Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they usually utilized heavy correlation operations to capture category-level characteristics only, and overlook the inherent merit of arbitrariness in contrast to multiple object tracking. In this work, we propose a radically novel one-stream network with the strength of the instance-level encoding, which avoids the correlation operations occurring in previous Siamese network, thus considerably reducing the computational effort. In particular, the proposed method mainly consists of a Template-aware Transformer Module (TTM) and a Multi-scale Feature Aggregation (MFA) module capable of fusing spatial and semantic information. The TTM stitches the specified template and the search region together and leverages an attention mechanism to establish the information flow, breaking the previous pattern of independentextraction-and-correlation. As a result, this module makes it possible to directly generate template-aware features that are suitable for the arbitrary and continuously changing nature of the target, enabling the model to deal with unseen categories. In addition, the MFA is proposed to make spatial and semantic information complementary to each other, which is characterized by reverse directional feature propagation that aggregates information from shallow to deep layers. Extensive experiments on KITTI and nuScenes demonstrate that our method has achieved considerable performance not only for class-specific tracking but also for class-agnostic tracking with less computation and higher efficiency.

We introduce a novel design of parallel-jaw grippers drawing inspiration from pin-pression toys. The proposed pin-pression gripper features a distinctive mechanism in which each finger integrates a 2D array of pins capable of independent extension and retraction. This unique design allows the gripper to instantaneously customize its finger's shape to conform to the object being grasped by dynamically adjusting the extension/retraction of the pins. In addition, the gripper excels in in-hand re-orientation of objects for enhanced grasping stability again via dynamically adjusting the pins. To learn the dynamic grasping skills of pin-pression grippers, we devise a dedicated reinforcement learning algorithm with careful designs of state representation and reward shaping. To achieve a more efficient grasp-while-lift grasping mode, we propose a curriculum learning scheme. Extensive evaluations demonstrate that our design, together with the learned skills, leads to highly flexible and robust grasping with much stronger generality to unseen objects than alternatives. We also highlight encouraging physical results of sim-to-real transfer on a physically manufactured pin-pression gripper, demonstrating the practical significance of our novel gripper design and grasping skill. Demonstration videos for this paper are available at https://github.com/siggraph-pin-pression-gripper/pin-pression-gripper-video.

We propose a novel Text-to-Image Generation Network, Attention-bridged Modal Interaction Generative Adversarial Network (AMI-GAN), to better explore modal interaction and perception for high-quality image synthesis. The AMI-GAN contains two novel designs: an Attention-bridged Modal Interaction (AMI) module and a Residual Perception Discriminator (RPD). In AMI, we mainly design a multi-scale attention mechanism to exploit semantics alignment, fusion, and enhancement between text and image, to better refine details and context semantics of the synthesized image. In RPD, we design a multi-scale information perception mechanism with our proposed novel information adjustment function, to encourage the discriminator to better perceive visual differences between the real and synthesized image. Consequently, the discriminator will drive the generator to improve the visual quality of the synthesized image. Besides, based on these novel designs, we can design two versions, a single-stage generation framework (AMI-GAN-S), and a multi-stage generation framework (AMI-GAN-M), respectively. The former can synthesize high-resolution images because of its low computational cost; the latter can synthesize images with realistic detail. Experimental results on two widely used T2I datasets showed that our AMI-GANs achieve competitive performance in T2I task.

The widespread deployment of deep neural networks (DNNs) for 3D point cloud processing contrasts sharply with their vulnerability to security breaches, particularly backdoor attacks. Studying these attacks is crucial for raising security awareness and mitigating potential risks. However, the irregularity of 3D data and the heterogeneity of 3D DNNs pose unique challenges. Existing methods frequently fail against basic point cloud preprocessing or require intricate manual design. Exploring simple, imperceptible, effective, and difficult-to-defend triggers in 3D point clouds remains challenging. To address this issue, we propose iBA, a novel solution utilizing a folding-based auto-encoder (AE). By leveraging united reconstruction losses, iBA enhances both effectiveness and imperceptibility. Its data-driven nature eliminates the need for complex manual design, while the AE core imparts significant nonlinearity and sample specificity to the trigger, rendering traditional preprocessing techniques ineffective. Additionally, a trigger smoothing module based on spherical harmonic transformation (SHT) allows for controllable intensity. We also discuss potential countermeasures and the possibility of physical deployment for iBA as an extensive reference. Both quantitative and qualitative results demonstrate the effectiveness of our method, achieving state-of-the-art attack success rates (ASR) across a variety of victim models, even with defensive measures in place. iBA’s imperceptibility is validated with multiple metrics as well.

Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, existing methods based on deep neural networks mainly focus on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes unseen during the training phase. In this work, we investigate a more challenging task in LiDAR point clouds, namely class-agnostic tracking, where a general model is supposed to be learned to handle targets of both observed and unseen categories. In particular, we first investigate the class-agnostic performance of state-of-the-art trackers by exposing the unseen categories to them during testing. It is found that as the distribution shifts from observed to unseen classes, how to constrain the fused features between the template and the search region to maintain generalization is a key factor in class-agnostic tracking. Therefore, we propose a feature decorrelation method to address this problem, which eliminates the spurious correlations of the fused features through a set of learned weights, and further makes the search region consistent among foreground points and distinctive between foreground and background points. Experiments on KITTI and NuScenes demonstrate that the proposed method can achieve considerable improvements by benchmarking against the advanced trackers P2B and BAT, especially when tracking unseen objects.

How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task.

This article presents a novel person reidentification model, named multihead self-attention network (MHSA-Net), to prune unimportant information and capture key local information from person images. MHSA-Net contains two main novel components: multihead self-attention branch (MHSAB) and attention competition mechanism (ACM). The MHSAB adaptively captures key local person information and then produces effective diversity embeddings of an image for the person matching. The ACM further helps filter out attention noise and nonkey information. Through extensive ablation studies, we verified that the MHSAB and ACM both contribute to the performance improvement of the MHSA-Net. Our MHSA-Net achieves competitive performance in the standard and occluded person Re-ID tasks.

This article presents a new text-to-image (T2I) generation model, named distribution regularization generative adversarial network (DR-GAN), to generate images from text descriptions from improved distribution learning. In DR-GAN, we introduce two novel modules: a semantic disentangling module (SDM) and a distribution normalization module (DNM). SDM combines the spatial self-attention mechanism (SSAM) and a new semantic disentangling loss (SDL) to help the generator distill key semantic information for the image generation. DNM uses a variational auto-encoder (VAE) to normalize and denoise the image latent distribution, which can help the discriminator better distinguish synthesized images from real images. DNM also adopts a distribution adversarial loss (DAL) to guide the generator to align with normalized real image distributions in the latent space. Extensive experiments on two public datasets demonstrated that our DR-GAN achieved a competitive performance in the T2I task. The code link: https://github.com/Tan-H-C/DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation.

This paper presents a novel approach for human pose transfer, progressive multi-attention network (PMAN), which generates a new human image by transferring the pose of a given person to a target pose. The network gradually updates the pose feature and the image feature through a series of multi-attention transfer blocks (MATBs). Each MATB consists of two attention mechanisms: pose-conditioned batch normalization (PCBN) and cooperative attention mechanism (CAM). Specifically, in low-level feature space, the PCBN layer with pose information is used to replace the BN layer of the image channel to realize the preliminary guidance of pose to image. The CAM is implemented as two gated mechanisms in high-level feature space, which reveals the essence of human pose transfer, that is, mutual guidance and dynamic control between pose and image. Gated memory writing is used to calculate the pixel-wise weight of the pose by using global image information to guide the update of the pose. Gated response utilizes an adaptive gating mechanism to dynamically control the pose information flow so as to update the image. A large number of subjective and objective experiments on DeepFashion and Market-1501 demonstrate the superiority of our method. The proposed multi-attention mechanism is well adapted to the human pose transfer task and provides a possible new idea for other cross-domain generation tasks.

In this paper, we propose a novel person Re-ID model, Consecutive Batch DropBlock Network (CBDB-Net), to capture the attentive and robust person descriptor for the person Re-ID task. The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform partition on the feature maps. And then, we independently and continuously drop each patch from top to bottom on the feature maps, which can output multiple incomplete feature maps. In the training stage, these multiple incomplete features can better encourage the Re-ID model to capture the robust person descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel weight control item to help the Re-ID model adaptively balance hard sample pairs and easy sample pairs in the whole training process. Through an extensive set of ablation studies, we verify that the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive performance on the three standard person Re-ID datasets (the Market-1501, the DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets (the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a general image retrieval dataset (In-Shop Clothes Retrieval dataset).

Three-dimensional (3-D) tracking in point cloud is a core competence of autonomous robots to perceive and forecast the environment. How to initialize bounding box seeds and optimize their position and orientation are very crucial for 3-D object tracking in point clouds. Nevertheless, existing methods mainly resort to developing a powerful classifier based on the initial bounding box seeds. In this article, we propose an end-to-end deep supervised descent method (SDM), which seamlessly integrates multiple seeds generation for the initialization of seeds and sequential updates for the estimation of accurate result. Specifically, we start with transforming the SDM iterative process into a trainable recurrent module. It explicitly learns a series of descent directions in the parameter space, to gradually optimize the initial seeds. Moreover, to alleviate drifting of this process, we initialize multiple seeds based on aggregated point sets generated by the deep Hough voting. Besides, a discrimination module is introduced to determine the bounding box with the highest score as the final result. Importantly, a specific multitask loss is proposed to train our model in an end-to-end way. Experiments on KITTI, PandaSet, and Waymo datasets show that our method could achieve significant improvements (up to 11.2% in success ratio) as compared to state-of-the-art trackers.

Synthesizing photo-realistic images based on text descriptions is a challenging image generation problem. Although many recent approaches have significantly advanced the performance of text-to-image generation, to guarantee semantic matchings between the text description and synthesized image remains very challenging. In this paper, we propose a new model, Cross-modal Semantic Matching Generative Adversarial Networks (CSM-GAN), to improve the semantic consistency between text description and synthesized image for a fine-grained text-to-image generation. Two new modules are proposed in CSM-GAN: Text Encoder Module (TEM) and Textual-Visual Semantic Matching Module (TVSMM). TVSMM is aimed at making the distance of the pairs of synthesized image and its corresponding text description closer, in global semantic embedding space, than those of mismatched pairs. This improves the semantic consistency and consequently, the generalizability of CSM-GAN. In TEM, we introduce Text Convolutional Neural Networks (Text_CNNs) to capture and highlight local visual features in textual descriptions. Thorough experiments on two public benchmark datasets demonstrated the superiority of CSM-GAN over other representative state-of-the-art methods.

Abstract Robot deburring is an effective method for improving the surface quality of the high‐voltage copper contact. The first step of robot deburring is to acquire the burr images. We propose a new burr mathematical model and build a real burr image dataset for burr image denoising. In order to improve burr image denoising effects of the high‐voltage copper contact, this study proposes an online burr image denoising algorithm, that is, block cosparsity overcomplete learning transform algorithm (BCOLTA). The penalty term and the condition number are affected by the burr parameter. The clustering and transform alternate minimisation algorithms are adopted to achieve lower computational cost and better denoising effect. In addition, BCOLTA also has a good adaptibility to inherent noise images, especially in Gaussian noise. Compared with other traditional and deep learning algorithms by no reference and full reference image quality assessment methods, BCOLTA has state‐of‐the‐art denoising effects and computational complexity on dealing with burr images. This research will play an important role in the intelligent manufacturing field.

This paper presents a new framework, Knowledge-Transfer Generative Adversarial Network (KT-GAN), for fine-grained text-to-image generation. We introduce two novel mechanisms: an Alternate Attention-Transfer Mechanism (AATM) and a Semantic Distillation Mechanism (SDM), to help generator better bridge the cross-domain gap between text and image. The AATM updates word attention weights and attention weights of image sub-regions alternately, to progressively highlight important word information and enrich details of synthesized images. The SDM uses the image encoder trained in the Image-to-Image task to guide training of the text encoder in the Text-to-Image task, for generating better text features and higher-quality images. With extensive experimental validation on two public datasets, our KT-GAN outperforms the baseline method significantly, and also achieves the competive results over different evaluation metrics.

Designing an effective and uniform framework to meliorate tracking performance is very meaningful and essential. However, existing methods merely focus on single positive and negative instances corresponding to the exemplar, thoroughly ignoring the effective information hidden in other instances. To tackle this issue, in this paper, we present an informative enhanced loss based Siamese tracking network. Specifically, we introduce an informative enhanced loss to enable the network to capture information from an overall perspective. In other words, we construct dense connections among instances and exemplar. More importantly, we prove that our proposed loss can be transformed into the logistic loss and the triplet loss under particular parameter settings. Experiments on prevalent benchmarks demonstrate that the Siamese frameworks trained with our proposed loss indeed obtain better tracking results than original ones, and achieve promising performance against several state-of-the-art trackers on the real-time challenge.

This paper presents a new model, Semantics-enhanced Generative Adversarial Network (SEGAN), for fine-grained text-to-image generation. We introduce two modules, a Semantic Consistency Module (SCM) and an Attention Competition Module (ACM), to our SEGAN. The SCM incorporates image-level semantic consistency into the training of the Generative Adversarial Network (GAN), and can diversify the generated images and improve their structural coherence. A Siamese network and two types of semantic similarities are designed to map the synthesized image and the groundtruth image to nearby points in the latent semantic feature space. The ACM constructs adaptive attention weights to differentiate keywords from unimportant words, and improves the stability and accuracy of SEGAN. Extensive experiments demonstrate that our SEGAN significantly outperforms existing state-of-the-art methods in generating photo-realistic images. All source codes and models will be released for comparative study.

The burrs on high‐voltage copper contact leads to point discharge and device damage. Since the high‐voltage copper contact has different machining batch and the contour various which the machine tool remove is not economic and robot usually is used to remove the burrs of high‐voltage copper contact. The first step for robot deburring is to identify burrs. In order to improve the performance of copper contact burr video denoising, this article presents online burr video denoising sparsifying transforms algorithm, which defined two alternative values between the optimal sparse signal and transform learning dictionary, simultaneously, calculated the mean of peak signal‐to‐noise ratio, the mean of execution time, the STD (STandard Deviation), and the VAR (VARiance), accordingly presented an burr video denoising algorithm and compared with state‐of‐the‐art video denoising algorithms. The experiment results show that compared with traditional methods, the burr video denoising algorithm has higher denoising precision, faster denoising speed, and stronger high‐noise‐level processing capacity, and so on. The numeric experiments show that the proposed approach has higher peak signal‐to‐noise ratio and less computation complexity than the existing video denoising methods.

The normals of feature points, i.e., the intersection points of multiple smooth surfaces, are ambiguous and undefined. This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals. This definition facilitates several succeeding operations, such as feature points extraction and point cloud filtering. We also develop a feature preserving normal estimation method which outputs multiple normals per feature point. The core of the method is a pair consistency voting scheme. All neighbor point pairs vote for the local tangent plane. Each vote takes the fitting residuals of the pair of points and their preliminary normal consistency into consideration. Thus the pairs from the same subspace and relatively far off features dominate the voting. An adaptive strategy is designed to overcome sampling anisotropy. In addition, we introduce an error measure compatible with traditional normal estimators, and present the first benchmark for normal estimation, composed of 152 synthesized data with various features and sampling densities, and 288 real scans with different noise levels. Comprehensive and quantitative experiments show that our method generates faithful feature preserving normals and outperforms previous cutting edge normal estimation methods, including the latest deep learning based method.

Traditional unsupervised salient object detection methods majorly rely on pre-defined assumptions about saliency. However, these assumptions may not be sufficient for handling test images of varied content and context. Meanwhile, supervised models learn saliency knowledge from thousands of annotated images, which are usually expensive to obtain. In this paper, we propose an exemplar-aided salient object detection method, which can complement heuristic saliency assumptions by leveraging only a few exemplar images. This is a challenging task since the appearances between the query images and the exemplars can be quite different. We handle it by learning the matching relationship of the intra-class instances in a latent embedding space in an online fashion. Given a test image and an annotated reference image (retrieved from several exemplar images), our method transfers the foreground and background information of the reference image to the test image via a joint latent embedding of image superpixels. Extensive experiments show that our method can easily improve the performance of existing unsupervised methods even when a very small reference image dataset (e.g. one image) is used. In addition, our method is able to attain competitive performance against fully supervised methods.

Benefiting from global rank constraints, the low-rank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank minimization for the entire data set whenever new samples are dynamically added, making it prohibitively expensive. Existing attempts to online LRR either take a stochastic approach or build the representation purely based on a small sample set and treat new input as out-of-sample data. The former often requires multiple runs for good performance and thus takes longer time to run, and the latter formulates online LRR as an out-of-sample classification problem and is less robust to noise. In this paper, a novel online LRR subspace learning method is proposed for both large-scale and dynamic data. The proposed algorithm is composed of two stages: static learning and dynamic updating. In the first stage, the subspace structure is learned from a small number of data samples. In the second stage, the intrinsic principal components of the entire data set are computed incrementally by utilizing the learned subspace structure, and the LRR matrix can also be incrementally solved by an efficient online singular value decomposition algorithm. The time complexity is reduced dramatically for large-scale data, and repeated computation is avoided for dynamic problems. We further perform theoretical analysis comparing the proposed online algorithm with the batch LRR method. Finally, experimental results on typical tasks of subspace recovery and subspace clustering show that the proposed algorithm performs comparably or better than batch methods, including the batch LRR, and significantly outperforms state-of-the-art online methods.

We study a new and elegant instance of geometric dissection of 2D shapes: reversible hinged dissection, which corresponds to a dual transform between two shapes where one of them can be dissected in its interior and then inverted inside-out , with hinges on the shape boundary, to reproduce the other shape, and vice versa. We call such a transform reversible inside-out transform or RIOT. Since it is rare for two shapes to possess even a rough RIOT, let alone an exact one, we develop both a RIOT construction algorithm and a quick filtering mechanism to pick, from a shape collection, potential shape pairs that are likely to possess the transform. Our construction algorithm is fully automatic. It computes an approximate RIOT between two given input 2D shapes, whose boundaries can undergo slight deformations, while the filtering scheme picks good inputs for the construction. Furthermore, we add properly designed hinges and connectors to the shape pieces and fabricate them using a 3D printer so that they can be played as an assembly puzzle. With many interesting and fun RIOT pairs constructed from shapes found online, we demonstrate that our method significantly expands the range of shapes to be considered for RIOT, a seemingly impossible shape transform, and offers a practical way to construct and physically realize these transforms.

Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models. This paper proposes a support-free hollowing framework to overcome the difficulty of fabricating voids inside a solid. The challenge of computing a support-free hollowing is decomposed into a sequence of shape optimization steps, which are repeatedly applied to interior mesh surfaces. The optimization of physical properties in different applications can be easily integrated into our framework. Comparing to prior approaches that can generate support-free inner structures, our hollowing operation can reduce more volume of material and thus provide a larger solution space for physical optimization. Experimental tests are taken on a number of 3D models to demonstrate the effectiveness of this framework.

Abstract We present an adaptive slicing scheme for reducing the manufacturing time for 3D printing systems. Based on a new saliency‐based metric, our method optimizes the thicknesses of slicing layers to save printing time and preserve the visual quality of the printing results. We formulate the problem as a constrained ℓ0 optimization and compute the slicing result via a two‐step optimization scheme. To further reduce printing time, we develop a saliency‐based segmentation scheme to partition an object into subparts and then optimize the slicing of each subpart separately. We validate our method with a large set of 3D shapes ranging from CAD models to scanned objects. Results show that our method saves printing time by 30–40% and generates 3D objects that are visually similar to the ones printed with the finest resolution possible.

In this paper, we propose least-squares images (LS-images) as a basis for a novel edge-preserving image smoothing method. The LS-image requires the value of each pixel to be a convex linear combination of its neighbors, i.e., to have zero Laplacian, and to approximate the original image in a least-squares sense. The edge-preserving property inherits from the edge-aware weights for constructing the linear combination. Experimental results demonstrate that the proposed method achieves high quality results compared to previous state-of-the-art works. We also show diverse applications of LS-images, such as detail manipulation, edge enhancement, and clip-art JPEG artifact removal.

In this paper, we present a novel bottom-up salient object detection approach by exploiting the relationship between the saliency detection and the Markov absorption probability. First, we calculate a preliminary saliency map by the Markov absorption probability on a weighted graph via partial image borders as background prior. Unlike most of the existing background prior-based methods which treated all image boundaries as background, we only use the left and top sides as background for simplicity. The saliency of each element is defined as the sum of the corresponding absorption probability by several left and top virtual boundary nodes, which are most similar to it. Second, a better result is obtained by ranking the relevance of the image elements with foreground cues extracted from the preliminary saliency map, which can effectively emphasize the objects against the background, whose computation is processed similarly as that in the first stage and yet substantially different from the former one. At last, three optimization techniques--content-based diffusion mechanism, superpixelwise depression function, and guided filter--are utilized to further modify the saliency map generalized at the second stage, which is proved to be effective and complementary to each other. Both qualitative and quantitative evaluations on four publicly available benchmark data sets demonstrate the robustness and efficiency of the proposed method against 17 state-of-the-art methods.

A novel representation of a triangular mesh surface using a set of scale-invariant measures is proposed. The measures consist of angles of the triangles (triangle angles) and dihedral angles along the edges (edge angles) which are scale and rigidity independent. The vertex coordinates for a mesh give its scale-invariant measures, unique up to scale, rotation, and translation. Based on the representation of mesh using scale-invariant measures, a two-step iterative deformation algorithm is proposed, which can arbitrarily edit the mesh through simple handles interaction. The algorithm can explicitly preserve the local geometric details as much as possible in different scales even under severe editing operations including rotation, scaling, and shearing. The efficiency and robustness of the proposed algorithm are demonstrated by examples.

This paper proposes a consolidation method for scanned point clouds that are usually corrupted by noises, outliers, and thickness. At the beginning, we construct neighborhood of a point based on shared nearest neighbor relationship. Then, the points with few number of neighbors are regarded as outliers and removed. After that, we propose a feature-aware projection operator to thin the thick point clouds by considering spatial distances, normal diversifications, and the squash directions of thick point clouds. Experiment results of scanned point clouds show that our method can consolidate the thick point clouds while preserving sharp features and geometry details.

3D printers have become popular in recent years and enable fabrication of custom objects for home users. However, the cost of the material used in printing remains high. In this paper, we present an automatic solution to design a skin-frame structure for the purpose of reducing the material cost in printing a given 3D object. The frame structure is designed by an optimization scheme which significantly reduces material volume and is guaranteed to be physically stable, geometrically approximate, and printable. Furthermore, the number of struts is minimized by solving an l 0 sparsity optimization. We formulate it as a multi-objective programming problem and an iterative extension of the preemptive algorithm is developed to find a compromise solution. We demonstrate the applicability and practicability of our solution by printing various objects using both powder-type and extrusion-type 3D printers. Our method is shown to be more cost-effective than previous works.

We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages: feature detection and feature refinement. First, the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.

ABSTRACT This paper presents a subdivision connectivity remeshing approach for closed genus 0 meshes. It is based on spherical parameterization and umbrella‐operator smoothing. Our main contribution lies in adopting a low‐distortion spherical parameterization approach to generate high‐quality subdivision connectivity meshes. Besides, a simple and efficient point location method on the sphere based on the uniform partition of the rectangle is presented, which is used to find the containing triangle in the spherical mesh for each point on the sphere rapidly. Our method can generate high‐quality subdivision connectivity meshes fast, which can be applied to level of detail and progressive transmission. All the application examples demonstrate that our remeshing procedure is robust and efficient. Copyright © 2011 John Wiley & Sons, Ltd.

One major goal of mesh parameterization is to minimize the conformal distortion. Measured boundary parameterizations focus on lowering the distortion by setting the boundary free with the help of distance from a center vertex to all the boundary vertices. Hence these parameterizations strongly depend on the determination of the center vertex. In this paper, we introduce two methods to determine the center vertex automatically. Both of them can be used as necessary supplements to the existing measured boundary methods to minimize the common artifacts as a result of the obscure choice of the center vertex. In addition, we propose a simple and fast measured boundary parameterization method based on the Poisson’s equation. Our new approach generates less conformal distortion than the fixed boundary methods. It also generates more regular domain boundaries than other measured boundary methods. Moreover, it offers a good tradeoff between computation costs and conformal distortion compared with the fast and robust angle based flattening (ABF++).

Two algorithms for evaluation of regular quad-mesh interpolatory subdivision surfaces are proposed. Algorithms are designed based on the parametric m-ary decomposition and construction of matrix sequence. The weights of the control points on the initial mesh can be obtained, through direct computation of the basic function values by multiplying the finite matrix sequence corresponding to the decomposition number sequence. Algorithm-I is based on 2D subdivision masks while the other is based on tensor-product. Numerical experiments show that algorithms are efficient with low storage cost.

In this paper, we present a method to simultaneously blend the corner of three coordinate planes with G2 continuity by cubic algebraic spline surfaces. This method is based on space partition and algebraic splines.