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A Survey on Cage-based Deformation of 3D Models

COMPUTER GRAPHICS FORUM 2024年第2期43卷 e15060-e15060页

作者： Stroeter, D. Thiery, J. M. Hormann, K. Chen, J. Chang, Q. Besler, S. Mueller-Roemer, J. S. Boubekeur, T. Stork, A. Fellner, D. W. Tech Univ Darmstadt Darmstadt Germany Adobe Res Paris France Univ Svizzera Italiana Lugano Switzerland Inria Le Chesnay Rocquencourt France Fraunhofer IGD Darmstadt Germany Graz Univ Technol Inst Comp Graph & Knowledge Visualizat Graz Austria

Interactive deformation via control handles is essential in computer graphics for the modeling of 3D geometry. Deformation control structures include lattices for free-form deformation and skeletons for character articulation, but this report focuses on cage-based deformation. Cages for deformation control are coarse polygonal meshes that encase the to-be-deformed geometry, enabling high-resolution deformation. Cage-based deformation enables users to quickly manipulate 3D geometry by deforming the cage. Due to their utility, cage-based deformation techniques increasingly appear in many geometry modeling applications. For this reason, the computer graphics community has invested a great deal of effort in the past decade and beyond into improving automatic cage generation and cage-based deformation. Recent advances have significantly extended the practical capabilities of cage-based deformation methods. As a result, there is a large body of research on cage-based deformation. In this report, we provide a comprehensive overview of the current state of the art in cage-based deformation of 3D geometry. We discuss current methods in terms of deformation quality, practicality, and precomputation demands. In addition, we highlight potential future research directions that overcome current issues and extend the set of practical applications. In conjunction with this survey, we publish an application to unify the most relevant deformation methods. Our report is intended for computer graphics researchers, developers of interactive geometry modeling applications, and 3D modeling and character animation artists.

关键词： CCS Concepts center dot computing methodologies -> shape modeling center dot Mathematics of computing -> Interpolation

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VRTree: Example-Based 3D Interactive Tree modeling in Virtual Reality

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COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Wu, Di Yang, Mingxin Liu, Zhihao Tu, Fangyuan Liu, Fang Cheng, Zhanglin Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Visual Comp & Analyt Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenyang Peoples R China Univ Tokyo Tokyo Japan

We present VRTree, an example-based interactive virtual reality (VR) system designed to efficiently create diverse 3D tree models while faithfully preserving botanical characteristics of real-world references. Our method employs a novel representation called Hierarchical Branch Lobe (HBL), which captures the hierarchical features of trees and serves as a versatile intermediary for intuitive VR interaction. The HBL representation decomposes a 3D tree into a series of concise examples, each consisting of a small set of main branches, secondary branches, and lobe-bounded twigs. The core of our system involves two key components: (1) We design an automatic algorithm to extract an initial library of HBL examples from real tree point clouds. These HBL examples can be optionally refined according to user intentions through an interactive editing process. (2) Users can interact with the extracted HBL examples to assemble new tree structures, ensuring the local features align with the target tree species. A shape-guided procedural growth algorithm then transforms these assembled HBL structures into highly realistic, finegrained 3D tree models. Extensive experiments and user studies demonstrate that VRTree outperforms current state-of-the-art approaches, offering a highly effective and easy-to-use VR tool for tree modeling.

关键词： CCS Concepts center dot computing methodologies -> shape modeling Virtual reality Graphics systems and interfaces

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Raster-to-Graph: Floorplan Recognition via Autoregressive Graph Prediction with an Attention Transformer

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COMPUTER GRAPHICS FORUM 2024年第2期43卷 e15007-e15007页

作者： Hu, Sizhe Wu, Wenming Su, Ruolin Hou, Wanni Zheng, Liping Xu, Benzhu Hefei Univ Technol Hefei Peoples R China

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: .

关键词： CCS Concepts center dot computing methodologies -> shape modeling Computer vision

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Cut-Cell Microstructures for Two-scale Structural Optimization

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COMPUTER GRAPHICS FORUM 2024年第5期43卷 e15139-e15139页

作者： Tozoni, Davi Colli Huang, Zizhou Panozzo, Daniele Zorin, Denis NYU New York NY 10003 USA nTop New York NY USA

Two-scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. The main idea of this approach is to optimize the distribution of material properties in the object partitioned into relatively coarse cells, and then replace each cell with microstructure geometry that mimics these material properties. In this paper, we focus on adapting this approach to complex shapes in situations when preserving the shape's surface is essential. Our approach extends any regular (i.e. defined on a regular lattice grid) microstructure family to complex shapes, by enriching it with tiles adapted to the geometry of the cut-cell. We propose a fully automated and robust pipeline based on this approach, and we show that the performance of the regular microstructure family is only minimally affected by our extension while allowing its use on 2D and 3D shapes of high complexity.

关键词： CCS Concepts center dot computing methodologies -> shape modeling

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Hierarchical Co-generation of Parcels and Streets in Urban modeling

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COMPUTER GRAPHICS FORUM 2024年第2期43卷 e15053-e15053页

作者： Chen, Zebin Song, Peng Ortner, F. Peter Singapore Univ Technol & Design Singapore Singapore

We present a computational framework for modeling land parcels and streets. In the real world, parcels and streets are highly coupled with each other since a street network connects all the parcels in a certain area. However, existing works model parcels and streets separately to simplify the problem, resulting in urban layouts with irregular parcels and/or suboptimal streets. In this paper, we propose a hierarchical approach to co-generate parcels and streets from a user-specified polygonal land shape, guided by a set of fundamental urban design requirements. At each hierarchical level, new parcels are generated based on binary splitting of existing parcels, and new streets are subsequently generated by leveraging efficient graph search tools to ensure that each new parcel has a street access. At the end, we optimize the geometry of the generated parcels and streets to further improve their geometric quality. Our computational framework outputs an urban layout with a desired number of regular parcels that are reachable via a connected street network, for which users are allowed to control the modeling process both locally and globally. Quantitative comparisons with state-of-the-art approaches show that our framework is able to generate parcels and streets that are superior in some aspects.

关键词： CCS Concepts center dot computing methodologies -> shape modeling

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Developable Approximation via Gauss Image Thinning

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COMPUTER GRAPHICS FORUM 2021年第5期40卷 289-300页

作者： Binninger, Alexandre Verhoeven, Floor Herholz, Philipp Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland

Approximating 3D shapes with piecewise developable surfaces is an active research topic, driven by the benefits of developable geometry in fabrication. Piecewise developable surfaces are characterized by having a Gauss image that is a 1D object - a collection of curves on the Gauss sphere. We present a method for developable approximation that makes use of this classic definition from differential geometry. Our algorithm is an iterative process that alternates between thinning the Gauss image of the surface and deforming the surface itself to make its normals comply with the Gauss image. The simple, local-global structure of our algorithm makes it easy to implement and optimize. We validate our method on developable shapes with added noise and demonstrate its effectiveness on a variety of non-developable inputs. Compared to the state of the art, our method is more general, tessellation independent, and preserves the input mesh connectivity.

关键词： CCS Concepts center dot computing methodologies -> shape modeling Mesh geometry models

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Surface Map Homology Inference

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COMPUTER GRAPHICS FORUM 2021年第5期40卷 193-203页

作者： Born, J. Schmidt, P. Campen, M. Kobbelt, L. Rhein Westfal TH Aachen Aachen Germany Osnabruck Univ Osnabruck Germany

A homeomorphism between two surfaces not only defines a (continuous and bijective) geometric correspondence of points but also (by implication) an identification of topological features, i.e. handles and tunnels, and how the map twists around them. However, in practice, surface maps are often encoded via sparse correspondences or fuzzy representations that merely approximate a homeomorphism and are therefore inherently ambiguous about map topology. In this work, we show a way to infer topological information from an imperfect input map between two shapes. In particular, we compute a homology map, a linear map that transports homology classes of cycles from one surface to the other, subject to a global consistency constraint. Our inference robustly handles imperfect (e.g., partial, sparse, fuzzy, noisy, outlier-ridden, non-injective) input maps and is guaranteed to produce homology maps that are compatible with true homeomorphisms between the input shapes. Homology maps inferred by our method can be directly used to transfer homological information between shapes, or serve as foundation for the construction of a proper homeomorphism guided by the input map, e.g., via compatible surface decomposition.

关键词： CCS Concepts center dot computing methodologies -> shape modeling

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Learning to Generate and Manipulate 3D Radiance Field by a Hierarchical Diffusion Framework with CLIP Latent

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COMPUTER GRAPHICS FORUM 2023年第7期42卷 e14930-e14930页

作者： Wang, Jiaxu Zhang, Ziyi Xu, Renjing Hong Kong Univ Sci & Technol Guangzhou Peoples R China

3D-aware generative adversarial networks (GAN) are widely adopted in generating and editing neural radiance fields (NeRF). However, these methods still suffer from GAN-related issues including degraded diversity and training instability. Moreover, 3D-aware GANs consider NeRF pipeline as regularizers and do not directly operate with 3D assets, leading to imperfect 3D consistencies. Besides, the independent changes in disentangled editing cannot be ensured due to the sharing of some shallow hidden features in generators. To address these challenges, we propose the first purely diffusion-based three-stage framework for generative and editing tasks, with a series of well-designed loss functions that can directly handle 3D models. In addition, we present a generalizable neural point field as our 3D representation, which explicitly disentangles geometry and appearance in feature spaces. For 3D data conversion, it simplifies the preparation pipeline of datasets. Assisted by the representation, our diffusion model can separately manipulate the shape and appearance in a hierarchical manner by image/text prompts that are provided by the CLIP encoder. Moreover, it can generate new samples by adding a simple generative head. Experiments show that our approach outperforms the SOTA work in the generative tasks of direct generation of 3D representations and novel image synthesis, and completely disentangles the manipulation of shape and appearance with correct semantic correspondence in the editing tasks.

关键词： CCS Concepts center dot computing methodologies -> shape modeling Image manipulation

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Reconstructing 3D Human Pose from RGB-D Data with Occlusions

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COMPUTER GRAPHICS FORUM 2023年第7期42卷 e14982-e14982页

作者： Dang, Bowen Zhao, Xi Zhang, Bowen Wang, He Xi An Jiao Tong Univ Xian Peoples R China UCL London England

We propose a new method to reconstruct the 3D human body from RGB-D images with occlusions. The foremost challenge is the incompleteness of the RGB-D data due to occlusions between the body and the environment, leading to implausible reconstructions that suffer from severe human-scene penetration. To reconstruct a semantically and physically plausible human body, we propose to reduce the solution space based on scene information and prior knowledge. Our key idea is to constrain the solution space of the human body by considering the occluded body parts and visible body parts separately: modeling all plausible poses where the occluded body parts do not penetrate the scene, and constraining the visible body parts using depth data. Specifically, the first component is realized by a neural network that estimates the candidate region named the "free zone", a region carved out of the open space within which it is safe to search for poses of the invisible body parts without concern for penetration. The second component constrains the visible body parts using the "truncated shadow volume" of the scanned body point cloud. Furthermore, we propose to use a volume matching strategy, which yields better performance than surface matching, to match the human body with the confined region. We conducted experiments on the PROX dataset, and the results demonstrate that our method produces more accurate and plausible results compared with other methods.

关键词： CCS Concepts center dot computing methodologies -> shape modeling Reconstruction

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A Family of Barycentric Coordinates for Co-Dimension 1 Manifolds with Simplicial Facets

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COMPUTER GRAPHICS FORUM 2019年第5期38卷 75-83页

作者： Yan, Z. Schaefer, S. Texas A&M Univ College Stn TX 77843 USA

We construct a family of barycentric coordinates for 2D shapes including non-convex shapes, shapes with boundaries, and skeletons. Furthermore, we extend these coordinates to 3D and arbitrary dimension. Our approach modifies the construction of the Floater-Hormann-Kos family of barycentric coordinates for 2D convex shapes. We show why such coordinates are restricted to convex shapes and show how to modify these coordinates to extend to discrete manifolds of co-dimension 1 whose boundaries are composed of simplicial facets. Our coordinates are well-defined everywhere (no poles) and easy to evaluate. While our construction is widely applicable to many domains, we show several examples related to image and mesh deformation.

关键词： CCS Concepts center dot computing methodologies -> shape modeling Parametric curve and surface models

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