RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection
📰 ArXiv cs.AI
RayMamba improves long-range 3D object detection by introducing ray-aligned serialization for state space model-based methods
Action Steps
- Identify the limitations of existing serialization strategies in state space model-based methods for long-range 3D object detection
- Understand how RayMamba's ray-aligned serialization approach preserves meaningful contextual neighborhoods
- Implement RayMamba's serialization strategy in existing SSM-based detectors to improve long-range modeling efficiency
- Evaluate the performance of RayMamba-enhanced detectors on benchmark datasets
Who Needs to Know This
Computer vision engineers and researchers working on 3D object detection tasks can benefit from RayMamba's approach to improve detection accuracy and efficiency in long-range scenarios
Key Insight
💡 Ray-aligned serialization preserves contextual neighborhoods, improving detection accuracy in long-range scenarios
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💡 RayMamba enhances long-range 3D object detection with ray-aligned serialization!
Key Takeaways
RayMamba improves long-range 3D object detection by introducing ray-aligned serialization for state space model-based methods
Full Article
Title: RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection
Abstract:
arXiv:2604.02903v1 Announce Type: cross Abstract: Long-range 3D object detection remains challenging because LiDAR observations become highly sparse and fragmented in the far field, making reliable context modeling difficult for existing detectors. To address this issue, recent state space model (SSM)-based methods have improved long-range modeling efficiency. However, their effectiveness is still limited by generic serialization strategies that fail to preserve meaningful contextual neighborhoo
Abstract:
arXiv:2604.02903v1 Announce Type: cross Abstract: Long-range 3D object detection remains challenging because LiDAR observations become highly sparse and fragmented in the far field, making reliable context modeling difficult for existing detectors. To address this issue, recent state space model (SSM)-based methods have improved long-range modeling efficiency. However, their effectiveness is still limited by generic serialization strategies that fail to preserve meaningful contextual neighborhoo
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