构建了一个高保真多模态自动驾驶合成数据集，支持2D与激光雷达任务。
RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving
- 基于真实驾驶场景生成多模态合成数据，涵盖图像与点云。
- 提供64类细粒度标注，性能超越现有合成基准。
- 适合自动驾驶感知模型训练与评估，开源可用。
随着感知模型的不断发展，大规模数据集的需求日益增长，但数据标注成本过高，难以有效扩展。合成数据集为提升模型性能提供了低成本解决方案。然而，现有合成数据集在覆盖范围、真实感以及任务通用性方面仍显不足。本文提出 RealDriveSim，一个面向自动驾驶的高保真多模态合成数据集，不仅支持主流的2D计算机视觉任务，也涵盖对应的激光雷达（LiDAR）任务，并提供最多64类的细粒度标注。我们对该数据集在多种应用和领域进行了广泛评估，结果表明其性能优于现有合成基准。数据集已公开，可通过 https://realdrivesim.github.io/ 获取。
原文摘要 · Abstract (English)
As perception models continue to develop, the need for large-scale datasets increases. However, data annotation remains far too expensive to effectively scale and meet the demand. Synthetic datasets provide a solution to boost model performance with substantially reduced costs. However, current synthetic datasets remain limited in their scope, realism, and are designed for specific tasks and applications. In this work, we present RealDriveSim, a realistic multi-modal synthetic dataset for autonomous driving that not only supports popular 2D computer vision applications but also their LiDAR counterparts, providing fine-grained annotations for up to 64 classes. We extensively evaluate our dataset for a wide range of applications and domains, demonstrating state-of-the-art results compared to existing synthetic benchmarks. The dataset is publicly available at https://realdrivesim.github.io/.
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