Core Viewpoint - The article discusses a novel multi-scale bilateral grid framework for 3D scene reconstruction in autonomous driving, addressing challenges such as lighting variations and dynamic objects, leading to improved geometric accuracy and visual quality [5][10][39]. Group 1: Methodology - The proposed framework combines the strengths of appearance codes and bilateral grids to achieve efficient and accurate scene reconstruction [11][13]. - The architecture employs Gaussian splatting to model complex driving scenes, decomposing them into a mixed scene graph that includes independent modeling of static and dynamic elements [14]. - The framework consists of three levels: coarse, intermediate, and fine, each addressing different aspects of lighting and detail adjustments [15]. Group 2: Experimental Results - Extensive experiments on datasets like Waymo, NuScenes, Argoverse, and PandaSet demonstrate that the proposed method significantly outperforms existing models in geometric accuracy and appearance consistency [19][39]. - In the Waymo dataset, the chamfer distance (CD) improved from 1.378 (OmniRe) to 0.989, a 28.2% enhancement [21]. - The method achieved a PSNR of 27.69 and an SSIM of 0.847 on the NuScenes dataset, surpassing OmniRe's scores of 26.37 and 0.837 respectively [23]. Group 3: Robustness and Versatility - The framework shows enhanced performance in extreme scenarios such as night scenes and varying lighting conditions, proving its robustness [27][39]. - The method can be integrated as a plug-and-play enhancement module into existing models like ChatSim and StreetGS, resulting in significant improvements in reconstruction quality [25][26]. Group 4: Future Directions - The research team plans to further optimize the framework for larger and more complex scenes and explore more efficient computational methods for practical applications in autonomous driving [40].

Core Insights - The article discusses a novel multi-scale bilateral grid framework that enhances the geometric accuracy and visual realism of dynamic scene reconstruction in autonomous driving, addressing challenges posed by photometric inconsistency in real-world environments [5][10][12]. Motivation - Neural rendering technologies are crucial for the development and testing of autonomous driving systems, but they heavily rely on photometric consistency among multi-view images. Variations in lighting conditions, weather, and camera parameters introduce significant color inconsistencies, leading to erroneous geometry and distorted textures [5][6]. Existing Solutions - Current solutions are categorized into two main types: global appearance coding and bilateral grids. The proposed framework combines the advantages of both methods to overcome their limitations [6][10]. Key Contributions - The framework introduces a multi-scale bilateral grid that seamlessly integrates global appearance coding and local bilateral grids, allowing adaptive color correction from coarse to fine scales. This significantly improves the geometric accuracy of dynamic driving scene reconstruction and effectively suppresses artifacts like "floaters" [9][10][12]. Method Overview 1. **Scene Representation and Initial Rendering**: The framework employs Gaussian splatting to model complex driving scenes, creating a hybrid scene graph that includes independently modeled elements like sky, static backgrounds, and dynamic objects [12]. 2. **Multi-Scale Bilateral Grid Correction**: The initial rendered image undergoes processing through a hierarchical multi-scale bilateral grid, resulting in a color-consistent, visually realistic high-quality image [13][14]. 3. **Optimization Strategy and Real-World Adaptability**: The model utilizes a coarse-to-fine optimization strategy and a composite loss function to ensure stable training and effective adaptation to real-world variations in image signal processing parameters [15][16]. Experimental Results - The proposed framework was extensively evaluated on four major autonomous driving datasets: Waymo, NuScenes, Argoverse, and PandaSet. The results demonstrate significant improvements in both geometric accuracy and visual realism compared to baseline models [17][18]. Quantitative Evaluation - The method achieved leading results in both geometric and appearance metrics. For instance, the Chamfer Distance (CD) metric on the Waymo dataset improved from 1.378 (baseline) to 0.989, showcasing the model's ability to handle color inconsistencies effectively [18][19]. Qualitative Evaluation - Visual comparisons illustrate the robustness of the proposed method in complex real-world scenarios, effectively reducing visual artifacts and maintaining high-quality outputs [23][24][29]. Generalizability and Plug-and-Play Capability - The method's core modules were integrated into advanced baseline models like ChatSim and StreetGS, resulting in substantial performance enhancements, such as an increase in reconstruction PSNR from 25.74 to 27.90 [20][21]. Conclusion - The multi-scale bilateral grid framework represents a significant advancement in the field of autonomous driving, providing a robust solution to the challenges of dynamic scene reconstruction and photometric inconsistency, thereby enhancing the overall safety and reliability of autonomous systems [10][12][18].