Abstract Point cloud generation aims to transfer a latent code to realistic 3D shapes through generative models. However, most of progressive generative methods ignore the spatial relationship among different stages and suffer from the loss of contextual information. To address this issue, we propose dual-stream progressive refinement adversarial network (DPR-GAN), which utilizes a dual-stream structure to establish the relationship between two adjacent stages. Such a mechanism can learn the spatial context and preserve more spatial details of point clouds at different stages. In addition, DPR-GAN adopts 3D gridding transformation to guide the shape deformation. In this way, 3D gridding transformation can learn a reasonable correspondence between the local regions of 3D shapes and latent codes. Benefiting from the uniformity and adaptability of the 3D grids, our proposed DPR-GAN can improve the quality and consistency of generated point clouds. We conduct comprehensive experiments to demonstrate that the proposed DPR-GAN is capable of generating pluralistic point clouds, as compared with state-of-the-art generation methods in terms of both visual and quantitative evaluations.

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in hyperspectral images (HSIs) classification, which is widely used for the analysis of remotely sensed images. HSI includes spectral and spatial information from several hundreds of spectral data channels. Recent CNN models deal with various bands of HSIs as independent channels, which may lead to the loss of dependencies between different channels or the loss of associated information between each channel and the global. This article proposes a novel CNN model based on geometric algebra (GA), dubbed GA-CNN, to process the HSIs in a holistic way without losing the interrelationship among channels. Specifically, taking advantage of GA, different band images are represented as GA multivectors to capture the inherent structures and preserve the correlation of those channels. In particular, all the basic modules of our model, such as convolutional layers and the backpropagation algorithm, are extended to the GA domain. We evaluate the performance of the proposed GA-CNN model in classification tasks on four well-known HSI datasets. The experimental results indicate that our GA-CNN model outperforms traditional and state-of-the-art real-valued CNNs with higher classification accuracy and fewer model parameters.

Currently, human pose estimation (HPE) methods mainly rely on the design framework of Convolutional Neural Networks (CNNs). These CNNs typically consist of high‐to‐low‐resolution subnetworks (encoder) to learn semantic information and low‐to‐high subnetworks (decoder) to raise the resolution for keypoint localization. Because too low‐resolution feature maps in encoder will inevitably lose some spatial information, which cannot be recovered in the upsampling stages, keeping high spatial resolution features is critical for human pose estimation. On the other hand, due to scale variation of human body parts, multiscale features are also very important for human pose estimation. In this paper, a novel backbone network is proposed specifically for HPE, named High Spatial Resolution and Multiscale Networks (HSR‐MSNet), which maintain high spatial resolution features in deeper layers of the encoder and meanwhile construct multiscale features within one single residual block via subgroup splitting and fusion of feature maps. Experiments show that our approach outperforms other state‐of‐the‐art methods with more accurate keypoint locations on COCO dataset.

Abstract Although different approaches of segmenting and assembling geometric models for 3D printing have been proposed, it is difficult to find any research studies, which investigate model segmentation and assembly in head‐mounted display (HMD) virtual reality (VR) environments for 3D printing. In this work, we propose a novel and interactive segmentation method for screwing assembly in the environments to tackle this problem. Our approach divides a large model into semantic parts with a screwing interface for repeated tight assembly. Specifically, after a user places the cutting interface, our algorithm computes the bounding box of the current part automatically for subsequent multicomponent semantic Boolean segmentations. Afterwards, the bolt is positioned with an improved K3M image thinning algorithm and is used for merging paired components with union and subtraction Boolean operations respectively. Moreover, we introduce a swept Boolean‐based rotation collision detection and location method to guarantee a collision‐free screwing assembly. Experiments show that our approach provides a new interactive multicomponent semantic segmentation tool that supports not only repeated installation and disassembly but also tight and aligned assembly.

Abstract Easy and efficient 3D modelling in virtual environments is an important and unsolved topic. This paper proposes a new modelling approach to tackle this issue. It invents convolution surface‐based brushes to directly draw 3D models in head‐mounted display‐based virtual environments. In order to maximize the efficiency, flexibility, and capacity of our proposed modelling approach, we propose three different skeleton‐based convolution surfaces to tackle different modelling tasks: point skeleton‐based convolution surfaces for metaball shapes, line skeleton‐based convolution surfaces for cylindrical shapes, and polygon skeleton‐based convolution surfaces for planar surfaces. Their combination makes 3D modelling more flexible and powerful. The high efficiency is further raised by our developed closed‐form solutions for point skeletons, ends of line skeletons, and edges of polygonal skeletons. Different user‐friendly sweeping schemes are provided to facilitate intuitive inputs for various complex shape generation. Unlike Google's Tilt Brush, which is used to create disconnected sheet‐like surfaces only, our proposed convolution surface‐based brushes can produce smoothly blended manifold surfaces, and novice users can easily learn and use them to create various interesting 3D models efficiently.

This letter proposes a novel framework to improve the saliency detection performance of an existing saliency model, which is used to generate the initial saliency map. First, a novel regional descriptor consisting of regional self-information, regional variance, and regional contrast on a number of features with local, global, and border context is proposed to describe the segmented regions at multiple scales. Then, regarding saliency computation as a regression problem, a multiple kernel boosting method based on support vector regression (MKB-SVR) is proposed to generate the complementary saliency map. Finally, an adaptive fusion method via learning a quality prediction model for saliency maps is proposed to effectively fuse the initial saliency map with the complementary saliency map and obtain the final saliency map with improvement on saliency detection performance. Experimental results on two public datasets with the state-of-the-art saliency models validate that the proposed method consistently improves the saliency detection performance of various saliency models.

In this paper, joint Haar-like feature is used for detecting faces in images. Our method is based on the co-occurrence Haar-like features which can capture the structural characteristic of the face, make it possible to construct more effective weak classifier. As the Haar-like, the joint Haar-like feature can be calculated very effective and has the robustness to addition of noise and change in illumination. The face detector is learned by stage wise selection which is different with Viola and Jones Detector is that we use LogitBoost. We perform two experiments: In the Experiment 1, we show that the LogitBoost [9] obtain higher performance than AdaBoost [2] [4] [11]. We have confirmed that our method based on LogitBoost yielded higher performance than AdaBoost. In the Experiment 2, the performance increase according with the num of the combined features. However, the time spent increase at huge growth rate, too. Therefore, we will research that how we can choose the optimal F at the acceptable training time in the latter work.

In this paper, we propose a new visual tracking algorithm, SRAPF, for object tracking, which is based on sparse representation and annealed particle filter. To find the tracking target at a new frame, each target candidate is sparsely represented by target templates and trivial templates. The sparsity is achieved by solving a l1-regularized least squares problem. After that, Instead of tracking objects in the common particle filter framework, we solve the sparse representation problem in an annealed particle filter framework. Then the candidate with the largest likelihood is taken as the tracking target. In the APF framework, the sampling covariance and annealing factor items are incorporated into the tracking process. The annealing strategy can achieve "Smart sampling" to avoid generating invalid particles corresponding to impossible target object. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs better in comparison with the L1 tracking algorithm.

This paper proposes a new algorithm, named Multi-Task Robust Principal Component Analysis (MTRPCA), to collaboratively integrate multiple visual features and motion priors for human motion segmentation. Given the video data described by multiple features, the human motion part is obtained by jointly decomposing multiple feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a convex optimization problem that minimizes a constrained combination of nuclear norm and ℓ2,1-norm, which can be solved efficiently with Augmented Lagrange Multiplier (ALM) method. Compared to previous methods, which usually make use of individual features, the proposed method seamlessly integrates multiple features and priors within a single inference step, and thus produces more accurate and reliable results. Experiments on the HumanEva human motion dataset show that the proposed MTRPCA is novel and promising.