3D shape analysis for point clouds using variational geometric

Three-dimensional (3D) shape models have recently been used in various fields including machine designing, internet mall, and medical application. Therefore, efficient management and reuse of 3D shape models is becoming important with research on object searching of 3D shape models. 3D shape model searching requires taking into consideration rotation, scaling, and translation of models. This paper proposes a method to search 3D objects to include and account for the above conditions. The proposed method acquires 2D range images by rendering a 3D object from many viewpoints, and discretely approximating two of the three degrees of rotational freedom by a finite number of viewpoints. Using the phase correlation method on the range image one can estimate the rotation around one axis, include scaling and translation, and compare the similarities between the input with models in a dictionary. Experimental results show that whether models match or not match can be distinguished even when the supposed viewpoints are limited to a finite number.

A new method for reconstructing the 3D shape of single cells in fruit

We propose a markerless end-to-end training framework for parametric 3D human shape models. The training of statistical 3D human shape models with minimal supervision is an important problem in computer vision. Contrary to prior work, the whole training process (i) uses a differentiable shape model surface and (ii) is trained end-to-end by jointly optimizing all parameters of a single, self-contained objective that can be solved with slightly modified off-the-shelf non-linear least squares solvers. The training process only requires a compact model definition and an off-the-shelf 2D RGB pose estimator. No pre-trained shape models are required. For training (iii) a medium-sized dataset of approximately 1000 low-resolution human body scans is sufficient to achieve competitive performance on the challenging FAUST surface correspondence benchmark. The training and evaluation code will be made available for research purposes to facilitate end-to-end shape model training on novel datasets with minimal setup cost.

The classification of urban landscape in aerial lidar point clouds is useful in 3D modeling and object recognition applications in urban environments. In this paper, we introduce a multi-category classification system for identifying water, ground, roof, and trees in airborne lidar. The system is organized as a cascade of binary classifiers, each of which performs unsupervised region growing followed by supervised, segment-wise classification. Categories with the most discriminating features, such as water and ground, are identified first and are used as context for identifying more complex categories, such as trees. We use 3D shape analysis and region growing to identify ¿planar¿ and ¿scatter¿ regions that likely correspond to ground/roof and trees respectively. We demonstrate results on two urban datasets, the larger of which contains 200 million lidar returns over 7km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We show that our ground, roof, and tree classifiers, when trained on one dataset, perform well on the other dataset.

Need to organize 3D shape data has prompted studies on comparison and retrieval of 3D shape models. Being able to query 3D shape models by words, in addition to 3D model examples and 2D sketches, would be quite beneficial. This paper proposes a method to associate whole 3D models (e.g., automobile) as well as their parts (e.g., tire, body, engine) with word labels so that the 3D model can be queried by words. The associations between 3D shapes and words are learned from a dataset of 3D models whose whole model and segmented parts are labeled with words. Feature vectors of these words (distributed representation) and feature vectors of whole and partial geometries of 3D models are embedded, by Word Shape embedding Network (WSN) into a common feature embedding space. As the word feature vectors are learned by Word2Vec trained on Wikipedia corpus, the common embedding space can be queried by a wide variety of words that are not included in the labeled 3D model dataset. Experimental evaluation has shown that, with the proposed algorithm, 3D shape can be queried by labels of either whole or part shape, or labels that are semantically close but not included in the original 3D model dataset.

The beam column connection is the most critical zone in a reinforced concrete frame. The strength of connection affects the overall behavior and performance of RC framed structures subjected to lateral load and axial loads. The study of critical parameters that affects the overall joint performances and response of the structure is important. Recent developments in computer technology have made possible the use of Finite element method for 3D modeling and analysis of reinforced concrete structures. Nonlinear finite element analysis of reinforced concrete interior beam column connection subjected to lateral loading was performed in order to investigate joint shear failure mode in terms of joint shear capacity, deformations and cracking pattern using ABAQUS software. A 3D solid shape model using 3D stress hexahedral element type (C3D8R) was implemented to simulate concrete behavior. Wire shape model with truss shape elements (T3D2) was used to simulate reinforcement’s behavior. The concrete and reinforcement bars were coupled using the embedded modeling technique. In order to define nonlinear behavior of concrete material, the concrete damage plasticity (CDP) was applied to the numerical model as a distributed plasticity over the whole geometry. The study was to investigate the most influential parameters affecting joint shear failure due to column axial load, beam longitudinal reinforcement ratio, joint panel geometry and concrete compressive strength. The Finite Element Model (FEM) was verified against experimental test of interior RC beam column connection subjected to lateral loading. The model showed good comparison with test results in terms of load-displacement relation, cracking pattern and joint shear failure modes. The FEA clarified that the main influential parameter for predicting joint shear failure was concrete compressive strength.

Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little diversity in race and gender), and (b) mesh convolutions for deep networks present several challenges that are not entirely tackled in the literature, leading to operator approximations and model instability, often failing to preserve high-frequency components of the distribution. As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face—such as eyelid and lip variations. In this work, we present 3DFaceGAN, the first GAN tailored towards modeling the distribution of 3D facial surfaces, while retaining the high frequency details of 3D face shapes. We conduct an extensive series of both qualitative and quantitative experiments, where the merits of 3DFaceGAN are clearly demonstrated against other, state-of-the-art methods in tasks such as 3D shape representation, generation, and translation.

Patellofemoral pain (PFP) is suggested as a precursor of patellofemoral osteoarthritis (PFOA) later in life. This hypothesis is based on shared risk factors for both diseases, such as deviating alignment parameters. In patients with PFOA, certain 2D alignment parameters and 3D shape variations are associated with the progression of PFOA. The relationship among alignment, shape, and PFP persistence remains unclear to date. To investigate the association between 2D alignment parameters, 3D bone shape variations, and PFP persistence. Questionnaire and magnetic resonance imaging (MRI) data from 64 patients between 14 and 40 years of age with PFP were selected from a case-control study. Participants who completed follow-up questionnaires at one or two years of follow-up were included. PFP persistence, measured using a 7-point Likert scale, was dichotomized. Structural abnormalities were scored using the MRI Osteoarthritis Knee Score (MOAKS), and alignment parameters were determined following standardized methods. The patellae were segmented from the MRI using a semi-automatic segmentation algorithm, and a 3D statistical shape model was created. Shape modes explaining at least 1% of total population variation were included. Regression analyses were performed to determine the association among baseline function (anterior knee pain scale (AKPS)), MOAKS features, alignment parameters, 3D patellar shape modes, and PFP persistence. Fifty-five participants completed follow-up questionnaires and were included in the current analyses. Participants had a mean (standard deviation) age of 23.2 (6.8), and 58.2% were female. AKPS increased statistically significantly from 65.7 (10.6) at baseline to 75.9 (14.9) at follow-up. Nineteen patellar shape modes were included in the analyses, of which four showed a P-value <0.05; however, they were not statistically significant after applying Bonferroni correction. No associations were found among MOAKS, alignment parameters, shape modes, and PFP persistence in the adjusted models. Only AKPS at baseline was associated with pain severity at follow-up (β​=​-0.13, 95% confidence interval: -0.20; -0.07). No statistically significant associations were found among structural abnormalities, 2D alignment, or 3D shape modes, and PFP persistence. Only worse AKPS at baseline was associated with persistent PFP, suggesting that baseline function might be a better predictor than structural abnormalities, alignment, or bone shape. III.

We used functional magnetic resonance imaging to investigate the human cortical areas involved in processing 3-dimensional (3D) shape from texture (SfT) and shading. The stimuli included monocular images of randomly shaped 3D surfaces and a wide variety of 2-dimensional (2D) controls. The results of both passive and active experiments reveal that the extraction of 3D SfT involves the bilateral caudal inferior temporal gyrus (caudal ITG), lateral occipital sulcus (LOS) and several bilateral sites along the intraparietal sulcus. These areas are largely consistent with those involved in the processing of 3D shape from motion and stereo. The experiments also demonstrate, however, that the analysis of 3D shape from shading is primarily restricted to the caudal ITG areas. Additional results from psychophysical experiments reveal that this difference in neuronal substrate cannot be explained by a difference in strength between the 2 cues. These results underscore the importance of the posterior part of the lateral occipital complex for the extraction of visual 3D shape information from all depth cues, and they suggest strongly that the importance of shading is diminished relative to other cues for the analysis of 3D shape in parietal regions.

We provide an important analysis of depth variation of human faces. Throughout an extensive analysis of 3D face shapes, we claim that 3D depth information (z) of faces is not significantly changing and can be synthesized from another person's depth or a generic depth information. We also show that gender and ethnicity specific average depth models can approximate the 3D shape of the input face image more accurately, achieving a better generalization of 3D face modeling and reconstruction compared to a global average depth model.

Shape analysis requires invariance under translation, scale, and rotation. Translation and scale invariance can be realized by normalizing shape vectors with respect to their mean and norm. This maps the shape feature vectors onto the surface of a hypersphere. After normalization, the shape vectors can be made rotational invariant by modeling the resulting data using complex scalar-rotation invariant distributions defined on the complex hypersphere, e.g., using the complex Bingham distribution. However, the use of these distributions is hampered by the difficulty in estimating their parameters and the nonlinear nature of their formulation. In the present paper, we show how a set of kernel functions that we refer to as rotation invariant kernels can be used to convert the original nonlinear problem into a linear one. As their name implies, these kernels are defined to provide the much needed rotation invariance property allowing one to bypass the difficulty of working with complex spherical distributions. The resulting approach provides an easy, fast mechanism for 2D & 3D shape analysis. Extensive validation using a variety of shape modeling and classification problems demonstrates the accuracy of this proposed approach.

Context. Detailed knowledge of shapes is necessary for modeling certain processes affecting small Solar System bodies, such as the influence of radiative effects – like the YORP effect – on rotation. For meteoroids and small monolithic asteroids, such shapes have not been available up till now. This has strongly limited the possibilities of theoretical studies of the non-gravitational phenomena acting on these bodies. Aims. Our goal is to create a database of digital 3D shape models that would be suitable for describing the shapes of meteoroids and small monolithic asteroids. Methods. We performed catastrophic disruption experiments on samples from L3-6 ordinary chondrite NWA 869 and terrestrial tephriphonolite, including one hypervelocity impact and two explosion experiments. We selected fragments with masses of m ≥ 0.2 g that originated from the interior of the samples and did not contain the original target surface. Their size range is approximately 5–24 mm. Their shapes were digitized using X-ray computed tomography, with voxel sizes of about 50 µm. Results. The resulting database contains 868 shape models in Wavefront OBJ format, as well as a list of the basic properties of each one. The numbers of triangular surface facets of these models range from ∼20 000 to ∼760 000. These shape models correspond to meteoroids and small asteroids created by hypervelocity collisions. When using this database for a particular purpose, it is necessary to consider the selection of appropriate models based on the absence or presence of certain morphological features, such as chondrules, significant cracks, or minor artefacts. The possible presence of these features in a specific shape model has been recorded in the database.

The goal of this paper is to present a new methodology designed to identify and quantify the location of the Center of Mass (CM) in Acheulean bifacial stone tools. Experimental protocols and new methodologies, made possible due to recent technological advances are outlined, and the importance of balance as one of the design variables behind bifacial tool morphology is briefly discussed. The location of the CM is determined by using a novel protocol for analysing the relationship between the Center of the Bounding Box (CoBB) of the three-dimensional (3D) model of a bifacial tool, and the tool’s CM. Although the methodology was initially designed for examining the role that balance might have played in Acheulean bifacial tool design, it can be adapted for use in analysing patterns in the location of the CM in numerous other categories of 3D objects.

This paper presents a novel method for face alignment under unknown head poses and nonrigid warp, within the framework of markov random field. The proposed method learns a 3D face shape model comprised of 31 facial features and a texture model for each facial feature from a 3D face database. The models are combined to serve as the unary, pairwise and high order constraints of the markov random field. The facial features are located by minimizing the potential function of markov random field, which is solved with dual decomposition. The main contribution of this paper is composed of three aspects. First, Random Project Tree is utilized to learn the manifold structure of the facial feature appearance under different view points. Second, a 3D face shape model is learned to capture the linear part of face shape distribution due to the change of identity and expression, which is served as the high order constraints. Third, Markov random field is introduced to model the nonlinear part of the face shape distribution, and also to deal with occlusion of facial features due to head pose variation or ornaments. Experiments was taken on the Texas 3D face database and face images downloaded from the Internet, results shows capability of adapting large head pose variations of the method.KeywordsFace ImageFacial FeatureMarkov Random FieldFace ShapeTexture ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

It is not only interesting to predict how an individual of a relatively young age will look in the future but also to reconstruct the facial appearance in the past during childhood. It can be even more desirable when different circumstances, behavior and lifestyle and their impacts on the facial shape appearance as a consequence are taken into account. Such may be applicable for many practical reasons in healthcare, forensics psychology, missing people and children, etc. This paper presents the 3D Face Time Machine Matrix (FT2M), a 3D Dynamic Shape Model which is a fusion of two models of ageing and rejuvenation with facial shape variations due to lifestyle and behavioral factors. This dynamic model is learned from a database of three dimensional facial images which is built by ten individual age groups between 3 to 75 years old. 3D facial aging modeling is a complex process since it affects both the shape and texture of the face. We propose a Dynamic face model to transform the given input face to his youthful or adulthood appearance by taking into account his lifestyle and behavioral traits and the probable changes may occur in perceptible appearance by altering its shape and texture simultaneously.