ObjectiveResearch on computer-assisted instruction in physics education remains at a relatively exploratory and developmental stage， particularly in the area of dynamics problem solving， where persistent and multifaceted challenges continue to restrict large-scale pedagogical effectiveness. These challenges include the incomplete and fragmented representation of physical states， insufficient formalization of object interactions， opaque and non-explainable reasoning processes， limited adaptability to complex multi-body systems， and weak alignment between computational outputs and instructional logic. Although intelligent tutoring systems， digital simulations， and adaptive learning technologies have achieved considerable technological advancement， many existing solutions rely either on purely numerical computation frameworks or on rigid， template-driven procedural scripts. Such approaches often fail to establish a coherent structural bridge between low-level simulation data and high-level conceptual reasoning， thereby limiting semantic consistency， interpretability， and cross-problem generalization. Consequently， learners frequently receive algorithmically generated answers without adequate exposure to intermediate inferential steps， causal dependencies among variables， constraint propagation mechanisms， or the theoretical principles governing the solution trajectory. This lack of transparency not only reduces instructional clarity but also weakens students’ ability to construct transferable mental models of physical systems. In response to these theoretical and methodological gaps， the present study seeks to develop a unified， extensible， and semantically grounded framework that enhances representational completeness， reasoning explicability， structural consistency， cognitive alignment， and domain generalizability in computer-assisted physics education through the systematic integration of structured scene modeling， knowledge-graph-based inference， and interactive visual analytics.MethodTo achieve this objective， this study proposes a hybrid reasoning architecture that integrates Physics Scene Graphs with a domain-specific knowledge graph to support intelligent physical process analysis， automated model selection， interpretable equation derivation， and structured problem solving. The framework begins by constructing a formally defined Physics Scene Graph derived from simulation data generated by a physics engine. Within this graph-based representation， physical entities are modeled as nodes enriched with multidimensional attributes， including mass， position， displacement， velocity， acceleration， net force， constraint conditions， and state-transition parameters. Mechanical interactions among entities—such as contact relationships， frictional forces， normal forces， tension links， collision events， composite bindings， and constraint hierarchies—are encoded as semantically annotated edges， forming a relational topology that captures both spatial configuration and dynamic dependency. This explicit structural formalization enables comprehensive modeling of instantaneous states， temporal evolution， constraint propagation， and multi-object coupling in a machine-interpretable and computationally robust manner.Building upon this structured representation layer， a knowledge-graph-driven rule-based reasoning mechanism is introduced to dynamically identify， activate， and instantiate appropriate physical models according to contextual features of the problem scenario. The knowledge graph encodes fundamental physical laws， mathematical expressions， symbolic transformation rules， boundary conditions， dimensional constraints， decomposition strategies， and pedagogical heuristics within a hierarchically organized semantic network. Through semantic matching， relational mapping， subgraph alignment， and rule triggering， entities and relations in the Physics Scene Graph are systematically mapped to corresponding theoretical constructs within the knowledge graph. This bidirectional mapping enables automated derivation of governing equations， symbolic manipulation of expressions， validation of physical consistency， incremental state updating， and stepwise numerical computation. Importantly， the reasoning pipeline is designed to be modular， traceable， and explainable， allowing explicit reconstruction of inference chains and transparent visualization of intermediate logical transitions. To operationalize the proposed architecture， an interactive physics teaching system was implemented， integrating automatic scene parsing， structured reasoning orchestration， real-time state synchronization， multimodal rendering modules， and user-interactive control mechanisms to facilitate both computational automation and pedagogical interpretability.ResultComprehensive experimental case analyses， benchmark evaluations， and user-centered empirical studies demonstrate that the proposed system is capable of automatically and reliably solving a broad spectrum of dynamics problems involving multi-object interactions， frictional constraints， composite systems， conditional equilibrium states， and dynamic state transitions. The generated analytical solutions exhibit high consistency with standard textbook methodologies and conventional classroom derivations， including systematic free-body diagram construction， equation decomposition， and sequential symbolic manipulation. In addition to producing accurate numerical results， the system dynamically generates synchronized two-dimensional visualizations that depict object configurations， force vectors， motion trajectories， constraint relationships， and state-evolution sequences. These visual representations are temporally aligned with symbolic derivations， formula transformations， and computational procedures， forming a coherent multimodal explanatory environment. User evaluation results indicate that the integration of structured graphical representations with explicit reasoning chains substantially enhances conceptual clarity， cognitive engagement， and problem-solving confidence. Students reported improved understanding of force decomposition strategies， system boundary selection， inter-object dependency modeling， and the logical progression from physical principles to quantitative solutions. Moreover， the modular architecture of both the Physics Scene Graph and the knowledge graph demonstrates strong extensibility， scalability， and adaptability， allowing systematic expansion to additional domains such as kinematics， energy conservation analysis， rotational dynamics， oscillatory systems， and electromagnetism through incremental enrichment of semantic attributes and domain-specific inference rules.ConclusionThis study presents a comprehensive， semantically coherent， and pedagogically interpretable framework that integrates structured scene representation， knowledge-graph-driven automated reasoning， and interactive multimodal visualization to advance the theoretical foundation and practical implementation of computer-assisted physics instruction. By systematically addressing limitations related to representational incompleteness， reasoning opacity， procedural rigidity， and limited cross-domain transferability， the proposed approach enhances both computational intelligence and educational effectiveness. The synergistic coupling of simulation-based state modeling with explicit conceptual inference establishes a transparent， explainable， and cognitively aligned problem-solving paradigm that supports deep conceptual comprehension， structured analytical thinking， and meaningful knowledge transfer. Beyond the specific context of dynamics， the architectural principles and methodological innovations underlying this framework provide a transferable reference model for other branches of physics as well as highly abstract disciplines in computer-assisted education， including advanced mathematics， engineering mechanics， and computational modeling. Overall， this work contributes a theoretically grounded， technically robust， and pedagogically valuable blueprint for the design of next-generation intelligent educational systems that harmonize structured representation， automated reasoning， semantic transparency， and interactive visualization to foster more rigorous， immersive， and engaging learning experiences.

ObjectiveIn the contemporary landscape of geospatial engineering and computer vision， modern industrial workflows dedicated to Unmanned Aerial Vehicle （UAV） aerial surveying and high-fidelity 3D scene reconstruction have become foundational. Rapidly acquiring sparse 3D point clouds of target areas—typically achieved through Structure-from-Motion （SfM） photogrammetry or airborne Light Detection and Ranging （LiDAR）—has been established as a standard procedure. However， practical data collection is frequently hindered by severe physical and regulatory bottlenecks. Restricted by limited UAV battery endurance， strict airspace control regulations， or rigid predefined flight routes， the actual optical imagery collected during field operations can rarely achieve the dense， omnidirectional overlapping coverage required by ideal reconstruction algorithms. Consequently， researchers are often forced to work with highly sparse visual observations. When attempting to perform large-baseline view extrapolation or traditional graphics rendering utilizing these discrete and occluded 3D priors， existing methods face insurmountable challenges. Traditional rendering pipelines often result in severe structural distortions， projection holes， and rendering artifacts due to the lack of dense multi-view continuity. On the other hand， the recent emergence of purely data-driven 2D generative diffusion models （such as Stable Diffusion） offers unprecedented capabilities in high-frequency texture hallucination. Yet， these models fundamentally lack explicit 3D physical constraints. During large-baseline view extrapolation， they easily violate epipolar geometric constraints， leading to severe perspective distortion， physical scale collapse， and the topological misalignment of critical ground objects. To address these critical limitations， this research proposes a novel generative view extrapolation method fused with sparse 3D priors. The primary objective is to utilize discrete physical geometric skeletons to constrain and guide continuous pixel generation. By bridging the gap between graphical rendering and generative modeling， this research aims to enable high-fidelity， geometrically consistent， and controllable novel view extrapolation under extreme， sparse UAV observation conditions.MethodTo achieve the aforementioned objectives， the proposed framework explicitly injects multi-source physical spatial constraints into a large-scale Latent Diffusion Model （SDXL）， fundamentally transforming it from a pure 2D image generator into a 3D-aware extrapolation engine. The methodology is meticulously structured into three core stages to ensure that geometric consistency and semantic richness are simultaneously preserved. First， the framework executes the Spatial Alignment of 3D Priors. Recognizing that raw point clouds are discrete and challenging for 2D networks to interpret， we project the sparse point cloud onto the target camera view using the corresponding extrinsic matrices. This projection yields an absolute depth map. By fusing this depth information with pixel-level coordinate maps， we construct a comprehensive Geometric Spatial Embedding （GSE） map. Furthermore， to provide the generative network with an optimal starting initialization， the source reference image undergoes a pre-alignment process via rigid pose transformation， mapping the source pixels to the approximate target perspective based on available geometric data. Second， we propose a Dual-Branch Forward Generation Pipeline with Decoupled Semantics and Geometry. To prevent the network from confusing structural boundaries with texture colors， the architecture handles these modalities independently. An IP-Adapter （Image Prompt Adapter） is employed as the semantic branch to extract global appearance characteristics—such as lighting conditions， material textures， and environmental atmosphere—directly from the source image. Simultaneously， a ControlNet architecture serves as the geometric branch， meticulously injecting the constructed GSE features into the denoising steps. This decoupled dual-branch design forces the network to render continuous and realistic high-frequency textures （guided by the semantic branch） while strictly anchoring them to precise spatial topologies （constrained by the geometric branch）. Third， the framework introduces 3D Perspective Supervision via Latent-Space Reprojection. Traditional pixel-space losses often fail to capture deep structural semantic errors during the diffusion process. Therefore， our method computes the geometric constraints directly within the latent space. At each denoising timestep， the model estimates the noise-free latent features. Using the known camera intrinsic and extrinsic matrices， these latent features are reprojected across views to compute a cross-view geometric reprojection loss. This rigorous supervision mechanism continuously penalizes any spatial drift during the reverse diffusion process， forcing the newly generated textures to strictly follow the physical perspective rules of epipolar geometry.ResultThe proposed framework was rigorously evaluated using datasets derived from real-world UAV aerial imagery and their corresponding reconstructed point clouds. Our experimental setup was explicitly designed to simulate extreme large-baseline view extrapolation tasks， where the target camera pose significantly deviates from the available sparse reference views. We conducted comprehensive qualitative and quantitative comparisons against state-of-the-art baselines， including purely data-driven models like VistaDream and adapted baselines such as SDXL equipped with standard ControlNet depth conditioning. Qualitative visual analyses demonstrate that the proposed method significantly outperforms all existing baseline models. While foundational SDXL models suffered from severe structural drift—often manifesting as fractured roads or the unnatural intertwining of distinct architectural elements—our method accurately preserved the targeted ground objects. The generated objects exhibited geometrically correct recession strictly along the depth axis， maintaining precise topological boundaries without perspective distortion. Furthermore， compared to traditional multi-view stereo reconstruction pipelines， the images synthesized by our generative framework eliminated rendering empty holes and appeared significantly more natural and visually realistic. Quantitatively， the proposed method achieved superior performance across multiple core perception and semantic metrics. Traditional pixel-level metrics like PSNR often penalize generative models due to minor， physically plausible texture variations. Therefore， we focused on metrics that reflect human visual perception and deep semantic alignment. Our method successfully reduced the Learned Perceptual Image Patch Similarity （LPIPS） score to an exceptional 0.466， objectively proving its capability to synthesize high-fidelity textures that closely mimic real UAV footage. Moreover， the framework exhibited distinct advantages in large-scale visual-language model evaluations， comprehensively outperforming baseline models in both LLaVA-IQA aesthetic scoring and CLIP-Score semantic consistency evaluation.ConclusionThis research represents a significant paradigm shift in the field of novel view synthesis， moving away from relying on dense pixel interpolation towards geometry-guided generative extrapolation. By seamlessly integrating the deterministic "rendering" principles of traditional computer graphics with the "generative" capabilities of advanced diffusion models， we have explored a highly effective solution for large-baseline view synthesis under sparse observation conditions. The proposed method successfully breaks the long-standing dependence on multi-view continuity. By treating discrete 3D point clouds not merely as rendering primitives， but as rigorous physical anchors to constrain the generative diffusion process， the framework effectively mitigates spatial drift and perspective distortion. From a practical engineering standpoint， this research directly addresses the severe objective constraints faced in actual UAV aerial surveying， such as airspace restrictions and limited flight endurance. It provides a robust， controllable， and highly practical algorithm pipeline capable of accurately deducing large-scale spatial structures， thereby unlocking new potentials for digital twin modeling and complex scene reconstruction in industrial applications.

Document Image Analysis and Recognition （DIAR） stands as a pivotal technological bridge， transforming static physical documents into dynamic， structured digital information. This field is currently undergoing a profound paradigm shift， evolving from a collection of isolated， task-specific pipelines towards an era of holistic， intelligent understanding powered by large-scale models. This comprehensive survey， synthesized from over twenty presentations at the “Document Image Micro-Salon” seminar series organized by the Technical Committee on Document Image Analysis and Recognition of the China Society of Image and Graphics， offers a systematic review of the groundbreaking contributions made by young Chinese scholars in recent years. We trace the trajectory of this evolution across three interconnected layers： the continuous refinement of foundational tasks， the architectural unification of these tasks into end-to-end systems， and the emergence of new intelligent parsing paradigms driven by large vision-language models （LVLMs）.At the foundational layer， significant innovations have redefined core challenges. In text recognition， research has moved decisively beyond closed-set， fully supervised settings. Pioneering work has formalized and addressed the Open-Set Text Recognition （OSTR） problem， enabling models to recognize novel characters—crucial for applications like ancient manuscript digitization or minority language processing—without costly retraining， through frameworks that learn to map character labels to visual prototypes. Concurrently， the field has embraced self-supervised and semi-supervised learning to overcome the bottleneck of expensive annotations. Novel frameworks that synergistically combine contrastive learning with masked image modeling have demonstrated remarkable gains in general representation power， while unified architectures for joint supervised and self-supervised learning have effectively bridged the domain gap between synthetic and real-world data. Furthermore， there has been a concerted push towards more efficient and universal models. Architectures like SVTR have shown that a single， pure vision model can outperform traditional hybrid designs， and new decoding strategies have reconciled the speed-accuracy trade-off in sequence prediction. The frontier has even expanded to instruction-guided recognition， where models are trained to understand text by predicting rich character attributes， showcasing a deeper level of semantic interaction.The deep structural complexities of mathematical expressions and tables have also seen dedicated advances. For handwritten mathematical expression recognition （HMER）， graph-to-graph learning paradigms have enabled explicit modeling of hierarchical symbol relationships， while structured string decoders have offered a balanced approach between sequential and tree-based methods. More recently， leveraging the power of pre-training， specialized LVLMs for HMER have been developed， featuring hierarchical adapters that separately handle primitive character recognition and structural relationship inference， achieving state-of-the-art results that surpass both general-purpose and previous specialized models. In table structure recognition （TSR）， solutions have been tailored to the wild variations found in real-world documents. Innovations include cycle-pairing modules for robust cell detection in distorted tables and visual-aligned sequential modeling that ensures precise physical bounding box prediction by enriching logical representations with fine-grained local visual features. The ultimate goal is a unified framework capable of end-to-end， multi-turn conversational parsing of complex tabular data.Building upon these refined components， the second layer of progress focuses on system-level integration. The traditional， error-prone pipeline of “detect-then-recognize” is being replaced by deeply fused， end-to-end architectures. Models like Text Perceptron and MANGO have pioneered methods to jointly optimize detection and recognition， using techniques such as fiducial point regression and mask attention to directly read character sequences from feature maps， thereby eliminating intermediate cropping steps and enabling seamless handling of arbitrary text shapes. This trend culminates in the vision of General OCR （OCR-2.0）， embodied by unified transformer models that can process all forms of human-readable optical signals—text， formulas， tables， and charts—in a single， elegant framework. Yet， the field pragmatically acknowledges that meticulously engineered， high-performance pipelines remain highly valuable， as evidenced by open-source parsers like MinerU that integrate multiple SOTA modules with sophisticated post-processing rules to achieve production-grade quality.Finally， the advent of the large model era has catalyzed a third wave of innovation， giving rise to new intelligent document parsing paradigms. Recognizing the limitations of generic LVLMs in professional OCR tasks （e.g.， hallucination， inefficiency）， the community has developed a new generation of specialized， lightweight OCR-LVMs. These models， such as PaddleOCR-VL and HunyuanOCR， strategically balance the benefits of end-to-end learning with the reliability of modular design， often through a two-stage “analyze-then-parse” workflow that first predicts a layout and then performs parallel， element-wise content recognition. This approach preserves structural integrity while maintaining high efficiency. To support this rapid development， a new ecosystem of comprehensive evaluation benchmarks has emerged. These benchmarks， including OCRBench， OmniDocBench， and TextHalu-Bench， move far beyond simple accuracy metrics. They provide systematic， fine-grained assessments of a model’s capabilities across diverse languages， layouts， and degradation conditions， while also quantifying critical factors like its susceptibility to semantic hallucination and the cascading impact of its errors on downstream applications like RAG systems.In summary， this survey delineates a clear and vibrant path of DIAR’s evolution—from the meticulous engineering of individual components， through their synergistic integration into robust systems， to the cognitive-level semantic understanding promised by the new generation of intelligent document parsers. It provides a crucial reference for the ongoing effort to build a universal document intelligence foundation that is not only highly accurate but also robust， interpretable， efficient， and truly capable of serving the complex demands of the real world. The algorithms， datasets， and evaluation metrics mentioned in this paper have been compiled athttps：//github.com/xhli-git/Micro-Salon-Survey.

ObjectiveFacial expression recognition （FER） has emerged as a pivotal research topic in the field of computer vision due to its broad applicability in diverse domains such as human–computer interaction， intelligent education systems， healthcare monitoring， mental health assessment， driver fatigue detection， and online behavior analysis. Accurate FER enables machines to interpret human emotions， thereby enhancing the naturalness and adaptability of interactive systems. Despite recent advances driven by convolutional neural network （CNN） and， more recently， Transformer-based architectures， the performance of FER models remains constrained in challenging scenarios characterized by high inter-class similarity and large intra-class variability. One of the primary limitations of existing methods lies in their insufficient ability to effectively model fine-grained discriminative features from facial key regions—such as the corners of the eyes and mouth—which are crucial for recognizing subtle emotional differences. This deficiency often results in feature representations that are dominated by global semantic patterns while failing to capture subtle but semantically critical local cues， ultimately reducing classification accuracy in complex and ambiguous expression categories.MethodsTo address these challenges， we propose a novel Cross-Fusion Multi-Level Receptive Field Network specifically designed for facial expression recognition. The central idea of our approach is to explicitly integrate local structural priors derived from facial landmarks with global semantic expression features， enabling a more balanced and discriminative feature representation that combines detailed regional cues with holistic facial context. Concretely， we first design a dual-stream feature extraction framework in which one stream encodes the global expression semantics of the entire face， while the other focuses on fine-grained geometric structures extracted from facial landmark points. These two feature streams are then fused using a Transformer-based cross-fusion module， allowing rich bidirectional interactions in the spatial domain. This fusion mechanism effectively guides the model to attend to expression-critical regions while simultaneously leveraging the contextual information provided by the full-face representation.A key technical innovation in our model is the Sliding Dilated Window Attention mechanism， which is designed to overcome the limitations of conventional global self-attention in standard Transformers. While global attention offers strong long-range dependency modeling and parallel computation efficiency， it lacks the local inductive bias inherent to CNNs， making it less effective for capturing small scale discriminative features. To remedy this， our SDWA mechanism restricts attention computation to localized sliding windows， significantly reducing computational complexity while concentrating attention resources on critical facial areas. Furthermore， inspired by the dilated convolution paradigm， we introduce varying dilation rates within these sliding windows to expand the receptive field without sacrificing resolution. Specifically， we assign different dilation rates to different attention heads， thereby constructing a Multi-Level receptive field encoder within the Transformer. This design allows the network to simultaneously perceive small-scale details， such as subtle movements in the eyes or mouth corners， and larger-scale structural variations， enabling a richer and more semantically aligned representation of facial expressions.ResultWe evaluate our method on three widely used benchmark datasets： RAF-DB， AffectNet， and FERPlus. The proposed model achieves top-tier performance， with classification accuracies of 92.14% on RAF-DB （seven-class classification）， 67.35% and 63.44% on AffectNet （seven- and eight-class classification settings）， and 91.67% on FERPlus （eight-class classification）. Our experiments are conducted using a standardized training pipeline with data augmentation strategies such as random cropping， horizontal flipping， and color jittering， and optimization via AdamW with a learning rate warm-up schedule. We also perform a comprehensive ablation study， demonstrating that removing any of the three major components—the cross-fusion framework， the SDWA mechanism， or the multi-dilation receptive field encoder—results in a significant performance drop. Additionally， qualitative analysis using attention heatmaps confirms that the proposed method consistently focuses on semantically meaningful facial regions， such as the eyes and mouth， across different expression categories and datasets.Beyond raw accuracy numbers， we compare our method with representative state-of-the-art FER models， including CNN-based architectures and pure Transformer baselines. Our network consistently outperforms these methods， particularly in categories with subtle local differences， validating the effectiveness of explicitly modeling key facial regions. Notably， the proposed architecture achieves this without incurring excessive computational cost， maintaining an inference speed suitable for real-time applications.ConclusionIn this work， the proposed Cross-Fusion Multi-Level Receptive Field Network offers a new paradigm for FER by effectively combining the strengths of global and local feature modeling within a unified Transformer-based framework. Its ability to attend to subtle local details while preserving global semantic coherence leads to notable improvements in recognition accuracy on challenging benchmark datasets. Future work will explore adaptive attention windowing strategies and integration with temporal modeling modules to extend the approach to video-based FER and other dynamic facial analysis tasks.

ObjectiveConstructing vivid personalized 3D head avatar models at the lowest possible cost from 2D images is an important research problem in the fields of computer graphics and virtual reality. Although recently the 2D video generation models have achieved significant breakthroughs， explicit 3D avatars still play an irreplaceable role in fields such as virtual reality and human–computer interaction. Using 3D Gaussian Splatting （3DGS） as the rendering and 3D representation method can effectively improve the rendering quality and efficiency of the avatar models. However， the existing avatar modeling methods based on 3DGS either require several hours or even days of training time or have difficulty reconstructing fine wrinkle details， making it hard to achieve both fast and high-quality reconstruction simultaneously. In the term of the implementation details of these methods， during the training of the avatar models， the existing methods all rely on the mesh that tracked from the original data in the preprocessing stage to provide geometric information for the avatar model， but at the same time， the texture map corresponding to the mesh model is discarded as an additional output of the preprocessing stage. This leads to the avatar model having to learn the appearance information from scratch from the original image. In addition， the existing methods achieves the expression animation of avatar models through the linear combination of Gaussian attribute blendshapes. However， taking the rotation attribute of Gaussians as an example， the addition of quaternions is mathematically meaningless. This will lead to unreasonable rotation interpolation during the expression animation process， affecting the stability of the animation and the rationality of the final avatar model's geometry. To solve these problems， we propose TPAvatar （Head Avatar with Texture Prior）， a novel method to create photorealistic head avatars from the multi-view video sequences or monocular video sequence of the subject based on 3DGS， achieving real-time and high-fidelity animation.MethodBy learning a latent feature space for Gaussian attributes， TPAvatar achieves a significant reduction in neural network parameters， resulting in a compact neural network model. Specifically， TPAvatar is the first to leverage a pre-trained DINOv2 model to extract view-independent identity appearance prior from the texture map of the specific subject， construct a UV-aligned identity feature map， and provide improved initialization for the Gaussian model. For expression driving， TPAvatar establishes a set of implicit expression feature blendshapes for each Gaussians within the local space of each triangle of the mesh. By combining mesh binding with linear combinations of these expression feature blendshapes， it enables efficient and expressive animation of the avatar. The identity feature map and the expression feature map are first summed pixel-by-pixel， and then decoded by a Gaussian decoder to obtain Gaussian attribute maps defined in the UV-local coordinate. These Gaussian attributes are subsequently transformed into the global coordinate， and the final images are rendered using 3DGS.ResultExperimental results on the multi-views dataset NeRSemble and the monocular dataset INSTA demonstrate that TPAvatar can effectively handle multi-view reconstruction and monocular reconstruction tasks. Comparing with existing methods such as GaussianAvatars， GEM， and RGBAvatar， TPAvatar achieves shorter training time， faster inference speed， and higher-quality reconstruction and animation， effectively balancing high fidelity and real-time performance. Specifically， we evaluate these methods on subjects of different ages and genders under two tasks： novel view synthesis on the validation set and novel expression synthesis on the test set. In the multi-view reconstruction scenario， compared with the baseline method GaussianAvatars/GEM， TPAvatar has shortened the reconstruction time from 8/12 hours to 1.5 hours while achieving higher reconstruction quality： on the test set， PSNR increased by 1.5608/10.3556， and LPIPS decreased by 0.0037/0.0131； compared with the baseline method RGBAvatar， TPAvatar significantly improved the generalization of new view synthesis while maintaining the advantage of fast reconstruction， with PSNR increasing by 0.5139 and LPIPS decreasing by 0.0155. In the monocular reconstruction scenario， compared with the existing SOTA baseline method RGBAvatar， TPAvatar's PSNR increased by 0.1176 and LPIPS decreased by 0.0016. TPAvatar achieves an animation speed of 164 FPS， enabling real-time animation performance. Ablation studies further verify the effectiveness of both the identity feature module and the expression driving module.ConclusionBy integrating texture features and constructing expression feature bases， TPAvatar enhances the animation quality and perspective generalization of the 3DGS head avatar model， making it a practical and efficient 3D avatar reconstruction method that capable of real-time rendering and animation. It suitable for personalized 3D head avatar reconstruction tasks under multi-view or monocular video input. The decoupled design of identity and expression endows the proposed method with the potential to be extended to single-image avatar reconstruction. Code is available in：https：//doi.org/10.57760/sciencedb.j00240.00128

ObjectiveAs a fundamental data modality for autonomous driving，robotic navigation，and large-scale environmental perception，3D point clouds pose stringent demands on place recognition technologies. Accurate place recognition relies on extracting robust environmental descriptors；however，despite recent advancements，mainstream deep learning methods are fundamentally constrained by three prominent limitations. First，conventional feature representations are strictly confined to the real-number domain，which inherently restricts models to capturing only geometric coordinates and local structural information. This paradigm discards crucial spatial attributes such as the point distribution phase，the relative positions of non-adjacent points，and complex global topological relations. Consequently，this leads to low recognition accuracy for places exhibiting similar local structures but possessing distinct global configurations. Second，existing feature interaction mechanisms struggle to align with the "unordered，sparse，and irregular" intrinsic properties of raw point clouds. Traditional grid-based convolutions，which rely on fixed local receptive fields，often suffer from "local information over-focus，" severely neglecting long-range semantic associations necessary for holistic s place understanding. Finally，resolving the pervasive trade-off between recognition performance and computational efficiency remains elusive. High-accuracy baseline models typically demand excessive network depth and parameter scales—often reaching tens or hundreds of megabytes—yielding inference latencies that far exceed the 10ms real-time threshold， thereby failing to meet the strict practical application requirements of autonomous systems. Aiming to systematically address these limitations of traditional real-number domain operations，this study explores a novel point cloud feature representation paradigm deeply integrated with interdisciplinary theories，striving to significantly enhance both the performance and interpretability of large-scale point cloud place recognition.MethodsInspired by the foundational wave-particle duality theory in physics，this study abstracts each individual point in the 3D point cloud as an elementary particle with fundamental physical states，innovatively constructing a comprehensive point cloud feature representation framework that embodies both "wave nature" and "particle nature". Correspondingly，an end-to-end place recognition method consisting of three synergistic core modules is proposed，with two specific implementation instances designed for each module to demonstrate the paradigm's remarkable flexibility. 1） Point Cloud Wave Property Expression Module： Leveraging the wave attribute of single particles，this initial module maps raw 3D point clouds from the constrained real-number space into a high-dimensional Hilbert space，representing each point as a complex-valued wave function. The amplitude component encodes fundamental geometric positions and local structural details，whereas the phase component intricately captures relative spatial relationships and global distribution trends. This dual encoding achieves comprehensive modeling of both geometric and semantic point information，vastly enhancing local context perception. 2） Point Cloud Particle Property Information Interaction Module： Emulating the "particle nature" behavior observed under multi-particle coherence，this phase-driven interaction mechanism constructs a dynamic spatial graph based on the wave function similarities between points，treating individual points as graph nodes and interaction intensities as adaptive edge weights. By mathematically simulating particle energy transfer and interactive information exchange，it facilitates the adaptive fusion of high-dimensional feature information across both immediate adjacent points and semantically correlated non-adjacent distant points. This efficiently characterizes the dynamic dependencies and high-order semantic interactions that conventional real-number convolutions miss. 3） Point Cloud Feature Compact Encoding Module： To address the inherent high dimensionality and computational burden of complex-valued features，this final module leverages parameter-sharing complex-valued convolutions for deep feature extraction. It then applies efficient aggregation strategies—specifically proposing a Generalized Mean （GeM） pooling approach or a structurally refined Soft-NetVLAD instance—to compress these high-dimensional interactions into a highly compact， representative global feature vector tailored for the final retrieval and recognition task. Results Comprehensive and rigorous evaluations were conducted across four foundational benchmark datasets （Oxford， U.S.， R.A.， B.D.） and two complex generalization datasets （KITTI Sequence 00， RobotCar Season）. The empirical findings confirm that all instances of the proposed interdisciplinary paradigm effectively and consistently enhance the performance of established benchmark models—including PointNet++，MinkLoc3D， and PCAN—in terms of both top-1% average recall and retrieval precision. Notably，the specific combination of direct wave function modeling and GeM pooling achieves an unprecedented balance between performance and deployment efficiency. Requiring merely 6.12MB of learnable parameters，this streamlined instance nearly doubles the model convergence speed during training and reduces the inference time to a mere 8ms， strictly satisfying the critical 10ms real-time processing requirement dictated by autonomous driving and robotic navigation systems. Furthermore， the wave-particle paradigm exhibits exceptional robustness； in rigorous perturbation tests，it delivered a 7.1% average performance gain under severe rotational variances and secured a 5.8% average performance enhancement in demanding cross-domain environmental tests. Extensive ablation studies unequivocally corroborate the structural necessity of modeling wave-particle coherence，demonstrating that the removal of any core module directly leads to immediate and notable performance degradation.ConclusionThe proposed wave-particle duality paradigm exhibits excellent architectural versatility and plug-and-play characteristics，enabling significant performance enhancements of existing baseline models without necessitating extensive structural modifications. It empirically verifies the profound effectiveness of interdisciplinary theoretical fusion in successfully breaking through traditional point cloud feature representation bottlenecks，resolving issues including real-number domain spatial limitations，mismatched feature interaction mechanisms，and rigid performance-efficiency trade-offs. This work provides a robust， interpretable， and highly efficient new research direction for point cloud place recognition， paving the way for future optimizations in complex-valued computation and expanding scenario-specific applications.