基于循环神经网络的双目视觉物体6D位姿估计

基于循环神经网络的双目视觉物体6D位姿估计

杨恒,李卓,康忠元,田兵,董青

Binocular vision object 6D pose estimation based on circulatory neural network

Heng YANG,Zhuo LI,Zhong-yuan KANG,Bing TIAN,Qing DONG

表 2 Binocular-RNN与其他方法在LM-O上的精确度比较

Tab.2 Comparison of Binocular-RNN with other methods on LM-O %

方法	m	训练数据	模型								MEAN
方法	m	训练数据	Ape	Can	Cat	Driller	Duck	Eggbox	Glue	Holep	MEAN
PoseCNN	1	Real+syn	9.6	45.2	0.9	41.4	19.6	22.0	38.5	22.1	24.9
PVNet	M	Real+syn	15.8	63.3	16.7	65.7	25.2	50.2	49.6	36.1	40.8
Single-Stage	M	Real+syn	19.2	65.1	18.9	69.0	25.3	52.0	51.4	45.6	43.3
GDR-NET	M	Real+syn	41.3	71.1	18.2	54.6	41.7	40.2	59.5	52.6	47.4
Binocular-RNN	1	Real+syn	49.6	78.2	40.3	67.4	50.6	45.4	60.5	68.2	57.5
	M	Real+syn	41.3	79.1	42.8	71.2	55.3	48.3	65.7	70.5	51.6
	1	Real+pbr	48.6	82.3	51.4	73.5	61.2	58.3	70.5	72.6	64.8
	M	Real+pbr	50.4	85.7	58.3	76.2	68.3	62.1	75.8	74.2	68.9
DPOD	1	Real+syn	—	—	—	—	—	—	—	—	47.3
DeepIM	1	Real+syn	59.2	63.5	26.2	55.6	52.4	63.0	71.7	52.5	55.5