Classification of 3D objects with curvedsurfaces based on cross-entropybetween shape histograms

This paper proposes a method for recognition and classification of 3D objects using 2D moments and HMLP network. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged using multiple views technique. 2D moments are commonly used for 2D pattern recognition. However, the current study proves that with some adaptation to multiple views technique, 2D moments are sufficient to model 3D objects. In addition, the simplicity of 2D moment's calculation reduces the processing time for feature extraction, thus decreases the recognition time. The 2D moments were then fed into a neural network for classification of the 3D objects. In the current study, hybrid multi-layered perceptron (HMLP) network is proposed to perform the classification. Two distinct groups of objects that are polyhedral and free-form objects were used to access the performance of the proposed method. The recognition results show that the proposed method has successfully classified the 3D object with the accuracy of up to 100%.

We propose a framework for three-dimensional (3D) object recognition and classification in very low illumination environments using convolutional neural networks (CNNs). 3D images are reconstructed using 3D integral imaging (InIm) with conventional visible spectrum image sensors. After imaging the low light scene using 3D InIm, the 3D reconstructed image has a higher signal-to-noise ratio than a single 2D image, which is a result of 3D InIm being optimal in the maximum likelihood sense for read-noise dominant images. Once 3D reconstruction has been performed, the 3D image is denoised and regions of interest are extracted to detect 3D objects in a scene. The extracted regions are then inputted into a CNN, which was trained under low illumination conditions using 3D InIm reconstructed images, to perform object recognition. To the best of our knowledge, this is the first report of utilizing 3D InIm and convolutional neural networks for 3D training and 3D object classification under very low illumination conditions.

Nowadays, point clouds are frequently gathered by 3D scanners such as Lidar and Kinect, which produces thousands of point cloud models. Point cloud processing is vital to 3D vision, especially 3D object recognition, positioning, and navigation technology. Addressing uneven data density caused by coordinate frame transformations and the inherent problem of insufficient context connection in point clouds, the DiM-PCNet (Multi-scale and Multi-level Point Clouds Classification Network, and the Di is a prefix to represent double M in Multi-scale and Multi-level) is proposed in this paper. DiM-PCNet is provided for object classification with multi-scale and multi-level features. We encode the point cloud in multi-scale and fully fuse the features with the raw point cloud for keeping the context relationship. In DiM-PCNet, we sample the point clouds from eight parts for multi-scales feature extraction. The multi-scales features are fully fused by multi-level pyramid models. The multi-scale and multi-level strategies are applied in DiM-PCNet, in which the abundant and important features of point clouds are extracted and utilized in the 3D object classification. It is worth noting that the DiM-PCNet feature block can be embedded into the segmentation net, where the accuracy achieved is 87.1%. We conducted experiments on ShapeNet and ModelNet40 and the experimental results show that DiM-PCNet achieves state-of-the-art performance in 3D object classification. The experiment shows competitive performance on robustness and segmentation tasks.

This paper proposes an effective method for recognition and classification of 3D objects using multiple views technique and neural networks system. In the processing stage, we propose to use 2D moment invariants as the features for modeling 3D objects. 2D moments have been commonly used for 2D object recognition. However, we have proved that with some adaptation to multiple views technique, 2D moments are sufficient to model 3D objects. In addition, the simplicity of 2D moments calculation reduces the processing time for feature extraction, hence increases the system efficiency. In the recognition stage, we propose a cascaded multilayered perceptron (c-MLP) network for matching and classification. The c-MLP contains two MLP networks which are arranged in a serial combination. This proposed method has been tested using two groups of object, polyhedral and free-form objects. We also compare our method with standard MLP network. Our results show that the proposed method can successfully be applied to 3D object recognition. In addition, the proposed network also achieved better performance and faster convergence rate compared to the than standard MLP

Multimodal learning for view-based 3D object classification

In this paper we propose to address the problem of 3D object categorization. We model the 3D object as a 2D Riemannian manifold and propose metric tensor and Christoffel symbols as a novel set of features. The proposed set of features capture the local and global geometry of 3D objects by exploiting the positional dependence of the features. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieve comparable results.

The continuous evolution of 3D sensor technology has increased the demand for robust perception systems that can handle real-world complexities.Although deep learning has advanced 3D object recognition and orientation estimation, point cloud data remain sparse, noisy, and irregular, posing significant challenges.To overcome these issues, we propose PointVoxelNet, a hybrid architecture that integrates voxel grid representations with point-level features enhanced by residual learning.Instead of binary voxelization, sampled point features are embedded within voxels to enrich the geometric details and improve generalization.Extensive experiments confirmed the model's effectiveness, with accuracies of 90.67 %, 92.29% on ModelNet10 and 99.64% on HumanPose (LiDAR-based).In occlusion scenarios, PointVoxelNet remained robust, achieving 86.67% accuracy at a 0.8 ratio, and outperformed VoxNet, PointNet, PointGrid, and VR5.Overall, the results demonstrate that combining residual learning with pointin-voxel representations enhances the accuracy, robustness under occlusion, and computational efficiency.Although it requires more resources than lightweight models, PointVoxelNet offers a favorable trade-off, establishing its strong potential for advancing 3D orientation recognition and object classification in both synthetic and real-world applications.

Deep learning for three-dimensional (3D) object classification is a promising task in digital holography. In this paper, we propose a convolutional neural network (CNN) that performs the multi-class classification of 3D objects information retrieved using off-axis Fresnel digital holography. The performance of the proposed method is evaluated for five different sets of 3D objects using loss and accuracy curves to demonstrate the proof of the concept.KeywordsDeep learningConvolutional neural network3-D objects classificationDigital holography

3D Point clouds are irregular and unordered. Recently, neural networks applying on point clouds have shown superior performance on 3D object classification and segmentation. PointNet has achieved competitive performance in classification tasks, however, it ignores the important local information of point clouds, which carries the important information in 3D geometric space. The informations lack will reduce the accuracy of object classification. In this paper, a new deep neural network model is proposed. Specifically, a multiscale network is used to extract local features of points to improve the accuracy of PointNet and a deep residual network is used to avoid the vanishing gradient problem in the process of deep training network. Experiments show that the proposed network architecture achieves better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.

Classification of 3D objects is becoming an increasingly important research area due to cheap and innovative sensor technology. Shadows, noise, viewing direction, and distance from the sensor all directly affect the quality and amount of surface information provided by the sensor. The recognition approach described in this paper converts surface information, a set of (x,y,z) points, into a discrete 3D binary image. This conversion step processes the surface points using a fuzzy technique to mitigate the effects of noise and minor distortions. These images are then processed by sequences of one or two randomly selected morphological operators. Each of the sequences' output is then fed into a simple transducer to obtain a set of scalar feature values. The feature values are classified using a K nearest neighbor (KNN) classifier that is trained using a sparse number of training samples. Experiments were conducted using the Air Force Research Laboratory's E3D data and experimental protocol. Experimental results for the tank classification problem using 10 tanks and 26 confusers are presented. The results show the combination of morphological processing and KNN classifier produced consistently good performance under variations in noise, viewing angle, or distance.

A novel algorithm for detection and classification of 3D objects in high-resolution microwave radar images for security screening purposes is proposed. To detect and automatically recognize concealed objects a computer vision approach is applied. A three-step detection procedure based on SIFT, Histogram thresholding and matched filtering is developed for 3D image processing. This novel procedure has proved to be capable of detecting and classifying a set of objects which are likely to be carried by an individual at an airport security checkpoint.

This study proposes three efficient 3D point cloud network models for reconstruction, object classification, and part segmentation to reduce costs and labor in 3D modeling. The proposed models achieve high accuracy and performance by optimizing 3D-LMNet with improved encoder depth, point attention mechanisms, and refined MLP structures. Extensive experiments on G3D and S3D datasets using 1024 and 2048 points demonstrate the effectiveness of the architectures. The best-performing configurations for encoders and decoders are identified across tasks. The results show that the proposed models significantly enhance modeling efficiency while maintaining high precision, making them suitable for real-world 3D applications.

Future multimedia high-quality systems will be, among all, based on improving 3D visual experience. To raise 3D visual content quality and interactivity it is necessary to enable segmentation and classification of content which involves dividing the scene into meaningful sub-regions with the same attributes. Partitioning the image into grouping objects has various different applications in a wide variety of areas, since distinctive features in raw images may appear unclear to the human eyes. Segmentation can be defined as the identification of meaningful image components. It is a fundamental task in image processing providing the basis for any kind of further high-level image analysis. There are many different ways of segmenting the 3D image, all of which can be considered as a good segmentations, depending on objects of interest on an image, and to a large extent, the user’s own subjectivity. Key issues in this chapter include different techniques for segmentation of 3D object based on classification on different regions and shapes.KeywordsGrey LevelSegmentation MethodCorner DetectionGlobal ThresholdingGrey Level IntensityThese 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.

3D object classification using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust against adversarial changes to the input data set. We present a preliminary evaluation of adversarial attacks on 3D point cloud classifiers by evaluating adversarial attacks that were proposed for 2D images, and extending those attacks to reduce the perceptibility of the perturbations in 3D space. We also show the effectiveness of simple defenses against those attacks. Finally, we attempt to explain the effectiveness of the defenses through the intrinsic structures of both the point clouds and the neural networks. Overall, we find that 3D point cloud classifiers are weak to adversarial attacks, but they are also more easily defensible compared to 2D image classifiers. Our investigation will provide the groundwork for future studies on improving the robustness of deep neural networks that handle 3D data.

Currently, lung cancer has one of the highest mortality rates because it is often caught too late. Therefore, early detection is essential to reduce the risk of death. Pulmonary nodules are considered key indicators of primary lung cancer. Developing an efficient and accurate computer-aided diagnosis system for pulmonary nodule detection is an important goal. Typically, a computer-aided diagnosis system for pulmonary nodule detection consists of two parts: candidate nodule extraction and false-positive reduction of candidate nodules. The reduction of false positives (FPs) of candidate nodules remains an important challenge due to morphological characteristics of nodule height changes and similar characteristics to other organs. In this study, we propose a novel multi-scale heterogeneous three-dimensional (3D) convolutional neural network (MSH-CNN) based on chest computed tomography (CT) images. There are three main strategies of the design: (1) using multi-scale 3D nodule blocks with different levels of contextual information as inputs; (2) using two different branches of 3D CNN to extract the expression features; (3) using a set of weights which are determined by back propagation to fuse the expression features produced by step 2. In order to test the performance of the algorithm, we trained and tested on the Lung Nodule Analysis 2016 (LUNA16) dataset, achieving an average competitive performance metric (CPM) score of 0.874 and a sensitivity of 91.7% at two FPs/scan. Moreover, our framework is universal and can be easily extended to other candidate false-positive reduction tasks in 3D object detection, as well as 3D object classification.