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[2602.19536] Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection
Summary
The paper presents Fore-Mamba3D, a novel approach for 3D object detection that enhances foreground encoding while addressing limitations of previous Mamba-based methods.
Why It Matters
As 3D object detection becomes increasingly vital in fields like robotics and autonomous vehicles, improving detection accuracy through innovative methods like Fore-Mamba3D can significantly enhance performance in real-world applications. This research addresses key challenges in existing models, making it relevant for ongoing advancements in computer vision.
Key Takeaways
- Fore-Mamba3D focuses on foreground enhancement for better 3D object detection.
- The method addresses response attenuation in linear modeling by using a regional-to-global slide window.
- A new semantic-assisted and state spatial fusion module enriches contextual representation.
- The approach demonstrates superior performance across various benchmarks.
- This research contributes to the ongoing development of effective 3D detection frameworks.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19536 (cs) [Submitted on 23 Feb 2026] Title:Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection Authors:Zhiwei Ning, Xuanang Gao, Jiaxi Cao, Runze Yang, Huiying Xu, Xinzhong Zhu, Jie Yang, Wei Liu View a PDF of the paper titled Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection, by Zhiwei Ning and 7 other authors View PDF HTML (experimental) Abstract:Linear modeling methods like Mamba have been merged as the effective backbone for the 3D object detection task. However, previous Mamba-based methods utilize the bidirectional encoding for the whole non-empty voxel sequence, which contains abundant useless background information in the scenes. Though directly encoding foreground voxels appears to be a plausible solution, it tends to degrade detection performance. We attribute this to the response attenuation and restricted context representation in the linear modeling for fore-only sequences. To address this problem, we propose a novel backbone, termed Fore-Mamba3D, to focus on the foreground enhancement by modifying Mamba-based encoder. The foreground voxels are first sampled according to the predicted scores. Considering the response attenuation existing in the interaction of foreground voxels across different instances, we design a regional-to-global slide window (RGSW) to propagate the information from regional split to the entire sequence. Furthermo...
