基于渐进式适配器的动态点云高效迁移学习方法

孙一丁; 祝继华; 王耀南; 程浩喆; 卢超翼; 陈林

doi:10.16383/j.aas.c250666

基于渐进式适配器的动态点云高效迁移学习方法

doi: 10.16383/j.aas.c250666 cstr: 32138.14.j.aas.c250666

孙一丁^{1, 2,},
祝继华^{1, 2,},
王耀南^{3, 4,},
程浩喆^1,,
卢超翼^1,,
陈林^1,

1.
西安交通大学软件学院西安 710049
2.
人机混合增强智能全国重点实验室西安 710049
3.
湖南大学人工智能与机器人学院长沙 410082
4.
机器人视觉感知与控制技术国家工程研究中心长沙 410082

基金项目: 国家自然科学基金(62125305), 陕西省杰出青年科学基金(2025JC-JCQN-091), 陕西省技术创新引导计划(基金)资助项目(2024QY-SZX-23)资助

详细信息

作者简介:
孙一丁：西安交通大学软件学院硕士研究生. 2024年获得西安工业大学计算机科学与技术学院学士学位. 主要研究方向为点云理解. E-mail: sunyiding@stu.xjtu.edu.cn

祝继华：西安交通大学软件学院教授. 2004年获得中南大学自动化专业学士学位. 2011年获得西安交通大学控制科学与工程专业博士学位. 主要研究方向为计算机视觉与机器学习. 本文通信作者. E-mail: zhujh@xjtu.edu.cn

王耀南：中国工程院院士, 湖南大学人工智能与机器人学院教授. 1995年获得湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

程浩喆：西安交通大学软件学院博士研究生. 2022年获得西安工程大学电子信息学院硕士学位. 主要研究方向为点云处理. E-mail: chz97@stu.xjtu.edu.cn

卢超翼：西安交通大学软件学院硕士研究生. 2024年获得浙江科技大学计算机科学与技术学院软件工程专业学士学位. 主要研究方向为计算机视觉. E-mail: chaoyi@stu.xjtu.edu.cn

陈林：西安交通大学软件学院助理教授. 2018年获得西北师范大学电子信息工程专业学士学位. 2021年获得中国科学院大学计算机技术专业硕士学位. 2025年获得湖南大学控制科学与工程专业博士学位. 主要研究方向是多机器人系统、深度强化学习和视觉导航. E-mail: chenlin21@hnu.edu.cn

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出版历程
- 收稿日期: 2025-11-24
- 录用日期: 2026-02-13
- 网络出版日期: 2026-04-27
- 刊出日期: 2026-07-23

Efficient Transfer Learning for Dynamic Point Clouds With Progressive Adapters

SUN Yi-Ding^{1, 2
,},
ZHU Ji-Hua^{1, 2
,},
WANG Yao-Nan^{3, 4
,},
CHENG Hao-Zhe^1
,,
LU Chao-Yi^1
,,
CHEN Lin^1
,

1.
School of Software Engineering, Xi＇an Jiaotong University, Xi＇an 710049
2.
State Key Laboratory of Human-machine Hybrid Augmented Intelligence, Xi＇an 710049
3.
School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082
4.
National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha 410082

Funds: Supported by National Natural Science Foundation of China (62125305), the Natural Science Basis Research Plan in Shaanxi Province of China (2025JC-JCQN-091), and the Technology Innovation Leading Program of Shaanxi (2024QY-SZX-23)

More Information

Author Bio:
SUN Yi-Ding　Master student at the School of Software Engineering, Xi＇an Jiaotong University. He received his bachelor degree from the School of Computer Science and Technology, Xi＇an Technological University in 2024. His main research interest is point cloud understanding

ZHU Ji-Hua　Professor at the School of Software Engineering, Xi＇an Jiaotong University. He received his bachelor degree in automation from Central South University in 2004 and his Ph.D. degree in control science and engineering from Xi＇an Jiaotong University in 2011. His research interests include computer vision and machine learning. Corresponding author of this paper

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interests include robotics, intelligent control, and image processing

CHENG Hao-Zhe　Ph.D. candidate at the School of Software Engineering, Xi＇an Jiaotong University. He received his master degree from the School of Electronic Information, Xi＇an Polytechnic University in 2022. His main research interest is point cloud processing

LU Chao-Yi　Master student at the School of Software Engineering, Xi＇an Jiaotong University. He received his bachelor degree in software engineering from the School of Computer Science and Technology, Zhejiang University of Science and Technology in 2024. His main research interest is computer vision

CHEN Lin　Assistant professor at the School of Software Engineering, Xi＇an Jiaotong University. He received his bachelor degree in electronic information engineering from Northwest Normal University in 2018, his master degree in computer technology from the University of Chinese Academy of Sciences in 2021, and his Ph.D. degree in control science and engineering from Hunan University in 2025. His research interests include multi-robot systems, deep reinforcement learning, and visual navigation

摘要

摘要: 动态点云视频时空耦合复杂, 现有端到端方法仅支持特定数据集与任务验证, 跨场景、跨任务泛化能力不足, 需重复训练. 为能够挖掘可迁移的通用先验, 本文将研究视角重新聚焦于静态点云基础模型. 首先分析现有迁移学习方法存在的两大掣肘, 即密集计算与冗余设计. 为实现轻量、紧凑的目标, 提出一种基于渐进式适配器的跨模态高效迁移学习方法. 该方法通过无重叠滑动窗口注意力, 将自注意力复杂度由二次阶降为线性阶. 其次引入洗牌希尔伯特-Z序双向扫描曲线, 将动态点云视频约束为兼容Mamba的特征序列, 并以渐进式门控融合实现基础模型与新增适配器的高效协同. 整体方法不改变基础模型权重, 不依赖外部辅助设计. 最后在MSR-Action3D、HOI4D和SHREC＇17上的实验结果表明, 仅微调1.8%参数即可获得超越现有基线模型的性能, 验证了该方法的优越性.
- 点云视频 /
- 迁移学习 /
- 混合架构 /
- 注意力机制 /
- 高效适配器
Abstract: Dynamic point cloud videos exhibit intricate spatio-temporal coupling. Current end-to-end methods are only evaluated on specific datasets and tasks, generalize poorly across scenes or tasks, and must be retrained from scratch. To uncover transferable universal priors, we refocus our investigation on static point cloud foundation models. In this paper, we first identify two bottlenecks in existing transfer learning methods: Heavy computation and redundant design. To obtain a compact and lightweight solution, we propose an efficient cross-modal transfer method that uses progressive adapters. It reduces self-attention from quadratic to linear complexity with non-overlapping sliding window attention. A shuffled Hilbert-Z bidirectional scan curve converts the dynamic point cloud video into a Mamba-compatible feature sequence; progressive gating fusion then lets the foundation model and the newly added adapter cooperate efficiently. The foundation model weights remain unchanged and no external auxiliary designs are required. Experiments on MSR-Action3D, HOI4D and SHREC＇17 show that tuning only 1.8% of the parameters already surpasses existing baselines, confirming the superiority of our method.
- point cloud video /
- transfer learning /
- hybrid architecture /
- attention mechanism /
- efficient adapter

HTML全文

图 1 本文与现有方法在性能与可学习参数比例上的对比

Fig. 1 Comparison between our method and existing methods on performance and learnable parameter ratio

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图 2 PointM4模型结构示意图

Fig. 2 Schematic diagram of PointM4 model architecture

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图 3 双向扫描与洗牌策略的示意图

Fig. 3 Schematic diagram of the bidirectional scan and shuffle strategy

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图 4 PointM4与基线模型的动作分割可视化对比

Fig. 4 Visualization comparison of action segmentation between PointM4 and baseline models

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图 5 两种适配方法的t-SNE可视化

Fig. 5 t-SNE visualization of two adaptation methods

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图 6 PointM4与现有方法在训练时长与GPU占用上的对比

Fig. 6 Comparison of training time and GPU memory usage between PointM4 and existing methods

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表 1 MSR-Action3D数据集动作识别准确率(%)

Table 1 Action recognition accuracy on the MSR-Action3D dataset (%)

训练方式	方法	12帧	16帧	24帧	32帧
有监督训练	MeteorNet^[28]	86.53	88.21	88.50	—
	PSTNet^[4]	87.88	89.90	91.20	—
	P4Transformer^[3]	87.54	89.56	90.94	87.93
	Kinet^[29]	88.53	91.92	93.27	—
	PPTr^[30]	89.89	90.31	92.33	—
	LeaF^[31]	—	91.50	93.84	—
	PST-Transformer^[19]	88.15	91.98	93.73	—
	X4D-SceneFormer^[32]	—	92.56	93.90	—
	Mamba4D^[20]	—	—	93.38	93.10
自监督训练 (端到端微调)	CPR^[5]	91.00	92.15	93.03	—
	C2P^[33]	—	91.89	94.76	—
	PointCMP^[34]	91.58	92.26	93.27	—
	PointCPSC^[35]	90.24	92.26	92.68	—
	MaST-Pre^[6]	—	—	94.08	—
跨模态迁移学习 (3D预训练模型适配)	PointCSA^[9]	92.04	93.73	95.12	95.47
	Point-BERT + M4	$ 92.33_{(+0.29)} $	$ \;{\bf{94.77}}_{(+1.04)} $	$ 95.47_{(+0.35)} $	$ 96.17_{(+0.70)} $
	PointMAE + M4	$ 93.37_{(+1.33)} $	$ 94.08_{(+0.35)} $	$ 95.12_{(+0.00)} $	$\; {\bf{96.86}}_{(+1.39)} $
	PointGPT-S + M4	$\; {\bf{93.72}}_{(+1.68)} $	$ 94.43_{(+0.70)} $	$\; {\bf{95.62}}_{(+0.50)} $	$ 96.52_{(+1.05)} $

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表 2 HOI4D数据集动作分割准确率

Table 2 Action segmentation accuracy on the HOI4D dataset

训练方式	方法	准确率(%)	编辑距离	F1@50 (%)
有监督训练	P4Trans^[3]	71.2	73.1	58.2
	PPTr^[30]	77.4	80.1	69.5
	Mamba4D^[20]	85.5	91.3	85.5
自监督训练 (端到端微调)	P4Trans + C2P^[33]	73.5	76.8	62.4
	PPTr + C2P^[33]	81.1	84.0	74.1
	X4D^[32]	84.1	91.1	84.8
	CrossVideo^[36]	83.7	86.0	76.0
跨模态迁移学习 (3D预训练模型适配)	P4Trans + M4	$ {{80.1}}_{(+8.9)} $	$ {{74.7}}_{(+1.6)} $	$ {{71.7}}_{(+13.5)} $

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表 3 HOI4D数据集语义分割准确率

Table 3 Semantic segmentation accuracy on the HOI4D dataset

方法	输入帧数	mIoU (%)
P4Trans^[3]	3	40.1
P4Trans + C2P^[33]	3	41.4
P4Trans + CrossVideo^[36]	3	42.1
PPTr^[30]	3	41.0
PPTr + C2P^[33]	3	42.3
P4Trans + M4	3	$ {\bf{42.8}}_{(+2.7)} $

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表 4 SHREC＇17数据集手势识别准确率(%)

Table 4 Gesture recognition accuracy on the SHREC＇17 dataset (%)

训练方式	方法	准确率
有监督训练	PLSTM-base^[38]	87.6
	PLSTM-early^[38]	93.5
	PLSTM-PSS^[38]	93.1
	PLSTM-middle^[38]	94.7
	PLSTM-late^[38]	93.5
	Kinet^[29]	95.2
自监督训练 (端到端微调)	PointCMP^[34]	93.3
自监督训练 (端到端微调)	MaST-Pre^[6]	92.4
跨模态迁移学习 (3D预训练模型适配)	Point-BERT + CSA^[9]	96.2
	PointMAE + CSA^[9]	95.2
	PointGPT-S + CSA^[9]	96.5
	Point-BERT + M4	$ {\bf{96.4}}_{(+0.2)} $
	PointMAE + M4	$ {\bf{96.3}}_{(+1.1)} $
	PointGPT-S + M4	$ {\bf{96.7}}_{(+0.2)} $

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表 5 滑动窗口尺寸对注意力效率以及模型性能的影响

Table 5 The impact of sliding window size on attention efficiency and model performance

模型	窗口尺寸(点)	每秒帧数 (FPS)	注意力占比(%)	准确率(%)
A0	$ 640 \times 240 \times 3 $	102.9	54.5	94.4
A1	$ 16 \times 16 \times 3 $	110.7	36.9	95.4
A2	$ 8 \times 8 \times 3 $	112.5	36.6	96.8
A3	$ 4 \times 4 \times 3 $	113.2	36.5	96.2

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表 6 Mamba扫描策略及变体种类对模型性能的影响(%)

Table 6 The impact of Mamba scanning strategy and variant types on model performance (%)

模型	双向扫描	洗牌策略	六种变体						准确率
模型	双向扫描	洗牌策略	XYZ	YXZ	XZY	ZXY	YZX	ZYX	准确率
B0	$ \times $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	94.9
B1	$ \checkmark $	$ \times $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	94.2
B2	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	96.8
B3	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \times $	$ \times $	$ \checkmark $	$ \checkmark $	95.8
B4	$ \checkmark $	$ \checkmark $	$ \times $	$ \times $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	95.1
B5	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \checkmark $	$ \times $	$ \times $	$ \times $	$ \times $	94.9
B6	$ \checkmark $	$ \checkmark $	$ \times $	$ \times $	$ \checkmark $	$ \checkmark $	$ \times $	$ \times $	94.7
B7	$ \checkmark $	$ \checkmark $	$ \times $	$ \times $	$ \times $	$ \times $	$ \checkmark $	$ \checkmark $	94.7
B8	$ \times $	$ \times $	$ \times $	$ \times $	$ \times $	$ \times $	$ \times $	$ \times $	92.3

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表 7 适配方法对模型性能的影响

Table 7 The impact of adaptation methods on model performance

模型	适配方法	具体操作	准确率(%)
C0	跳过	$ X $	92.4
C1	相加	$ X+Y $	92.3
C2	最大值	$ \max (X,\; Y) $	90.2
C3	拼接	$ {\rm{Linear}}([X,\; Y]) $	95.5
C4	渐进式适配	$ {\rm{ProAda}}\left(X,\; Y_1,\; Y_2\right) $	96.8

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表 8 级联顺序对模型性能的影响

Table 8 The impact of cascade order on model performance

模型	是否适配	级联顺序	准确率(%)
D0	$ \checkmark $	[TM]$ \times $12	96.8
D1	$ \times $	[T]$ \times $12 [M]$ \times $12	90.6
D2	$ \times $	[T]$ \times $6 [TMM]$ \times $6	92.5
D3	$ \checkmark $	[TTMM]$ \times $6	93.8

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表 9 整体推理流程的时间分析

Table 9 Overall reasoning process time analysis

方法	帧数	预处理时间 (ms)	模型推理时间 (ms)	后处理时间 (ms)	准确率(%)
PointCSA	24	3.3	6.7	0.9	95.1
PointCSA	36	不适用(OOM)
PointM4	24	4.8	3.2	0.9	95.1
PointM4	36	5.0	3.8	1.0	96.8

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