors for point cloud upsampling.
Specifically, we train a local distance indicator (LDI) that predicts the
unsigned distance from a query point to a local implicit surface. Utilizing the
learned LDI, we learn an unsigned distance field to represent the sparse point
cloud with patch consistency. At inference time, we randomly sample queries
around the sparse point cloud, and project these query points onto the
zero-level set of the learned implicit field to generate a dense point cloud.
We justify that the implicit field is naturally continuous, which inherently
enables the application of arbitrary-scale upsampling without necessarily
retraining for various scales. We conduct comprehensive experiments on both
synthetic data and real scans, and report state-of-the-art results under widely
used benchmarks.

提出了一种学习局部先验引导的无符号距离场的点云上采样方法，稀疏点云通过局部一致性表示，通过在学习的隐式场的零级集上生成稠密点云，实验结果在综合数据和真实扫描中显示出最先进的效果。

使用本地距离指示器学习任意尺度点云上采样的连续隐式场

Learning Continuous Implicit Field with Local Distance Indicator for  Arbitrary-Scale Point Cloud Upsampling

We introduce Neural Marching Cubes (NMC), a data-driven approach for
extracting a triangle mesh from a discretized implicit field. Classical MC is
defined by coarse tessellation templates isolated to individual cubes. While
more refined tessellations have been proposed, they all make heuristic
assumptions, such as trilinearity, when determining the vertex positions and
local mesh topologies in each cube. In principle, none of these approaches can
reconstruct geometric features that reveal coherence or dependencies between
nearby cubes (e.g., a sharp edge), as such information is unaccounted for,
resulting in poor estimates of the true underlying implicit field. To tackle
these challenges, we re-cast MC from a deep learning perspective, by designing
tessellation templates more apt at preserving geometric features, and learning
the vertex positions and mesh topologies from training meshes, to account for
contextual information from nearby cubes. We develop a compact per-cube
parameterization to represent the output triangle mesh, while being compatible
with neural processing, so that a simple 3D convolutional network can be
employed for the training. We show that all topological cases in each cube that
are applicable to our design can be easily derived using our representation,
and the resulting tessellations can also be obtained naturally and efficiently
by following a few design guidelines. In addition, our network learns local
features with limited receptive fields, hence it generalizes well to new shapes
and new datasets. We evaluate our neural MC approach by quantitative and
qualitative comparisons to all well-known MC variants. In particular, we
demonstrate the ability of our network to recover sharp features such as edges
and corners, a long-standing issue of MC and its variants. Our network also
reconstructs local mesh topologies more accurately than previous approaches.

本文提出了一种基于深度学习的 Neural Marching Cubes 方法，利用紧凑的每个立方体参数化来表示输出三角网格，并且是与神经处理兼容的，通过从训练网格中学习顶点位置和网格拓扑形状，以考虑周围立方体的上下文信息，从而更准确地重构几何特征和局部网格拓扑。

神经 Marching Cubes

Neural Marching Cubes

We present the first single-view 3D reconstruction network aimed at
recovering geometric details from an input image which encompass both
topological shape structures and surface features. Our key idea is to train the
network to learn a detail disentangled reconstruction consisting of two
functions, one implicit field representing the coarse 3D shape and the other
capturing the details. Given an input image, our network, coined D$^2$IM-Net,
encodes it into global and local features which are respectively fed into two
decoders. The base decoder uses the global features to reconstruct a coarse
implicit field, while the detail decoder reconstructs, from the local features,
two displacement maps, defined over the front and back sides of the captured
object. The final 3D reconstruction is a fusion between the base shape and the
displacement maps, with three losses enforcing the recovery of coarse shape,
overall structure, and surface details via a novel Laplacian term.

本文提出了一种用于从单一视角对三维模型进行重建的模型，使用隐式函数来表示模型的粗略形状，采用细节表征的重建方法来捕捉表面细节。