3D Gaussian Splatting has emerged as a promising technique for high-quality 3D rendering, leading to increasing interest in integrating 3DGS into realism SLAM systems. However, existing methods face challenges such as Gaussian primitives redundancy, forgetting problem during continuous optimization, and difficulty in initializing primitives in monocular case due to lack of depth information. In order to achieve efficient and photorealistic mapping, we propose RP-SLAM, a 3D Gaussian splatting-based vision SLAM method for monocular and RGB-D cameras. RP-SLAM decouples camera poses estimation from Gaussian primitives optimization and consists of three key components. Firstly, we propose an efficient incremental mapping approach to achieve a compact and accurate representation of the scene through adaptive sampling and Gaussian primitives filtering. Secondly, a dynamic window optimization method is proposed to mitigate the forgetting problem and improve map consistency. Finally, for the monocular case, a monocular keyframe initialization method based on sparse point cloud is proposed to improve the initialization accuracy of Gaussian primitives, which provides a geometric basis for subsequent optimization. The results of numerous experiments demonstrate that RP-SLAM achieves state-of-the-art map rendering accuracy while ensuring real-time performance and model compactness.

Accurate detection of surface defects on magnetic chips is a necessary and difficult task, especially for small defects, which lack discriminative and robust features due to their small size and weak characteristics. At present, the low accuracy of small defect detection seriously restricts the development of automated visual inspection and needs to be solved urgently. Thus, we propose a novel small defects detection network based on context-weighted aggregation and feature multiscale loop fusion. First, a context-weighted aggregation module (CAM) that enriches feature representations by combining context and attention mechanisms is proposed. We believe that any further improvements will be futile if robust feature representations cannot be obtained. Second, inspired by the mechanism of looking and thinking twice, we aggregate left-right feedback connections into feature pyramids and creatively propose a loop-shaped feature pyramid network (Loop-FPN), enabling multiscale features to be fused up and down and connected left and right. This loop-shaped structure makes the connection of each layer more direct and allows the features at each scale to be fully integrated, which improves the utilization of multiscale features and facilitates the detection of small defects. Finally, we apply the proposed network to practical detection and the results show that our network achieves 97.57% precision, 91.91% recall, and 98.39% AP, which are 0.13%, 1.43%, and 0.99% higher than the current best-performing comparative methods, respectively. Note to Practitioners—Current vision inspection technology has low accuracy and poor stability in small defect detection, which cannot meet the industrial inspection requirements. Small defect detection has become the biggest challenge in the field of visual inspection and has seriously restricted its development. Our proposed network can solve this problem well by mining small defect context, fusing multiscale features and utilizing attention mechanisms, and has been successfully applied in magnetic chip production line. Furthermore, we developed an image acquisition system that can capture defects on all surfaces with high accuracy and without dead space, allowing our network to not only detect small defects on magnetic chips of varying sizes and irregular shapes, but also to adapt to changes in lighting conditions, backgrounds, and viewpoints. Our work provides an effective, reliable, and convenient quality control solution for magnetic chip production.

Construction waste classification (CWC) is a crucial and challenging task. However, this promising CWC system has not yet been fully implemented worldwide. The similar appearance of construction waste makes it difficult to be classified. We believe that if the characteristics of the appearance between different materials can not be fully extracted and effectively propagated, any further improvements of CWC models will be futile. To this end, this paper develops a texture and pixel gradient convolutional network (TPCNet) to capture material features. We find that the texture features as the additional input features can provide intrinsic fine-grained patterns. First, we propose the early-stage fusion strategy, where natural texture features and RGB are concatenated together as input. Second, we propose a texture and pixel gradient convolution (TPC) module to consider subtle texture information. TPC focuses on the texture guidance and introduces it to the modern CNNs, which enjoy the best of both worlds. We extend TPC to GTPC using a lossless synthetic large kernel (SLK) to achieve global contextual dependencies and seamlessly incorporate channel-wise adaptability. Moreover, we propose a two-stage feature pyramid network (TFPN) to fuse richer and denser spatial multi-scale contextual information. We also construct a large-scale real-world dataset and a production line to demonstrate the effectiveness of our model. Note to Practitioners—This paper is motivated by the problem in real-world construction waste classification task. The construction waste have similar 2D appearance makes them hard to be classified. Current models often blindly feed raw RGB images into CNNs instead of considering specific features, such as textured material surface textures. This paper proposes the texture guidance mechanism and introduces it to the training of CNN. Furthermore, we propose a lossless synthetic large kernel (SLK) to achieve global texture information. By introducing texture information as a basis for classification, our model can effectively classify similarly shaped construction waste and improve segmentation performance, thus providing the robot with more accurate location information.

RGB-D semantic segmentation can be advanced with convolutional neural networks due to the availability of Depth data. Although objects cannot be easily discriminated by just the 2D appearance, with the local pixel difference and geometric patterns in Depth, they can be well separated in some cases. Considering the fixed grid kernel structure, CNNs are limited to lack the ability to capture detailed, fine-grained information and thus cannot achieve accurate pixel-level semantic segmentation. To solve this problem in the CNN structure, we propose a Pixel Difference Convolutional Network (PDCNet) to capture detailed intrinsic patterns by aggregating both intensity and gradient information in the local range for Depth data and global range for RGB data, respectively. Precisely, PDCNet consists of a Depth branch and an RGB branch. For the Depth branch, we propose a Pixel Difference Convolution (PDC) to consider local and detailed geometric information in Depth data via aggregating both intensity and gradient information. For the RGB branch, we contribute a lightweight Cascade Large Kernel (CLK) to extend PDC, namely CPDC, to enjoy global contexts for RGB data and further boost performance. Consequently, the local and global pixel differences from both modal data are seamlessly incorporated into PDCNet during the information propagation process. Experiments on three challenging benchmark datasets,$i.e.$, NYUDv2 (78.4 Pixel Acc., 53.5 mIoU), SUN RGB-D (83.3 Pixel Acc., 49.6 mIoU) and SID Dataset (83.1 Pixel Acc., 61.4 mIoU) reveal that our PDCNet achieves state-of-the-art performance for the semantic segmentation task.

Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. Existing detection methods either blindly utilize deep learning or use biosignal features, but neither considers spatial and temporal relevance of face features. These methods are increasingly unable to resist the growing realism of fake videos and lack generalization. In this paper, we identify a reliable fingerprint through the consistency of AR coefficients and extend the original PPG signal to 3-dimensional fingerprints to effectively detect fake content. Using these reliable fingerprints, we propose a novel model-agnostic method to expose Deepfake by analyzing temporal and spatial faint synthetic signals hidden in portrait videos. Specifically, our method extracts two types of faint information, i.e., PPG features and AR features, which are used as the basis for forensics in temporal and spatial domains, respectively. PPG allows remote estimation of the heart rate in face videos, and irregular heart rate fluctuations expose traces of tampering. AR coefficients reflect pixel-wise correlation and spatial traces of smoothing caused by up-sampling in the process of generating fake faces. Furthermore, we employ two ACBlock-based DenseNets as classifiers. Our method provides state-of-the-art performance on multiple deep forgery datasets and demonstrates better generalization.