RiO-DETRv2: Geometry-Aware Uncertainty Modeling Meets Real-Time Oriented Object Detection Transformer

Key points

• Although distributional regression has succeeded in horizontal DETRs, extending it to oriented detection introduces geometry-misaligned boundary modeling and topology-inconsistent orientation refinement.1
• We present RiO-DETRv2, a geometry-aware uncertainty modeling framework for real-time oriented object detection.1
• For better orientation topology consistency, we introduce Circular-to-Local Angular Residual Refinement that transitions from global periodic search to local fine-grained estimation.1
• Unified OBB Quality-Aligned Learning further aligns classification, boundary and angular refinement with complete-OBB localization quality.1

Sources (1)

• [1]RiO-DETRv2: Geometry-Aware Uncertainty Modeling Meets Real-Time Oriented Object Detection Transformer
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 03:48 AM

  RiO-DETR is the first real-time end-to-end transformer for oriented object detection, yet further localization gains require a more expressive box representation.

  Although distributional regression has succeeded in horizontal DETRs, extending it to oriented detection introduces geometry-misaligned boundary modeling and topology-inconsistent orientation refinement.

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