DSpace Repository :: Browsing by Author "Liu, Ligang"

Browsing by Author "Liu, Ligang"

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Computational Design of Steady 3D Dissection Puzzles
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Tang, Keke; Song, Peng; Wang, Xiaofei; Deng, Bailin; Fu, Chi-Wing; Liu, Ligang; Alliez, Pierre and Pellacini, Fabio
Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles. We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state-of-the-art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness.
Computing Surface PolyCube-Maps by Constrained Voxelization
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Yang, Yang; Fu, Xiao-Ming; Liu, Ligang; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
We present a novel method to compute bijective PolyCube-maps with low isometric distortion. Given a surface and its preaxis- aligned shape that is not an exact PolyCube shape, the algorithm contains two steps: (i) construct a PolyCube shape to approximate the pre-axis-aligned shape; and (ii) generate a bijective, low isometric distortion mapping between the constructed PolyCube shape and the input surface. The PolyCube construction is formulated as a constrained optimization problem, where the objective is the number of corners in the constructed PolyCube, and the constraint is to bound the approximation error between the constructed PolyCube and the input pre-axis-aligned shape while ensuring topological validity. A novel erasing-and-filling solver is proposed to solve this challenging problem. Centeral to the algorithm for computing bijective PolyCube-maps is a quad mesh optimization process that projects the constructed PolyCube onto the input surface with high-quality quads. We demonstrate the efficacy of our algorithm on a data set containing 300 closed meshes. Compared to state-of-the-art methods, our method achieves higher practical robustness and lower mapping distortion.
Constrained Remeshing Using Evolutionary Vertex Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhang, Wen-Xiang; Wang, Qi; Guo, Jia-Peng; Chai, Shuangming; Liu, Ligang; Fu, Xiao-Ming; Chaine, Raphaëlle; Kim, Min H.
We propose a simple yet effective method to perform surface remeshing with hard constraints, such as bounding approximation errors and ensuring Delaunay conditions. The remeshing is formulated as a constrained optimization problem, where the variables contain the mesh connectivity and the mesh geometry. To solve it effectively, we adopt traditional local operations, including edge split, edge collapse, edge flip, and vertex relocation, to update the variables. Central to our method is an evolutionary vertex optimization algorithm, which is derivative-free and robust. The feasibility and practicability of our method are demonstrated in two applications, including error-bounded Delaunay mesh simplification and error-bounded angle improvement with a given number of vertices, over many models. Compared to state-of-the-art methods, our method achieves higher remeshing quality.
Deep Video-Based Performance Synthesis from Sparse Multi-View Capture
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Chen, Mingjia; Wang, Changbo; Liu, Ligang; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
We present a deep learning based technique that enables novel-view videos of human performances to be synthesized from sparse multi-view captures. While performance capturing from a sparse set of videos has received significant attention, there has been relatively less progress which is about non-rigid objects (e.g., human bodies). The rich articulation modes of human body make it rather challenging to synthesize and interpolate the model well. To address this problem, we propose a novel deep learning based framework that directly predicts novel-view videos of human performances without explicit 3D reconstruction. Our method is a composition of two steps: novel-view prediction and detail enhancement. We first learn a novel deep generative query network for view prediction. We synthesize novel-view performances from a sparse set of just five or less camera videos. Then, we use a new generative adversarial network to enhance fine-scale details of the first step results. This opens up the possibility of high-quality low-cost video-based performance synthesis, which is gaining popularity for VA and AR applications. We demonstrate a variety of promising results, where our method is able to synthesis more robust and accurate performances than existing state-of-the-art approaches when only sparse views are available.
Interactive Editing of Discrete Chebyshev Nets
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Li, Rui-Zeng; Guo, Jia-Peng; Wang, Qi; Chai, Shuangming; Liu, Ligang; Fu, Xiao-Ming; Chaine, Raphaëlle; Kim, Min H.
We propose an interactive method to edit a discrete Chebyshev net, which is a quad mesh with edges of the same length. To ensure that the edited mesh is always a discrete Chebyshev net, the maximum difference of all edge lengths should be zero during the editing process. Hence, we formulate an objective function using lp-norm (p > 2) to force the maximum length deviation to approach zero in practice. To optimize the nonlinear and non-convex objective function interactively and efficiently, we develop a novel second-order solver. The core of the solver is to construct a new convex majorizer for our objective function to achieve fast convergence. We present two acceleration strategies to further reduce the optimization time, including adaptive p change and adaptive variables reduction. A large number of experiments demonstrate the capability and feasibility of our method for interactively editing complex discrete Chebyshev nets.
Large-Scale Worst-Case Topology Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhang, Di; Zhai, Xiaoya; Fu, Xiao-Ming; Wang, Heming; Liu, Ligang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
We propose a novel topology optimization method to efficiently minimize the maximum compliance for a high-resolution model bearing uncertain external loads. Central to this approach is a modified power method that can quickly compute the maximum eigenvalue to evaluate the worst-case compliance, enabling our method to be suitable for large-scale topology optimization. After obtaining the worst-case compliance, we use the adjoint variable method to perform the sensitivity analysis for updating the density variables. By iteratively computing the worst-case compliance, performing the sensitivity analysis, and updating the density variables, our algorithm achieves the optimized models with high efficiency. The capability and feasibility of our approach are demonstrated over various large-scale models. Typically, for a model of size 512×170×170 and 69934 loading nodes, our method took about 50 minutes on a desktop computer with an NVIDIA GTX 1080Ti graphics card with 11 GB memory.
Learning Part Generation and Assembly for Sketching Man‐Made Objects
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Du, Dong; Zhu, Heming; Nie, Yinyu; Han, Xiaoguang; Cui, Shuguang; Yu, Yizhou; Liu, Ligang; Benes, Bedrich and Hauser, Helwig
Modeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure and thus has been researched for decades. However, modelling a man‐made shape with complex structures remains challenging. Existing methods adopt advanced deep learning techniques to map holistic sketches to 3D shapes. They are still bottlenecked to deal with complicated topologies. In this paper, we decouple the task of sketch2shape into a part generation module and a part assembling module, where deep learning methods are leveraged for the implementation of both modules. By changing the focus from holistic shapes to individual parts, it eases the learning process of the shape generator and guarantees high‐quality outputs. With the learned automated part assembler, users only need a little manual tuning to obtain a desired layout. Extensive experiments and user studies demonstrate the usefulness of our proposed system.
Memory-Efficient Bijective Parameterizations of Very-Large-Scale Models
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ye, Chunyang; Su, Jian-Ping; Liu, Ligang; Fu, Xiao-Ming; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
As high-precision 3D scanners become more and more widespread, it is easy to obtain very-large-scale meshes containing at least millions of vertices. However, processing these very-large-scale meshes is still a very challenging task due to memory limitations. This paper focuses on a fundamental geometric processing task, i.e., bijective parameterization construction. To this end, we present a spline-enhanced method to compute bijective and low distortion parameterizations for very-large-scale disk topology meshes. Instead of computing descent directions using the mesh vertices as variables, we estimate descent directions for each vertex by optimizing a proxy energy defined in spline spaces. Since the spline functions contain a small set of control points, it significantly decreases memory requirement. Besides, a divide-and-conquer method is proposed to obtain bijective initializations, and a submesh-based optimization strategy is developed to reduce distortion further. The capability and feasibility of our method are demonstrated over various complex models. Compared to the existing methods for bijective parameterizations of very-large-scale meshes, our method exhibits better scalability and requires much less memory.
Numerical Coarsening with Neural Shape Functions
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Ni, Ning; Xu, Qingyu; Li, Zhehao; Fu, Xiao‐Ming; Liu, Ligang; Hauser, Helwig and Alliez, Pierre
We propose to use nonlinear shape functions represented as neural networks in numerical coarsening to achieve generalization capability as well as good accuracy. To overcome the challenge of generalization to different simulation scenarios, especially nonlinear materials under large deformations, our key idea is to replace the linear mapping between coarse and fine meshes adopted in previous works with a nonlinear one represented by neural networks. However, directly applying an end‐to‐end neural representation leads to poor performance due to over‐huge parameter space as well as failing to capture some intrinsic geometry properties of shape functions. Our solution is to embed geometry constraints as the prior knowledge in learning, which greatly improves training efficiency and inference robustness. With the trained neural shape functions, we can easily adopt numerical coarsening in the simulation of various hyperelastic models without any other preprocessing step required. The experiment results demonstrate the efficiency and generalization capability of our method over previous works.
Practical Fabrication of Discrete Chebyshev Nets
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liu, Hao-Yu; Liu, Zhong-Yuan; Zhao, Zheng-Yu; Liu, Ligang; Fu, Xiao-Ming; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
We propose a computational and practical technique to allow home users to fabricate discrete Chebyshev nets for various 3D models. The success of our method relies on two key components. The first one is a novel and simple method to approximate discrete integrable, unit-length, and angle-bounded frame fields, used to model discrete Chebyshev nets. Central to our field generation process is an alternating algorithm that takes turns executing one pass to enforce integrability and another pass to approach unit length while bounding angles. The second is a practical fabrication specification. The discrete Chebyshev net is first partitioned into a set of patches to facilitate manufacturing. Then, each patch is assigned a specification on pulling, bend, and fold to fit the nets. We demonstrate the capability and feasibility of our method in various complex models.
Practical Foldover-Free Volumetric Mapping Construction
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Su, Jian-Ping; Fu, Xiao-Ming; Liu, Ligang; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
In this paper, we present a practically robust method for computing foldover-free volumetric mappings with hard linear constraints. Central to this approach is a projection algorithm that monotonically and efficiently decreases the distance from the mapping to the bounded conformal distortion mapping space. After projection, the conformal distortion of the updated mapping tends to be below the given bound, thereby significantly reducing foldovers. Since it is non-trivial to define an optimal bound, we introduce a practical conformal distortion bound generation scheme to facilitate subsequent projections. By iteratively generating conformal distortion bounds and trying to project mappings into bounded conformal distortion spaces monotonically, our algorithm achieves high-quality foldover-free volumetric mappings with strong practical robustness and high efficiency. Compared with existing methods, our method computes mesh-based and meshless volumetric mappings with no prescribed conformal distortion bounds. We demonstrate the efficacy and efficiency of our method through a variety of geometric processing tasks.
Precise High-order Meshing of 2D Domains with Rational Bézier Curves
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Yang, Jinlin; Liu, Shibo; Chai, Shuangming; Liu, Ligang; Fu, Xiao-Ming; Campen, Marcel; Spagnuolo, Michela
We propose a novel method to generate a high-order triangular mesh for an input 2D domain with two key characteristics: (1) the mesh precisely conforms to a set of input piecewise rational domain curves, and (2) the geometric map on each curved triangle is injective. Central to the algorithm is a new sufficient condition for placing control points of a rational Bézier triangle to guarantee that the conformance and injectivity constraints are theoretically satisfied. Taking advantage of this condition, we provide an explicit construct that robustly creates higher-order 2D meshes satisfying the two characteristics. We demonstrate the robustness and effectiveness of our algorithm over a data set containing 2200 examples.
Real-time Denoising Using BRDF Pre-integration Factorization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhuang, Tao; Shen, Pengfei; Wang, Beibei; Liu, Ligang; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
Path tracing has been used for real-time renderings, thanks to the powerful GPU device. Unfortunately, path tracing produces noisy rendered results, thus, filtering or denoising is often applied as a post-process to remove the noise. Previous works produce high-quality denoised results, by accumulating the temporal samples. However, they cannot handle the details from bidirectional reflectance distribution function (BRDF) maps (e.g. roughness map). In this paper, we introduce the BRDF preintegration factorization for denoising to better preserve the details from BRDF maps. More specifically, we reformulate the rendering equation into two components: the BRDF pre-integration component and the weighted-lighting component. The BRDF pre-integration component is noise-free, since it does not depend on the lighting. Another key observation is that the weighted-lighting component tends to be smooth and low-frequency, which indicates that it is more suitable for denoising than the final rendered image. Hence, the weighted-lighting component is denoised individually. Our BRDF pre-integration demodulation approach is flexible for many real-time filtering methods. We have implemented it in spatio-temporal varianceguided filtering (SVGF), ReLAX and ReBLUR. Compared to the original methods, our method manages to better preserve the details from BRDF maps, while both the memory and time cost are negligible.

Browsing by Author "Liu, Ligang"

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