We propose a method to allow precise and extremely fast mesh extraction from
3D Gaussian Splatting. Gaussian Splatting has recently become very popular as
it yields realistic rendering while being significantly faster to train than
NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D
gaussians as these gaussians tend to be unorganized after optimization and no
method has been proposed so far. Our first key contribution is a regularization
term that encourages the gaussians to align well with the surface of the scene.
We then introduce a method that exploits this alignment to extract a mesh from
the Gaussians using Poisson reconstruction, which is fast, scalable, and
preserves details, in contrast to the Marching Cubes algorithm usually applied
to extract meshes from Neural SDFs. Finally, we introduce an optional
refinement strategy that binds gaussians to the surface of the mesh, and
jointly optimizes these Gaussians and the mesh through Gaussian splatting
rendering. This enables easy editing, sculpting, rigging, animating,
compositing and relighting of the Gaussians using traditional softwares by
manipulating the mesh instead of the gaussians themselves. Retrieving such an
editable mesh for realistic rendering is done within minutes with our method,
compared to hours with the state-of-the-art methods on neural SDFs, while
providing a better rendering quality.

我们提出了一种方法，可以从 3D 高斯散点图中精确且非常快速地提取网格。该方法使用高斯散点图进行实际渲染，相较于 NeRFs 具有更快的训练速度。我们首先引入了一个鼓励高斯散点图与场景表面对齐的正则项，然后利用此对齐性通过 Poisson 重建方法从高斯散点图中提取网格，该方法快速、可扩展且能保留细节信息，与通常用于从神经隐式函数中提取网格的 Marching Cubes 算法形成对比。最后，我们引入了可选的优化策略，通过高斯散点图渲染将高斯散点图与网格表面绑定，在传统软件中通过操纵网格而非高斯散点图本身实现了易于编辑、雕刻、绑定、动画制作、合成和调光。与基于神经隐式函数的最先进方法相比，我们的方法可在几分钟内获取逼真渲染所需的可编辑网格，而不是使用数小时。同时，我们的方法提供更好的渲染质量。

SuGaR: 表面对齐的高斯点精炼用于高效 3D 网格重构和高质量网格渲染

SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh  Reconstruction and High-Quality Mesh Rendering

Recently, synthetic data generation and realistic rendering has advanced
tasks like target tracking and human pose estimation. Simulations for most
robotics applications are obtained in (semi)static environments, with specific
sensors and low visual fidelity. To solve this, we present a fully customizable
framework for generating realistic animated dynamic environments (GRADE) for
robotics research, first introduced in [1]. GRADE supports full simulation
control, ROS integration, realistic physics, while being in an engine that
produces high visual fidelity images and ground truth data. We use GRADE to
generate a dataset focused on indoor dynamic scenes with people and flying
objects. Using this, we evaluate the performance of YOLO and Mask R-CNN on the
tasks of segmenting and detecting people. Our results provide evidence that
using data generated with GRADE can improve the model performance when used for
a pre-training step. We also show that, even training using only synthetic
data, can generalize well to real-world images in the same application domain
such as the ones from the TUM-RGBD dataset. The code, results, trained models,
and the generated data are provided as open-source at
this https URL

提出了用于机器人研究的可定制的、真实动态环境生成框架 (GRADE)，利用该框架生成数据集用于检测和分割人物，并表明使用该数据集进行预训练能够提高模型性能

利用 GRADE 生成的合成数据进行学习

Learning from synthetic data generated with GRADE

This paper reviews the second AIM realistic bokeh effect rendering challenge
and provides the description of the proposed solutions and results. The
participating teams were solving a real-world bokeh simulation problem, where
the goal was to learn a realistic shallow focus technique using a large-scale
EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs
captured using the Canon 7D DSLR camera. The participants had to render bokeh
effect based on only one single frame without any additional data from other
cameras or sensors. The target metric used in this challenge combined the
runtime and the perceptual quality of the solutions measured in the user study.
To ensure the efficiency of the submitted models, we measured their runtime on
standard desktop CPUs as well as were running the models on smartphone GPUs.
The proposed solutions significantly improved the baseline results, defining
the state-of-the-art for practical bokeh effect rendering problem.

本文回顾了第二届 AIM 逼真的浅景深效果渲染挑战，并提供所提出的解决方案和结果的描述。参与团队通过学习大规模数据集，成功实现了只基于一个框架的实时计算和感知质量较高的浅景深效果渲染，将该领域的实际应用推向了新的高度。