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.

Estimating 3D information about objects from a single image is a challenging problem in computer vision due to the lack of multi-view information for depth estimation. The Transformer-based methods propagate the target's 3D center depth within its 2D bounding box to construct object-level depth labels. By treating the 2D box area as a unified entity, these methods can perform sufficient feature sampling and parsing within the aforementioned area. This rough but robust detection strategy effectively avoids the dependence of 3D detection on accurate depth estimation of the target center and a few key points nearby. However, due to the lack of effective foreground constraints, these methods struggle to ensure that sampling points are located inside the target, while exterior points lack valid depth values for supervision, which affects both detection accuracy and stability. To address this issue, we propose a Foreground-Constrained Monocular 3D Object Detector (FC-MonoDETR). First, we leverage 2D annotations to generate target segmentation masks using the Segment Anything Model (SAM), directly establishing depth supervision under foreground constraints. Second, we design an attention-based feature fusion module that utilizes contour information to refine visual features and emphasizes the role of foreground regions in depth estimation, guiding the network to focus more effectively on the foreground during holistic 3D information parsing. Finally, we model the relative depth relationships between targets and optimize the estimation of the target's center depth through a specially designed target center depth loss function. Considering the stability issues of Transformer-based methods, we recommend using a more comprehensive evaluation strategy. The sufficient migration experiments have verified the effectiveness of our constructed foreground-constrained depth supervision and feature fusion module in optimizing Transformer-based methods.

Generalizable 3D Gaussian Splatting (G-3DGS) has recently emerged as a promising solution for efficient 3D scene representation and novel view synthesis. However, sparse-view scenarios pose a critical challenge for accurate depth estimation. In such cases, viewpoint overlaps are minimal, and many regions are visible from only a single view. As a result, reliable multi-view matching is unavailable in these areas, leading to significant reconstruction quality degradation. To tackle this bottleneck, we propose GraphSplat, a feed-forward framework for novel view synthesis that dynamically incorporates both cross-view and monocular cues through a graph-based feature aggregation strategy. Central to our approach is a Multi-view Aggregate Graph Attention (MAGA) mechanism, which adaptively reweights intra-view and inter-view node connections to compensate for unreliable multi-view correspondences with robust single-view depth priors. In addition, we design a Hierarchical Depth Fusion Estimator (HDFE) module to integrate monocular and multi-view depth cues, effectively reducing ghosting artifacts and improving geometric consistency. Extensive evaluations on RealEstate10K and ACID benchmarks show that GraphSplat achieves competitive performance against prior SOTA methods, with improvements in appearance fidelity and cross-dataset generalization particularly under challenging sparse-view conditions.

The irregular and chaotically interwoven distributions of vegetation and rock mass in natural environments pose significant challenges for vegetation filtering of rock point clouds, as existing methods struggle with inefficient contextual modeling and insufficient feature discriminability. To address these limitations, we propose a robust vegetation filtering method for rock mass scene point clouds featuring two key innovations: (1) Bidirectional Point Cloud Mamba module that adopts an alternating allocation strategy to divide the sampled superpoints into two complementary groups, and constructs optimized sequences from edge to center for each group via the Traveling Salesman Problem (TSP) algorithm to enable spatially continuous context capture while eliminating dependency on regular geometric priors; (2) Structure-based adaptive Tri-Plane feature representation includes Principal Component Analysis (PCA)-based support plane selection, projective 2D feature mapping, and learnable multi-scale aggregation. By exploiting the distribution differences of rock mass and vegetation structure information in 2D projections while emulating human visual perception mechanisms that rely on optimal viewing perspectives and different scales, this module enables more discriminative feature extraction in complex natural rock-mass scenarios. Extensive comparative and ablation experiments on real-world datasets demonstrate that our method achieves superior accuracy, robustness, and generalization, and excels in preserving intricate details compared to existing methods.

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.

Obtaining complete point cloud data is the basis of rock surface segmentation and related rock-mass numerical simulation. The existing rock-mass point cloud registration methods usually extract point features as the key information for registration. However, the calculation results of point features may be affected by many factors such as data integrity, point density, and noise, which limits the application of existing algorithms in some complex rock scenes (complex collection conditions and complex surface structures). In this article, we propose to divide the rock-mass registration task into two stages: global matching and local matching. During global matching, we extract structure-level features (interrelationships between planes) and shape features (point distribution information in a specific region) instead of point features as the basis for establishing preliminary correspondence between point clouds, achieving robust and efficient region-to-region matching. In the local matching stage, the method based on feature point extraction and matching is proposed to establish accurate point-to-point correspondences in the local region, thus effectively solving the influence of the error of structure-level feature matching on the registration accuracy. In this article, the registration accuracy and efficiency of our method are fully validated by using both repository datasets and real-scene datasets. The robustness of the method is also demonstrated under various conditions. The experimental results show that the root-mean-square error of this method is less than 0.05 m when dealing with mountain data with a length greater than 200 m, which is obviously better than the existing best method.