Traditional 3D object detectors use BEV (bird’s eye view) feature maps to generate 3D object proposals, but in a single BEV feature map, there are inevitably the problems of high compression and information loss. To solve this problem, we propose a multi-view joint learning and BEV feature-fusion network. In this network, we mainly propose two fusion modules: the multi-view feature-fusion module and the multi-BEV feature-fusion module. The multi-view feature fusion module performs joint learning from multiple views, fusing features learned from multiple views, and supplementing missing information in the BEV feature map. The multi-BEV feature-fusion module fuses BEV feature map outputs from different feature extractors to further enrich the feature information in the BEV feature map, in order to generate better quality 3D object proposals. We conducted experiments on a widely used KITTI dataset. The results show that our method has significantly improved the detection accuracy of the cyclist category.For cyclist detection tasks at the easy, moderate, and hard levels of the KITTI test dataset, our method improves by $1.57\%$, $2.03\%$, and $0.67\%$, respectively, compared to PV-RCNN.

The existing 3-D multiobject tracking (MOT) methods suffer from object occlusion in real-world traffic scenes. However, previous works have faced challenges in providing a reasonable solution to the fundamental question: “How can the interference of the perception data loss caused by occlusion be overcome?” Therefore, this article attempts to provide a reasonable solution by developing a novel pretrained movement-constrained masked autoencoder (M-MAE) for an antiocclusion 3-D MOT called MAEMOT. Specifically, for the pretrained M-MAE, this article adopts an efficient multistage transformer (MST) encoder and a spatiotemporal-based motion decoder to predict and reconstruct occluded point cloud data, following the properties of object motion. Afterward, the well-trained M-MAE model extracts the global features of occluded objects, ensuring that the features of the intraobjects between interframes are as consistent as possible throughout the spatiotemporal sequence. Next, a proposal-based geometric graph aggregation (PG $^2$ A) module is utilized to extract and fuse the spatial features of each proposal, producing refined region-of-interest (RoI) components. Finally, this article designs an object association module that combines geometric and corner affinities, which helps to match the predicted occlusion objects more robustly. According to an extensive evaluation, the proposed MAEMOT method can effectively overcome the interference of occlusion and achieve improved 3-D MOT performance under challenging conditions.

In this paper, a new spectral–spatial classification method based on texture pattern separation (TPS) is proposed for hyperspectral image (HSI) classification that consists of the following steps. First, the principal component analysis (PCA) method is used to reduce the dimensions of the original HSI. Then, the processed image is partitioned into several three-dimensional subcube images, each of which can be decomposed into a texture layer and background layer using joint convolutional analysis and synthesis sparse representation (JCAS) to reflect meaningful information and disturbing information, respectively. Next, texture layer images are fed into different kernel pixelwise classifiers for classification. Finally, majority voting is utilized to obtain the final classification result. Through comparisons with other well-known classification methods, the proposed TPS method shows outstanding performance, even with a quite limited number of training samples.