Visible-Infrared (RGB-IR) Unmanned Aerial Vehicle (UAV) object detection integrates complementary cues from visible and infrared sensors, offering broad application potential. However, due to sensor parallax, it still faces the challenge of weak spatial misalignment, which significantly limits its performance in UAV-based object detection. Existing methods emphasize strict alignment, overlooking spectral heterogeneity under varying illumination. To address these issues, we propose the Illumination Guided Implicit Alignment Network (IGIANet) to mitigate modality heterogeneity without explicit alignment. Specifically, we integrate three novel modules. First, we propose an illumination-guided frequency modulation module that adaptively allocates fusion weights to visible and infrared features based on global illumination estimation, effectively alleviating modality imbalance under varying lighting conditions. Second, we introduce a frequency-guided cross-modality differential enhancement module, which computes differential cues across frequency domains to enhance complementary information and highlight weakly aligned and low-contrast regions. Finally, we introduce an implicit alignment-driven dynamic fusion module that actively estimates offsets and generates dynamic, position-adaptive fusion kernels to align and fuse modalities. Extensive experiments demonstrate that IGIANet outperforms state-of-the-art models on various benchmarks, achieving 80.9% mAP on DroneVehicle, 57.1% mAP on VEDAI, and 49.4% mAP on FLIR.

Despite the progress made through deep learning, existing Visual Object Tracking (VOT) frameworks struggle with real-world challenges. Recent approaches incorporate additional modalities like Depth, Thermal Infrared, and Language to enhance the robustness of VOT, particularly with the improvement of the depth sensor precision, facilitating RGB-D tracking. However, current RGB-D trackers often copy RGB tracking paradigms, leading to inefficiency due to two-stream architectures that fail to exploit heterogeneous features, and reliance on simplistic or large-parameter fusion methods. To address these challenges, we propose AMTrack, a one-stream RGB-D tracker leveraging Mamba's linear complexity for simultaneous feature extraction and two-stage cross-modal feature fusion. Our innovation also includes a low-parameter Multimodal Mix Mamba (3M) module, which optimizes deep feature fusion and reduces computational overhead. The advantage of the 3M module stems from our Multimodal State Space Model (MSSM), a multimodal feature interaction component reconstructed based on SSM. Experiments across multiple RGB-D tracking datasets indicate that AMTrack achieves superior performance with lower parameters and memory demands compared to state-of-the-arts.

Visible-infrared remote sensing object detection aims to achieve all-weather object detection by leveraging the complementary information from paired visible and infrared (RGB-IR) images. However, modality differences and weak alignment often limit its performance. Existing methods largely neglect the frequency discrepancies between modalities and require strict alignment, increasing complexity. To address these challenges, this study proposes a novel mask-guided frequency feature fusion (MGFF) method for RGB-IR object detection in remote sensing. Specifically, we develop a feature frequency decomposition and enhancement module using wavelet transform to reduce modality differences between RGB and IR images by restructuring and enhancing their frequency components. Additionally, we introduce a mask-guided feature reconstruction module and a feature-guided consistency loss, ensuring that even under weak alignment, the focus remains on integrating the target features from different modalities. Meanwhile, this loss is used to guide the reconstruction of features from different modalities. Finally, We design a multi-directional perception cross-modality fusion module to achieve deep fusion of multimodal information, which enhances object perception from different directions across modalities. Extensive evaluations on the widely recognized RGB-IR remote sensing benchmarks, including DroneVehicle and VEDAI, as well as the RGB-IR pedestrian dataset KAIST, substantiate the effectiveness of the proposed MGFF method. The results consistently demonstrate that the MGFF achieves a superior performance in terms of detection accuracy and robustness compared to existing state-of-the-art approaches.

3D keypoint detection is of great interest to researchers in computer vision and graphics because it is an integral part of realizing many tasks, such as object tracking, 3D reconstruction, and shape registration. However, it is challenging to detect 3D keypoints quickly and stably due to the ambiguity of the keypoints and the presence of noise, density changes, and geometric distortions in the 3D point cloud. This paper proposes a novel 3D keypoint detection method based on point cloud structural saliency (PCSS) to realize stable and efficient 3D keypoint detection. First, we propose an effective point cloud feature descriptor called local spatial geometric feature, which can effectively combine spatial and geometric information to improve feature distinguishability. Second, we define a point cloud structural saliency representation that effectively characterizes the structured information in the point cloud. Finally, we generate 3D keypoints based on point cloud structural saliency using a non-maximum suppression method. We evaluate our method on five 3D keypoint benchmark datasets, and the experimental results demonstrate that it achieves state-of-the-art performance in 3D keypoint detection. Comparing it with previous keypoint detection methods further demonstrates the effectiveness and superiority of our method.

Keypoint detection plays a fundamental role in many applications, such as 3D reconstruction, object registration, and shape retrieval, and has attracted significant interest from researchers in computer vision and graphics. However, due to the ambiguity of the keypoint and the complexity of 3D objects, it is still tricky for existing 3D keypoint detection methods to generate stable keypoints with good coverage, especially for unsupervised detection methods. This paper proposes a 3D keypoint detection method based on hierarchical point saliency. This method can effectively and accurately locate the keypoints of a 3D point cloud, and it does not require complex training processes. First, we propose a simple and effective point descriptor called the local geometric structure feature, which can effectively characterize the geometric structure changes of 3D point clouds and has a strong feature identification ability. Second, we define two saliency measures used to characterize the saliency of points in the point cloud, which are low-level and high-level saliency. Third, we hierarchically characterize the saliency of points by combining the low-level and high-level saliency, thus measuring the probability that a point belongs to a keypoint. Finally, we extensively test our method on three benchmark 3D point cloud datasets, and the experimental results demonstrate that our method achieves state-of-the-art performance in keypoint detection tasks, significantly superior to the prior hand-crafted and deep-learning-based 3D keypoint detection methods.

Content-based 3D object retrieval is a challenging problem in computer vision and graphics, especially for non-rigid 3D shapes. This article proposes a multiview-based robust point representation approach for 3D non-rigid shape retrieval. First, we propose an efficient local descriptor called the local point histogram, which is robust to non-rigid changes in shape. Second, we encode local point histogram features into high-level point features (HPF) using Fisher vectors. Finally, we present an efficient feature fusion method that can further enhance the performance of 3D non-rigid shape retrieval. We extensively tested our approach on two benchmark 3D non-rigid shape datasets, including the SHREC2015 non-rigid shape and SHREC2015 canonical forms. Our method achieves 98.33% and 90.55% retrieval accuracy on the SHREC2015 non-rigid shape and SHREC2015 canonical forms datasets, surpassing previous state-of-the-art methods by nearly 2% and 7%, respectively. In addition, we further tested our method on the well-known 3D rigid shape dataset ModelNet, and the experimental results demonstrate that our method is also effective for 3D rigid shape retrieval. We also combine the proposed HPF shape features with deep convolutional features for the 3D rigid shape retrieval task, achieving a retrieval performance comparable to the prior state-of-the-art methods, which indicates a strong complementarity between HPF shape features and deep convolutional features.

Plant identification based on leaf images is a widely concerned application field in artificial intelligence and botany. The key problem is extracting robust discriminative features from leaf images and assigning a measure of similarity. This study proposes an effective, robust shape descriptor to identify plant species from images of their leaves, which we call the high-level triangle shape descriptor (HTSD). First, we extract a leaf image's external contour and internal salient point information. We then use triangle features to describe the leaf contour, which we call the contour point based on triangle features (CPTFs). The internal information of the leaf image is based on salient point triangle features (SPTFs). The third step is to apply the Fisher vector to encode the two kinds of point-based local triangle features into the HTSD. Finally, we employ the simple euclidean distance to calculate the dissimilarities between the HTSD characteristics of leaf images. We have extensively evaluated the proposed approach on several public leaf datasets successfully. Experimental results show that our method has superior recognition accuracy, outperforming current state-of-the-art shape-based and deep-learning plant identification approaches.

The shapes of plant leaves are very important to plant ecologists and botanists because these can help distinguish plant species as well as serve as health indicators. In this paper, we present a novel contour-based shape descriptor named multiscale triangular centroid distance (MTCD) for plant leaf recognition. MTCD features at different triangles are extracted from each contour point to provide a compact, multiscale shape descriptor. Both local and global features of a plant leaf are effectively captured by the proposed method. A simple cosine distance is used to calculate the dissimilarity measurement between MTCD descriptors. Therefore, MTCD is a rapid approach for shape matching and is suitable for real-time application. The proposed method has been evaluated using four publicly available plant leaf datasets, including the Swedish Leaf dataset, the Smithsonian Leaf dataset, the Flavia Leaf dataset, and the ImageCLEF2012 Leaf dataset. The experimental results show that this novel approach can achieve high recognition accuracy. Comparisons with other state-of-the-art shape-based plant leaf recognition methods further demonstrate the effectiveness and efficiency of MTCD.