At present, effectively aggregating and transferring the local features of point cloud is still an unresolved technological conundrum. In this study, we propose a new space-cover convolutional neural network (SC-CNN) for tasks such as point cloud classification and segmentation. The core of this network is space-cover convolution (SC-Conv), which implements depthwise separable convolution on the point cloud. In addition, a newly designed space-cover operator (SCOP) replaces depthwise convolution. The key to SC-Conv is constructing anisotropic spatial geometry in the local point cloud. The SCOP achieves this by utilizing the positional and feature relationships to learn the high-order relationship expression between points. First, data-driven adaptive learning from the 3-D coordinate relationship between the local points is used to determine the weight of the SCOP. Then, the edge feature of the neighboring point relative to the sampling point is used as the input of the SCOP. Finally, a deformable spatial geometry is constructed in the feature space between local points to aggregate the local high-order features. By stacking SC-Conv to construct SC-CNN with a hierarchical network structure for point cloud analysis, we can better perceive the shape information of point cloud and improve network robustness. Finally, we provide numerous experiments to verify that SC-CNN parallels or even outperforms advanced methods in shape classification, part segmentation, and large-scale indoor scene segmentation tasks. The open-source code was published at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/changshuowang/SC-CNN</uri> . 

Learning Discriminative Features by Covering Local Geometric Space for Point Cloud Analysis

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications of 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, view-based, convolution-based, graph-based, generative model-based, transformer-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field. 

/pdf/comprehensive-review-of-deep-learning-based-3d-point-cloud-2d3pco81.pdf

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

Point cloud completion aims to infer the complete point clouds from incomplete ones, which is used in remote sensing applications such as reconstructing and autonomous driving. However, most existing methods cannot recover accurate structure details of the object. In this article, we propose point shift network (PS-Net). Our main contributions lie in the following three-folds. First, we propose a multiresolution encoder, which extracts and fuses multiresolution point cloud features hierarchically, thus avoiding information loss caused by a single global feature. Second, we design a multiresolution point cloud generation structure, which can be combined with the multiresolution encoder to generate gradually dense point clouds, avoiding the problem of nonuniformly density of the single-layer decoder. Third, we design the shift network (SN), which is used to generate shift vectors to shift the coordinates of each point cloud, so as to further fine-tune the coordinate positions of point clouds, achieving more accurate prediction. We conduct comprehensive experiments on the ShapeNet, KITTI, ScanObjectNN, and ModelNet40 datasets, which demonstrate that the proposed PS-Net achieves better performance than the existing methods and verify the robustness of the proposed method. This article contributes a new method to point cloud completion, realizes fine point cloud shape completion, and brings new possibilities to the research of autonomous driving, registration, and reconstruction.

PS-Net: Point Shift Network for 3-D Point Cloud Completion

Most of the existing synthetic aperture radar (SAR) image superpixel generation methods are designed based on the raw SAR images or artificially designed features. However, such methods have the following limitations: (1) SAR images are severely affected by speckle noise, resulting in unstable pixel distance estimation. (2) Artificially designed features cannot be well-adapted to complex SAR image scenes, such as the building regions. Aiming to overcome these shortcomings, we propose a multitask learning-based superpixel generation network (ML-SGN) for SAR images. ML-SGN firstly utilizes a multitask feature extractor to extract deep features, and constructs a high-dimensional feature space containing intensity information, deep semantic informantion, and spatial information. Then, we define an effective pixel distance measure based on the high-dimensional feature space. In addition, we design a differentiable soft assignment operation instead of the non-differentiable nearest neighbor operation, so that the differentiable Simple Linear Iterative Clustering (SLIC) and multitask feature extractor can be combined into an end-to-end superpixel generation network. Comprehensive evaluations are performed on two real SAR images with different bands, which demonstrate that our proposed method outperforms other state-of-the-art methods.

/pdf/multitask-learning-based-for-sar-image-superpixel-generation-1nu2faqm.pdf

Multitask Learning-Based for SAR Image Superpixel Generation

Generative adversarial networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation of synthetic images of human faces, landscapes, and buildings, among others. Not only image generation, but also image manipulation is possible with GANs. Generative deep learning models are inherently limited in their creative abilities because of a focus on learning for perfection. We investigated the potential of GAN’s latent spaces to encode human expressions, highlighting creative interest for suboptimal solutions rather than perfect reproductions, in pursuit of the artistic concept. We have trained Deep Convolutional GAN (DCGAN) and StyleGAN using a collection of portraits of detained persons, portraits of dead people who died of violent causes, and people whose portraits were taken during an orgasm. We present results which diverge from standard usage of GANs with the specific intention of producing portraits that may assist us in the representation and recognition of otherness in contemporary identity construction.

GANs and Artificial Facial Expressions in Synthetic Portraits

Point cloud completion aims to reconstruct complete point clouds from partial point clouds, which is widely used in various fields such as autonomous driving and robotics. Most existing methods are sparse point cloud completion, where the number of point clouds after completion is relatively small and the details are insufficient. This article proposes a novel end-to-end generative adversarial network-based dense point cloud completion architecture (DPCG-Net). We design two generative adversarial network (GAN)-based modules that translate point cloud completion into mapping between global feature distributions obtained by encoding partial point clouds and ground truth, respectively. The first designed generator module proposes skip connections to fully connected layer-based network for regenerating global feature and changing the global feature distribution derived from the encoder module to approximate the ground truth global feature distribution. The second proposed discriminator module divides high-dimensional global feature vectors into several smaller batches for judgment to guarantee the similarity between the regenerated global feature and the ground truth. We perform quantitative and qualitative experiments on the ShapeNet and KITTI datasets. Experiments on ShapeNet demonstrate that our model outperforms other models in cases where the lack of a large proportion of point clouds results in a large loss of spatial structure, especially when 80% of point clouds are missing. Moreover, KITTI experiments reveal that it is also valid for realistic situations. In addition, application in classification shows that the classification accuracy of point clouds completed with DPCG-Net is as high as 86.5% under the condition of 80% missing point clouds.

Dense Point Cloud Completion Based on Generative Adversarial Network

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Dense Point Cloud Completion Based on Generative Adversarial Network