Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves
training a neural network to regress the signed distance from any point to the
nearest obstacle, which has the advantages of lightweight storage and
continuous querying. However, existing algorithms usually rely on conflicting
raw observations as training data, resulting in poor map performance. In this
paper, we propose LGSDF, an ESDF continual Global learning algorithm aided by
Local updating. At the front end, axis-aligned grids are dynamically updated by
pre-processed sensor observations, where incremental fusion alleviates
estimation error caused by limited viewing directions. At the back end, a
randomly initialized implicit ESDF neural network performs continual
self-supervised learning guided by these grids to generate smooth and
continuous maps. The results on multiple scenes show that LGSDF can construct
more accurate ESDF maps and meshes compared with SOTA (State Of The Art)
explicit and implicit mapping algorithms. The source code of LGSDF is publicly
available at this https URL

该论文提出了一种 LGSDF 算法，通过动态更新轴对齐网格和持续自监督学习的神经网络来实现 ESDF 的隐式重建，从而构建更准确的 ESDF 地图和网格。

LGSDF: 基于局部更新的连续全局有符号距离场学习

LGSDF: Continual Global Learning of Signed Distance Fields Aided by  Local Updating

Recently, neural implicit functions have demonstrated remarkable results in
the field of multi-view reconstruction. However, most existing methods are
tailored for dense views and exhibit unsatisfactory performance when dealing
with sparse views. Several latest methods have been proposed for generalizing
implicit reconstruction to address the sparse view reconstruction task, but
they still suffer from high training costs and are merely valid under carefully
selected perspectives. In this paper, we propose a novel sparse view
reconstruction framework that leverages on-surface priors to achieve highly
faithful surface reconstruction. Specifically, we design several constraints on
global geometry alignment and local geometry refinement for jointly optimizing
coarse shapes and fine details. To achieve this, we train a neural network to
learn a global implicit field from the on-surface points obtained from SfM and
then leverage it as a coarse geometric constraint. To exploit local geometric
consistency, we project on-surface points onto seen and unseen views, treating
the consistent loss of projected features as a fine geometric constraint. The
experimental results with DTU and BlendedMVS datasets in two prevalent sparse
settings demonstrate significant improvements over the state-of-the-art
methods.

利用表面先验信息实现稀疏视角下高保真度的重构，通过全局几何对齐和本地几何细化，优化粗糙形状和细节，实现的稀疏视角重构框架在稀疏视图下得到了显著提升。

NeuSurf：基于表面先验的稀疏输入视图下的神经表面重建

NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse  Input Views

Recovering detailed interactions between humans/hands and objects is an
appealing yet challenging task. Existing methods typically use template-based
representations to track human/hand and objects in interactions. Despite the
progress, they fail to handle the invisible contact surfaces. In this paper, we
propose Ins-HOI, an end-to-end solution to recover human/hand-object
reconstruction via instance-level implicit reconstruction. To this end, we
introduce an instance-level occupancy field to support simultaneous human/hand
and object representation, and a complementary training strategy to handle the
lack of instance-level ground truths. Such a representation enables learning a
contact prior implicitly from sparse observations. During the complementary
training, we augment the real-captured data with synthesized data by randomly
composing individual scans of humans/hands and objects and intentionally
allowing for penetration. In this way, our network learns to recover individual
shapes as completely as possible from the synthesized data, while being aware
of the contact constraints and overall reasonability based on real-captured
scans. As demonstrated in experiments, our method Ins-HOI can produce
reasonable and realistic non-visible contact surfaces even in cases of
extremely close interaction. To facilitate the research of this task, we
collect a large-scale, high-fidelity 3D scan dataset, including 5.2k
high-quality scans with real-world human-chair and hand-object interactions. We
will release our dataset and source codes. Data examples and the video results
of our method can be found on the project page.

通过实例级隐式重建，我们提出了 Ins-HOI，一种端到端解决方案，用于恢复人与物体之间的详细交互。我们引入了实例级占据场来支持同时表示人 / 手和对象，并使用补充训练策略处理实例级基准缺失的问题。我们的方法 Ins-HOI 能够在极近交互的情况下生成合理和真实的非可见接触表面，并提供了一个大规模高保真度的 3D 扫描数据集用于研究。