In this work, we present the local patch mesh representation for neural
signed distance fields. This technique allows to discretize local regions of
the level sets of an input SDF by projecting and deforming flat patch meshes
onto the level set surface, using exclusively the SDF information and its
gradient. Our analysis reveals this method to be more accurate than the
standard marching cubes algorithm for approximating the implicit surface. Then,
we apply this representation in the setting of handle-guided deformation: we
introduce two distinct pipelines, which make use of 3D neural fields to compute
As-Rigid-As-Possible deformations of both high-resolution meshes and neural
fields under a given set of constraints. We run a comprehensive evaluation of
our method and various baselines for neural field and mesh deformation which
show both pipelines achieve impressive efficiency and notable improvements in
terms of quality of results and robustness. With our novel pipeline, we
introduce a scalable approach to solve a well-established geometry processing
problem on high-resolution meshes, and pave the way for extending other
geometric tasks to the domain of implicit surfaces via local patch meshing.

本研究提出了一种基于局部补丁网格表示的神经符号距离场方法，结合 3D 神经场和约束集，实现高效的几何变形，并在结果质量和稳定性方面取得显著改进。通过局部补丁网格化，我们解决了高分辨率网格的几何处理问题，并为将其他几何任务应用于隐式曲面领域铺平了道路。

隐式 - ARAP: 通过局部补丁网格化实现高分辨率网格和神经场的高效手柄导向变形

Implicit-ARAP: Efficient Handle-Guided Deformation of High-Resolution  Meshes and Neural Fields via Local Patch Meshing

In this work, we use multi-view aerial images to reconstruct the geometry,
lighting, and material of facades using neural signed distance fields (SDFs).
Without the requirement of complex equipment, our method only takes simple RGB
images captured by a drone as inputs to enable physically based and
photorealistic novel-view rendering, relighting, and editing. However, a
real-world facade usually has complex appearances ranging from diffuse rocks
with subtle details to large-area glass windows with specular reflections,
making it hard to attend to everything. As a result, previous methods can
preserve the geometry details but fail to reconstruct smooth glass windows or
verse vise. In order to address this challenge, we introduce three spatial- and
semantic-adaptive optimization strategies, including a semantic regularization
approach based on zero-shot segmentation techniques to improve material
consistency, a frequency-aware geometry regularization to balance surface
smoothness and details in different surfaces, and a visibility probe-based
scheme to enable efficient modeling of the local lighting in large-scale
outdoor environments. In addition, we capture a real-world facade aerial 3D
scanning image set and corresponding point clouds for training and
benchmarking. The experiment demonstrates the superior quality of our method on
facade holistic inverse rendering, novel view synthesis, and scene editing
compared to state-of-the-art baselines.

使用多视角航拍图像，通过神经符号距离场重建立面的几何形状、光照和材质，从而实现基于物理和照片般逼真的新视角渲染、重照和编辑。通过引入三种自适应优化策略，包括基于零样本分割技术的语义正则化方法来改善材料一致性，基于频率感知的几何正则化来平衡不同表面的表面光滑性和细节，以及基于可见性探针的方案来实现大规模户外环境中局部照明的有效建模。实验结果通过与最先进的基线方法进行比较，展示了我们的方法在立面整体反渲染、新视角合成和场景编辑方面的卓越质量。

复杂立面的空中三维扫描的整体逆渲染

Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning

In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene
reconstruction and editing using differentiable Monte Carlo raytracing on
neural signed distance fields (SDFs). Our holistic neural SDF-based framework
jointly recovers the underlying shapes, incident radiance and materials from
multi-view images. We introduce a novel bubble loss for fine-grained small
objects and error-guided adaptive sampling scheme to largely improve the
reconstruction quality on large-scale indoor scenes. Further, we propose to
decompose the neural radiance field into spatially-varying material of the
scene as a neural field through surface-based, differentiable Monte Carlo
raytracing and emitter semantic segmentations, which enables physically based
and photorealistic scene relighting and editing applications. Through a number
of qualitative and quantitative experiments, we demonstrate the superior
quality of our method on indoor scene reconstruction, novel view synthesis, and
scene editing compared to state-of-the-art baselines.

本研究提出了一种基于神经距离场的方法，利用可微分的蒙特卡罗光线跟踪技术，从多视角图像中恢复室内场景的形状、辐射和材料，并进行物理模拟和逼真的场景编辑。实验结果表明，该方法在室内场景重建、新视角合成和场景编辑方面优于现有的基线方法。