Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection
Zhiwei Ning, Xuanang Gao, Jiaxi Cao, Runze Yang, Huiying Xu, Xinzhong Zhu, Jie Yang, Wei Liu
TL;DR
Fore-Mamba3D introduces a novel foreground-enhanced encoding backbone for 3D object detection, improving focus on relevant scene regions and enriching contextual understanding, leading to superior benchmark performance.
Contribution
The paper proposes Fore-Mamba3D, a Mamba-based encoder that emphasizes foreground voxels and incorporates regional-to-global information propagation and semantic-aware fusion.
Findings
Achieves state-of-the-art results on multiple 3D detection benchmarks.
Effectively enhances foreground feature representation.
Improves detection accuracy by focusing on relevant scene regions.
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…
Peer Reviews
Decision·ICLR 2026 Poster
1. The paper clearly identifies a major inefficiency in prior Mamba-based 3D object detectors: unnecessary computation over background voxels, and the resulting context loss when naïvely restricting to the foreground. 2. Foreground selection is performed using a trainable scoring mechanism with effective sampling and serialization, alleviating regional truncation problems. It is well-motivated both by experiments and design illustrations. 3. The RGSW and the SASFMamba fusion modules are describe
1. Limited discussion of causal sequence modeling approaches: Although the method addresses linear sequence encoding with foreground focus, it omits explicit comparison to other causal sequence modeling alternatives or other advanced state-space models. 2. Modeling limitations: The method largely treats background voxels as non-informative, but in autonomous driving, background structure or clutter can contain subtle contextual cues (for object contextual priors, occlusion estimation, etc.). The
This paper is well-structured and supported by sufficient experiments, showing great potential for acceptance. 1. Experiments are conducted in the mainstreams datasets, such as KITTI, NuScenes, and Waymo datasets. These results show the effectinvess of the proposed method. 2. Overview of the proposed framework is very clear. It shows the novelty of the proposed method. 3. Motivation of the proposed method is also clear.
The following points are suggested for further improvement. Q1. In the Introduction, the mention of "incomplete and imprecise sampling" seems disconnected from the preceding context, which might confuse readers about the purpose of this sampling operation. Please clarify its objective. For instance, if foreground-only encoding is used, does it require sampling foreground voxels? Please explain this clearly. Q2. In the Related Work section, the authors highlight the limitations of existing me
1. The paper presents an interesting and effective idea by selectively adapting foreground voxels for Mamba-based modeling. This approach substantially reduces computational overhead while maintaining high representational quality, showing a smart trade-off between efficiency and performance. 2. The method is validated across multiple benchmark datasets (nuScenes, KITTI, Waymo) with consistent performance gains. The visual results Fig3 are clear and insightful, effectively illustrating how the
1. Although the proposed method introduces innovative foreground-focused encoding, the overall performance gain compared to prior Mamba-based or Transformer-based 3D detectors is relatively modest. The improvements, while consistent, may not fully justify the added architectural complexity or the additional design components. 2. Unclear Spatial Consistency in the Regional-to-Global Sliding Window (RGSW): In the proposed RGSW strategy, the authors directly fuse the later half of the sequence wit
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques