Abstract Creating floorplans lays the foundation for architectural design and scene modeling. We propose a novel framework for generating diverse high‐quality floorplans under predefined constraints. Central to our method is an iterative refinement process for optimizing the bounding boxes of rooms and the floorplan semantics image, which defines a vector floorplan together. Vector floorplans can be generated through a learning‐based refinement process. Our framework supports various constraints, such as floorplan boundaries, topological graphs, and bubble diagrams. Extensive experiments demonstrate that our method is superior to state‐of‐the‐art techniques, particularly in generating a wider variety of solutions that cater to various architectural needs.

In large-scale urban areas, edge computing, with flexible and low-latency services, enriches various pioneering applications such as the internet of vehicles (IoVs) and smart cities. These location-sensitive applications raise a critical concern, i.e., the mismatch between the spatial distributions of computing requirements and computing capacities. And this mismatch gives a huge challenge for load balancing among edge servers. However, existing approaches do not account for this spatial unevenness, which undermines the high-quality implementations of urban edge computing systems. Regarding this load balancing problem, we propose a novel Power Diagram based Edge Balancing (PDEB) approach, pursuing computing capacities self-adapted with computing requirements via the power diagram. This paper makes three key contributions. First, we propose a distribution modeling framework that formulates spatial mismatch as an optimization problem. Second, we introduce a decentralized power diagram constructed from modeled distributions, serving as the mathematical foundation for decentralized edge coordination. Third, we develop a structured pairing and scheduling strategy based on the power diagram to proactively redistribute load across edge servers. Experimentally, PDEB achieves$42.78\%$better load balance and$16.69\%$lower queuing delay than leading baselines, validating its theoretical and practical advantages.

Automating architectural floorplan design is vital for housing and interior design, offering a faster, cost-effective alternative to manual sketches by architects. However, existing methods, including rule-based and learning-based approaches, face challenges in design complexity and constrained generation with extensive post-processing, and tend to obvious geometric inconsistencies such as misalignment, overlap, and gaps. In this work, we propose a novel generative framework for vector floorplan design via structural graph generation, called GSDiff, focusing on wall junction generation and wall segment prediction to capture both geometric and semantic aspects of structural graphs. To improve the geometric rationality of generated structural graphs, we propose two innovative geometry enhancement methods. In wall junction generation, we propose a novel alignment loss function to improve geometric consistency. In wall segment prediction, we propose a random self-supervision method to enhance the model’s perception of the overall geometric structure, thereby promoting the generation of reasonable geometric structures. Employing the diffusion model and the Transformer model, as well as the geometry enhancement strategies, our framework can generate wall junctions, wall segments and room polygons with structural and semantic information, resulting in structural graphs that accurately represent floorplans. Extensive experiments show that the proposed method surpasses existing techniques, enabling free generation and constrained generation, marking a shift towards structure generation in architectural design.

Automated vector floorplan generation is valuable for designers to explore potential spatial designs. However, existing learning-based methods rely on complex post-processing or optimization to obtain plausible vector floorplans, which disrupts the end-to-end design flow. In this paper, we propose FloorplanSBS, a patch-based segmentation framework for directly synthesizing vector floorplans. Our method leverages the strengths of box-based representation and segmentation-based generation, following a division-and-labeling scheme. The framework operates in two stages: given input design constraints, a division model first divides the design space into rectangular patches, followed by a labelling model that assigns semantic labels to each patch. FloorplanSBS supports constraints such as boundaries and layout graphs. Extensive evaluations show that it surpasses state-of-the-art methods in generating high-quality vector floorplans. With its end-to-end neural framework, FloorplanSBS eliminates the need for post-processing, offering a simple, efficient, and user-friendly tool for vector floorplan design.

Abstract Point cloud normal estimation underpins many 3D vision and graphics applications. Precise normal estimation in regions of sharp curvature and high‐frequency variation remains a major bottleneck; existing learning‐based methods still struggle to isolate fine geometry details under noise and uneven sampling. We present FAHNet, a novel frequency‐aware hierarchical network that precisely tackles those challenges. Our Frequency‐Aware Hierarchical Geometry (FAHG) feature extraction module selectively amplifies and merges cross‐scale cues, ensuring that both fine‐grained local features and sharp structures are faithfully represented. Crucially, a dedicated Frequency‐Aware geometry enhancement (FA) branch intensifies sensitivity to abrupt normal transitions and sharp features, preventing the common over‐smoothing limitation. Extensive experiments on synthetic benchmarks (PCPNet, FamousShape) and real‐world scans (SceneNN) demonstrate that FAHNet outperforms state‐of‐the‐art approaches in normal estimation accuracy. Ablation studies further quantify the contribution of each component, and downstream surface reconstruction results validate the practical impact of our design.

With the rapid development of Mobile Edge Computing (MEC), various heterogeneous applications have being deployed on edge servers in close proximity to end-users for the low-latency responses. In this circumstance, since the resources on edge servers are limited, it is critical to deploy these applications on suitable edge servers. However, due to the heterogeneity of the applications and the mobility of end-users in real MEC circumstances, the requests each edge server received may undergo temporal fluctuations in both views of quantity and type. In other words, it is crucial to re-deploy these heterogeneous applications to match these dynamic circumstances, instead of permanent deployments without adjustments. Nevertheless, frequent re-deployment causes service interruptions and resource wastage, leading to system instability. Existing approaches struggle to handle redeployment effectively in heterogeneous, dynamic, and stability-critical MEC environments. In this paper, we first formulate the Edge Application Re-Deployment problem on the basis of constrained multi-objective optimization and prove its$\mathcal {NP}$-hardness. Then we propose an optimal re-deployment approach based on the Integer Programming technique for small-scale edge application re-deployment scenarios. And we also propose a Decompose-Solve-Merge approximation approach which balances the effectiveness and efficiency with a configurable parameter for large-scale scenarios. Extensive experiments on a real-world data set evaluate our novel approaches against four existing representative approaches. Additionally, we perform the ablation experiment to validate the effectiveness of our approaches and explore the impact of configurable parameter on the performance. The results show the superior performance of our approaches on re-deployment in terms of heterogeneous, dynamic, and stability.

Abstract Recognizing the detailed information embedded in rasterized floorplans is at the research forefront in the community of computer graphics and vision. With the advent of deep neural networks, automatic floorplan recognition has made tremendous breakthroughs. However, co‐recognizing both the structures and semantics of floorplans through one neural network remains a significant challenge. In this paper, we introduce a novel framework Raster‐to‐Graph, which automatically achieves structural and semantic recognition of floorplans. We represent vectorized floorplans as structural graphs embedded with floorplan semantics, thus transforming the floorplan recognition task into a structural graph prediction problem. We design an autoregressive prediction framework using the neural network architecture of the visual attention Transformer, iteratively predicting the wall junctions and wall segments of floorplans in the order of graph traversal. Additionally, we propose a large‐scale floorplan dataset containing over 10,000 real‐world residential floorplans. Our autoregressive framework can automatically recognize the structures and semantics of floorplans. Extensive experiments demonstrate the effectiveness of our framework, showing significant improvements on all metrics. Qualitative and quantitative evaluations indicate that our framework outperforms existing state‐of‐the‐art methods. Code and dataset for this paper are available at: https://github.com/HSZVIS/Raster-to-Graph .

Edge computing deploys edges close to end-users to provide highly accessible resources and latency-sensitive services. It is invaluable for urban crowd/hazard management services, e.g., real-time dynamic route planning and hazard monitoring/analysis, etc. However, in such scenarios, various types of urban hazards jeopardize the usability of edge servers. Worsely, these hazards could be integrated, like gas fires caused by urban earthquakes. In this regard, the formulation of usability risks that servers face is intractable due to the complexity, incomplete real-time data and insufficient expert knowledge of these integrated hazards. Therefore, we innovatively define the usability risks asServer Hazard Riskmodel from the view of the spatial data field by utilizingInformation Diffusion techniquewhich can overcome the adverse conditions above. Then we involve it to formulate theServer Hazard Risk User Allocation(SR-UA) problem, and analyze three typical solutions from the perspective of optimality and efficiency, which are the Lexicographic Goal Programming approach (SR-UA-LGP), the Approximation approach (SR-UA-A) and the Particle Swarm Optimization-based approach (SR-UA-PSO). The extensive experiments based on two real-world datasets illustrate the superior performance of our model and solutions.

Abstract Bubble diagrams serve as a crucial tool in the field of architectural planning and graphic design. With the surge of Artificial Intelligence Generated Content (AIGC), there has been a continuous emergence of research and development efforts focused on utilizing bubble diagrams for layout design and generation. However, there is a lack of research efforts focused on bubble diagram generation. In this paper, we propose a novel generative model, BubbleFormer, for generating diverse and plausible bubble diagrams. BubbleFormer consists of two improved Transformer networks: NodeFormer and EdgeFormer. These networks generate nodes and edges of the bubble diagram, respectively. To enhance the generation diversity, a VAE module is incorporated into BubbleFormer, allowing for the sampling and generation of numerous high‐quality bubble diagrams. BubbleFormer is trained end‐to‐end and evaluated through qualitative and quantitative experiments. The results demonstrate that BubbleFormer can generate convincing and diverse bubble diagrams, which in turn drive downstream tasks to produce high‐quality layout plans. The model also shows generalization capabilities in other layout generation tasks and outperforms state‐of‐the‐art techniques in terms of quality and diversity. In previous work, bubble diagrams as input are provided by users, and as a result, our bubble diagram generative model fills a significant gap in automated layout generation driven by bubble diagrams, thereby enabling an end‐to‐end layout design and generation. Code for this paper is at https://github.com/cgjiahui/BubbleFormer .

Abstract Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT‐based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider surface curvature. Topological errors and outliers can also occur in the close sheet surface remeshing, resulting in wrong meshes. With this regard, we present a novel method named PowerRTF, an extension of the restricted tangent face (RTF) in conjunction with the power diagram, to better approximate the original surface with curvature adaption. The idea is to introduce a weight property to each sample point and compute the power diagram on the tangent face to produce area‐controlled polygonal facets. Based on this, we impose the variable‐capacity constraint and centroid constraint to the PowerRTF, providing the trade‐off between mesh quality and computational efficiency. Moreover, we apply a normal verification‐based inverse side point culling method to address the topological errors and outliers in close sheet surface remeshing. Our method independently computes and optimizes the PowerRTF per sample point, which is efficiently implemented in parallel on the GPU. Experimental results demonstrate the effectiveness, flexibility, and efficiency of our method.

Floorplan generation has drawn widespread interest in the community. Recent learning-based methods for generating realistic floorplans have made significant progress while a complex heuristic post-processing is still necessary to obtain desired results. In this paper, we propose a novel wall-oriented method, called WallPlan , for automatically and efficiently generating plausible floorplans from various design constraints. We pioneer the representation of the floorplan as a wall graph with room labels and consider the floorplan generation as a graph generation. Given the boundary as input, we first initialize the boundary with windows predicted by WinNet. Then a graph generation network GraphNet and semantics prediction network LabelNet are coupled to generate the wall graph progressively by imitating graph traversal. WallPlan can be applied for practical architectural designs, especially the wall-based constraints. We conduct ablation experiments, qualitative evaluations, quantitative comparisons, and perceptual studies to evaluate our method's feasibility, efficacy, and versatility. Intensive experiments demonstrate our method requires no post-processing, producing higher quality floorplans than state-of-the-art techniques.

In virtual reality (VR), the virtual scenes are pre-designed by creators. Our physical surroundings, however, comprise significantly varied sizes, layouts, and components. To bridge the gap and further enable natural navigation, recent solutions have been proposed to redirect users or recreate the virtual content. However, they suffer from either interrupted experience or distorted appearances. We present a novel VR-oriented algorithm that automatically restructures a given virtual scene for a user’s physical environment. Different from the previous methods, we introduce neither interrupted walking experience nor curved appearances. Instead, a perception-aware function optimizes our retargeting technique to preserve the fidelity of the virtual scene that appears in VR head-mounted displays. Besides geometric and topological properties, it emphasizes the unique first-person view perceptual factors in VR, such as dynamic visibility and objectwise relationships. We conduct both analytical experiments and subjective studies. The results demonstrate our system’s versatile capability and practicability for natural navigation in VR: It reduces the virtual space by 40% without statistical loss of perceptual identicality.

We propose a novel data-driven technique for automatically and efficiently generating floor plans for residential buildings with given boundaries. Central to this method is a two-stage approach that imitates the human design process by locating rooms first and then walls while adapting to the input building boundary. Based on observations of the presence of the living room in almost all floor plans, our designed learning network begins with positioning a living room and continues by iteratively generating other rooms. Then, walls are first determined by an encoder-decoder network, and then they are refined to vector representations using dedicated rules. To effectively train our networks, we construct RPLAN - a manually collected large-scale densely annotated dataset of floor plans from real residential buildings. Intensive experiments, including formative user studies and comparisons, are conducted to illustrate the feasibility and efficacy of our proposed approach. By comparing the plausibility of different floor plans, we have observed that our method substantially outperforms existing methods, and in many cases our floor plans are comparable to human-created ones.

Abstract We propose a hierarchical framework for the generation of building interiors. Our solution is based on a mixed integer quadratic programming (MIQP) formulation. We parametrize a layout by polygons that are further decomposed into small rectangles. We identify important high‐level constraints, such as room size, room position, room adjacency, and the outline of the building, and formulate them in a way that is compatible with MIQP and the problem parametrization. We also propose a hierarchical framework to improve the scalability of the approach. We demonstrate that our algorithm can be used for residential building layouts and can be scaled up to large layouts such as office buildings, shopping malls, and supermarkets. We show that our method is faster by multiple orders of magnitude than previous methods.