Learning semantic representations from point sets of 3D object shapes is often challenged by significant geometric variations, primarily due to differences in data acquisition methods. Typically, training data is generated using point simulators, while testing data is collected with distinct 3D sensors, leading to a simulation-to-reality (Sim2Real) domain gap that limits the generalization ability of point classifiers. Current unsupervised domain adaptation (UDA) techniques struggle with this gap, as they often lack robust, domain-insensitive descriptors capable of capturing global topological information, resulting in overfitting to the limited semantic patterns of the source domain. To address this issue, we introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds. Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures, and by modeling the topological relations of local geometric features through a novel self-supervised learning task. Additionally, we propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training, effectively reducing the impact of noisy pseudo-labels and enhancing the robustness of the adaptation process. Experimental results on three public Sim2Real benchmarks validate the effectiveness of our TAM framework, showing consistent improvements over state-of-the-art methods across all evaluated tasks. The source code of this work will be available athttps://github.com/zou-longkun/TAG.git.

In urban environments, numerous IoT devices rely on accurate radio map information to enable critical applications such as localization, trajectory planning, and communication. The effectiveness of these applications hinges on the availability of high-fidelity and spatially comprehensive radio maps. To address this, we propose an optimal path planning approach with distance constraints for efficient sampling in urban radio map reconstruction. Unlike traditional random sampling strategies, our method restricts sampling points to lie along a feasible road network and imposes a fixed distance constraint on the sampling path. We design a structure-aware genetic algorithm (GA_s) to optimize the path for mobile sampling, incorporating an unsupervised population evaluation metric to assess the fitness of candidate solutions. Experimental results show that, under equal sampling distance conditions, GA_s achieves a Root Mean Square Error (RMSE) of 0.0626 in radio map recovery—outperforming random sampling (0.0796), A* algorithm (0.0748), and a conventional genetic algorithm (GA_c). These results demonstrate the effectiveness of our method in enabling efficient and accurate radio map reconstruction for mobile sampling platforms in real-world urban settings.

Global Navigation Satellite Systems (GNSS) and base station (BS)-based wireless positioning are the predominant technologies for outdoor user equipment (UE) localization. However, their performance deteriorates significantly in dense urban environments with complex architectural structures. This degradation arises because obstructions disrupt line-of-sight (LoS) conditions between the UE and satellites or base stations, reducing positioning accuracy. To address this challenge, this paper introduces LocVMunet, a novel deep neural network designed for high-precision localization based on received signal strength (RSS) from a limited number of base stations. The proposed LocVMunet seamlessly integrates the Cross-Scan Module (CSM) with signal distribution modeling, enhancing spatial feature extraction and improving localization accuracy. Unlike traditional methods, this neural network-based approach inherently mitigates the impact of non-line-of-sight (NLOS) conditions, enabling real-time localization across any region covered by the radio map. Experimental evaluations demonstrate that the LocVMunet establishes a new benchmark for RSS-based urban localization, significantly outperforming state-of-the-art neural network-based methods. Notably, it achieves a 24.8% positioning error reduction over LocUNet and a 22.7% improvement over LocSwinUnet in the five-base-station setup, highlighting its robustness and effectiveness in urban positioning.

Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy-free. Therefore, representations of those ideal synthetic point clouds have limited variations in the geometric perspective and can gain good performance on a number of 3D vision tasks such as point cloud classification. In the context of unsupervised domain adaptation (UDA), representation learning designed for synthetic point clouds can hardly capture domain invariant geometric patterns from incomplete and noisy point clouds. To address such a problem, we introduce a novel scheme for induced geometric invariance of point cloud representations across domains, via regularizing representation learning with two self-supervised geometric augmentation tasks. On one hand, a novel pretext task of predicting translation distances of augmented samples is proposed to alleviate centroid shift of point clouds due to occlusion and noises. On the other hand, we pioneer an integration of the self-supervised relational learning on geometrically-augmented point clouds in a cascade manner, utilizing the intrinsic relationship of augmented variants and other samples as extra constraints of cross-domain geometric features. Experiments on the PointDA-10 dataset demonstrate the effectiveness of the proposed method, achieving the state-of-the-art performance.

Semantic pattern of an object point cloud is determined by its topological configuration of local geometries. Learning discriminative representations can be challenging due to large shape variations of point sets in local regions and incomplete surface in a global perspective, which can be made even more severe in the context of unsupervised domain adaptation (UDA). In specific, traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries, which greatly limits their cross-domain generalization. Recently, the transformer-based models have achieved impressive performance gain in a range of image-based tasks, benefiting from its strong generalization capability and scalability stemming from capturing long range correlation across local patches. Inspired by such successes of visual transformers, we propose a novel Relational Priors Distillation (RPD) method to extract relational priors from the well-trained transformers on massive images, which can significantly empower cross-domain representations with consistent topological priors of objects. To this end, we establish a parameter-frozen pre-trained transformer module shared between 2D teacher and 3D student models, complemented by an online knowledge distillation strategy for semantically regularizing the 3D student model. Furthermore, we introduce a novel self-supervised task centered on reconstructing masked point cloud patches using corresponding masked multi-view image features, thereby empowering the model with incorporating 3D geometric information. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification. The source code of this work is available athttps://github.com/zou-longkun/RPD.git.

The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel self-supervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-of-the-art methods. Source codes and pre-trained models are available at https://github.com/zou-longkun/GAST.