Toward knowledge-based automatic 3D spatial topological modeling from LiDAR point clouds for urban areas

Le traitement d'un très grand nombre de données LiDAR demeure très coûteux et nécessite des approches de modélisation 3D automatisée. De plus, les nuages de points incomplets causés par l'occlusion et la densité ainsi que les incertitudes liées au traitement des données LiDAR compliquent la création automatique de modèles 3D enrichis sémantiquement. Ce travail de recherche vise à développer de nouvelles solutions pour la création automatique de modèles géométriques 3D complets avec des étiquettes sémantiques à partir de nuages de points incomplets. Un cadre intégrant la connaissance des objets à la modélisation 3D est proposé pour améliorer la complétude des modèles géométriques 3D en utilisant un raisonnement qualitatif basé sur les informations sémantiques des objets et de leurs composants, leurs relations géométriques et spatiales. De plus, nous visons à tirer parti de la connaissance qualitative des objets en reconnaissance automatique des objets et à la création de modèles géométriques 3D complets à partir de nuages de points incomplets. Pour atteindre cet objectif, plusieurs solutions sont proposées pour la segmentation automatique, l'identification des relations topologiques entre les composants de l'objet, la reconnaissance des caractéristiques et la création de modèles géométriques 3D complets. (1) Des solutions d'apprentissage automatique ont été proposées pour la segmentation sémantique automatique et la segmentation de type CAO afin de segmenter des objets aux structures complexes. (2) Nous avons proposé un algorithme pour identifier efficacement les relations topologiques entre les composants d'objet extraits des nuages de points afin d'assembler un modèle de Représentation Frontière. (3) L'intégration des connaissances sur les objets et la reconnaissance des caractéristiques a été développée pour inférer automatiquement les étiquettes sémantiques des objets et de leurs composants. Afin de traiter les informations incertitudes, une solution de raisonnement automatique incertain, basée sur des règles représentant la connaissance, a été développée pour reconnaître les composants du bâtiment à partir d'informations incertaines extraites des nuages de points. (4) Une méthode heuristique pour la création de modèles géométriques 3D complets a été conçue en utilisant les connaissances relatives aux bâtiments, les informations géométriques et topologiques des composants du bâtiment et les informations sémantiques obtenues à partir de la reconnaissance des caractéristiques. Enfin, le cadre proposé pour améliorer la modélisation 3D automatique à partir de nuages de points de zones urbaines a été validé par une étude de cas visant à créer un modèle de bâtiment 3D complet. L'expérimentation démontre que l'intégration des connaissances dans les étapes de la modélisation 3D est efficace pour créer un modèle de construction complet à partir de nuages de points incomplets.The processing of a very large set of LiDAR data is very costly and necessitates automatic 3D modeling approaches. In addition, incomplete point clouds caused by occlusion and uneven density and the uncertainties in the processing of LiDAR data make it difficult to automatic creation of semantically enriched 3D models. This research work aims at developing new solutions for the automatic creation of complete 3D geometric models with semantic labels from incomplete point clouds. A framework integrating knowledge about objects in urban scenes into 3D modeling is proposed for improving the completeness of 3D geometric models using qualitative reasoning based on semantic information of objects and their components, their geometric and spatial relations. Moreover, we aim at taking advantage of the qualitative knowledge of objects in automatic feature recognition and further in the creation of complete 3D geometric models from incomplete point clouds. To achieve this goal, several algorithms are proposed for automatic segmentation, the identification of the topological relations between object components, feature recognition and the creation of complete 3D geometric models. (1) Machine learning solutions have been proposed for automatic semantic segmentation and CAD-like segmentation to segment objects with complex structures. (2) We proposed an algorithm to efficiently identify topological relationships between object components extracted from point clouds to assemble a Boundary Representation model. (3) The integration of object knowledge and feature recognition has been developed to automatically obtain semantic labels of objects and their components. In order to deal with uncertain information, a rule-based automatic uncertain reasoning solution was developed to recognize building components from uncertain information extracted from point clouds. (4) A heuristic method for creating complete 3D geometric models was designed using building knowledge, geometric and topological relations of building components, and semantic information obtained from feature recognition. Finally, the proposed framework for improving automatic 3D modeling from point clouds of urban areas has been validated by a case study aimed at creating a complete 3D building model. Experiments demonstrate that the integration of knowledge into the steps of 3D modeling is effective in creating a complete building model from incomplete point clouds

Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization

Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Based Non-Photorealistic Radiance Fields (Ref-NPR) to address this limitation. This controllable method stylizes a 3D scene using radiance fields with a single stylized 2D view as a reference. We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views. Then we exploit semantic correspondences in content images to fill occluded regions with perceptually similar styles, resulting in non-photorealistic and continuous novel view sequences. Our experimental results demonstrate that Ref-NPR outperforms existing scene and video stylization methods regarding visual quality and semantic correspondence. The code and data are publicly available on the project page at https://ref-npr.github.io.Comment: Accepted by CVPR2023. 17 pages, 20 figures. Project page: https://ref-npr.github.io, Code: https://github.com/dvlab-research/Ref-NP

Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7

<p>Abstract</p> <p>Background</p> <p>The three-dimensional shape of grain, measured as grain length, width, and thickness (GL, GW, and GT), is one of the most important components of grain appearance in rice. Determining the genetic basis of variations in grain shape could facilitate efficient improvements in grain appearance. In this study, an F_7:8recombinant inbred line population (RIL) derived from a cross between <it>indica </it>and <it>japonica </it>cultivars (Nanyangzhan and Chuan7) contrasting in grain size was used for quantitative trait locus (QTL) mapping. A genetic linkage map was constructed with 164 simple sequence repeat (SSR) markers. The major aim of this study was to detect a QTL for grain shape and to fine map a minor QTL, <it>qGL7</it>.</p> <p>Results</p> <p>Four QTLs for GL were detected on chromosomes 3 and 7, and 10 QTLs for GW and 9 QTLs for GT were identified on chromosomes 2, 3, 5, 7, 9 and 10, respectively. A total of 28 QTLs were identified, of which several are reported for the first time; four major QTLs and six minor QTLs for grain shape were also commonly detected in both years. The minor QTL, <it>qGL7</it>, exhibited pleiotropic effects on GL, GW, GT, 1000-grain weight (TGW), and spikelets per panicle (SPP) and was further validated in a near isogenic F₂population (NIL-F₂). Finally, <it>qGL7 </it>was narrowed down to an interval between InDel marker RID711 and SSR marker RM6389, covering a 258-kb region in the Nipponbare genome, and cosegregated with InDel markers RID710 and RID76.</p> <p>Conclusion</p> <p>Materials with very different phenotypes were used to develop mapping populations to detect QTLs because of their complex genetic background. Progeny tests proved that the minor QTL, <it>qGL7</it>, could display a single mendelian characteristic. Therefore, we suggested that minor QTLs for traits with high heritability could be isolated using a map-based cloning strategy in a large NIL-F₂population. In addition, combinations of different QTLs produced diverse grain shapes, which provide the ability to breed more varieties of rice to satisfy consumer preferences.</p

MicroRNA-96 Promotes Schistosomiasis Hepatic Fibrosis in Mice by Suppressing Smad7

Infection with Schistosoma causes aberrant expression of host microRNAs (miRNAs), and normalizing the levels of dysregulated miRNAs can attenuate pathology. Here, we show that the host miRNA, miR-96, is markedly upregulated during the progression of hepatic schistosomiasis. We demonstrate that elevation of miR-96 induces hepatic fibrosis in infected mice by suppressing the expression of its target gene, Smad7. We show that infection with Schistosoma induces the expression of transforming growth factor beta1 (TGF-beta1), which in turn upregulates the expression of miR-96 through SMAD2/3-DROSHA-mediated post-transcriptional regulation. Furthermore, inhibition of miR-96 with recombinant adeno-associated virus 8 (rAAV8)-mediated delivery of Tough Decoy RNAs in mice attenuated hepatic fibrosis and prevented lethality following schistosome infection. Taken together, our data highlight the potential for rAAV8-mediated inhibition of miR-96 as a therapeutic strategy to treat hepatic schistosomiasis

Synthesis and Electrochemical Performance of Graphene Wrapped SnxTi1−xO2 Nanoparticles as an Anode Material for Li-Ion Batteries

Ever-growing development of Li-ion battery has urged the exploitation of new materials as electrodes. Here, SnxTi1-xO2 solid-solution nanomaterials were prepared by aqueous solution method. The morphology, structures, and electrochemical performance of SnxTi1-xO2 nanoparticles were systematically investigated. The results indicate that Ti atom can replace the Sn atom to enter the lattice of SnO2 to form substitutional solid-solution compounds. The capacity of the solid solution decreases while the stability is improved with the increasing of the Ti content. Solid solution with x of 0.7 exhibits the optimal electrochemical performance. The Sn0.7Ti0.3O2 was further modified by highly conductive graphene to enhance its relatively low electrical conductivity. The Sn0.7Ti0.3O2/graphene composite exhibits much improved rate performance, indicating that the SnxTi1-xO2 solid solution can be used as a potential anode material for Li-ion batteries

Study of Cluster Formation Algorithm for Aquaculture WSN Based on Cross-Layer Design

International audienceAquaculture WSN is composed of a large number of nodes with different monitoring function. In this paper, we present a cluster formation algorithm in Sensor MAC for event-driven Aquaculture WSN. The technique proposed is based on cross-layer design which is adopted to reduce the energy waste and guarantee the data transmissions. The event-driven Aquaculture WSN is simulated by OPNET and MATLAB. Simulation results show that the proposed protocol saves node energy, shortens average packet latency, and improves event detection reliability

Experimental Study on Optimal Recycling Mechanical Parameters of Cotton Field Mulch film based on Small Soil Trough System

Film mulching agriculture in arid areas is faced with pollution caused by film mulching, and currently mainly adopts the mechanized recycling of mulch film. However, residual mulch film in the soil will bind with soil under the farming environment, which affects the recycling effect. The main factors affecting the recycling of mulch film in the soil are not clear. In order to find out the specific factors, the actual dry-wet cycle water environment was simulated by using a small soil trough system based on the film lifting, separation and recycling problem of residual mulch film in the soil. The film lifting force and recycling efficiency of the residual mulch film under the action of wet-dry cycle were studied. The following results were obtained: soil compaction, film lifting angle, and the dry-wet cycle had a significant influence on the film lifting force value, indicating that the dry-wet cycle including water fertilizer had an impact on the soil structure. After mechanical loosening, the film lifting force decreased and the recycling rate of residual mulch film increased obviously. The optimal film recycling effect could be obtained under the following conditions, namely, a film lifting angle of 21.37&ndash;45.37&deg;, the number of dry-wet cycles &lt;3.8, a soil moisture of 22.43&ndash;23.18%, a soil compaction of 132.51&ndash;144.06 KPa, and a residual mulch film area of 45.85&ndash;64.5 cm2. The experimental results can provide technical reference for residual mulch film pollution control and mechanized recycling

Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified

Detection of Cotton Seed Damage Based on Improved YOLOv5

The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds