动量更新与重构约束的限制视角下3D物品识别

doi:10.3969/j.issn.1000-5641.2023.06.006

摘要/Abstract

摘要：

提出了一个基于动量更新表示与重构约束的神经网络训练框架: 在视角标签信息缺失的限制性条件下, 使用2D (two-dimensional)图像进行3D (three-dimensional)物品识别. 首先, 使用自监督学习来解决训练过程中标签缺失的问题. 其次, 在动态队列基础上, 使用动量更新来保持物品表示的稳定性. 更进一步地, 在训练框架中加入自编码器模块, 利用重构约束使模型学习到的表示具有更多的语义信息. 最后, 提出动态队列递减策略, 解决训练过程中数据分布不均衡带来的准确度下降问题. 在2个广泛使用的多视角数据集ModelNet和ShapeNet上进行了实验, 结果表明所提方法具有良好的性能表现.

关键词: 多视角物品识别, 自监督学习, 自编码器

Abstract:

We propose a neural network training framework called momentum-updated representation with reconstruction constraint for 3D (three-dimensional) object recognition using 2D (two-dimensional) images without angle labels. First, self-supervised learning is employed to address the lack of angle labels. Second, we use momentum updating based on a dynamic queue to maintain the stability of the object representation. Furthermore, the reconstruction constraint is applied to the learning process with an auto-encoder module, which enables the representation to capture more semantic information of the objects. Finally, during training, a dynamic queue reduction strategy is proposed for handling the imbalanced data distribution. Experiments on two popular multi-view datasets, ModelNet and ShapeNet, demonstrate that the proposed method outperforms existing methods.

Key words: multi-view object recognition, self-supervised learning, auto-encoder

中图分类号:

TP391.41

崔瑞博, 王峰. 动量更新与重构约束的限制视角下3D物品识别[J]. 华东师范大学学报（自然科学版）, 2023, 2023(6): 61-72.

Ruibo CUI, Feng WANG. Momentum-updated representation with reconstruction constraint for limited-view 3D object recognition[J]. Journal of East China Normal University(Natural Science), 2023, 2023(6): 61-72.

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方法	准确率(ModelNet)/%		准确率(ShapeNet) /%
方法	Seen	Unseen	Seen	Unseen
UEL^[17]	47.9	46.5	68.7	53.4
ViT^[13]	65.0	55.0	76.8	61.7
SimCLR^[15]	66.3	58.4	79.2	63.8
MoCo^[14]	69.3	59.7	78.5	60.1
MAE^[12]	67.3	56.4	72.9	54.9
VISPE^[20]	71.2	64.4	82.9	65.5
MUR +WithoutDQ+Pret	75.4	68.5	79.3	63.8
MUR +Pret	76.2	68.6	80.7	66.1
MUR+AE + Pret	78.1	71.0	81.8	67.1
MUR+AE+DQR	74.0	67.8	83.7	67.4
MUR +AE+DQR+Pret	78.0	71.8	83.0	67.2
MUR+AE+DQR+Pre(batch size 32)	76.5	69.3	84.8	67.3

方法	召回率(k-means)/%				NMI值/%
方法	$ k=1 $	$ k=2 $	$ k=4 $	$ k=8 $	NMI值/%
UEL^[17]	77.8	85.4	91.6	95.7	24.6
ViT^[13]	86.8	91.1	94.9	97.2	23.8
SimCLR^[15]	89.4	93.4	96.4	98.0	31.1
MoCo^[14]	90.7	94.3	97.1	98.4	34.2
MAE^[12]	89.7	94.0	96.8	98.3	27.6
VISPE^[20]	95.5	97.7	98.6	99.2	51.1
MUR+WithoutDQ+Pret	97.4	98.6	99.4	99.7	56.6
MUR+Pret	97.9	99.0	99.5	99.8	57.0
MUR +AE+Pret	98.5	99.2	99.7	99.9	56.6
MUR+AE+DQR	97.7	98.6	99.4	99.7	58.8
MUR+AE+DQR+Pret	98.2	99.1	99.7	99.8	57.2
MUR+AE+DQR+Pre(batch size 32)	98.4	99.1	99.6	99.8	55.1

方法	准确率(K-shot)/%
方法	$ K=1 $	$ K=3 $	$ K=5 $
UEL^[17]	23.8	30.9	34.2
ViT^[13]	38.5	39.9	43.0
SimCLR^[15]	40.3	41.8	43.9
MoCo^[14]	40.8	44.3	45.5
MAE^[12]	41.3	42.5	42.7
VISPE^[20]	43.1	52.5	55.9
MUR+WithoutDQ+Pret	43.9	57.7	63.3
MUR+Pret	44.6	58.3	64.7
MUR+AE+Pret	46.8	59.7	65.1
MUR+AE+DQR	45.1	57.6	63.8
MUR+AE+DQR+Pret	46.7	59.6	65.8
MUR+ AE+DQR+Pre(batch size 32)	44.6	59.3	64.7

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