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pith:KAT6LIHW

pith:2026:KAT6LIHWZM5VB4PI53PSXJRL6N

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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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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

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

44 extracted · 44 resolved · 1 Pith anchors

[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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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())"
# expect: 5027e5a0f6cb3b50f1e8eedf2ba62bf3420e9afb803cabf8c0bda4d8a7a19a58

Canonical record JSON

{
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-15T02:33:19Z",
    "title_canon_sha256": "35610f8e2f24716cab3c6d0d9576981ce78cb1048c943f4cf53d3fcbb4d9029f"
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  "source": {
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