RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection
Summary: 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 neighborhoods in sparse scenes.
To tackle this challenge, researchers have proposed RayMamba, a geometry-aware plug-and-play enhancement for voxel-based 3D detectors. This innovative approach organizes sparse voxels into sector-wise ordered sequences through a ray-aligned serialization strategy, effectively preserving directional continuity and occlusion-related context for subsequent Mamba-based modeling. Notably, RayMamba is compatible with both LiDAR-only and multimodal detectors, introducing only modest overhead in computational requirements.
Key Features of RayMamba
- Ray-Aligned Serialization: This method organizes voxel data into ordered sequences that maintain contextual relationships, addressing issues of sparsity in far-field observations.
- Compatibility: RayMamba is designed to work seamlessly with existing LiDAR-only and multimodal detectors, ensuring broad applicability in the field of 3D object detection.
- Minimal Overhead: The enhancement introduces only modest computational overhead, making it feasible for real-time applications without significantly burdening system resources.
- Improved Performance: Extensive experiments conducted on datasets such as nuScenes and Argoverse 2 demonstrate consistent improvements in detection metrics, showcasing the effectiveness of RayMamba.
Experimental Results
Research findings indicate that RayMamba achieves significant performance gains in long-range 3D object detection tasks. In particular, it shows an impressive increase of up to 2.49 mAP (mean Average Precision) and 1.59 NDS (NuScenes Detection Score) in the challenging 40–50 m range on the nuScenes dataset. Additionally, when integrating RayMamba with the VoxelNeXt architecture on the Argoverse 2 dataset, the performance improves from 30.3 to 31.2 mAP, highlighting its efficacy in enhancing existing models.
Conclusion
RayMamba represents a significant step forward in addressing the challenges posed by long-range 3D object detection in sparse environments. Its ray-aligned serialization strategy not only enhances model performance but also retains critical contextual information that is often lost in traditional serialization methods. As the field of 3D detection continues to evolve, innovations like RayMamba will be essential for improving the robustness and efficiency of detection systems, paving the way for more advanced applications in autonomous driving and robotics.
For more details, refer to the full paper available on arXiv.