Point cloud semantic segmentation has achieved considerable progress in the past decade. To alleviate expensive data annotation efforts, weakly supervised learning methods are preferable, and traditional approaches are typically based on siamese neural networks. To enhance the feature learning capability, in this work, we introduce a dual-teacher-guided contrastive learning framework for weakly supervised point cloud semantic segmentation. A dual-teacher framework can reduce sub-network coupling and facilitate feature learning. In addition, a cross-validation approach can filter out low-quality samples, and a pseudo-label correction module can improve the quality of pseudo-labels. Cleaned unlabeled data are used to construct contrastive loss based on the prototypes of each class, which further boost the segmentation performance. Extensive experimental results conducted on the S3DIS, ScanNet-v2, and SemanticKITTI datasets demonstrate that our proposed DCL outperforms state-of-the-art methods.

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study’s image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people’s average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.

This paper proposed a method for reconstructing floorplans from indoor point clouds. Unlike existing corner and line primitive detection algorithms, this method uses a generative adversarial network to learn the complex distribution of indoor layout graphics, and repairs incomplete room masks into more regular segmentation areas. Automatic learning of the structure information of layout graphics can reduce the dependence on geometric priors, and replacing complex optimization algorithms with Deep Neural Networks (DNN) can improve the efficiency of data processing. The proposed method can retain more shape information from the original data and improve the accuracy of the overall structure details. On this basis, the method further used an edge optimization algorithm to eliminate pixel-level edge artifacts that neural networks cannot perceive. Finally, combined with the constraint information of the overall layout, the method can generate compact floorplans with rich semantic information. Experimental results indicated that the algorithm has robustness and accuracy in complex 3D indoor datasets; its performance is competitive with those of existing methods.

Semi-supervised semantic segmentation relieves the reliance on large-scale labeled data by leveraging unlabeled data. Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data. However, unreliable pseudo-labeling can undermine the semi-supervision processes. In this paper, we propose an algorithm called Multi-Level Label Correction (MLLC), which aims to use graph neural networks to capture structural relationships in Semantic-Level Graphs (SLGs) and Class-Level Graphs (CLGs) to rectify erroneous pseudo-labels. Specifically, SLGs represent semantic affinities between pairs of pixel features, and CLGs describe classification consistencies between pairs of pixel labels. With the support of proximate pattern information from graphs, MLLC can rectify incorrectly predicted pseudo-labels and can facilitate discriminative feature representations. We design an end-to-end network to train and perform this effective label corrections mechanism. Experiments demonstrate that MLLC can significantly improve supervised baselines and outperforms state-of-the-art approaches in different scenarios on Cityscapes and PASCAL VOC 2012 datasets. Specifically, MLLC improves the supervised baseline by at least 5% and 2% with DeepLabV2 and DeepLabV3+ respectively under different partition protocols.

The identification of plant nematodes is crucial in the fields of pest control, soil ecology, and biogeography. The automated recognition of plant nematodes based on deep-learning technology can significantly improve the accuracy and efficiency of their recognition. In this study, we devised a method for the multi-mode, multi-feature identification of plant nematodes using deep-learning techniques which emulated the recognition logic of domain experts. Beginning with a multi-featured plant nematode dataset, we not only designed key feature extraction strategies to address the problem of weak key feature points and small inter-specific differences in plant nematodes but also proposed a multi-feature joint training scheme and constructed a neural network structure with interpretability. Finally, an intelligent decision-making expert identification system for plant nematodes was implemented, and its performance was tested on the multi-feature plant nematode dataset. The results indicate that our model achieves an accuracy of up to 96.74% in identifying 23 species across two-body parts, which is 17.5% higher than the single-part feature identification. The accuracy of identifying 11 species in three-body parts reached 98.46%, an improvement of 1.24% over that of the two-part feature identification. Our novel model demonstrates that the accuracy of the expert system can be increased by incorporating more nematode feature parts.

Neural radiation field (NeRF)-based novel view synthesis methods are gaining popularity. NeRF can generate more detailed and realistic images than traditional methods. Conventional NeRF reconstruction of a room scene requires at least several hundred images as input data and generates several spatial sampling points, placing a tremendous burden on the training and prediction process with respect to memory and computational time. To address these problems, we propose a prior-driven NeRF model that only accepts sparse views as input data and reduces a significant number of non-functional sampling points to improve training and prediction efficiency and achieve fast high-quality rendering. First, this study uses depth priors to guide sampling, and only a few sampling points near the controllable range of the depth prior are used as input data, which reduces the memory occupation and improves the efficiency of training and prediction. Second, this study encodes depth priors as distance weights into the model and guides the model to quickly fit the object surface. Finally, a novel approach combining the traditional mesh rendering method (TMRM) and the NeRF volume rendering method was used to further improve the rendering efficiency. Experimental results demonstrated that our method had significant advantages in the case of sparse input views (11 per room) and few sampling points (8 points per ray).