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3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds

Ben Liang, Bingwen Qiu, Fangzhou Chen, Junqi Bai, Qian Chen, Tong Jiang, Xiubao Sui, Yuan Liu

A hybrid Transformer and state space model network better detects objects in sparse distant point clouds.

arxiv:2605.15546 v1 · 2026-05-15 · cs.CV

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Claims

C1strongest claim

Extensive experiments conducted on the KITTI and ONCE datasets have shown that 3DTMDet outperforms state-of-the-art detectors.

C2weakest assumption

The SSM-Attention-SSM pipeline in the proposed 3D Hybrid Mamba Transformer block can effectively balance global context understanding with preservation of fine-grained local geometric structures in sparse distant point sets.

C3one line summary

3DTMDet proposes a hybrid Mamba-Transformer architecture with a 3DHMT block and LiDAR-inspired voxel generation to improve 3D object detection in point clouds, outperforming prior methods on KITTI and ONCE datasets.

References

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[1] A survey on deep-learning-based lidar 3d object detection for autonomous driving 2022
[2] Ao, L., Wan, W., Ouyang, N., Li, J., Li, Q., Gong, M., Sheng, K., 2026.Svp:stratifiedverticalpriorsforlidar-based3dobjectdetection. Neurocomputing 659, 131737. URL:https://www.sciencedirect. com/scien 2026
[3] Dstr: Dual scenes transformer for cross-modal fusion in 3d object detection 2025
[4] Kptr:Key point transformer for lidar-based 3d object detection 2025 · doi:10.1016/j.measurement
[5] Eb-lg module for 3d point cloud classification and segmentation 2023
Receipt and verification
First computed 2026-05-20T00:01:04.644903Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

5027e5a0f6cb3b50f1e8eedf2ba62bf3420e9afb803cabf8c0bda4d8a7a19a58

Aliases

arxiv: 2605.15546 · arxiv_version: 2605.15546v1 · doi: 10.48550/arxiv.2605.15546 · pith_short_12: KAT6LIHWZM5V · pith_short_16: KAT6LIHWZM5VB4PI · pith_short_8: KAT6LIHW
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/KAT6LIHWZM5VB4PI53PSXJRL6N \
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Canonical record JSON
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