Unveiling an empty street from crowded observations captured by in-car
cameras is crucial for autonomous driving. However, removing all temporary
static objects, such as stopped vehicles and standing pedestrians, presents a
significant challenge. Unlike object-centric 3D inpainting, which relies on
thorough observation in a small scene, street scenes involve long trajectories
that differ from previous 3D inpainting tasks. The camera-centric moving
environment of captured videos further complicates the task due to the limited
degree and time duration of object observation. To address these obstacles, we
introduce StreetUnveiler to reconstruct an empty street. StreetUnveiler learns
a 3D representation of the empty street from crowded observations. Our
representation is based on the hard-label semantic 2D Gaussian Splatting (2DGS)
for its scalability and ability to identify Gaussians to be removed. We inpaint
rendered image after removing unwanted Gaussians to provide pseudo-labels and
subsequently re-optimize the 2DGS. Given its temporal continuous movement, we
divide the empty street scene into observed, partial-observed, and unobserved
regions, which we propose to locate through a rendered alpha map. This
decomposition helps us to minimize the regions that need to be inpainted. To
enhance the temporal consistency of the inpainting, we introduce a novel
time-reversal framework to inpaint frames in reverse order and use later frames
as references for earlier frames to fully utilize the long-trajectory
observations. Our experiments conducted on the street scene dataset
successfully reconstructed a 3D representation of the empty street. The mesh
representation of the empty street can be extracted for further applications.
Project page and more visualizations can be found at:
this https URL

通过 Crowded Observations 捕获的车载相机，我们介绍了 StreetUnveiler 来重建空旷道路的 3D 表示，通过去除不需要的高斯分布，我们使用时间逆转框架对帧进行填充以增强填充的时序一致性。

具有语义感知的 2DGS 的 3D StreetUnveiler

3D StreetUnveiler with Semantic-Aware 2DGS

Synthesizing healthy brain scans from diseased brain scans offers a potential
solution to address the limitations of general-purpose algorithms, such as
tissue segmentation and brain extraction algorithms, which may not effectively
handle diseased images. We consider this a 3D inpainting task and investigate
the adaptation of 2D inpainting methods to meet the requirements of 3D magnetic
resonance imaging(MRI) data. Our contributions encompass potential
modifications tailored to MRI-specific needs, and we conducted evaluations of
multiple inpainting techniques using the BraTS2023 Inpainting datasets to
assess their efficacy and limitations.

从疾病大脑扫描中综合出健康脑扫描为解决一般算法的局限性，我们考虑了 3D 修复任务，并研究将 2D 修复方法调整为满足 3D 磁共振成像（MRI）数据要求的方法。我们的贡献包括针对 MRI 特定需求的潜在修改，并使用 BraTS2023 修复数据集对多个修复技术进行评估，以评估它们的效果和限制。

用生成模型推进脑部肿瘤修复

Advancing Brain Tumor Inpainting with Generative Models

Removing clutter from scenes is essential in many applications, ranging from
privacy-concerned content filtering to data augmentation. In this work, we
present an automatic system that removes clutter from 3D scenes and inpaints
with coherent geometry and texture. We propose techniques for its two key
components: 3D segmentation from shared properties and 3D inpainting, both of
which are important porblems. The definition of 3D scene clutter
(frequently-moving objects) is not well captured by commonly-studied object
categories in computer vision. To tackle the lack of well-defined clutter
annotations, we group noisy fine-grained labels, leverage virtual rendering,
and impose an instance-level area-sensitive loss. Once clutter is removed, we
inpaint geometry and texture in the resulting holes by merging inpainted RGB-D
images. This requires novel voting and pruning strategies that guarantee
multi-view consistency across individually inpainted images for mesh
reconstruction. Experiments on ScanNet and Matterport dataset show that our
method outperforms baselines for clutter segmentation and 3D inpainting, both
visually and quantitatively.

本文提出了一种自动系统，在 3D 场景中移除杂物并填补缺失部位，解决了 3D 分类和 3D 修复两个关键问题，实现了杂物与纹理准确的合成，实验表明该方法在杂物分割和 3D 修复方面都表现优秀。