SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, they require ground-truth dense point sets as the supervision information, which can only trained on synthetic paired training data and are not suitable for training under real-scanned sparse data. However, it is expensive and tedious to obtain large scale paired sparse-dense point sets for training from real scanned sparse data. To address this problem, we propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse-to-fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate self-attention module with graph convolution network (GCN) to simultaneously capture context information inside and among local regions. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate the upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self-projection optimization associated with uniform and reconstruction terms, as a joint loss, to facilitate the self-supervised point cloud upsampling. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performance to the state-of-the-art supervised methods

Comparison of the volatiles composition between healthy and buprestid infected Juglans regia (Juglandaceae).

Meliboeus ohbayashii primoriensis (Coleoptera: Buprestidae) is an important pest of the walnut tree Juglans regia (Juglandaceae), but the volatiles mediating this plant–herbivore interaction are unknown. In this study, volatiles emitted by healthy J. regia and by plants infested with M. ohbayashii primoriensis (Coleoptera: Buprestidae) were obtained by a dynamic headspace method and analyzed by gas chromatography-mass spectrometry (Shanxi, China). We identified 26 major compounds and compared the volatile composition of healthy and buprestid-infected J. regia. Green leaf volatiles were detected in all damaged plants, including the monoterpenoids β-phellandrene and (E)-β-ocimene, the sesquiterpenoids (-)-β-bourbonene, β-ylangene, and (E,E)-α-farnesene, the alcohols linalool, myrtenol, and (E)-(-)-pinocarveol, the ketones (E)-pinocamphone and (Z)-pinocamphone, and the ester methyl salicylate. The major volatiles detected in healthy plants were β-pinene (36.26 %), α-pinene (23.81 %), D-limonene (12.03 %), sabinene (8.63 %), and β-myrcene (4.35 %). The main volatiles from M. ohbayashii primoriensis larva-infested plants were β-pinene (37.82 %), α-pinene (20.36 %), D-limonene (14.71 %), germacrene D (5.24 %), sabinene (4.52 %), and β-phellandrene (3.80 %). These results enrich our understanding of volatiles of healthy plants and plants infested with M. ohbayashii primoriensis. Furthermore, they provide a theoretical basis and scientific foundation for integrated pest management and for effective ecologically sustainable pest control strategie

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.Comment: To be published in AAAI 201

L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learning of global structure by global shape reconstruction, while ignoring the learning of local structures. To resolve this issue, we propose Local-to-Global auto-encoder (L2G-AE) to simultaneously learn the local and global structure of point clouds by local to global reconstruction. Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time. In addition, we introduce a novel hierarchical self-attention mechanism to highlight the important points, scales and regions at different levels in the information aggregation of the encoder. Simultaneously, L2G-AE employs a recurrent neural network (RNN) as decoder to reconstruct a sequence of scales in a local region, based on which the global point cloud is incrementally reconstructed. Our outperforming results in shape classification, retrieval and upsampling show that L2G-AE can understand point clouds better than state-of-the-art methods

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike. Prior attempts on the challenging Semantic Scene Completion task - which entails the inference of dense 3D structure and associated semantic labels from "sparse" representations - have been, to a degree, successful in small indoor scenes when provided with dense point clouds or dense depth maps often fused with semantic segmentation maps from RGB images. However, the performance of these systems drop drastically when applied to large outdoor scenes characterized by dynamic and exponentially sparser conditions. Likewise, processing of the entire sparse volume becomes infeasible due to memory limitations and workarounds introduce computational inefficiency as practitioners are forced to divide the overall volume into multiple equal segments and infer on each individually, rendering real-time performance impossible. In this work, we formulate a method that subsumes the sparsity of large-scale environments and present S3CNet, a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. We show that our proposed method outperforms all counterparts on the 3D task, achieving state-of-the art results on the SemanticKITTI benchmark. Furthermore, we propose a 2D variant of S3CNet with a multi-view fusion strategy to complement our 3D network, providing robustness to occlusions and extreme sparsity in distant regions. We conduct experiments for the 2D semantic scene completion task and compare the results of our sparse 2D network against several leading LiDAR segmentation models adapted for bird's eye view segmentation on two open-source datasets.Comment: 14 page

Enhancing Subtask Performance of Multi-modal Large Language Model

Multi-modal Large Language Model (MLLM) refers to a model expanded from a Large Language Model (LLM) that possesses the capability to handle and infer multi-modal data. Current MLLMs typically begin by using LLMs to decompose tasks into multiple subtasks, then employing individual pre-trained models to complete specific subtasks, and ultimately utilizing LLMs to integrate the results of each subtasks to obtain the results of the task. In real-world scenarios, when dealing with large projects, it is common practice to break down the project into smaller sub-projects, with different teams providing corresponding solutions or results. The project owner then decides which solution or result to use, ensuring the best possible outcome for each subtask and, consequently, for the entire project. Inspired by this, this study considers selecting multiple pre-trained models to complete the same subtask. By combining the results from multiple pre-trained models, the optimal subtask result is obtained, enhancing the performance of the MLLM. Specifically, this study first selects multiple pre-trained models focused on the same subtask based on distinct evaluation approaches, and then invokes these models in parallel to process input data and generate corresponding subtask results. Finally, the results from multiple pre-trained models for the same subtask are compared using the LLM, and the best result is chosen as the outcome for that subtask. Extensive experiments are conducted in this study using GPT-4 annotated datasets and human-annotated datasets. The results of various evaluation metrics adequately demonstrate the effectiveness of the proposed approach in this paper

Experiments and transient simulation on spring-loaded pressure relief valve under high temperature and high pressure steam conditions

Reliable performances of high temperature and high pressure operating steam pressure relief valves (HTHP PRVs) are extremely important for the safety of nuclear power plants. It is still a challenge to accurately describe the dynamic performance of HTHP PRVs. In this study, the accuracy of computational fluid dynamics (CFD) based modelling of the transient processes is examined. For one of the HTHP PRVs named DWPRV, the effects of different parameters on the dynamic performance were investigated by combining CFD simulation and experiments. In the simulation, the domain decomposition method (DDM) and the Grid Pre-deformation Method (GPM) were adopted to handle the moving disk geometry and the large mesh deformation. The effect of damping was also studied. It is confirmed that the use of CFD simulation can improve the design and settings of a HTHP PRV in a highly energetic service that is difficult to test due to safety reasons. For the DWPRV, it was found that the maximum flow rate occurs when the curtain area is 1.18 times the throat area. The degree of superheat ranging from 0 C to 100 C has a negligible effect on the performance of DWPRV regardless of the changes in the material mechanical properties with operating temperatures. The reseating pressure increases linearly with the rise in the distance between the upper adjusting ring and the sealing face. The lower adjusting ring exhibits a weak effect on the reseating pressure. For the ratios of rated lift to throat diameter equalling to 0.3 and 0.35, the DWPRV exhibits the higher blowdown for the ratio of 0.3

Potential of tropical maize populations for improving an elite maize hybrid

Identifying exotic maize (Zea mays L) populations possessing favorable new alleles lacking in local elite hybrids is an important strategy for improving maize hybrids. Selection of an appropriate breeding method will increase the chance of successfully transferring these favorable new alleles into elite inbred lines of local hybrids. The objec¬tives of this study were to: (i) evaluate 14 maize populations from CIMMYT and identify those containing favorable alleles for grain yield, ear length, ear diameter, kernel length, plant height, and ear height that are lacking in a local super hybrid [Jidan261 (W9706 × Ji853)], and to (ii) determine which inbred parent should be improved. These re¬sults showed that the populations Pob43, Pob501, and La Posta had positive and significant numbers of favorable alleles not found in hybrid W9706 × Ji853 that could be used for simultaneous improvement of its grain yield, ear length, and kernel length, and that population QPM-Y was also a good donor for improvement of ear diameter and kernel length in the hybrid. Based on allele frequencies in the two inbred lines and the donor population, when the populations Pob43, La Posta, Pob501, and QPM-Y were used as donors, inbred line W9706 would be improved by selfing the F1 of the cross W9706 × donor population. These results suggested that CIMMYT germplasm has potential to improve temperate elite hybrids. The relationship between GCA and SCA from a previous study and the parameters obtained from the Dudley method are discussed. The results showed that the values of Lplμ’ esti¬mates obtained by applying the Dudley method had the same trend as GCA effects for grain yield but a less clear trend for ear length, while the trends in the relationship value were reversed for SCA between these populations and Lancaster-derived lines

Advances in myopia prevention strategies for school-aged children: a comprehensive review

Myopia has significantly risen in East and Southeast Asia, and the pathological outcomes of this condition, such as myopic maculopathy and optic neuropathy linked to high myopia, have emerged as leading causes of irreversible vision loss. Addressing this issue requires strategies to reduce myopia prevalence and prevent progression to high myopia. Encouraging outdoor activities for schoolchildren and reducing near-work and screen time can effectively prevent myopia development, offering a safe intervention that promotes healthier habits. Several clinical approaches can be employed to decelerate myopia progression, such as administering low-dose atropine eye drops (0.05%), utilizing orthokeratology lenses, implementing soft contact lenses equipped with myopia control features, and incorporating spectacle lenses with aspherical lenslets. When choosing an appropriate strategy, factors such as age, ethnicity, and the rate of myopia progression should be considered. However, some treatments may encounter obstacles such as adverse side effects, high costs, complex procedures, or limited effectiveness. Presently, low-dose atropine (0.05%), soft contact lenses with myopia control features, and orthokeratology lenses appear as promising options for managing myopia. The measures mentioned above are not necessarily mutually exclusive, and researchers are increasingly exploring their combined effects. By advocating for a personalized approach based on individual risk factors and the unique needs of each child, this review aims to contribute to the development of targeted and effective myopia prevention strategies, thereby minimizing the impact of myopia and its related complications among school-aged children in affected regions