We introduce HouseCrafter, a novel approach that can lift a floorplan into a
complete large 3D indoor scene (e.g., a house). Our key insight is to adapt a
2D diffusion model, which is trained on web-scale images, to generate
consistent multi-view color (RGB) and depth (D) images across different
locations of the scene. Specifically, the RGB-D images are generated
autoregressively in a batch-wise manner along sampled locations based on the
floorplan, where previously generated images are used as condition to the
diffusion model to produce images at nearby locations. The global floorplan and
attention design in the diffusion model ensures the consistency of the
generated images, from which a 3D scene can be reconstructed. Through extensive
evaluation on the 3D-Front dataset, we demonstrate that HouseCraft can generate
high-quality house-scale 3D scenes. Ablation studies also validate the
effectiveness of different design choices. We will release our code and model
weights. Project page: this https URL

HouseCrafter 使用 2D 扩散模型生成与全景平面图一致的多视角 RGB-D 图像，从而重建高质量的 3D 室内场景。

HouseCrafter：使用 2D 扩散模型将平面图提升为 3D 场景

HouseCrafter: Lifting Floorplans to 3D Scenes with 2D Diffusion Model

In this paper we propose an efficient data-driven solution to
self-localization within a floorplan. Floorplan data is readily available,
long-term persistent and inherently robust to changes in the visual appearance.
Our method does not require retraining per map and location or demand a large
database of images of the area of interest. We propose a novel probabilistic
model consisting of an observation and a novel temporal filtering module.
Operating internally with an efficient ray-based representation, the
observation module consists of a single and a multiview module to predict
horizontal depth from images and fuses their results to benefit from advantages
offered by either methodology. Our method operates on conventional consumer
hardware and overcomes a common limitation of competing methods that often
demand upright images. Our full system meets real-time requirements, while
outperforming the state-of-the-art by a significant margin.

我们提出了一种高效的数据驱动的解决方案，用于楼层平面图的自我定位。该方法不需要每张地图和位置的重新训练，也不需要大量图片数据库。我们提出了一个由观测模块和新型时间滤波模块组成的新颖概率模型。我们的方法适用于传统消费硬件，并克服了其他方法常常需要直立图像的共同限制。我们的系统满足实时要求，并在性能上显著超过现有技术。

F$^3$Loc：地板布局定位的融合与过滤

F$^3$Loc: Fusion and Filtering for Floorplan Localization

The automatic generation of floorplans given user inputs has great potential
in architectural design and has recently been explored in the computer vision
community. However, the majority of existing methods synthesize floorplans in
the format of rasterized images, which are difficult to edit or customize. In
this paper, we aim to synthesize floorplans as sequences of 1-D vectors, which
eases user interaction and design customization. To generate high fidelity
vectorized floorplans, we propose a novel two-stage framework, including a
draft stage and a multi-round refining stage. In the first stage, we encode the
room connectivity graph input by users with a graph convolutional network
(GCN), then apply an autoregressive transformer network to generate an initial
floorplan sequence. To polish the initial design and generate more visually
appealing floorplans, we further propose a novel panoptic refinement
network(PRN) composed of a GCN and a transformer network. The PRN takes the
initial generated sequence as input and refines the floorplan design while
encouraging the correct room connectivity with our proposed geometric loss. We
have conducted extensive experiments on a real-world floorplan dataset, and the
results show that our method achieves state-of-the-art performance under
different settings and evaluation metrics.

本文提出了一种基于向量序列而非图片格式生成楼层平面图的方法，并详细介绍了一个基于图卷积网络这一新颖框架的二阶段流程，最终在实际数据集上取得了最佳的表现。

端到端图约束矢量平面图生成与全景细化

End-to-end Graph-constrained Vectorized Floorplan Generation with  Panoptic Refinement

This paper presents a novel algorithm that utilizes a 2D floorplan to align
panorama RGBD scans. While effective panorama RGBD alignment techniques exist,
such a system requires extremely dense RGBD image sampling. Our approach can
significantly reduce the number of necessary scans with the aid of a floorplan
image. We formulate a novel Markov Random Field inference problem as a scan
placement over the floorplan, as opposed to the conventional scan-to-scan
alignment. The technical contributions lie in multi-modal image correspondence
cues (between scans and schematic floorplan) as well as a novel coverage
potential avoiding an inherent stacking bias. The proposed approach has been
evaluated on five challenging large indoor spaces. To the best of our
knowledge, we present the first effective system that utilizes a 2D floorplan
image for building-scale 3D pointcloud alignment. The source code and the data
will be shared with the community to further enhance indoor mapping research.

该文提出了一种新算法，利用 2D 平面图对全景 RGBD 扫描进行校准，该算法可以大幅减少所需扫描数，并在五个大型室内空间上进行了评估。这是第一个利用 2D 平面图进行大型室内三维点云重建的有效系统，提出的算法使用多模态图像对应线索 (在扫描和平面图之间)，并避免了固有的堆叠偏差