检索结果-内蒙古大学图书馆

A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts

COMPUTER GRAPHICS FORUM 2025年

作者： Lu, Jiaxin Liang, Yongqing Han, Huijun Hua, Jiacheng Jiang, Junfeng Li, Xin Huang, Qixing Univ Texas Austin Comp Sci Dept Austin TX 78712 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77840 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Hohai Univ Coll Artificial Intelligence & Automa Nanjing Peoples R China Texas A&M Univ Sect Visual Comp & Computat Media College Stn TX 77840 USA

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics.

关键词： CCS Concepts • computing methodologies → shape analysis

来源：评论

学校读者我要写书评

暂无评论

S-ACORD: Spectral analysis of COral Reef Deformation

引用

COMPUTER GRAPHICS FORUM 2025年

作者： Alon-Borissiouk, Naama Yuval, Matan Treibitz, Tali Ben-Chen, Mirela Technion Israel Inst Technol Haifa Israel Univ Haifa Haifa Israel

We propose an efficient pipeline to register, detect, and analyze changes in 3D models of coral reefs captured over time. Corals have complex structures with intricate geometric features at multiple scales. 3D reconstructions of corals (e.g., using Photogrammetry) are represented by dense triangle meshes with millions of vertices. Hence, identifying correspondences quickly using conventional state-of-the-art algorithms is challenging. To address this gap we employ the Globally Optimal Iterative Closest Point (GO-ICP) algorithm to compute correspondences, and a fast approximation algorithm (FastSpectrum) to extract the eigenvectors of the Laplace-Beltrami operator for creating functional maps. Finally, by visualizing the distortion of these maps we identify changes in the coral reefs over time. Our approach is fully automatic, does not require user specified landmarks or an initial map, and surpasses competing shape correspondence methods on coral reef models. Furthermore, our analysis has detected the changes manually marked by humans, as well as additional changes at a smaller scale that were missed during manual inspection. We have additionally used our system to analyse a coral reef model that was too extensive for manual analysis, and validated that the changes identified by the system were correct.

关键词： CCS Concepts Mesh geometry models • computing methodologies → shape analysis

来源：评论

学校读者我要写书评

暂无评论

Localized shape Modelling with Global Coherence: An Inverse Spectral Approach

引用

COMPUTER GRAPHICS FORUM 2022年第5期41卷 13-24页

作者： Pegoraro, M. Melzi, S. Castellani, U. Marin, R. Rodola, E. Sapienza Univ Rome Rome Italy Univ Milano Bicocca Milan Italy Univ Verona Verona Italy

Many natural shapes have most of their characterizing features concentrated over a few regions in space. For example, humans and animals have distinctive head shapes, while inorganic objects like chairs and airplanes are made of well-localized functional parts with specific geometric features. Often, these features are strongly correlated - a modification of facial traits in a quadruped should induce changes to the body structure. However, in shape modelling applications, these types of edits are among the hardest ones;they require high precision, but also a global awareness of the entire shape. Even in the deep learning era, obtaining manipulable representations that satisfy such requirements is an open problem posing significant constraints. In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand. Modifications to these spectra are translated to semantically valid deformations of the corresponding surface. By explicitly decoupling the global from the local surface features, our pipeline allows to perform local edits while simultaneously maintaining a global stylistic coherence. We empirically demonstrate how our learning-based model generalizes to shape representations not seen at training time, and we systematically analyze different choices of local operators over diverse shape categories.

关键词： CCS Concepts • computing methodologies → shape analysis shape representations

来源：评论

学校读者我要写书评

暂无评论

MendNet: Restoration of Fractured shapes Using Learned Occupancy Functions

引用

COMPUTER GRAPHICS FORUM 2022年第5期41卷 65-78页

作者： Lamb, N. Banerjee, S. Banerjee, N. K. Clarkson Univ Potsdam NY 13676 USA

We provide a novel approach to perform fully automated generation of restorations for fractured shapes using learned implicit shape representations in the form of occupancy functions. Our approach lays the groundwork to perform automated object repair via additive manufacturing. Existing approaches for restoration of fractured shapes either require prior knowledge of object structure such as symmetries between the restoration and the fractured object, or predict restorations as voxel outputs that are impractical for repair at current resolutions. By leveraging learned occupancy functions for restoration prediction, our approach overcomes the curse of dimensionality with voxel approaches, while providing plausible restorations. Given a fractured shape, we fit a function to occupancy samples from the shape to infer a latent code. We apply a learned transformation to the fractured shape code to predict a corresponding code for restoration generation. To ensure physical validity and well-constrained shape estimation, we contribute a loss that models feasible occupancy values for fractured shapes, restorations, and complete shapes obtained by joining fractured and restoration shapes. Our work overcomes deficiencies of shape completion approaches adapted for repair, and enables consumer-driven object repair and cultural heritage object restoration. We share our code and a synthetic dataset of fractured meshes from 8 shapeNet classes at: https://***/Terascale- All- sensing-Research-Studio/MendNet.

关键词： CCS Concepts • computing methodologies → shape analysis Neural networks

来源：评论

学校读者我要写书评

暂无评论

WTFM Layer: An Effective Map Extractor for Unsupervised shape Correspondence

引用

COMPUTER GRAPHICS FORUM 2022年第7期41卷 51-61页

作者： Liu, Shengjun Xu, Haojun Yan, Dong-Ming Hu, Ling Liu, Xinru Li, Qinsong Cent South Univ Inst Engn Modeling & Sci Comp Changsha 410083 Hunan Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Hunan First Normal Univ Sch Math & Stat Changsha 410205 Hunan Peoples R China Cent South Univ Big Data Inst Changsha 410083 Hunan Peoples R China

We propose a novel unsupervised learning approach for computing correspondences between non-rigid 3D shapes. The core idea is that we integrate a novel structural constraint into the deep functional map pipeline, a recently dominant learning framework for shape correspondence, via a powerful spectral manifold wavelet transform (SMWT). As SMWT is isometrically invariant operator and can analyze features from multiple frequency bands, we use the multiscale SMWT results of the learned features as function preservation constraints to optimize the functional map by assuming each frequency-band information of the descriptors should be correspondingly preserved by the functional map. Such a strategy allows extracting significantly more deep feature information than existing approaches which only use the learned descriptors to estimate the functional map. And our formula strongly ensure the isometric properties of the underlying map. We also prove that our computation of the functional map amounts to filtering processes only referring to matrix multiplication. Then, we leverage the alignment errors of intrinsic embedding between shapes as a loss function and solve it in an unsupervised way using the Sinkhorn algorithm. Finally, we utilize DiffusionNet as a feature extractor to ensure that discretization-resistant and directional shape features are produced. Experiments on multiple challenging datasets prove that our method can achieve state-of-the-art correspondence quality. Furthermore, our method yields significant improvements in robustness to shape discretization and generalization across the different datasets. The source code and trained models will be available at https://***/HJ-Xu/WTFM-Layer.

关键词： CCS Concepts • computing methodologies → shape analysis Matching

来源：评论

学校读者我要写书评

暂无评论

Topological Simplification of Nested shapes

引用

COMPUTER GRAPHICS FORUM 2022年第5期41卷 161-173页

作者： Zeng, D. Chambers, E. Letscher, D. Ju, T. Washington Univ St Louis St Louis MO 63110 USA St Louis Univ St Louis MO 63103 USA

We present a method for removing unwanted topological features (e.g., islands, handles, cavities) from a sequence of shapes where each shape is nested in the next. Such sequences can be found in nature, such as a multi-layered material or a growing plant root. Existing topology simplification methods are designed for single shapes, and applying them independently to shapes in a sequence may lose the nesting property. We formulate the nesting-constrained simplification task as an optimal labelling problem on a set of candidate shape deletions ("cuts") and additions ("fills"). We explored several optimization strategies, including a greedy heuristic that sequentially propagates labels, a state-space search algorithm that is provably optimal, and a beam-search variant with controllable complexity. Evaluation on synthetic and real-world data shows that our method is as effective as single-shape simplification methods in reducing topological complexity and minimizing geometric changes, and it additionally ensures nesting. Also, the beam-search strategy is found to strike the best balance between optimality and efficiency.

关键词： CCS Concepts • computing methodologies → shape analysis Volumetric models

来源：评论

学校读者我要写书评

暂无评论

引用

COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Lu, Z. A. Xiong, W. D. Ren, P. Jia, J. Y. Tongji Univ Shanghai Peoples R China Shenzhen Univ Shenzhen Peoples R China Jilin Animat Inst Changchun Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China

Large-scale urban point clouds play a vital role in various applications, while rendering and transmitting such data remains challenging due to its large volume, complicated structures, and significant redundancy. In this paper, we present LightUrban, the first point cloud instancing framework for efficient rendering and transmission of fine-grained complex urban scenes. We first introduce a segmentation method to organize the point clouds into individual buildings and vegetation instances from coarse to fine. Next, we propose an unsupervised similarity detection approach to accurately group instances with similar shapes. Furthermore, a fast pose and size estimation component is applied to calculate the transformations between the representative instance and the corresponding similar instances in each group. By replacing individual instances with their group's representative instances, the data volume and redundancy can be dramatically reduced. Experimental results on large-scale urban scenes demonstrate the effectiveness of our algorithm. To sum up, our method not only structures the urban point clouds but also significantly reduces data volume and redundancy, filling the gap in lightweighting urban landscapes through instancing.

关键词： CCS Concepts • computing methodologies → shape analysis Point-based models

来源：评论

学校读者我要写书评

暂无评论

Strictly Conservative Neural Implicits

引用

COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Ludwig, I. Campen, M. Osnabruck Univ Osnabruck Germany

We describe a method to convert 3D shapes into neural implicit form such that the shape is approximated in a guaranteed conservative manner. This means the input shape is strictly contained inside the neural implicit or, alternatively, vice versa. Such conservative approximations are of interest in a variety of applications, including collision detection, occlusion culling, or intersection testing. Our approach is the first to guarantee conservativeness in this context of neural implicits. We support input given as mesh, voxel set, or implicit function. Adaptive affine arithmetic is employed in the neural network fitting process, enabling the reasoning over infinite sets of points despite using a finite set of training data. Combined with an interior point style optimization approach this yields the desired guarantee.

关键词： CCS Concepts • computing methodologies → shape analysis Mesh models Neural networks

来源：评论

学校读者我要写书评

暂无评论

Multiscale Spectral Manifold Wavelet Regularizer for Unsupervised Deep Functional Maps

引用

COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Liu, Shengjun Meng, Jing Hu, Ling Guo, Yueyu Liu, Xinru Yang, Xiaoxia Wang, Haibo Li, Qinsong Cent South Univ Inst Engn Modeling & Sci Comp Changsha 410083 Hunan Peoples R China Cent South Univ Big Data Inst Changsha 410083 Hunan Peoples R China Hunan First Normal Univ Sch Math & Stat Changsha 410205 Hunan Peoples R China

In deep functional maps, the regularizer computing the functional map is especially crucial for ensuring the global consistency of the computed pointwise map. As the regularizers integrated into deep learning should be differentiable, it is not trivial to incorporate informative axiomatic structural constraints into the deep functional map, such as the orientation-preserving term. Although commonly used regularizers include the Laplacian-commutativity term and the resolvent Laplacian commutativity term, these are limited to single-scale analysis for capturing geometric information. To this end, we propose a novel and theoretically well-justified regularizer commuting the functional map with the multiscale spectral manifold wavelet operator. This regularizer enhances the isometric constraints of the functional map and is conducive to providing it with better structural properties with multiscale analysis. Furthermore, we design an unsupervised deep functional map with the regularizer in a fully differentiable way. The quantitative and qualitative comparisons with several existing techniques on the (near-)isometric and non-isometric datasets show our method's superior accuracy and generalization capabilities. Additionally, we illustrate that our regularizer can be easily inserted into other functional map methods and improve their accuracy.

关键词： CCS Concepts • computing methodologies → shape analysis • Theory of computation → Computational geometry

来源：评论

学校读者我要写书评

暂无评论

Attention And Positional Encoding Are (Almost) All You Need For shape Matching

引用

COMPUTER GRAPHICS FORUM 2023年第5期42卷 e14912-e14912页

作者： Raganato, Alessandro Pasi, Gabriella Melzi, Simone Univ Milano Bicoccca Dept Informat Syst & Commun DISCo Milan Italy

The fast development of novel approaches derived from the Transformers architecture has led to outstanding performance in different scenarios, from Natural Language Processing to Computer Vision. Recently, they achieved impressive results even in the challenging task of non-rigid shape matching. However, little is known about the capability of the Transformer-encoder architecture for the shape matching task, and its performances still remained largely unexplored. In this paper, we step back and investigate the contribution made by the Transformer-encoder architecture compared to its more recent alternatives, focusing on why and how it works on this specific task. Thanks to the versatility of our implementation, we can harness the bi-directional structure of the correspondence problem, making it more interpretable. Furthermore, we prove that positional encodings are essential for processing unordered point clouds. Through a comprehensive set of experiments, we find that attention and positional encoding are (almost) all you need for shape matching. The simple Transformer-encoder architecture, coupled with relative position encoding in the attention mechanism, is able to obtain strong improvements, reaching the current state-of-the-art.

关键词： CCS Concepts • computing methodologies → shape analysis • Theory of computation → Computational geometry

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：