Unordered Point Sets Research Articles

Adaptive educational game design incorporating the psychology of early childhood development: a study of enhancing learning experiences and promoting cognitive development

Abstract Grounded in the principles of developmental psychology, this study develops an innovative online educational game for young children titled “Fill in the Picture.” Utilizing the AppCan platform in conjunction with Photoshop technology, the game involves the automatic generation of randomly sampled, unordered point sets using network depth search algorithms. It captures the sequential order in which children connect these points onscreen to form coherent graphical representations, subsequently assessing the correctness of these connections against computer-judged standards. The effectiveness of “Fill in the Picture” was evaluated through a questionnaire survey, which revealed a weighting coefficient of 0.248 for the game’s educational quality. Additionally, it scored above 4 on various dimensions, including educational value, scientific basis, playfulness, artistic integration, immersion, and technological engagement. Importantly, in terms of pedagogical design satisfaction, 81.3% of respondents recognized that the game significantly enhances understanding and mastery of scientific concepts, while 81% appreciated the logical organization of its content. Thus, the “Fill in the Picture” game, designed and developed by integrating a tailored educational game design model based on developmental psychology, demonstrates substantial educational efficacy and user satisfaction.

Emotional Music EEG Decoded by an End-to-End Deep Neural Network Model

Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the deep layers, which makes the feature learning insufficient in complex environments. In this paper, we propose a deep neural network for semantic labeling task. It iteratively learns deep features in a hierarchical structure, and provides a simple but efficient way to make interactions between different hierarchical levels. Since iteration process will greatly increase the number of layers, we employ the residual network to improve the performance. In addition, we also introduce dilated k-nearest neighbors and multi-scale grouping to increase the receptive field. The experiments on both Vaihingen 3D dataset and Dayton Annotated LiDAR Earth Scan (DALES) dataset demonstrate the effectiveness of the proposed method in point cloud classification.

A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds

Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the deep layers, which makes the feature learning insufficient in complex environments. In this paper, we propose a deep neural network for semantic labeling task. It iteratively learns deep features in a hierarchical structure, and provides a simple but efficient way to make interactions between different hierarchical levels. Since iteration process will greatly increase the number of layers, we employ the residual network to improve the performance. In addition, we also introduce dilated k-nearest neighbors and multi-scale grouping to increase the receptive field. The experiments on both Vaihingen 3D dataset and Dayton Annotated LiDAR Earth Scan (DALES) dataset demonstrate the effectiveness of the proposed method in point cloud classification.

Point Cloud Processing via Recurrent Set Encoding

We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometric features by a shared recurrent neural network (RNN). The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation. Our network is effective at spatial feature learning, and competes favorably with the state-of-the-arts (SOTAs) on a number of benchmarks. Meanwhile, it is significantly more efficient compared to the SOTAs.

ELF-Nets: Deep Learning on Point Clouds Using Extended Laplacian Filter

We propose a deep learning framework for various 3D vision tasks, which takes a point cloud as input. The convolution is a basic operator for feature extraction in deep learning. However, it is not directly applicable to a point cloud, which is an irregular, unordered point set. This makes deep learning on point clouds challenging. To address this issue, we propose the extended Laplacian filter (ELF) for point clouds, which adopts the design principles of discrete Laplacian filters in 2D image processing. In other words, ELF extends the Laplacian filters and has the following two properties: 1) it is a two-state filter using two filter matrices (one for a center point and the other for neighboring points), and 2) it employs a scalar weighting function to predict the relative importance of the neighboring points. Then, we develop ELF-Nets, which consist of ELF convolution layers and fully connected layers. Experimental results demonstrate that the proposed ELF-Nets are capable of recognizing the 3D shape of a point cloud effectively and efficiently. In particular, ELF-Nets provide better or comparable performances than the state-of-the-art techniques in both object classification and part segmentation tasks.

A multi-scale fully convolutional network for semantic labeling of 3D point clouds

When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features – usually derived from the 3D-covariance matrix – are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a novel 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data (if available) to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. This unique approach allows us to operate on unordered point sets with varying densities, without relying on expensive hand-crafted features; thus reducing the time needed for testing by an order of magnitude over existing approaches. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds, i.e. from multiple-view imagery. We train our network by splitting the data into square regions and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.

Reconstructing Open Surfaces via Graph-Cuts

A novel graph-cuts-based method is proposed for reconstructing open surfaces from unordered point sets. Through a Boolean operation on the crust around the data set, the open surface problem is translated to a watertight surface problem within a restricted region. Integrating the variational model, Delaunay-based tetrahedral mesh and multiphase technique, the proposed method can reconstruct open surfaces robustly and effectively. Furthermore, a surface reconstruction method with domain decomposition is presented, which is based on the new open surface reconstruction method. This method can handle more general surfaces, such as nonorientable surfaces. The algorithm is designed in a parallel-friendly way and necessary measures are taken to eliminate cracks and conflicts between the subdomains. Numerical examples are included to demonstrate the robustness and effectiveness of the proposed method on watertight, open orientable, open nonorientable surfaces and combinations of such.

On the representation of a digital contour with an unordered point set for visual perception

In this paper, we study the problem of representing the boundary of a digital object with an unordered set of points (pixels) chosen from its 1-pixel wide contour such that its shape is visually perceptible and uniquely reconstructible. Extraction of such a set is important from the viewpoint of shape description and may also offer potential solutions to various applications like object representation, recognition, and discrimination. We propose a novel technique of determining an irredundant point set from a digital contour using the classical concept of pointillism. Pointillism, a movement of painting with dots that would blend in the viewer’s eye, was developed by certain Neo-Impressionists of France late in the 19th century. In order to extract the representative point set, we first consider the digitally straight pieces constituting the contour and then obtain a digital polygon P that approximates the bounding curve in a compact form. The polygon P , defined in terms of its ordered set of vertices, is replaced, in turn, with an irredundant set P ′ of pseudo-vertices lying on its digital edges, so that the union of P and P ′ produces an unordered point set that obviates the vertex ordering but captures the underlying geometric orderliness and the neighborhood relations defining the boundary of the original object. The pseudo-vertices may be chosen by controlling a parameter called pointillist factor that governs our visual perception with the nearest-neighbor correlation of a point set. The pointillist factor can be regulated to control the prominence of the underlying object with its unordered set of points — a strong outcome that establishes the technique about its ability to capture the shape information by an order-free point set of optimal or suboptimal size. We have also given a reconstruction procedure along with an error analysis related with the concerned descriptor. Experimental results on several databases demonstrate its elegance and effectiveness.