As 3D technologies advance rapidly, 3D printing, 3D animation, and 3D Movie are springing out in different areas. It becomes a remarkable challenge to generate a large number of 3D models effectively and efficiently. This paper proposes a novel editing method based on the feature lines of images (i.e., image contour and principal axis) for generating new 3D models. Our method takes as input an existing 3D model (as the original model) and an image selected by the user (or a sketch hand-drawn by the users), and performs the model editing to generate a new 3D model. In particular, our method first takes as input an original 3D model and an image selected by the user. Second, the selected image is processed to produce the feature lines, i.e., contour and principal axis of the image. Third, the silhouette of the original model from a given view is acquired, and projected on a projection plane to produce a contour and principal axis of the model, which is the feature lines of the original model. Fourth, by comparing the feature lines of the image and the original model, the constraint conditions are established to control the editing of the 3D model. Finally, 3D model editing is conducted through the as-rigid-as-possible mesh deformation to produce a new 3D model with the appearance resembling the selected image. Furthermore, this paper proposes an energy function to guide the detailed model editing, and measure the similarity between the generated 3D model and the corresponding image. We have conducted extensive experiments to evaluate the proposed method. The results show that comparing with the existing editing methods in literature, the proposed model editing method is able to construct various types of 3D models more effectively and more efficiently.

In the fields of computer graphics and computer vision, a great amount of research and analysis has been conducted on expression-carrying face models. How to construct more realistic and effective 3D face models has become an immense challenge to researchers. This paper proposes a parametric 3D face model editing algorithm based on existing 3D face models. The algorithm takes a number of existing 3D face models with different expressions as input, edits the models through mostly through model deformation and interpolation, and generates a new 3D face model. In particular, the face model editing process begins with selecting multiple face models with different expressions as input. Second, with one of the selected models as the source model and others as the target models, the source model and all target models are registered one by one; meanwhile, the vertex correspondence between the registered models is established. Third, the selected 3D models are parameterized to a planar disc through quasi-conformal mapping. Fourth, relying on the vertex correspondence, a set of corresponding control points between different models are established. The model is then deformed and interpolated under the guidance of the control points and by using the quasi-conformal iteration method, which produces the 2D face models with transitional expressions between the source model and the target models. Finally, the 2D models are restored to the corresponding 3D face models using the model restoration algorithm. Additionally, this paper proposes to use the Beltrami coefficient to guide the quasi-conformal iteration in performing the mapping between two planes. This coefficient then serves as a measure to evaluate the similarity between the edited model and the original one. The proposed algorithm has been evaluated through extensive experiments. The results suggest that compared with existing editing methods, the proposed method is more effective and efficient in constructing various 3D face models.

Although current 3D semantic segmentation methods have achieved significant success, they suffer from catastrophic forgetting when confronted with dynamic, open environments. To address this issue, class incremental learning is introduced to update models while maintaining a balance between plasticity and stability. In this work, we propose CosPrompt, a rehearsal-free approach for class incremental semantic segmentation. Specifically, we freeze the prompts for existing classes and incrementally expand and fine-tune the prompts for new classes, thereby generating discriminative and customized features. We employ clamping operations to regulate backward propagation, ensuring smooth training. Furthermore, we utilize the learning without forgetting loss and pseudo-label generation to further mitigate catastrophic forgetting. We conduct comparative and ablation experiments on the S3DIS dataset and ScanNet v2 dataset, demonstrating the effectiveness and feasibility of our method.

3D mesh saliency prediction aims to identify the most visually attractive regions of a model and is widely applied in tasks such as mesh simplification, reconstruction, and retrieval. Traditional methods often rely on manually designed geometric features, which struggle to accurately characterize saliency distributions under complex deformations. Deep learning-based approaches, on the other hand, are limited by insufficient feature fusion. To address these issues, this paper proposes a lightweight neural network based on depthwise separable convolution for more efficient and precise saliency prediction. The method first constructs local patch features to represent geometric structures; then, multi-layer depthwise separable convolutions are employed to efficiently extract saliency information; finally, fully connected layers integrate the features to output saliency scores for each face, enabling the prediction of the overall saliency distribution of the mesh. Extensive experiments on multiple mesh datasets demonstrate that the proposed method improves prediction accuracy and effectively identifies salient regions.

Time difference of arrival (TDOA) positioning results obtained using commonly adopted algebraic methods lack uncertainty information. In this letter, we propose to incorporate interval computation into TDOA-based hyperbolic localization and employ an iterative contraction strategy to generate interval positioning results that guarantee to enclose the true solution. With the newly developed algorithm, interval TDOA measurements are considered as interval hyperbolas and partitioned into non-overlapping sets of rectangles using the dichotomy method. The intersection of these rectangles is determined and applied to update the target location interval through an iterative contraction process to shrink the location interval until convergence. Simulations are conducted to evaluate the accuracy, uncertainty and validity of the proposed interval hyperbolic localization algorithm. It is shown that the new algorithm can attain the Cramér-Rao lower bound accuracy under high level Gaussian noise and produce, with a probability close to one, positioning intervals enclosing the true target location.

Cable composite insulation interfaces are susceptible to pressure distribution distortion under combined electric, thermal, and mechanical stresses, potentially triggering insulation failure. To investigate this phenomenon, this research develops a comprehensive three-stage approach. Initially, a bilateral shaft-sleeve contact model is established to theoretically analyze the mechanism of uneven interfacial pressure distribution under bending stress and temperature at cable terminal interfaces. Subsequently, a specialized bending-temperature coupling experimental platform validates the theoretical findings, confirming that extreme temperatures and bending stresses significantly distort interfacial pressure. Finally, surface flashover discharge experiments on XLPE-SiR interfaces are conducted, examining discharge characteristics through multiple parameters including starting discharge voltage, grey scale histograms, and infrared spectra. The results reveal that mechanical-thermal stress coupling alters the interference fit of cable composite interfaces, leading to non-uniform pressure distribution. Furthermore, the study demonstrates that pressure distribution transition modes directly influence discharge path characteristics. The extent of discharge damage is found to correlate with the combined effects of discharge channel broadening at initiation sites and dielectric polarization. These findings provide crucial insights for improving cable terminal design and reliability in high-voltage power systems.