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Multi-agent perceptual map construction and long-term maintenance constitute an important paradigm for improving adaptability and real-world applicability. With the outstanding capability of 3D Gaussian Splatting in preserving fine-grained texture details, a number of 3DGS-based real-time mapping approaches have recently emerged. However, these methods often struggle to cope with complex dynamics in real-world environments and lack the generalization needed to scale to multi-agent systems. Existing solutions typically rely on direct parameter concatenation or locally confined optimization, which are unable to explicitly model cross-agent observation reliability under temporal asynchrony and dynamic inconsistency, and therefore tend to amplify conflicting updates rather than resolve them. To address these limitations, we propose DGOMapping, an online system for multi-agent dynamic perceptual mapping. DGOMapping leverages an uncertainty-coupled 4DGS scene representation and a collaborative interaction mechanism via Gaussian perception-score exchange, enabling both real-time 4DGS construction and long-term map memory adjustment. Experiments on multiple real-world datasets demonstrate that DGOMapping effectively suppresses dynamic interference and exploits multi-agent collaboration, achieving state-of-the-art performance in both tracking and reconstruction. The proposed system therefore provides a practical sensing-oriented solution for collaborative perception and real-time dynamic environment mapping.

Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. Current TIR image-based 3D reconstruction methods primarily focus on static conditions, which only capture the spatial distribution of thermal radiation but lack the ability to represent its temporal dynamics. The absence of dedicated datasets and effective methods for dynamic 3D representation are two key challenges that hinder progress in this field. To address these challenges, we propose a novel dynamic thermal 3D reconstruction method, named ThermalGS, based on 3D Gaussian Splatting (3DGS). ThermalGS employs a data-driven approach to directly learn both scene structure and dynamic thermal representation, using RGB and TIR images as input. The position, orientation, and scale of Gaussian primitives are guided by the RGB mesh. We introduce feature encoding and embedding networks to integrate semantic and temporal information into the Gaussian primitives, allowing them to capture dynamic thermal radiation characteristics. Moreover, we construct the Thermal Scene Day-and-Night (TSDN) dataset, which includes multi-view, high-resolution aerial RGB reference images and TIR images captured at five different times throughout the day and night, providing a benchmark for dynamic thermal 3D reconstruction tasks. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the TSDN dataset, with an average absolute temperature error of 1 °C and the ability to predict surface temperature variations over time.

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs.

Dynamic scene reconstruction from thermal infrared imagery remains insufficiently studied due to several inherent challenges, including low texture, low contrast, and radiometric ambiguity. In this paper, we present Thermal4D, a novel framework for reconstructing high-fidelity dynamic 3D scenes using only thermal images, without requiring visible-light inputs or auxiliary sensors. Built upon the 3D Gaussian Splatting paradigm, the proposed method introduces two key components. First, a frequency-aware attention module, termed TherHiLo, is designed to disentangle structural features across different frequency bands. Second, a physics-inspired atmospheric transmission module (ATM) is developed to model radiometric distortions caused by thermal imaging conditions. Although the reconstruction pipeline takes 8-bit thermal video sequences as input, high-precision 14-bit thermal frames are further exploited in TherHiLo to enhance attention learning with richer radiometric information. In addition, feature-level supervision from pretrained DINOv2 models is incorporated to improve structural consistency. To facilitate systematic evaluation, we also construct MVTD, a new multi-view dynamic thermal dataset. Experimental results on the MVTD and TI-NSD benchmarks show that Thermal4D consistently outperforms existing methods in both dynamic and static scenes, providing an effective framework for physics-consistent dynamic thermal scene reconstruction.

Novel space–time view synthesis for monocular video is a highly challenging task: both static and dynamic objects usually appear in the video, but only a single view of the current scene is available, resulting in inaccurate synthesis results. To address this challenge, we propose FRNeRF, a novel space–time view synthesis method with a fusion regularization field. Specifically, we design a 2D–3D fusion regularization field for the original dynamic neural field, which helps reduce blurring of dynamic objects in the scene. In addition, we add image prior features to the hierarchical sampling to solve the problem that the traditional hierarchical sampling strategy cannot obtain sufficient sampling points during training. We evaluate our method extensively on multiple datasets and show the results of dynamic space–time view synthesis. Our method achieves state-of-the-art performance both qualitatively and quantitatively. Code is available for research purposes at https://cic.tju.edu.cn/faculty/likun/projects/FRNerf.

Monocular dynamic scene reconstruction is a challenging task due to the inherent limitation of observing the scene from a single viewpoint at each timestamp, particularly in the presence of object motion and illumination changes. Recent methods combine Gaussian Splatting with deformation modeling to enable fast training and rendering; however, their performance in real-world scenarios strongly depends on accurate point cloud initialization. When such initialization is unavailable and random point clouds are used instead, reconstruction quality degrades significantly. To address this limitation, we propose an optimization strategy that relaxes the requirement for accurate initialization in Gaussian-Splatting-based monocular dynamic scene reconstruction. The scene is first reconstructed under a static assumption using all monocular frames, allowing stable convergence of background regions. Based on reconstruction errors, a subset of Gaussians is then activated as dynamic to model motion and deformation. In addition, an annealing jitter regularization term is introduced to improve robustness to camera pose inaccuracies commonly observed in real-world datasets. Extensive experiments on established benchmarks demonstrate that the proposed method enables stable training from randomly initialized point clouds and achieves reconstruction performance comparable to approaches relying on accurate point cloud initialization.

The 3DGS (3D Gaussian Splatting) series of works has achieved significant success in novel view synthesis, but further research is needed for dynamic scene reconstruction tasks. In this paper, we propose a new framework based on 3DGS for handling dynamic scene reconstruction problems involving color changes. Our approach employs a multi-stage training strategy combining motion and color deformation fields to accurately model dynamic geometry and appearance changes. Additionally, we design two modular components: the Dynamic Component for capturing motion variations and the Color Component for managing material and color changes. These components flexibly adapt to different scenes, enhancing our method’s versatility. Experimental results demonstrate that our method achieves real-time rendering at 80 FPS on an RTX 4090 and achieves higher reconstruction accuracy than baseline methods such as HexPlane and Deformable3DGS. Furthermore, it reduces training time by approximately 10%, indicating improved training efficiency. These quantitative results confirm the effectiveness of our approach in delivering high-fidelity 4D reconstruction of complex dynamic environments.

Dynamic scene reconstruction has achieved significant milestones with the advent of 3D Gaussian Splatting (3DGS). However, extending this technology from geometric reconstruction to semantic understanding in dynamic environments remains a challenge. Existing methods often rely on external 2D trackers, which lead to temporal inconsistencies and semantic drift, or suffer from the high computational costs of high-dimensional feature fields. In this paper, we propose a novel framework, Gaussian Semantic Segmentation based on Color and Shape Deformation Fields (GSSBC), to address these issues. Building upon our GBC dynamic scene representation, we bind learnable semantic features to deformable Gaussian primitives. We introduce a spatiotemporal contrastive learning strategy guided by the Segment Anything Model (SAM) to enforce semantic consistency without explicit tracking. Furthermore, we employ a density-based clustering algorithm with label propagation to extract discrete object entities efficiently. Experimental results on the HyperNeRF and Neu3D datasets demonstrate that our method achieves superior segmentation accuracy and spatiotemporal stability compared to state-of-the-art approaches, enabling effective semantic understanding in complex dynamic scenes.

This paper introduces a novel and efficient approach for Gaussian Splatting in dynamic scenes that leverages symmetry principles for enhanced computational efficiency and visual fidelity. First, we diverge from conventional methods that process static and dynamic regions uniformly by implementing an adaptive separation mechanism. This approach exploits the inherent symmetry-breaking properties between static and dynamic Gaussian points, utilizing motion differentials to identify and isolate dynamic elements. This symmetry-aware partitioning allows for the application of specialized processing techniques to each region type, with static regions benefiting from their temporal symmetry while dynamic regions receive targeted deformation modeling. Second, through this fine-grained partitioning of static and dynamic components guided by symmetry analysis, we achieve more judicious allocation of computational resources. The symmetric treatment of spatially coherent static regions and the focused processing of symmetry-breaking dynamic elements substantially reduce memory requirements and training time while preserving reconstruction quality. This optimization effectively conserves valuable computational resources without compromising visual fidelity. Third, we introduce a sophisticated deformation modeling framework that learns the transformational characteristics of grids composed of multiple Gaussian points. By incorporating radial basis function principles, which inherently preserve local rotational and translational symmetries, our method efficiently encodes complex motion information of dynamic Gaussian points. This symmetry-preserving deformation approach not only enables high-fidelity reconstruction of dynamic regions but also significantly improves the rendering of continuously evolving shadow interactions by maintaining physical consistency. The result is a marked reduction in visual distortion and rendering outputs that demonstrate exceptional correspondence to ground truth imagery across diverse dynamic scenes.

Dynamic street-scene reconstruction from sparse viewpoints over long temporal spans is challenged by temporal instability, ghosting near occlusions, and background drift. This paper presents SPT-Gauss (Semantic Prior and Temporal constraint-enhanced Gaussian splatting), a Gaussian-splatting framework that improves dynamic reconstruction without object-level annotations by combining dense semantic priors with lightweight, parameter-level temporal regularization. SPT-Gauss distills per-pixel semantic features from a frozen 2D foundation model into 4D Gaussian primitives, estimates static and dynamic regions via a dual-evidence motion mask, and regularizes temporal parameters through a semantic-guided velocity constraint and a static-lifetime prior to suppress spurious background motion. Experiments on the Waymo Open Dataset and KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) show consistent improvements over representative baselines in both 4D reconstruction and novel-view synthesis, with reduced temporal artifacts and improved fidelity in motion-challenging regions.