We present DiffRoom, a novel framework for tackling the problem of
high-quality 3D indoor room reconstruction and generation, both of which are
challenging due to the complexity and diversity of the room geometry. Although
diffusion-based generative models have previously demonstrated impressive
performance in image generation and object-level 3D generation, they have not
yet been applied to room-level 3D generation due to their computationally
intensive costs. In DiffRoom, we propose a sparse 3D diffusion network that is
efficient and possesses strong generative performance for Truncated Signed
Distance Field (TSDF), based on a rough occupancy prior. Inspired by
KinectFusion's incremental alignment and fusion of local SDFs, we propose a
diffusion-based TSDF fusion approach that iteratively diffuses and fuses TSDFs,
facilitating the reconstruction and generation of an entire room environment.
Additionally, to ease training, we introduce a curriculum diffusion learning
paradigm that speeds up the training convergence process and enables
high-quality reconstruction. According to the user study, the mesh quality
generated by our DiffRoom can even outperform the ground truth mesh provided by
ScanNet.

DiffRoom 提出了一种稀疏的 3D 扩散网络，它基于粗糙的占用先验和分层扩散学习结构，实现了高质量的 3D 室内重建和生成。

DiffRoom: 基于扩散的高质量三维房间重建与生成

DiffRoom: Diffusion-based High-Quality 3D Room Reconstruction and  Generation

In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regularize the ICP registration technique. We also present a
technique to filter the pairs of 3D matched points based on the distribution of
their distances. This filter is implemented efficiently on the GPU. Estimating
the distribution of the distances helps control the number of iterations
necessary for the convergence of the ICP algorithm. Finally, we show
experimental results that highlight improvements in robustness, a speed-up of
almost 12%, and a gain in tracking quality of 53% for the ATE metric on the
Freiburg benchmark.

本文介绍了如何结合低成本的 IMU 传感器的绝对定位测量结果到 KinectFusion 管道中，并展示了在 3D 重建中，这种集成可以改善运行时间、鲁棒性和质量的效果。我们使用这些方向数据来启动和规范 ICP 注册技术，并介绍了一种基于距离分布的 3D 匹配点对过滤技术。实验结果表明，与原有管道相比，我们的方法在鲁棒性、速度和跟踪质量上均有所提高。

KinectFusion 重建流程中绝对定位测量的整合

Integration of Absolute Orientation Measurements in the KinectFusion  Reconstruction pipeline

In this paper, we propose a novel method to jointly solve scene layout
estimation and global registration problems for accurate indoor 3D
reconstruction. Given a sequence of range data, we first build a set of scene
fragments using KinectFusion and register them through pose graph optimization.
Afterwards, we alternate between layout estimation and layout-based global
registration processes in iterative fashion to complement each other. We
extract the scene layout through hierarchical agglomerative clustering and
energy-based multi-model fitting in consideration of noisy measurements. Having
the estimated scene layout in one hand, we register all the range data through
the global iterative closest point algorithm where the positions of 3D points
that belong to the layout such as walls and a ceiling are constrained to be
close to the layout. We experimentally verify the proposed method with the
publicly available synthetic and real-world datasets in both quantitative and
qualitative ways.

本文提出了一种新颖的方法，用于精确的室内 3D 重建，通过 KinectFusion 先建立场景碎片并通过姿态图优化进行注册，然后通过层次凝聚聚类和基于能量的多模型拟合的方式提取场景布局，最后通过全局迭代最近点算法进行点云的全局配准。

室内重建的联合布局估计和全局多视图配准

Joint Layout Estimation and Global Multi-View Registration for Indoor  Reconstruction

Volumetric models have become a popular representation for 3D scenes in
recent years. One of the breakthroughs leading to their popularity was
KinectFusion, where the focus is on 3D reconstruction using RGB-D sensors.
However, monocular SLAM has since also been tackled with very similar
approaches. Representing the reconstruction volumetrically as a truncated
signed distance function leads to most of the simplicity and efficiency that
can be achieved with GPU implementations of these systems. However, this
representation is also memory-intensive and limits the applicability to small
scale reconstructions. Several avenues have been explored for overcoming this
limitation. With the aim of summarizing them and providing for a fast and
flexible 3D reconstruction pipeline, we propose a new, unifying framework
called InfiniTAM. The core idea is that individual steps like camera tracking,
scene representation and integration of new data can easily be replaced and
adapted to the needs of the user. Along with the framework we also provide a
set of components for scalable reconstruction: two implementations of camera
trackers, based on RGB data and on depth data, two representations of the 3D
volumetric data, a dense volume and one based on hashes of subblocks, and an
optional module for swapping subblocks in and out of the typically limited GPU
memory.

InfiniTAM 是一个新的统一框架，它可以快速灵活地进行 3D 重建，基于 KinectFusion 方法和单目 SLAM 方法，使用截断有符号距离函数来表示体积模型，提供了一系列组件来实现可扩展的 3D 重建。