pith:KAT6LIHW
3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds
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
Extensive experiments conducted on the KITTI and ONCE datasets have shown that 3DTMDet outperforms state-of-the-art detectors.
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.
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
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
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/KAT6LIHWZM5VB4PI53PSXJRL6N \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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