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GeoCode: Interpretable Shape Programs

computer graphics FORUM 2025年第1期44卷

作者： Pearl, Ofek Lang, Itai Hu, Yuhua Yeh, Raymond A. Hanocka, Rana Tel Aviv Univ Sch Elect Engn Tel Aviv Israel Univ Chicago Dept Comp Sci Chicago IL 60611 USA Purdue Univ Dept Comp Sci W Lafayette IN USA

The task of crafting procedural programs capable of generating structurally valid 3D shapes easily and intuitively remains an elusive goal in computer vision and graphics. Within the graphics community, generating procedural 3D models has shifted to using node graph systems. They allow the artist to create complex shapes and animations through visual programming. Being a high-level design tool, they made procedural 3D modelling more accessible. However, crafting those node graphs demands expertise and training. We present GeoCode, a novel framework designed to extend an existing node graph system and significantly lower the bar for the creation of new procedural 3D shape programs. Our approach meticulously balances expressiveness and generalization for part-based shapes. We propose a curated set of new geometric building blocks that are expressive and reusable across domains. We showcase three innovative and expressive programs developed through our technique and geometric building blocks. Our programs enforce intricate rules, empowering users to execute intuitive high-level parameter edits that seamlessly propagate throughout the entire shape at a lower level while maintaining its validity. To evaluate the user-friendliness of our geometric building blocks among non-experts, we conduct a user study that demonstrates their ease of use and highlights their applicability across diverse domains. Empirical evidence shows the superior accuracy of GeoCode in inferring and recovering 3D shapes compared to an existing competitor. Furthermore, our method demonstrates superior expressiveness compared to alternatives that utilize coarse primitives. Notably, we illustrate the ability to execute controllable local and global shape manipulations. Our code, programs, datasets and Blender add-on are available at .

关键词： curves and surfaces image-based modelling modelling modelling interfaces

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Genetic Subdivision Curve and Surface Reconstruction 32

Genetic Subdivision Curve and Surface Reconstruction

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32nd International Conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2024

作者： Komar, Alexander Riegler, Marco Preiner, Reinhold Augsdörfer, Ursula Institute of Computer Graphics and Knowledge Visualisation Graz University of Technology Austria

In this paper we employ a new genetic algorithm approach for CAD shape reconstruction, where a mathematical shape representation is reconstructed from point data. We reconstruct planar subdivision curves and 3D subdivision meshes from ordered input point data by fitting the corresponding subdivision control polygon or control mesh respectively, from which the smooth subdivision limit surfaces can be derived. For the reconstruction of curves the system estimates the number and position of control points required to approximate the curve closely. To reconstruct subdivision surfaces from points, the system determines a sequence of CAD operations which is subject to mutation in the course of a genetic optimization. We discuss implementation details of the proposed genetic algorithms and demonstrate our approach on a number of example data. © 2024 University of West Bohemia. All rights reserved.

关键词： Mesh generation

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31st International Conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2023 - Proceedings

31st International Conference in Central Europe on Computer ...

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31st International Conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2023

ISBN: (纸本)9788086943329

The proceedings contain 49 papers. The topics discussed include: investigation on encoder-decoder networks for segmentation of very degraded x-ray CT tomograms;self-checkout product class verification using center loss approach;why existing multimodal crowd counting datasets can lead to unfulfilled expectations in real-world applications;perceptions of color pickers in virtual reality art-making;modeling and rendering with expressive B-Spline curves;sail: semantic analysis of information in light fields: results from synthetic and real-world data;texture spectral similarity criteria comparison;designing a lightweight edge-guided convolutional neural network for segmenting mirrors and reflective surfaces;synthetic-real domain adaptation for probabilistic pose estimation;error-robust indoor augmented reality navigation: evaluation criteria and a new approach;and visualization of deviations between different geometries using a multi-level voxel-based representation.

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ECHO: Extended Convolution Histogram of Orientations for Local Surface Description

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computer graphics FORUM 2021年第1期40卷 180-194页

作者： Mitchel, Thomas W. Rusinkiewicz, Szymon Chirikjian, Gregory S. Kazhdan, Michael Johns Hopkins Univ Baltimore MD 21218 USA Princeton Univ Princeton NJ 08544 USA Natl Univ Singapore Singapore Singapore

This paper presents a novel, highly distinctive and robust local surface feature descriptor. Our descriptor is predicated on a simple observation: instead of describing the points in the vicinity of a feature point relative to a reference frame at the feature point, all points in the region describe the feature point relative to their own frames. Isometry invariance is a byproduct of this construction. Our descriptor is derived relative to the extended convolution - a generalization of the standard convolution that allows the filter to adaptively transform as it passes over the domain. As such, we name our descriptor the Extended Convolution Histogram of Orientations (ECHO). It exhibits superior performance compared to popular surface descriptors in both feature matching and shape correspondence experiments. In particular, the ECHO descriptor is highly stable under near-isometric deformations and remains distinctive under significant levels of noise, tessellation, complex deformations and the kinds of interference commonly found in real data.

关键词： 3D shape matching modeling curves and surfaces modeling computer vision – shape recognition methods and applications

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Toward Automatic Interpretation of 3D Plots 16th

Toward Automatic Interpretation of 3D Plots

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Brandt, Laura E. Freeman, William T. MIT CSAIL Cambridge MA 02139 USA NSF AI Inst Artificial Intelligence & Fundamental Cambridge MA USA

ISBN: (纸本)9783030863319

This paper explores the challenge of teaching a machine how to reverse-engineer the grid-marked surfaces used to represent data in 3D surface plots of two-variable functions. These are common in scientific and economic publications;and humans can often interpret them with ease, quickly gleaning general shape and curvature information from the simple collection of curves. While machines have no such visual intuition, they do have the potential to accurately extract the more detailed quantitative data that guided the surface's construction. We approach this problem by synthesizing a new dataset of 3D grid-marked surfaces (SurfaceGrid) and training a deep neural net to estimate their shape. Our algorithm successfully recovers shape information from synthetic 3D surface plots that have had axes and shading information removed, been rendered with a variety of grid types, and viewed from a range of viewpoints.

关键词： 3D plot interpretation Figure analysis Data extraction Shape-from-x graphics recognition Neural network computer vision

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Filling the Holes on 3D Heritage Object Surface based on Automatic Segmentation Algorithm

arXiv

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arXiv 2023年

作者： Van, Sinh Nguyen, B. Le, Son Thanh Tran, Minh Khai Sach, Le Thanh School of Computer Science and Engineering International University - VNUHCM Viet Nam School of Computer Science and Engineering Ho Chi Minh City University of Technology VNUHCM Viet Nam

Reconstructing and processing the 3D objects are popular activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on the methods like geometric modeling (a branch of applied mathematics and computational geometry) or the machine learning algorithms based on image processing. The computation of geometrical objects includes processing the curves and surfaces, subdivision, simplification, meshing, holes filling, reconstructing and refining the 3D surface’s objects on both point cloud data and triangular mesh. While the machine learning methods are developed using deep learning models. With the support of 3D laser scan devices and LiDAR techniques, the obtained dataset is close to original’s shape of the real objects. Besides, the photography and its application based on the modern techniques in recent years help us collect data and process the 3D models more precise. This article propose an improved method for filling holes on the 3D object’s surface based on an automatic segmentation. Instead of filling the hole directly as the existing methods, we now subdivide the hole before filling it. The hole is first determined and segmented automatically based on computation of its local curvature. It is then filled on each part of the hole to match its local curvature shape. The method can work on both 3D point cloud surfaces and triangular mesh surface. Comparing to the state-of-the-art (SOTA) methods, our proposed method obtained higher accuracy of the reconstructed 3D objects. Copyright © 2023, The Authors. All rights reserved.

关键词： Filling

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图形图像积分与微分不变量的构造与应用

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图学学报 2022年第6期43卷 1182-1192页

作者：墨瀚林郝优郭锐郝宏翔张贺李琪李华中国科学院计算技术研究所智能信息处理重点实验室北京100190 中国科学院大学计算机科学与技术学院北京100049

作为图形图像数据的常用特征,微分不变量和以矩不变量为代表的积分不变量在计算机视觉、模式识别和计算机图形学等领域扮演了重要角色。在过去二十年中,本研究团队利用基本生成函数构造了灰度图像、彩色图像、向量场、点云、曲线和网格... 详细信息

作为图形图像数据的常用特征,微分不变量和以矩不变量为代表的积分不变量在计算机视觉、模式识别和计算机图形学等领域扮演了重要角色。在过去二十年中,本研究团队利用基本生成函数构造了灰度图像、彩色图像、向量场、点云、曲线和网格曲面等图形图像数据在几何变换、颜色变换、图像模糊和全变换下的矩不变量;证明了仿射变换下几何矩不变量与微分不变量之间满足同构关系,提出了一种获取仿射微分不变量的简单方法,并进一步得到了射影变换和莫比乌斯变换下图形图像的微分不变量;为了增强深度神经网络对常见图形图像变换的不变性,探索了如何将图形图像不变量引入深度神经网络模型。系统回顾与总结了上述工作,简要介绍了如何使用基本生成函数构造图形图像在仿射变换下的几何矩不变量与微分不变量,分析了图形图像不变量的典型应用场景及优缺点,并对未来的研究进行了展望。

关键词：图形图像变换特征提取矩不变量微分不变量图像分类形状分析模板匹配

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A Variational Approach to Registration with Local Exponential Coordinates

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computer graphics FORUM 2019年第1期38卷 482-506页

作者： Paman, Ashish Rangarajan, Ramsharan Indian Inst Sci Bangalore Dept Mech Engn Bengaluru India

We identify a novel parameterization for the group of finite rotations (SO3), consisting of an atlas of exponential maps defined over local tangent planes, for the purpose of computing isometric transformations in registration problems that arise in machine vision applications. Together with a simple representation for translations, the resulting system of coordinates for rigid body motions is proper, free from singularities, is unrestricted in the magnitude of motions that can be represented and poses no difficulties in computer implementations despite their multi-chart nature. Crucially, such a parameterization helps to admit varied types of data sets, to adopt data-dependent error functionals for registration, seamlessly bridges correspondence and pose calculations, and inspires systematic variational procedures for computing optimal solutions. As a representative problem, we consider that of registering point clouds onto implicit surfaces without introducing any discretization of the latter. We derive coordinate-free stationarity conditions, compute consistent linearizations, provide algorithms to compute optimal solutions and examine their performance with detailed examples. The algorithm generalizes naturally to registering curves and surfaces onto implicit manifolds, is directly adaptable to handle the familiar problem of pairwise registration of point clouds and allows for incorporating scale factors during registration.

关键词： computer vision - tracking methods and applications implicit surfaces modelling computational geometry

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A heat flow based relaxation scheme for n dimensional discrete hyper surfaces

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computerS & graphics-UK 2018年第Apr.期71卷 124-131页

作者： Livesu, Marco CNR IMATI Genoa Italy

We consider the problem of relaxing a discrete (n - 1) dimensional hyper surface defining the boundary between two adjacent n dimensional regions in a discrete segmentation. This problem often occurs in computer graphics and vision, where objects are represented by discrete entities such as pixel/voxel grids or polygonal/polyhedral meshes, and the resulting boundaries often expose a typical jagged behavior. We propose a relaxation scheme that replaces the original boundary with a smoother version of it, defined as the level set of a continuous function. The problem has already been considered in recent years, but current methods are specifically designed to relax curves on triangulated discrete 2-manifolds embedded in R-3, and do not clearly scale to multiple discrete representations or to higher dimensions. Our biggest contribution is a smoothing operator entirely based on three canonical differential operators: namely the Laplacian, gradient and divergence. These operators are ubiquitous in applied mathematics, are available for a variety of discretization choices, and exist in any dimension. To the best of the author's knowledge, this is the first intrinsically dimension-independent method, and can be used to relax curves on 2-manifolds, surfaces in R-3, or even hyper-surfaces in R-n. We demonstrate our method on a variety of discrete entities, spanning from triangular, quadrilateral and polygonal surfaces, to solid tetrahedral meshes. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Diffusion Smoothing Implicit hyper surfaces Heat flow

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A Complex-Valued Field Model for Shape Representation with Applications in computer vision and graphics

A Complex-Valued Field Model for Shape Representation with A...

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作者： Corring, John R. University of Florida

学位级别：Ph.D.

Shape processing and analysis is a growing area of research straddling computer vision and computer graphics. Probability density and signed distance representations have become central to the field. While modeling uncertainty about observations and providing a convex representation, probability densities lack geometric precision and often exhibit topological inaccuracies. Signed distances lack the robustness of densities and the geometry of the feature space is very complicated. In this thesis, we develop a parametric model for approximating signed distance functions. This allows us to build up a useful shape representation with accurate topological and geometric data from point and normal estimates. The representation is approximately linear under union of parameters, thus allowing for easy computation and conventional statistical methods to be leveraged for shape processing. We develop an algorithm for registering oriented point-sets that leverages the representation. We compare this algorithm with various contenders in registration. We also develop algorithms for mesh extraction from sparsely scattered oriented points, dimensionality reduction on a collection of shapes, and approximation of distance functions on Riemannian surfaces. Empirical validation of all of the approaches outlined in this work is performed. Theoretical contributions include the description of a family of curves for which the representation converges uniformly, clarification of the regularizing principle on the geometric component of the representation, and an analysis of the asymptotic behavior of the representation with respect to the magnitude variance and frequency variance parameters.

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