Machining feature recognition plays a crucial role in the effective integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) systems. However, current machining feature datasets lack diversity and authenticity in both machining features and base models. Existing recognition methods are limited in capturing the geometric information of long-range complex intersecting features. In this paper, we first construct a novel Multi-Machining Feature model Dataset (MMFD) that includes various non-machining feature shapes as base models and complex machining features commonly encountered in real industrial manufacturing processes. We also propose CRNet, a contrastive learning-based machining feature recognition network with a Transformer encoder and Face Geometric Position Encoding (FGPE) to capture long-range contextual and spatial geometric relationship information between complex features. Our experimental results demonstrate the effectiveness and accuracy of our CRNet on MMFD compared to the state-of-the-art methods.
Seamlessly integrating virtual objects into real scenes is a critical challenge in augmented reality. To achieve this, it is important to provide globally consistent estimations of lighting conditions in the real scene. In this study, we propose a method to reproduce the appearance of metallic materials using an image-to-image translation network incorporating Vision Transformer (ViT). The network receives an image consisting of a background and a normal map of a virtual object, and transforms the normal map into a virtual object with the metallic material appearance. Specifically, ViT, which effectively captures long-range dependencies in the input image, is introduced into the network’s encoder to extract lighting information, and the network produces the natural appearance of the virtual object that reflects the extracted lighting information in the output image. We created a synthetic dataset and compared images generated by the proposed method with those generated by a CNN-based image-to-image translation network. The results showed that the proposed method outperformed the CNN-based method in three quantitative metrics and reproduced a more natural appearance of metallic materials that is consistent with the lighting conditions.
Video anomaly detection is crucial for public safety and traffic management but faces challenges due to the rarity, context - dependence, and high - dimensionality of anomalies. Traditional fully - supervised methods rely on expensive frame - level annotations. Existing weakly - supervised approaches, dependent on single - modality or static clip modeling, struggle to balance semantic discrimination and temporal coherence. To address these, we propose a novel weakly - supervised video anomaly detection framework using only video - level labels. It employs a Temporal Dynamic Modeling (TDM) module for capturing long - and short - term dependencies, a Semantic - Assisted Anomaly Recognition (SAAR) module to enhance anomaly discrimination, and a Time Series Confidence Smoothing (TCS) strategy to optimize result stability. Our method achieved an AUC of 97.64
While Convolutional Neural Networks (CNNs) excel at local feature modeling, they struggle with global context in lightweight super-resolution (SR) tasks and suffer from high computational complexity. Vision Transformers (ViTs) are effective at capturing long-range dependencies but tend to be computationally expensive and less efficient at handling local details. To address these challenges, we propose Hybrid-SANet, a novel hybrid self-attention network architecture that combines the strengths of both CNNs and ViTs. To this end, we design a Local Self-Attention (LSA) module consisting of wavelet transform and variable large kernel block to efficiently extract high-frequency details and capture short-range dependencies within the image. This allows the model to effectively preserve fine textures and local features, which are crucial for high-quality SR reconstruction. To enhance the global feature extraction, Hybrid-SANet also incorporates frequency projection into sparse GSA, which enables the model to capture long-range dependencies and global context. Experimental results demonstrate that Hybrid-SANet outperforms existing SR methods on benchmark datasets, particularly in terms of detail recovery and computational efficiency.
In the field of 3D generation, producing high quality physically based material maps has long been a key research challenge. However, mainstream diffusion-based material generation methods often lack fine-grained details and logical consistency. In contrast, manual material creation typically relies on extensive material layering and grouping to enrich details and ensure logical coherence. In this paper, we propose MatLayerNet, a multi-agent-based method that organizes large language model (LLM) agents to simulate the human workflow of material creation for material generation tasks. Firstly, we construct an iterative material segmentation pipeline that decomposes textual descriptions of objects into multiple material texts. Secondly, we deconstruct the logic of material layering, guiding the LLM to generate structured layer plans based on substrate material, material texture, and material aging. Thirdly, we introduce a vertically structured multi-agent architecture to generate consistent and coherent material parameters across all layers. Finally, we incorporate a retrieval-augmented generation (RAG) approach, where the LLM invokes a material mask generator to produce layer-specific masks and determine spatial distribution. Experimental results demonstrate that our method can generate more accurate material details while maintaining consistency and logical integrity among all material components.
