Table 2 Pose registration analysis with PartNet dataset46.

From: Unsupervised learning-based approach for detecting 3D edges in depth maps

 

Object

\(||{A}||_F\)*

\(\textbf{R}_{err}\)*

\(\textbf{T}_{err}\) *

\(\varvec{Z}_E\)*

 

\(||{A}||_F\)*

\(\textbf{R}_{err}\)*

\(\textbf{T}_{err}\)*

\(\varvec{Z}_E\)*

 

\(||{A}||_F\)*

\(\textbf{R}_{err}\)*

\(\textbf{T}_{err}\)*

\(\varvec{Z}_E\)*

Choi et al.2

Knife

3.06 ± 1.14

2.15 ± 0.97

1.84 ± 1.27

2.49 ± 0.56e1

JSENet20

1.47 ± 1.36

1.54 ± 1.50

0.14 ± 0.24

0.75 ± 0.17e1

Ours

1.45 ± 1.37

1.54 ± 1.50

0.10 ± 0.14

0.72 ± 0.15e1

Scissors

2.75 ± 1.19

2.04 ± 1.09

1.54 ± 1.13

7.01 ± 0.58e1

1.74 ± 1.27

1.77 ± 1.41

0.24 ± 0.32

6.27 ± 0.95e1

1.72 ± 1.22

1.73 ± 1.38

0.22 ± 0.24

5.44 ± 0.62e1

Bowl

3.05 ± 0.81

2.16 ± 0.70

1.74 ± 1.06

9.78 ± 1.08e1

1.89 ± 0.86

1.64 ± 0.92

0.25 ± 0.2

8.31 ± 1.42e1

1.75 ± 1.00

1.49 ± 0.95

0.24 ± 0.55

8.12 ± 1.38e1

Bottle

2.99 ± 1.07

2.09 ± 0.81

1.69 ± 1.29

8.61 ± 0.80e1

2.03 ± 0.87

1.85 ± 0.99

0.24 ± 0.22

1.24 ± 2.68e1

1.93 ± 0.96

1.77 ± 1.06

0.23 ± 0.26

1.09 ± 3.76e1

Mug

2.98 ± 0.77

2.15 ± 0.70

1.66 ± 0.97

1.11 ± 1.63e1

1.85 ± 1.60

1.68 ± 1.16

0.29 ± 1.24

1.01 ± 1.14e1

1.53 ± 1.20

1.46 ± 1.26

0.16 ± 0.14

8.27 ± 1.20e1

Avg.

2.96 ± 0.99

2.12 ± 0.70

1.69 ± 1.14

7.79 ± 0.93e1

1.79 ± 1.19

1.69 ± 1.19

0.23 ± 0.44

7.56 ± 1.27e1

1.67 ± 1.15

1.59 ± 1.00

0.19 ± 0.26

6.69 ± 1.42e1

Fast Edge13

Knife

2.69 ± 1.31

1.83 ± 1.13

1.65 ± 1.22

1.63 ± 0.23e1

Sung et al.15

2.23 ± 0.65

2.02 ± 0.77

0.40 ± 0.17

5.3 ± 2.7e1

     

Scissors

2.71 ± 1.10

1.96 ± 1.05

1.50 ± 1.08

7.04 ± 0.79e1

2.31 ± 0.63

2.14 ± 0.77

0.4 ± 0.16

6.2 ± 2.4e1

     

Bowl

2.99 ± 0.84

2.15 ± 0.70

1.65 ± 1.04

1.10 ± 3.09e1

2.29 ± 0.56

2.08 ± 0.71

0.38 ± 0.17

9.4 ± 2.7e1

     

Bottle

2.62 ± 0.88

2.01 ± 0.86

1.24 ± 0.90

9.58 ± 1.98e1

2.26 ± 0.60

2.03 ± 0.72

0.39 ± 0.18

7.2 ± 2.6e1

     

Mug

2.87 ± 0.80

2.09 ± 0.75

1.55 ± 0.93

1.35 ± 2.81e1

2.17 ± 0.68

1.96 ± 0.79

0.38 ± 0.16

8.0 ± 2.6e1

     

Avg.

2.77 ± 0.98

2.00 ± 0.89

1.52 ± 0.70

8.55 ± 1.78e1

2.24 ± 0.63

2.03 ± 0.76

0.39 ± 0.17

7.4 ± 3.0e1

     
  1. *Smaller values indicate better performance. Significant values are in bold.
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