Unmanned aerial vehicles (UAVs) have become essential platforms for 3D object detection. In contrast to DETR-style 2D detectors, the Euclidean distance between 3D predictions and the ground truth does not serve as an accurate proxy for sampling error due to complex projection geometry. To overcome this limitation, we propose the perpendicular distance from a predicted point to the ray connecting the camera optical center and the ground-truth, which serves as a precise geometric measure of sampling difficulty. Although this ray distance provides an ideal sampling reference, the isotropic circularity resulting from equal ray distances constrains the approach's ability to capture object shape and orientation. We therefore introduce an anisotropy-aware elliptical mapping that reformulates the circular projection into an ellipse aligned with the object's geometry. By integrating the ellipse-corrected ray distances into both the bipartite matching cost and the regression loss, we develop RayDETR-a geometry-consistent, multi-view 3D detection framework. Experiments on the UAV3D dataset demonstrate that RayDETR achieves state-of-the-art performance without compromising inference speed.

Oriented objects in images are typically embedded in complex backgrounds and exhibit arbitrary orientations. When using oriented bounding boxes (OBBs) to represent these objects, the periodicity of the angles and associated variations in side lengths lead to discontinuities in the angle loss. This paper fundamentally addresses this problem by proposing a trigonometric loss function in the complex plane. Moreover, a conformer RPN head is designed with convolution and multi-head self-attention, which can dynamically capture angular and classification information. The proposed loss function and conformer RPN head jointly generate high-quality oriented proposals. A category-aware dynamic label assignment based on predicted category feedback is proposed to address the limitations of solely relying on IoU for oriented proposal label assignment. This method makes negative sample selection more representative, ensuring consistency between classification and regression features. Experiments were conducted on five realistic oriented detection datasets, and the results demonstrate superior performance in oriented object detection with minimal parameter tuning and time costs. Specifically, mean average precision (mAP) scores of 82.02%, 71.99%, 69.87%, 46.45%, and 98.77% were achieved on the DOTA-v1.0, DOTA-v1.5, DIOR-R, STAR, and HRSC2016 datasets, respectively.

This correspondence proposes an efficient algorithm for removing Gaussian noise from corrupted image by incorporating a wavelet-based trivariate shrinkage filter with a spatial-based joint bilateral filter. In the wavelet domain, the wavelet coefficients are modeled as trivariate Gaussian distribution, taking into account the statistical dependencies among intrascale wavelet coefficients, and then a trivariate shrinkage filter is derived by using the maximum a posteriori (MAP) estimator. Although wavelet-based methods are efficient in image denoising, they are prone to producing salient artifacts such as low-frequency noise and edge ringing which relate to the structure of the underlying wavelet. On the other hand, most spatial-based algorithms output much higher quality denoising image with less artifacts. However, they are usually too computationally demanding. In order to reduce the computational cost, we develop an efficient joint bilateral filter by using the wavelet denoising result rather than directly processing the noisy image in the spatial domain. This filter could suppress the noise while preserve image details with small computational cost. Extension to color image denoising is also presented. We compare our denoising algorithm with other denoising techniques in terms of PSNR and visual quality. The experimental results indicate that our algorithm is competitive with other denoising techniques.

In this letter, we present an efficient algorithm for the removal of random-valued impulse noise from a corrupted image by using a reference image. The proposed method uses a statistic of rank-ordered relative differences to identify pixels which are likely to be corrupted by impulse noise. Once a noisy pixel is identified, its value is restored by a simple weighted mean filter. Simulation results indicate that our algorithm provides a significant improvement over many other existing techniques.