RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving
This provides a scalable solution for autonomous driving research by reducing annotation costs, though it is incremental as it builds on existing synthetic dataset approaches.
The authors tackled the problem of expensive data annotation for autonomous driving perception models by creating RealDriveSim, a realistic multi-modal synthetic dataset that supports 2D and LiDAR applications with fine-grained annotations for 64 classes, and demonstrated state-of-the-art results compared to existing synthetic benchmarks.
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/.