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About this paper
Chunliang Li, Wencheng Han, Junbo Yin, Sanyuan Zhao, Jianbing Shen
This research from Beijing Institute of Technology and University of Macau introduces RepVF, a unified vector fields representation, and RFTR, a single-head Transformer network, to perform multi-task 3D perception. The system achieves competitive performance on 3D object and lane detection benchmarks while significantly reducing task-specific gradient disparity.
Key insights
- A unified, task-agnostic representation inherently resolves representational discrepancies, allowing diverse 3D perception tasks to share a single output head without explicit task-specific parameters.
- The single-head multi-task architecture, when combined with a unified representation, fundamentally mitigates feature competition and gradient imbalance across tasks, leading to more stable training.
- Moving beyond shared backbones to unified output representations offers a more profound solution to multi-task conflict, improving overall system efficiency and robustness in autonomous driving perception.
Summary by alphaXiv.
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