Dirichlet Harmonic Shape Compression with Feature Preservation for Parameterized Surfaces

In this paper, we investigate a novel problem of using generative adversarial networks in the task of 3D shape generation according to semantic attributes. Recent works map 3D shapes into 2D parameter domain, which enables training Generative Adversarial Networks (GANs) for 3D shape generation task. We extend these architectures to the conditional setting, where we generate 3D shapes with respect to subjective attributes defined by the user. Given pairwise comparisons of 3D shapes, our model performs two tasks: it learns a generative model with a controlled latent space, and a ranking function for the 3D shapes based on their multi-chart representation in 2D. The capability of the model is demonstrated with experiments on HumanShape, Basel Face Model and reconstructed 3D CUB datasets. We also present various applications that benefit from our model, such as multi-attribute exploration, mesh editing, and mesh attribute transfer.

Deep point-to-subspace metric learning for sketch-based 3D shape retrieval

Shape analysis finds many important applications in shape understanding, matching and retrieval. Among the various shape analysis methods, spectral shape analysis aims to study the spectrum of the Laplace–Beltrami operator of some well‐designed shape‐dependent equations and obtain a spectral shape descriptor that can in turn be used for shape analysis purposes. The success of such approaches depends greatly on the discriminating power of a shape descriptor. On the other hand, the medial axis of a shape is widely known for its complete shape representation. It is sensitive to small perturbation of the boundary of a shape which often poses difficulty in its effective use for shape analysis. In this paper, we propose a new spectral shape descriptor, called the medial axis spectrum for 2D shapes, which directly applies spectral analysis to the medial axes of the shapes. We extend the Laplace–Beltrami operator onto the medial axis, and take the solution to an extended Laplacian eigenvalue problem defined on the axis as the medial axis spectrum. The medial axis spectrum is robust in the presence of shape boundary noise, and is invariant under rigid transformations, uniform scaling and isometry of the medial axis. We demonstrate these benefits of such a medial axis spectrum representation through extensive experiments. The medial axis spectrum is further used for 2D shape retrieval, and its superiority over previous work is shown by comparison.

We present a set of techniques to address the problem of scalable creation of visual overview representations of large 3D shape databases based on dimensionality reduction of feature vectors extracted from shape descriptions. We address the problem of feature extraction by exploring both combinations of hand-engineered geometric features and using the latent feature vectors generated by a deep learning classification method, and discuss the comparative advantages of both approaches. Separately, we address the problem of generating insightful 2D projections of these feature vectors that are able to separate well different groups of similar shapes by two approaches. First, we create quality projections by both automatic search in the space of feature combinations and, alternatively, by leveraging human insight to improve projections by iterative feature selection. Secondly, we use deep learning to automatically construct projections from the extracted features. We show that our three variations of deep learning, which jointly treat feature extraction, selection, and projection, allow efficient creation of high-quality visual overviews of large shape collections, require minimal user intervention, and are easy to implement. We demonstrate our approach on several real-world 3D shape databases.KeywordsContent-based shape retrievalMultidimensional projectionsFeature selectionDeep learningVisual analytics

Large scale sketch-based 3D shape retrieval has received more and more attentions in the community of content-based 3D object retrieval. The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a comprehensive 3D model dataset. The benchmark contains 12,680 sketches and 8,987 3D models, divided into 171 distinct classes. In this track, 12 runs were submitted by 4 groups and their retrieval performance was evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote the progress of this research direction for the 3D model retrieval community.

PurposeThis paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.Design/methodology/approachResearch papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose.FindingsLAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction.Research limitations/implicationsThe literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing.Practical implicationsContact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely.Social implicationsLAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace.Originality/valueThis paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

3D small strain large deflection beam shape sensing including poisson effect

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a single reference 3D shape. Specifically, we present a multi-scale GAN-based model designed to capture the input shape's geometric features across a range of spatial scales. To avoid large memory and computational cost induced by operating on the 3D volume, we build our generator atop the tri-plane hybrid representation, which requires only 2D convolutions. We train our generative model on a voxel pyramid of the reference shape, without the need of any external supervision or manual annotation. Once trained, our model can generate diverse and high-quality 3D shapes possibly of different sizes and aspect ratios. The resulting shapes present variations across different scales, and at the same time retain the global structure of the reference shape. Through extensive evaluation, both qualitative and quantitative, we demonstrate that our model can generate 3D shapes of various types. 1

A heat initiated 3D shape recovery and biodegradable thermoplastic tolerating a strain of 5

In this paper, we present a method for estimating full 3D shapes from given 2D silhouette images of human bodies. Because the silhouette only consists of the partial information on the true shape, it is an ill-posed problem. To address the problem, we use the statistical human shape space obtained from the existing large 3D human shape database. The method consists of three steps. First, we extract the boundary pixels and their appropriate normal vectors from the input silhouette images. Then, we initialize the correspondences of each pixel to the vertex of the statistically-deformable 3D human model. Finally, we numerically optimize the parameters of the statistical model to fit best to the given silhouettes. The viability and the robustness of the method is demonstrated with various experiments.

This paper presents a shape analysis on orthogonal feature curves of 3D bald head scans with the intention of predicting scalp shape under the hair. While there are currently a number of large scale 3D head data collections available around the world, they unfortunately all suffer from hair obstruction preventing an accurate description of true scalp shape. This study is aimed at exploring the relationship between a small set of head anthropometric measurements and the feature curves of the scalp shape based on a small set of 3D bald head scans. The feature curves include scalp profile along the sagittal plane, coronal plane and a cross-sectional curve at the level of glabella.Introduction: The ever increasing availability of 3D scans of the human head has been a valuable source of information for improving head shape related equipment design and engineering with the goal of improving overall fit, sizing and comfort. However the existence of the hair prevents the actual recording and analysis of true scalp shape, and thus the analysis of scalp shape remains an elusive work. For this reason, much of the data analysis based on 3D head scans is mostly limited on facial shape and some facial landmarks. To overcome this limitation, it is desirable to be able to reconstruct the cranium shape from a few easy to obtain anthropometric measurements. In order to achieve this goal we obtained three orthogonal feature curves from 83 bald head scans and build regression equation between principal component scores of these curves and a number of head measurements such as head length, head breadth and tragion-to-top of head distance. The prediction error of these equations are evaluated. The approach: A total of 83 bald head scans taken in the 2012 anthropometric survey of US Army personnel (ANSUR II) from male soldiers are available for the analysis. From these bald head scans we identified three feature curves that principally define the scalp shape. These curves are a profile along the sagittal plane, a profile curve along the coronal plane and a cross-sectional curve at glabella level. These curves are then sampled in an equal angular space and grouped by their respective plane. The collection of each feature curves forms a shape space. We applied Principal Component Analysis (PCA) to those shape curves and decomposed the shape variation of each curve group into its respective principal axes. After conducting the PCA each curve group has a reduced dimension of 4~5 principal components that account for 95% of total variance. It shows major shape variations within each feature curve group. Then we apply multiple linear regression to the above head measurements and PCA scores of each curve group (Principal component regression or PCR). The regression equations for each curve and each PC were evaluated for their predicting power. From these regression equations the constituent feature curves can be selected from respective shape space based on head anthropometric measurements. Then PCR was also applied to the shape space of combined three curves as compared to prediction power in individual curve spaces.The results and discussion: PCR results from all orthogonal curve spaces and combined three curve space have similar predicting power as their R-squared value falling between 0.6 ~ 0.7. With a set of feature curves it is possible to further derive a 3D shape of the scalp. Although the three head measurements used in this study are effectively correlated to the first and second principal component in each group, other principal components contribute to subtle shape variations. This is an area need further analysis. We will also investigate methods to predicting 3D scalp shape from three feature curves and accuracy of the reconstruction of 3D scalp from those curves.

Fast and accurate 3D measurement of large stack-yard is important job in bulk load-and-unload and logistics management. Stack-yard holds its special characteristics as: complex and irregular shape, single surface texture and low material reflectivity, thus its 3D measurement is quite difficult to be realized by traditional non-contacting methods, such as LiDAR(LIght Detecting And Ranging) and photogrammetry. Light-section is good at the measurement of small bulk-flow but not suitable for large-scale bulk-yard yet. In the paper, an improved method based on stereo cameras and laser-line projector is proposed. The due theoretical model is composed from such three key points: corresponding point of contour edge matching in stereo imagery based on gradient and epipolar-line constraint, 3D point-set calculating for stereo imagery projected-contour edge with least square adjustment and forward intersection, then the projected 3D-contour reconstructed by RANSAC(RANdom SAmpling Consensus) and contour spatial features from 3D point-set of single contour edge. In this way, stack-yard surface can be scanned easily by the laser-line projector, and certain region's 3D shape can be reconstructed automatically by stereo cameras on an observing position. Experiment proved the proposed method is effective for bulk-yard 3D measurement in fast, automatic, reliable and accurate way.

Structure reconstruction of 3D anatomy from bi-planar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh. Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen&#x02BC s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field. Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and 1.2 mm, separately. The average reconstruction time is 3 minutes.

3D scanning is a technique to measure the 3D shape information of the object. Shape information obtained by 3D scanning is expressed either as point cloud or as polygon mesh type data that can be widely used in various areas such as reverse engineering and quality inspection. 3D scanning should be performed as accurate as possible since the scanned data is highly required to detect the features on an object in order to scan the shape of the object more precisely. In this study, we propose the method on finding the location of feature more accurately, based on the extended Biplane SNAKE with global optimization. In each iteration, we project the feature lines obtained by the extended Biplane SNAKE into each image plane and move the feature lines to the features on each image. We have applied this approach to real models to verify the proposed optimization algorithm.

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven methods, which reduce this problem to feature classification, we propose to regress a scalar field representing the distance from point samples to the closest feature line on local patches. Our approach is the first that scales to massive point clouds by fusing distance-to-feature estimates obtained on individual patches. We extensively evaluate our approach against related state-of-the-art methods on newly proposed synthetic and real-world 3D CAD model benchmarks. Our approach not only outperforms these (with improvements in Recall and False Positives Rates), but generalizes to real-world scans after training our model on synthetic data and fine-tuning it on a small dataset of scanned data. We demonstrate a downstream application, where we reconstruct an explicit representation of straight and curved sharp feature lines from range scan data. We make code, pre-trained models, and our training and evaluation datasets available at https://github.com/artonson/def.