Nighttime hazy vision is severely limited by the presence of haze and multi-colored light sources. Different from the daytime image dehazing task which has been widely studied, less progress has been made in nighttime image dehazing. In this paper, through extensive analysis and experimentation, we find that game engine simulations offer strong real-world generalization but suffer from unrealistic brightness. To tackle this, we introduce a three-step, brightness-aware synthetic-to-real learning approach. First, we use supervised learning to train a spatial-frequency network (SFN) on synthetic data to produce pseudo-labels. With these pseudo-labels, we develop a semi-supervised dehazing model (SFN+) that minimizes domain discrepancy through a brightness consistency loss applied to local windows. Building on SFN+, we fine-tune the model for better vision using a relative brightness improvement strategy that accounts for color shifts from lighting and brightness shifts during enhancement (SFN++). Experiments on popular benchmark datasets confirm our method's superiority over state-of-the-art approaches.

Contrastive adversarial training emerges as an effective approach to enhancing model robustness in safety-critical applications, particularly point cloud recognition for autonomous driving and medical imaging. However, existing point cloud adversarial training methods mainly emphasize global contrastive learning while overlooking local geometric variations induced by adversarial perturbations. Motivated by the spatial and intensity variations of perturbations across axial views, we propose AVOC, a novel local-global adversarial training framework that utilizes axial-view-oriented contrastive learning. This framework leverages the smallest axial view for local contrastive learning, as it exhibits the highest perturbation differences, and utilizes the largest axial view for global contrastive learning, as it preserves global structural consistency. We conduct comprehensive experiments across four representative architectures, demonstrating significant robustness improvements on widely-adopted recognition benchmarks, including ModelNet40, ShapeNetPart, ModelNet40-C, and ScanObjectNN-C, and further validate its effectiveness on the large-scale KITTI benchmark for 3D object detection. Our results across diverse perturbation scenarios, encompassing white-box attacks, black-box attacks, and natural perturbations, demonstrate the consistent and significant model robustness enhancement of our proposed method.

Contrastive adversarial training has shown great potential in enhancing model robustness and has been adopted in point cloud classification. There are varying spatial distributions and densities across different regions in point cloud data, which makes adversarial perturbations always exhibit non-uniform patterns of attack intensity and distribution in different regions. However, existing approaches always rely on uniform feature contrast without considering the granularity in the context of point cloud data, limiting their capacities to counter adversarial perturbations effectively. To address this issue, we propose a novel frequency-aware contrastive adversarial training framework, which considers feature contrast via a “divide-and-conquer” method. Specifically, we systematically “divide” point clouds into distinct frequency components and “conquer” feature contrast within each frequency band, which fosters fine-grained feature consistency learning and leads to more informative as well as robust representations. Besides, existing methods typically apply group-level contrastive learning, which emphasizes category-wise similarity but often overlooks the nuanced structural variations among instances. To remedy this, we incorporate instance-level contrastive learning to capture per-instance geometric variations. Moreover, a frequency-specific hard-masked sample generation module is designed to construct challenging sample pairs by masking keypoint features in each frequency band, thereby promoting the model to learn more robust feature representations. Extensive experiments on multiple benchmark datasets demonstrate that our proposed method significantly outperforms existing state-of-the-art approaches in adversarial robustness for point cloud classification. The code is available on DiCon-FAT.

Partial point cloud registration aims to align partial scans into a shared coordinate system. While learning-based partial point cloud registration methods have achieved remarkable progress, they often fail to take full advantage of the relative positional relationships both within (intra-) and between (inter-) point clouds. This oversight hampers their ability to accurately identify overlapping regions and search for reliable correspondences. To address these limitations, a diverse inlier consistency (DIC) method has been proposed that adaptively embeds the positional information of a reliable correspondence in the intra- and inter-point cloud. Firstly, a diverse inlier consistency-driven region perception (DICdRP) module is devised, which encodes the positional information of the selected correspondence within the intra-point cloud. This module enhances the sensitivity of all points to overlapping regions by recognizing the position of the selected correspondence. Secondly, a diverse inlier consistency-aware correspondence search (DICaCS) module is developed, which leverages relative positions in the inter-point cloud. This module studies an inter-point cloud DIC weight to supervise correspondence compatibility, allowing for precise identification of correspondences and effective outlier filtration. Thirdly, diverse information is integrated throughout our framework to achieve a more holistic and detailed registration process. Extensive experiments on object-level and scene-level datasets demonstrate the superior performance of the proposed algorithm. The code is available at https://github.com/yxzhang15/DIC.