Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance.In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation—the local spherical curve—to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions. The source code is available at https://github.com/miraclelzk/star-edge.

Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D scene reconstruction often requires densely captured views, whereas 3D generation typically relies on a single or no input view, which significantly limits their applications. We found that the source of this phenomenon lies in the misalignment between 3D constraints and generative priors. To address this problem, we propose a reconstruction-driven video diffusion model that learns to condition video frames on artifact-prone RGB-D renderings. Moreover, we propose a cyclical fusion pipeline that iteratively adds restoration frames from the generative model to the training set, enabling progressive expansion and addressing the viewpoint saturation limitations seen in previous reconstruction and generation pipelines. Our evaluation, including view synthesis from sparse view and masked input, validates the effectiveness of our approach.

Fast and accurate registration of outdoor LiDAR point clouds poses a considerable challenge for their large-scale (e.g., 300 K points) and intricate (e.g., noise and outliers) distributions. In this article, we present a fast low-frequency coarse registration method for large-scale outdoor LiDAR point clouds, dubbed FlyCore. Different from existing methods, FlyCore is very fast for practical applications and bridges current refinement registration methods smoothly for their accuracy improvements. Specifically, we first construct spherical feature spaces for a pair of point clouds based on their keypoints and saliency uncertainties independently. Then, we perform harmonic decomposition on these spherical feature spaces, utilizing the low-frequency components of spherical harmonics (SHs) to implement point cloud registration. FlyCore demonstrates less sensitivity to noise and outliers compared to feature-based registration techniques. Also, FlyCore achieves exceptionally low time complexity by eliminating the need for feature matching and iterative procedures, ensuring fine alignment with only a few iterations. Experimental validations, utilizing two extensive LiDAR datasets featuring urban and natural scenarios, confirm the effectiveness and accuracy improvement of existing fine registration methods facilitated by our FlyCore.

Remote sensing images serve a significant role in earth observation to tackle climate change and post-disaster reconstruction concerns. However, optical images are obscured by clouds or haze, preventing precise earth observation; hence, cloud removal has been a hot topic among concerned scholars. The objective of this article is to make cloud removal more efficient and explicable by proposing three principles: identifying clouds, guessing objects beneath the clouds, and reconstructing the cloudy area. In addition, a modified dual contrastive learning Generative Adversarial Network is proposed based on these three principles by adding cloud detection and weight sharing strategy to obtain cloud semantics. In particular, we align two datasets by forming a quaternary sample pair that includes not only optical pictures and SAR images, but also region information for a more precise reconstruction. Our experiment results on the integrated dataset reveal the superiority of proposed method over previous cloud removal methods and the effectiveness of added modules through ablation experiments, with PSNR and SSIM values of 26.2 and 0.728, respectively.