123D: Unifying Multi-Modal Autonomous Driving Data at Scale
The pursuit of autonomous driving has produced one of the richest sensor data collections in all of robotics. However, its scale and diversity remain largely untapped. Each dataset adopts different 2D and 3D modalities, such as cameras, lidar, ego states, annotations, traffic lights, and HD maps, with different rates and synchronization schemes. They come in fragmented formats requiring complex dependencies that cannot natively coexist in the same development environment. Further, major inconsistencies in annotation conventions prevent training or measuring generalization across multiple datasets. We present 123D, an open-source framework that unifies such multi-modal driving data through...
| Authors | Daniel Dauner, Valentin Charraut, Bastian Berle et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
Models and datasets referencing this paper
- ln2697/lead-123dDataset
- kesai-labs/nuplanDataset
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