Abstract
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.
Abstract (translated)
这篇论文描述了高通AI研究为ICCV 2025举办的RealADSim Workshop中的RealADSim-NVS挑战提供的解决方案。该挑战涉及街景中视点新颖性合成问题，参赛者需要从训练过程中拍摄的以汽车为中心的画面出发，生成同一城市环境在不同视角下的渲染图（例如不同的街道车道或汽车方向）。我们的方案受到场景生成混合方法和结合高斯光斑技术和扩散模型的生成模拟器的启发，由两个阶段组成：首先，我们拟合出一个3D重建的场景，并从目标相机的角度重新渲染新颖视图；然后，我们使用专门设计的一步式扩散模型来增强所得帧。本文讨论了初始化高斯原语的具体选择以及细化增强模型及其训练数据整理方面的决策。我们报告了我们的模型的设计性能，并根据PSNR（峰值信噪比）、SSIM（结构相似性指数）和LPIPS（感知线性像差感知感知图像相似度）来衡量新颖视图质量，对其组件进行了消融研究。在公布的结果排行榜上，以测试结果为基础，我们方案的综合得分为0.432，取得了总成绩第二名的成绩。
URL
https://arxiv.org/abs/2510.12308
