Real-Time Radar Semantic and Panoptic Scene Understanding
We introduce StreamRPS, a novel framework for streaming FMCW radar panoptic segmentation, comprising an automated label-generation pipeline via LiDAR scene completion and a real-time streaming network for semantic and panoptic segmentation.
ProofPaper ↗
Key points
- Robust scene understanding under varying environmental conditions is essential for safe autonomous driving.
- Our encoder-decoder streaming architecture comprises a radar-specific hierarchical Transformer encoder and task-specific decoders for semantic or panoptic prediction.
- Our encoder introduces a stem to capture range-dependent signatures, anisotropic patch embeddings to preserve angular resolution, and axial polar attention to separately model the periodic azimuth and bounded range axes.
- We further introduce azimuth roll rotation augmentation to simulate ego-vehicle rotation during training.
Sources (1)
- [1]Real-Time Radar Semantic and Panoptic Scene UnderstandingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:51 PM
We introduce StreamRPS, a novel framework for streaming FMCW radar panoptic segmentation, comprising an automated label-generation pipeline via LiDAR scene completion and a real-time streaming network for semantic and panoptic segmentation.
Robust scene understanding under varying environmental conditions is essential for safe autonomous driving.
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