生成3D行人纹理多样化以实现自动驾驶感知的鲁棒性
Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception
AI总结：
本文提出一种简单有效的方法，通过StyleGAN2生成多样化的3D行人资产，用于合成场景生成，提升自动驾驶感知的鲁棒性。
摘要：
近年来，自动驾驶显著增加了对高质量数据的需求，以训练2D和3D感知模型以应对安全关键场景。现实世界数据集难以满足这一需求，因为要求不断演变，而大规模标注数据收集仍然昂贵且耗时，使得合成数据成为可扩展、实用且可控的替代方案。行人检测是自动驾驶中最关键的安全任务之一。本文提出了一种简单而有效的方法，通过合成多样化的面部纹理和身份级外观变化，从单个3D基础资产生成多个不同的行人实例。该方法在不需为每个实例设计新几何体的情况下，实现了可扩展的外观级资产多样化。使用这些资产，我们构建了合成数据集，并研究了混合真实和合成数据对RGB基于目标检测的影响。通过互补实验，我们分析了点云感知中几何驱动的分布偏移对3D目标检测的影响。我们的发现表明，受控的合成多样化提高了2D检测的鲁棒性，同时揭示了3D感知模型对几何域差距的敏感性。总体而言，本文强调了生成式AI如何通过受控的面部纹理合成实现可扩展的仿真准备行人多样化，并探讨了自动驾驶流水线中跨域训练策略的优势和限制。
英文摘要：
In recent years, autonomous driving has significantly in creased the demand for high-quality data to train 2D and 3D perception models for safety-critical scenarios. Real world datasets struggle to meet this demand as require ments continuously evolve and large-scale annotated data collection remains costly and time-consuming making syn thetic data a scalable, practical and controllable alterna tive. Pedestrian detection is among the most safety-critical tasks in autonomous driving. In this paper, we propose a simple yet effective method for scaling variability in 3D pedestrian assets for synthetic scene generation. Starting from a single 3D base asset, we generate multiple distinct pedestrian instances by synthesizing diverse facial textures and identity-level appearance variations using StyleGAN2 and automatically mapping them onto 3D meshes. This ap proach enables scalable appearance-level asset diversifica tion without requiring the design of new geometries for each instance. Using the assets, we construct synthetic datasets and study the impact of mixing real and synthetic data for RGB-based object detection. Through complementary ex periments, we analyze geometry-driven distribution shifts in point cloud perception for 3D object detection. Our findings demonstrate that controlled synthetic diversifica tion improves robustness in 2D detection while revealing the sensitivity of 3D perception models to geometric domain gaps. Overall, this work highlights how generative AI en ables scalable, simulation-ready pedestrian diversification through controlled facial texture synthesis, along with the benefits and limitations of cross-domain training strategies in autonomous driving pipelines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
