Ying Wang

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.

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.

High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may lead to unnecessary storage and computational burdens, while also struggling to capture clear, sharp, and continuous edges. This paper argues that the key to high-fidelity reconstruction lies in preserving sharp features. Therefore, we introduce a novel sharp-feature-preserving reconstruction framework based on primitive detection. It includes an improved deep-learning-based primitive detection module and two novel mesh splitting and selection modules that we propose. Our framework can accurately and reasonably segment primitive patches, fit meshes in each patch, and split overlapping meshes at the triangle level to ensure true sharpness while obtaining lightweight mesh models. Quantitative and visual experimental results demonstrate that our framework outperforms both the state-of-the-art learning-based primitive detection methods and traditional reconstruction methods. Moreover, our designed modules are plug-and-play, which not only apply to learning-based primitive detectors but also can be combined with other point cloud processing tasks such as edge extraction or random sample consensus (RANSAC) to achieve high-fidelity results.

Panoramic depth estimation has become a hot topic in 3D reconstruction techniques with its omnidirectional spatial field of view. However, panoramic RGB-D datasets are difficult to obtain due to the lack of panoramic RGB-D cameras, thus limiting the practicality of supervised panoramic depth estimation. Self-supervised learning based on RGB stereo image pairs has the potential to overcome this limitation due to its low dependence on datasets. In this work, we propose the SPDET, an edge-aware self-supervised panoramic depth estimation network that combines the transformer with a spherical geometry feature. Specifically, we first introduce the panoramic geometry feature to construct our panoramic transformer and reconstruct high-quality depth maps. Furthermore, we introduce the pre-filtered depth-image-based rendering method to synthesize the novel view image for self-supervision. Meanwhile, we design an edge-aware loss function to improve the self-supervised depth estimation for panorama images. Finally, we demonstrate the effectiveness of our SPDET with a series of comparison and ablation experiments while achieving the state-of-the-art self-supervised monocular panoramic depth estimation. Our code and models are available at https://github.com/zcq15/SPDET .

The surface mesh reconstruction from point clouds has been a fundamental research topic in Computer Vision and Computer Graphics. Recently, the Neural Implicit Representation (NIR)-based reconstruction has revolutionized this research topic. This work summarizes and analyzes representative works on NIR-based reconstruction and highlights several important insights. However, one major problem with existing works is that they struggle to handle high-resolution meshes. To address this, this paper introduces HRE-NDC, a novel High-Resolution and Efficient Neural Dual Contouring approach for mesh reconstruction from point clouds, which takes the previous state-of-the-art as a baseline and adopts a coarse-to-fine network structure design, along with feature-preserving downsampling and other improvements. HRE-NDC significantly reduces training time and memory usage while achieving better surface reconstruction results. Experimental results demonstrate the superiority of our method in both visualization and quantitative results, and it shows excellent generalization performance on various data including large indoor scenes, real-scanned urban buildings, clothing, and human bodies.

We address the problem of 3D human motion estimation from original MoCap optical markers. The original markers are noisy, disordered, and unlabeled, hence recovering 3D human motion from them is non-trivial. Existing works are either time-consuming or assuming the knowledge of the marker labels. We address these problems by presenting an end-to-end method for 3D human motion estimation by leveraging the capability of Transformer to model long-range dependencies. The method takes original markers as inputs and learns joint poses with a Transformer-like architecture. Experimental results show that our method is able to achieve better than centimeter-level errors.

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 paper, 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 paper, 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 0.05m when dealing with mountain data with a length greater than 200m, which is obviously better than the existing best method.

Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years. Panorama images maintain the complete spatial information but introduce distortion with equirectangular projection. In this paper, we propose an ACDNet based on the adaptively combined dilated convolution to predict the dense depth map for a monocular panoramic image. Specifically, we combine the convolution kernels with different dilations to extend the receptive field in the equirectangular projection. Meanwhile, we introduce an adaptive channel-wise fusion module to summarize the feature maps and get diverse attention areas in the receptive field along the channels. Due to the utilization of channel-wise attention in constructing the adaptive channel-wise fusion module, the network can capture and leverage the cross-channel contextual information efficiently. Finally, we conduct depth estimation experiments on three datasets (both virtual and real-world) and the experimental results demonstrate that our proposed ACDNet substantially outperforms the current state-of-the-art (SOTA) methods. Our codes and model parameters are accessed in https://github.com/zcq15/ACDNet.