Lifting multi-view 2D masks generated by the Segment Anything Model (SAM) into 3D space offers a promising direction for zero-shot 3D scene segmentation, but view-dependent occlusions and limited fields of view often cause incomplete observations and cross-view inconsistencies, resulting in fragmented semantics and geometric misalignment. To address this, we propose SAM-Zero3D, which extends SAM to the 3D domain through a structured fusion pipeline with two complementary branches. The global anchor point-guided branch projects 3D anchors into multi-view masks to construct a cross-view affinity graph, identifies consistent mask groups via connected component analysis, and assigns 3D masks via majority voting and nearest-neighbor propagation. The local geometry-driven branch partitions the point cloud into fine-grained regions, estimates region-level semantic similarity from aggregated mask distributions, and progressively merges similar regions through a multi-stage merging strategy. An iterative global–local interaction further refines both branches by aligning global semantic priors with local geometric cues. Extensive experiments on ShapeNetPart, ScanNetV2, and ScanNet200 show that SAM-Zero3D significantly outperforms existing zero-shot baselines, achieving accurate and structure-aware segmentation without any 3D training or supervision.

In the field of autonomous driving and intelligent transportation systems, 3D object detection plays a critical role in ensuring safe and efficient driving. Achieving reliable object detection relies on robust sensing technologies, where LiDAR provides accurate spatial perception, and radar offers extended detection range and speed information because of its longer wave-lengths. To capitalize on the strengths of both sensors, a novel RCS-based LiDAR-Radar fusion network, named RLRFusion, is proposed for 3D object detection in this paper. The network takes LiDAR and radar point clouds as inputs and processes them through dual bird's-eye view (BEV) feature extraction streams, followed by the BEV fusion and detection module to produce the detection results. In input-level fusion, a cross-modal pillar encoder is introduced to address the sparse radar data and its lack of height information. In feature-level fusion, an RCS-aware fusion encoder leverages the Radar Cross Section (RCS) distribution by mapping pillar features to their surroundings, enhancing object size estimation and mitigating the challenges faced by LiDAR in adverse weather conditions. Experimental results show that RLRFusion achieves competitive performance on the nuScenes dataset, with strong detection results even in rainy conditions. The source code of our method is available at: https://github.com/djZzgroupIRLRFusion.

Abstract While pre‐trained 3D vision‐language models are becoming increasingly available, there remains a lack of frameworks that can effectively harness their capabilities for few‐shot classification. In this work, we propose PointGMDA, a training‐free framework that combines Gaussian Mixture Models (GMMs) with Gaussian Discriminant Analysis (GDA) to perform robust classification using only a few labeled point cloud samples. Our method estimates GMM parameters per class from support data and computes mixture‐weighted prototypes, which are then used in GDA with a shared covariance matrix to construct decision boundaries. This formulation allows us to model intra‐class variability more expressively than traditional single‐prototype approaches, while maintaining analytical tractability. To incorporate semantic priors, we integrate CLIP‐style textual prompts and fuse predictions from geometric and textual modalities through a hybrid scoring strategy. We further introduce PointGMDA‐T, a lightweight attention‐guided refinement module that learns residuals for fast feature adaptation, improving robustness under distribution shift. Extensive experiments on ModelNet40 and ScanObjectNN demonstrate that PointGMDA outperforms strong baselines across a variety of few‐shot settings, with consistent gains under both training‐free and fine‐tuned conditions. These results highlight the effectiveness and generality of our probabilistic modeling and multimodal adaptation framework. Our code is publicly available at https://github.com/djzgroup/PointGMDA .

The contours of objects effectively represent essential 3D information such as shapes and boundaries. Existing contour detection methods mostly rely on thresholding or neural networks to classify points as edge or non-edge points. However, these methods often lack generalization ability on datasets with different shapes, leading to issues such as missing contours and discontinuous distribution. To address this, we propose a 3D point cloud contour generation method based on the denoising diffusion probabilistic model (DDPM). Our method treats the complete point cloud as an explicit condition to guide the generation of contour from noise. Specifically, our method trains the DDPM to be a conditional generative network customized for contour generation tasks. In the network, we design the Conditional Feature Extraction (CFE) module that obtains multi-scale feature information, and the Conditional Feature Fusion (CFF) module embeds this information in the generation process to guide contour generation. The experimental results demonstrate the effectiveness of our method. The source code of our method is available at: https://github.com/djzgroup/ContourGeneration.

Due to the significant intra-class variance of 3D point clouds, it becomes challenging to characterize prototype features with a small number of instances in few-shot classification. The significant feature discrepancies among instances also hinder category determination. In this paper, we propose a few-shot point cloud classification network based on prototype learning. We mitigate intra-class variance and enhance classification performance from three aspects of the network. Firstly, we enrich point cloud features through a multi-scale grouping and pooling strategy. Subsequently, we engage in learning compensatory information from support features to update preliminary prototype features. Finally, we enhance both prototype and query features through instance feature fusion. We conducted few-shot point cloud classification experiments on benchmark datasets, and the results indicate that our approach achieves state-of-the-art performance. The source code of our method is available at https://github.com/djzgroup/FewshotClassification.

Deep learning-based 3D object detectors often require large-scale labeled 3D datasets, which can be expensive to annotate. To tackle this issue, we introduce a core-set sampling strategy within an active learning framework, selecting highly informative data from a data pool to reduce reliance on such datasets. Additionally, we introduce a scale-sensitive loss function into the 3D object detector to mitigate the disparities in the influence of small and large objects on model learning, thereby enhancing the accuracy of small object detection. Our experiments on the KITTI dataset demonstrate that our method achieves comparable results to the baseline using only 20% of the data. Notably, our approach outperforms the baseline in small object detection, with an 8% accuracy improvement for pedestrians and a 4% improvement for cyclists. The source code of the proposed method is available at https://github.com/djzgroup/al-cs-ssl.

As a new design and manufacture paradigm, Cloud-Based Collaborative Design (CBCD) has motivated designers to outsource their product data and design computation onto the cloud service. Despite non-negligible benefits of CBCD, there are potential security threats for the outsourced product data, such as intellectual property, design intentions and private identity, which has become an interest point. This paper presents a novel secure product data exchange (PDE) in the processes of CBCD. Different from general cloud security mechanism, our method is content-based. We first show an outline of the collaborative scenario to describe the architecture of the proposed secure CBCD, in which a security mechanism is combined with the data exchange service to achieve secure PDE. Second, we present a novel grid-based geometric deformation method for the security mechanism with three processes: the original shapes of a source Computer Aided Design (CAD) model can be hidden by deforming the control grid; then the deformed grid can be exchanged to target system where a deformed target CAD model can be reconstructed; at last, the deformed target CAD model can be recovered to the original shape after recovering the deformed grid. Finally, typical CAD model tests demonstrate that our method can keep the sensitive information of source model and also maintain the same level of data exchange error.

With the rapid development of service-oriented computing (SOC)/service-oriented architecture (SOA), cloud computing and web services, cloud-based design and manufacture (CBDM) is emerging as state-of-the-art technologies and methodologies to enable collaborative product development (CPD). CBDM-enabled CPD can provide cost-effective, flexible and scalable solutions to collaborative partners by sharing the resources in the applications of design and manufacturing. Feature-based data exchange (FBDE) has been one of the key issues in history of CPD and should be adapted in lasted CBDM-enabled CPD. Firstly this paper presents a service-oriented architecture for data exchange in CBDM. Within this architecture, FBDE was registered as service and FBDE users in the CBDM environment can acquire a set of FBDE services to replace the traditional FBDE functions among heterogeneous CAD systems. Secondly, in orderto put the philosophy of FBDE-as-a-Service into practice for CBDM, this paper proposes a peerto peer (P2P) approach for service-oriented FBDE, which revolutionizes the traditional centralized and neutral-file based approach. Thirdly, technique issues of FBDE-as-a-Service in P2P architecture are discussed in details, including constituting of the P2P FBDE service, procedure of service-oriented P2P FBDE, pre-P2P FBDE service, topological entity matching between pre/post-P2P service and post-P2P FBDE service. Finally, a case study of data exchange is tested to demonstrate the proposed idea of service-oriented FBDE for CBDM.

As the popularization of 3D print technology, in the future cloud manufacturing environment, it makes the customization of personalized product with low cost to be possible. The design in the customization of personalized product is particularly important. And the key issues are the expression and understanding of design intentions from designers with different backgrounds in collaborative CAD which is main design tool for industrial products. An operation-effects merging for collaborative design method of personalized product is presented, which treats the operation effects reflecting the design intention as the research object, and merges the operation effects based on the geometric semantics to support the real-time collaborative product design freely. The advantage of the proposed method is that, the design intentions are expressed without any delay in the design which can improve the design efficiency and product quality.

Feature-based data exchange (FBDE) for heterogeneous CAD systems is one of the key issues in Collaborative Product Development (CPD) which is now enabled by Cloud-Based Design and Manufacturing (CBDM). Firstly this paper presents a FBDE-as-a-Service architecture for data exchange in CBDM. Within this architecture, FBDE users in the CBDM environment can acquire a set of Peer to Peer (P2P) services to realize the FBDE functions among heterogeneous CAD systems. Secondly, in order to integrate FBDE services into CBDM, we present a P2P approach for FBDE, which is totally different from traditional centralized and neutral file-based approach. Thirdly, some key issues of FBDE-as-a-Service, such as Pre-P2P FBDE service, Post-P2P FBDE service and topological entity matching are researched. Finally, a prototype system of FBDE-as-a-Service for data exchange is implemented to demonstrate the proposed ideas.

Product data exchange is one of most important key issues in Collaborative Product Development. Since feature-based parametric CAD systems have been dominated by industrial applications, Feature-Based Data Exchange (FBDE) is getting real growth. However the main feature-based method still lacks the ability to exchange complex shape among the heterogeneous CAD Systems. This paper attacks the problem by exchanging the parametric curve (such as Spline) which is sketched in 2D and will be used to prepare complex 3D shapes by various extrusion features. Therefore the data exchange of complex shape is divided into two layers: 3D extrusion layer and 2D sketch layer. The exchange of spline proceed in the 2D sketch layer between different CAD systems. We innovatively convert the problem of spline exchange into the problem of spline fitting, and employ the Genetic Algorithm (GA) to solve the problem. A new coding strategy in stage of initialization population is presented to improve the GA so it works well with the spline fitting among the heterogeneous CAD systems. Finally, a Hausdorff Distance (HD) is adopted to calculate the fitness. Experimental results demonstrate the effectiveness of our method.

Model security for collaborative product design in a networked environment (or called networked manufacture, grid manufacture, and cloud manufacture) is an important and also challenging research issue. In order to support collaborative product design in a secure and flexible means, a multi-granularity partial encryption method has been proposed in this paper. Base on the above method, parts of a Computer Aided Design (CAD) model can be selected flexibly by users for encrypting with multi-granularity, according to different users' requirements. The secret keys for the different parts of the CAD model can be customized to meet the requirements of users. Case studies have been developed to demonstrate the effectiveness of the proposed method.