Accurate 3D instance segmentation is crucial for high-quality scene understanding in the 3D vision domain. However, 3D instance segmentation based on 2D-to-3D lifting approaches struggle to produce precise instance-level segmentation, due to accumulated errors introduced during the lifting process from ambiguous semantic guidance and insufficient depth constraints. To tackle these challenges, we propose Splitting and Growing reliable Semantic mask for high-fidelity 3D instance segmentation (SGS-3D), a novel "split-then-grow" framework that first purifies and splits ambiguous lifted masks using geometric primitives, and then grows them into complete instances within the scene. Unlike existing approaches that directly rely on raw lifted masks and sacrifice segmentation accuracy, SGS-3D serves as a training-free refinement method that jointly fuses semantic and geometric information, enabling effective cooperation between the two levels of representation. Specifically, for semantic guidance, we introduce a mask filtering strategy that leverages the co-occurrence of 3D geometry primitives to identify and remove ambiguous masks, thereby ensuring more reliable semantic consistency with the 3D object instances. For the geometric refinement, we construct fine-grained object instances by exploiting both spatial continuity and high-level features, particularly in the case of semantic ambiguity between distinct objects. Experimental results on ScanNet200, ScanNet++, and KITTI-360 demonstrate that SGS-3D substantially improves segmentation accuracy and robustness against inaccurate masks from pre-trained models, yielding high-fidelity object instances while maintaining strong generalization across diverse indoor and outdoor environments.

Adversarial attack strategies for 3D object detection have highlighted the critical importance of addressing security concerns in this domain. However, white-box methods require full access to the victim model in large-scale point cloud applications. To this end, we propose a novel Policy-Driven Black-box Attack (BAT) that is designed to optimize attack locations without necessitating detailed knowledge of the victim models. First, we introduce a density-aware pattern generator that creates scene-adaptive attack clusters. Second, we leverage the deep deterministic policy gradient in deep reinforcement learning to train an attack agent capable of targeting the victim model. Ultimately, the attack agent is iteratively directed towards optimal attack locations through the joint application of critic loss and actor loss. To the best of our knowledge, this represents the first reinforcement learning-based black-box attack applied to practical 3D object detection. Experimental results on the KITTI, nuScenes, and Waymo datasets demonstrate that BAT effectively diminishes the accuracy of notable models. Importantly, BAT significantly enhances the attack success rate (surpassing state-of-the-art both white-box and black-box methods) and increases transferability (by 20 times) through simple deep deterministic policy gradient, thus establishing a new baseline for adversarial attacks in 3D object detection.

Open-vocabulary 3D instance segmentation (OV3IS) has emerged as a promising field, successfully bridging point cloud data and text descriptions through intermediate image modalities. However, existing approaches often rely on strong 3D clustering priors or a single, isolated 2D mask branch, which typically results in insufficient and inefficient feature interaction between the point cloud and image domains. To address these limitations, we propose VoxelFlow, a novel framework that effectively combines 2D mask semantics with 3D geometric features. Our core insight is to leverage the implicit semantic and spatial information embedded in 2D masks and leverage them to replace rigid 3D priors. Specifically, voxel masks, initially derived from 2D projections, are meticulously refined through a unique two-stage process: growth and cross-frame merging, ensuring precise alignment with true instance masks under both semantic and geometric constraints. The methodology unfolds in three key steps. Firstly, we project 2D masks onto a voxelized 3D scene to establish initial seed voxel masks in 3D. Secondly, these voxel masks are grown based on local 3D geometric features and progressively merged across frames using a dynamic threshold, yielding robust super voxel masks. Finally, these semantically consistent and geometrically aligned super voxel masks are integrated with a visual language model and text embeddings to achieve comprehensive open-vocabulary instance segmentation. Extensive experiments on ScanNet200 and ScanNet++ demonstrate that VoxelFlow not only significantly outperforms robust 3D prior baselines but also achieves superior performance in both class-agnostic and open-vocabulary 3D instance segmentation.

Foundation Models (FMs) have demonstrated significant potential for geospatial analysis. As geospatial big data (e.g., geo-tagged text and images) continues to grow, FMs have evolved into geospatial foundation models (GeoFMs), the FMs capable of geospatial reasoning. However, two critical tasks remain challenging: (1) how to align various geospatial modalities for model training, and (2) how to enhance FMs' geospatial context awareness. To fill these gaps, this work develops CLIP4Geo, a unified GeoFM that integrates satellite imagery, LiDAR point clouds, geo-tagged points of interest (POIs), and textual descriptions into a shared representation space. Specifically, to empower the model with geospatial intelligence, we design a Geospatial Expert Knowledge System that tailors both textual and visual information and encodes them into shared geospatial embeddings. Furthermore, we propose a novel Geospatial Context Awareness Transformer to guide the cross-attention mechanism, which leverages geospatial context position embedding to effectively capture spatial dependencies across modalities. We also introduce CityVerse, a large-scale, spatially aligned, and multimodal benchmark dataset covering diverse urban regions in the U.S. and China, supporting a range of geospatial downstream tasks (e.g., zero-shot classification, semantic segmentation / localization, and cross-modality retrieval and generation). Extensive experiments demonstrate that CLIP4Geo consistently outperforms state-of-the-art FM baselines, highlighting the importance of expert-guided multimodal alignment and geospatial context embeddings. Our work provides a scalable framework and comprehensive benchmark to facilitate the future development of GeoFMs for various geospatial applications. Code and dataset are available at https://github.com/Ting-Devin-Han/CLIP4Geo.git.

Open-Vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS) highlight a subtle complementarity that motivates Open-Vocabulary Domain-Generalized Semantic Segmentation (OV-DGSS). OV-DGSS aims to generate pixel-level masks for unseen categories while maintaining robustness across unseen domains, a critical capability for real-world scenarios such as autonomous driving in adverse conditions. We introduce Vireo, a novel single-stage framework for OV-DGSS that unifies the strengths of OVSS and DGSS for the first time. Vireo builds upon the frozen Visual Foundation Models (VFMs) and incorporates scene geometry via Depth VFMs to extract domain-invariant structural features. To bridge the gap between visual and textual modalities under domain shift, we propose three key components: (1) GeoText Query, which align geometric features with language cues and progressively refine VFM encoder representations; (2) Coarse Mask Prior Embedding (CMPE) for enhancing gradient flow for faster convergence and stronger textual influence; and (3) the Domain-Open-Vocabulary Vector Embedding Head (DOV-VEH), which fuses refined structural and semantic features for robust prediction. Comprehensive evaluation on these components demonstrates the effectiveness of our designs. Our proposed Vireo achieves the state-of-the-art performance and surpasses existing methods by a large margin in both domain generalization and open-vocabulary recognition, offering a unified and scalable solution for robust visual understanding in diverse and dynamic environments. Code is available at https://github.com/SY-Ch/Vireo.

Accurately identifying urban building layouts plays a crucial role in understanding the complexity of urban construction and the level of economic development. Previous footprint segmentation methods have struggled to adapt to the diverse morphology of buildings, limiting the accurate extraction of building footprints from remote sensing imagery and impeding insights and understanding of urban areas. To this end, we propose a framework named CityInsight for analyzing urban building morphology from remote sensing imagery. First, we establish a semantic segmentation network, dual-condition diffusion network (DC-Net), based on a diffusion model to accurately identify building footprints from remote sensing images. Second, we use uncertainty attention and condition attention to generate spatial and semantic priors. Finally, we design a condition injection module to incorporate spatial and semantic information into the diffusion learning. Comprehensive experiments demonstrate the accuracy, robustness, and generalization of the proposed method. The$F_{1}$-scores of the DC-Net on the large-scale remote sensing datasets SpaceNet, WHU Building, Inria, and Massachusetts are 92.05%, 96.59%, 92.17%, and 92.86%, respectively. Furthermore, the footprint segmentation is utilized for subsequent urban function identification and urban analysis of the Zona Oeste of Rio de Janeiro, to emphasize the application value of CityInsight. Our code is available athttps://github.com/Ting-Devin-Han/CityInsight

We present a real-time monocular simultaneous localization and mapping (SLAM) system with a new distributed structure for multi-UAV collaboration tasks. The system is different from other general SLAM systems in two aspects: First, it does not aim to build a global map, but to estimate the latest relative position between nearby vehicles; Second, there is no centralized structure in the proposed system, and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles’. To realize the above characteristics in real time, we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs. Based on the hash and principal component analysis, the matching time complexity of this algorithm can be reduced from O (log N ) to O (1). To evaluate the performance, the algorithm is verified on the acknowledged multi-view stereo benchmark dataset, and excellent results are obtained. Finally, through the simulation and real flight experiments, this improved SLAM system with the proposed algorithm is validated.