Yunbiao Wang

Multi-view 3D detection in bird’s-eye-view (BEV) has attracted significant attention from both industry and academia due to its unified, intuitive representation and low cost. By aggregating features along cast rays based on their spatial order, existing methods aim to address the false positive (FP) artifacts commonly observed in detections. However, this strategy may also suppress features in occluded regions along the path, which leads to both inaccurate localization and missed detections. In this paper, we propose AF-BEV, an Adaptive Frustum-based BEV detector, which leverages visible object information to compensate for occluded regions, thereby achieving more accurate localization. Our framework first extends each ray into a frustum and employs an offset prediction module to guide the adaptive frustum angle based on the perceived spatial range of objects. A learnable occlusion-aware aggregation module is then introduced to effectively aggregate frustum features for 3D detection. Finally, we incorporate a unique object-level supervision to guide the generation of appropriate frustum ranges. Extensive experiments on nuScenes demonstrate the effectiveness of our proposed AF-BEV, achieving consistent improvements over the baseline. Additionally, qualitative visualizations provide interpretable evidence of the contributions.

Hyperspectral image (HSI) classification demands models that can jointly capture long-range spatial relations and high-dimensional spectral structures while remaining scalable to large scenes and robust under limited supervision. Existing CNN-, Transformer-, and state-space-based approaches either suffer from restricted receptive fields, quadratic attention complexity, or directional biases that hinder dense pixel-wise prediction. To address these limitations, we propose Hi-RWKV, a hierarchical recurrent weighted key–value framework tailored for hyperspectral analysis. Hi-RWKV introduces three key innovations: (1) a spatial structure–guided bidirectional propagation mechanism that integrates global spatial context while preserving boundary fidelity via edge-aware gating; (2) a spectral identity–driven channel mixing module that incorporates learnable band embeddings and whitening transforms to enhance cross-band discriminability; and (3) a multi-stage hierarchical encoder that progressively refines spectral–spatial representations with strictly linear complexity. Together, these designs enable efficient, direction-free spectral–spatial reasoning essential for large-scale HSI interpretation. Extensive experiments on four benchmarks demonstrate that Hi-RWKV consistently achieves state-of-the-art accuracy under diverse training regimes. Ablation studies confirm that each proposed module offers complementary gains in boundary preservation, spectral discrimination, and data efficiency. By unifying scalable recurrence with hyperspectral-specific structural modeling, Hi-RWKV establishes a strong and efficient paradigm for high-resolution remote sensing. The logs and source data of this article are available at https://github.com/HSI-Lab/Hi-RWKV.

Hyperspectral image (HSI), with their rich spectral information and spatial details, have demonstrated significant potential for classification tasks in fields such as remote sensing, agriculture, and environmental monitoring. However, existing methods still exhibit limitations in feature representation, primarily manifested in insufficient contextual modeling and the inability to effectively address spectral redundancy and significant variations in spatial scales. To address these challenges, this paper proposes an enhanced MambaHSI-based framework for HSI classification, focusing on improving the representation capability of spatial-spectral features. The proposed method consists of three key innovations: (1) A hierarchical DualGroupMamba module that progressively models intra-group and inter-group spectral dependencies to enhance fine-grained spectral discrimination and global contextual awareness; (2) a lightweight Hyperspectral Channel Attention (HCA) that dynamically adjusts the importance of feature channels based on the spatial–spectral information of different bands, effectively suppressing redundant information and highlighting discriminative features. (3) a Hybrid Feature Enhancer (HFE) module that effectively represents and fuses multi-scale spatial features by extracting local texture details and perceiving the overall spatial distribution of scenes, thereby enhancing the model’s adaptability to complex spatial structures. Through a systematic evaluation on four benchmark hyperspectral datasets, the proposed method achieved an average overall classification accuracy of 95.64%, outperforming the current best method by 1.89%. The experimental results validate the superior performance of the proposed approach in enhancing the representation of spatial–spectral features. The latest logs are now available at https://github.com/Tomyaya/EFR.

Hyperspectral image classification faces significant challenges due to high-dimensional spectral redundancy and complex spatial-spectral dependencies. While existing MambaHSI models leverage state-space modeling to enhance representation learning, their unidirectional formulation fails to fully capture bidirectional spatial interactions and cross-band contextual dependencies. Moreover, the uniform projection mechanism struggles to effectively distinguish spectral variations across different wavelengths. To address these limitations, we propose MambaHSI+, a novel framework that integrates bidirectional state-space modeling with spectral trajectory learning. The proposed architecture introduces three key innovations: (1) A bidirectional context modeling module, enhanced by reverse-order modeling, enables multi-directional spatial information propagation through recursive state transitions, facilitating comprehensive local-global feature aggregation while maintaining linear computational efficiency; (2) a spectral trajectory learning paradigm that formulates spectral evolution as continuous state-space process with bidirectional propagation, effectively encoding cross-band relationships; and (3) a Mamba-enhanced channel attention mechanism that adaptively emphasizes discriminative spectral features via selective state-space transformations. Extensive experiments on four benchmark datasets demonstrate state-of-the-art performance, achieving an average accuracy improvement of 2.51% over MambaHSI. By integrating state-space systems with advanced Mamba mechanisms, MambaHSI+ establishes a new paradigm for spectral-spatial representation learning, significantly advancing classification accuracy while ensuring computational efficiency. The latest logs are now available at https://github.com/RockAilab/MambaHSI_Plus.

Registration will directly affect the quality of overall rock-mass point cloud, which is the basis of 3-D reconstruction for rock mass. Advanced methods establish correspondence by extracting various features that remain unchanged. Although these methods have made great progress, they analyze the local characteristics of each sample point, which leads to be inefficient. In this article, we select registration interesting points from feature lines that were extracted based on supervoxel and innovatively introduce the “clustering, primary matching, and coarse registration” strategy, which effectively reduces the complexity of calculating the corresponding relationship during point cloud registration. Finally, the iterative closest point (ICP) algorithm is used to optimize the result of coarse registration. By selecting registration interesting points from the extracted feature lines, the proposed method inherits the robustness of feature lines to noise, initial position, and so on. The experimental results prove that the coarse registration and refined registration results of the proposed method both have high accuracy and efficiency.

Point cloud registration is one of the basic research hotspots in the field of 3D reconstruction. Although many previous studies have made great progress, the registration of rock point clouds remains an ongoing challenge, due to the complex surface, arbitrary shape, and high resolution of rock masses. To overcome these challenges, a novel registration method for rock point clouds, based on local invariants, is proposed in this paper. First, to handle the massive point clouds, a point of interest filtering method based on a sum vector is adopted to reduce the number of points. Second, the remaining points of interest are divided into several cluster point sets and the centroid of each cluster is calculated. Then, we determine the correspondence between the original point cloud and the target point cloud by proving the inherent similarity (using the trace of the covariance matrix) of the remaining point sets. Finally, the rotation matrix and translation vector are calculated, according to the corresponding centroids, and a correction method is used to adjust the positions of the centroids. To illustrate the superiority of our method, in terms of accuracy and efficiency, we conducted experiments on multiple datasets. The experimental results show that the method has higher accuracy (about ten times) and efficiency than similar existing methods.

Existing 3-D object extraction methods on terrestrial laser point clouds are further developed through filtering and labeling. However, such predefined features are heuristically designed to process generic object point clouds. Thus, existing abilities are insufficient to handle specific rock-mass point clouds. Given the complexity and diversity of terrestrial environments, the effective removal of vegetation points from rock-mass point clouds is particularly challenging. To address such problems, this study presents a novel approach for 3-D rock-mass point clouds labeling by using convolutional feature learning based on distribution priors with multiple scales and views. First, to extract discriminative features of each point for classification, we propose novel multiview supporting planes to analyze the spatial distribution and structure of its neighboring points for each category. Second, we define the multiscale spatial distribution matrix on a grid representation (e.g., the number of points projected into each cell). Last, the statistical information of points is nonlinearly combined and hierarchically compressed to generate a compact and effective convolutional feature representation for classification. The effectiveness of the proposed method is evaluated via experiments on rock-mass point clouds from different scenes. Compared with existing extraction approaches, experimental results indicate the superiority of the proposed method in terms of the precision and recall.