2025年，已经不是一英寸横飞的时代了。 现在市面上还能买到的一英寸主摄旗舰，只有小米 15 Ultra和OPPO Find X8 Ultra，这类机型明确是为少数死忠影像玩家准备的「玩具」，而不是走量产品。 （图片来源：雷科技摄制） 到了25年底再看，手机影像在过去一年已经发生了剧烈的变化。 把时间回拨一年，各家手机厂商还在大力推崇自己的「一英寸大底+两亿像素长焦」，几乎把手机影像推到了「硬件天花板」的高度。而到了2025年，影像 旗舰们不再靠堆料吸引眼球，真正被厂商和用户反复提起的，反而是「色彩一致性」、「AI 后期」、「抓拍成功率」这类听起来很「软」的东西。 这一年，手机影像的主线可以用一句话概括——硬件不再疯狂冲锋，算法和 AI 开始主导画面。 一英寸传感器退烧了 手机厂商们在2025年的主流选择，是主动从一英寸退一步，例如最新发布的vivo X300 Pro和OPPO Find X9 Pro的主摄都使用了1/1.28英寸的LYT-828传感器， 再搭配一颗 1/1.4英寸的200MP潜望长焦，而其他旗舰也大多停在1/1.3英寸左右，再辅以大底长焦传感器，如今厂商更注重全焦段协同表现与机身握持感之 间 ...

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].