融合高斯点云与扩散模型，实现街景新视角合成
Hybrid Gaussian Splatting for Novel Urban View Synthesis
- 先用高斯点云重建3D场景，再用单步扩散模型增强图像
- 在公开榜单上取得0.432综合得分，排名第二
- 适合关注城市环境生成与视觉质量提升的研究者
本文介绍高通人工智能研究院在ICCV 2025年RealADSim研讨会举办的RealADSim-NVS挑战赛中的解决方案。该挑战要求从训练阶段采集的车载视角视频中，生成不同行驶路线或方向下的同一城市环境的新视角渲染图。我们的方法受混合场景生成与生成模拟器启发，结合高斯点云与扩散模型，分为两个阶段：首先拟合场景的3D结构并渲染目标相机视角；随后利用专用的单步扩散模型对生成结果进行细节增强。我们讨论了高斯原语初始化策略、增强模型微调及训练数据筛选的具体设计。报告了模型在PSNR、SSIM和LPIPS指标上的表现，并对各组件进行了消融实验。在公开排行榜上，我们的方案取得0.432的综合得分，位列第二。
原文摘要 · Abstract (English)
This paper describes the Qualcomm AI Research solution to the RealADSim-NVS challenge, hosted at the RealADSim Workshop at ICCV 2025. The challenge concerns novel view synthesis in street scenes, and participants are required to generate, starting from car-centric frames captured during some training traversals, renders of the same urban environment as viewed from a different traversal (e.g. different street lane or car direction). Our solution is inspired by hybrid methods in scene generation and generative simulators merging gaussian splatting and diffusion models, and it is composed of two stages: First, we fit a 3D reconstruction of the scene and render novel views as seen from the target cameras. Then, we enhance the resulting frames with a dedicated single-step diffusion model. We discuss specific choices made in the initialization of gaussian primitives as well as the finetuning of the enhancer model and its training data curation. We report the performance of our model design and we ablate its components in terms of novel view quality as measured by PSNR, SSIM and LPIPS. On the public leaderboard reporting test results, our proposal reaches an aggregated score of 0.432, achieving the second place overall.
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