3D Reconstruction and Accuracy Evaluation of Ancient Chinese Architectural Patches Based on Depth Learning from Single Image

Lihua Hu; Wenzhuang Yin; Siyuan Xing; Jifu Zhang; Qiulei Dong; Zhanyi Hu

doi:10.3788/LOP202259.1415020

Evaluating index

Definition

A b s E

\frac{1}{|D|} \sum_{d \in D} |d^{'} - d|

A b s R e l

\frac{1}{|D|} \sum_{d \in D} |d^{'} - d| / d^{'}

R M S E

\sqrt[]{\frac{1}{|D|} \sum_{d \in D} {|d^{'} - d|}^{2}}

R M S E l o g

\sqrt[]{\frac{1}{|D|} \sum_{d \in D} {|l o g d^{'} - l o g d|}^{2}}

δ_{t}

\begin{array}{l} \frac{1}{|D|} |\{d \in D |m a x (\frac{d^{'}}{d}, \frac{d}{d^{'}}) < 1 . 25^{t}\}| \times 100 %, \\ (t = 1,2, 3) \end{array}

Scene

Parameter

AbsE

AbsRel

RMSElog

RMSE

δ₁

δ₂

δ₃

S-DN

P-DN

S-DN

P-DN

S-DN

P-DN

S-DN

P-DN

S-DN

P-DN

S-DN

P-DN

S-DN

P-DN

Mean

92.059

69.396

0.044

0.033

0.019

0.015

130.047

101.968

0.992

0.996

0.998

0.999

Std

20.119

13.009

0.011

0.008

0.004

0.003

30.701

26.728

0.012

0.007

0.005

0.003

0.002

Mean

142.004

85.449

0.069

0.042

0.031

0.018

218.779

134.206

0.939

0.986

0.997

0.998

1.000

Std

85.910

50.506

0.033

0.022

0.017

0.010

104.946

56.033

0.086

0.017

0.021

0.004

0.005

0.001

Mean

76.176

47.532

0.143

0.062

0.044

0.026

172.549

116.678

0.890

0.970

0.967

0.987

0.986

0.991

Std

47.523

33.026

0.104

0.036

0.027

0.015

134.621

79.858

0.107

0.075

0.036

0.02

0.018

0.012

Mean

81.869

53.993

0.053

0.037

0.023

0.016

118.610

84.116

0.985

0.991

0.995

0.999

0.998

0.999

Std

27.412

18.137

0.018

0.013

0.008

0.005

46.794

31.098

0.022

0.012

0.011

0.003

0.005

0.001

Mean

79.434

53.465

0.048

0.035

0.021

0.015

127.196

87.650

0.986

0.990

0.995

0.998

0.999

Std

13.332

15.185

0.007

0.011

0.003

0.005

28.824

31.470

0.012

0.009

0.005

0.003

0.002

0.001

Mean

73.650

51.223

0.060

0.044

0.024

0.018

146.474

121.922

0.977

0.981

0.986

0.988

0.993

0.995

Std

32.010

24.641

0.034

0.026

0.009

0.008

102.214

92.644

0.021

0.018

0.016

0.013

0.012

0.009

Mean

147.347

77.696

0.119

0.061

0.040

0.023

262.657

153.315

0.941

0.969

0.960

0.984

0.968

0.990

Std

77.375

39.067

0.099

0.046

0.024

0.014

199.390

110.325

0.073

0.039

0.062

0.025

0.052

0.017

Mean

61.501

45.311

0.059

0.039

0.024

0.017

112.967

91.378

0.965

0.987

0.991

0.994

0.995

0.997

Std

29.071

15.768

0.036

0.016

0.013

0.007

52.998

42.383

0.048

0.019

0.015

0.012

0.009

0.008

Mean

61.198

57.930

0.036

0.034

0.016

0.015

89.288

82.843

0.991

0.995

0.999

1.000

Std

17.013

13.090

0.011

0.007

0.004

0.003

25.556

19.684

0.013

0.006

0.003

0.002

0.001

0.000

S10

Mean

98.554

79.701

0.057

0.046

0.024

0.020

148.852

118.570

0.979

0.988

0.995

0.999

1.000

Std

28.063

27.118

0.019

0.016

0.008

0.007

39.469

35.626

0.024

0.014

0.006

0.002

0.001

S11

Mean

112.137

94.995

0.053

0.045

0.022

0.020

171.664

149.130

0.984

0.986

0.997

0.998

0.999

1.000

Std

16.456

21.645

0.008

0.009

0.003

0.004

30.734

31.393

0.011

0.010

0.006

0.003

0.002

0.001

S12

Mean

60.529

46.035

0.065

0.044

0.024

0.018

111.509

89.939

0.967

0.981

0.988

0.993

0.992

0.995

Std

67.468

39.070

0.155

0.057

0.030

0.016

102.026

68.838

0.069

0.050

0.048

0.031

0.040

0.026

S13

Mean

67.791

51.239

0.043

0.034

0.019

0.015

109.101

89.085

0.989

0.992

0.996

0.998

0.999

Std

21.200

14.242

0.013

0.010

0.005

0.004

37.649

34.578

0.011

0.009

0.007

0.004

0.006

0.003

S14

Mean

138.875

62.554

0.056

0.026

0.025

0.011

161.117

83.849

0.996

0.999

1.000

Std

49.425

10.958

0.017

0.004

0.008

0.002

47.469

13.856

0.009

0.002

0.000

0.001

0.000

S15

Mean

37.833

31.337

0.044

0.035

0.017

0.015

76.269

58.719

0.985

0.988

0.992

0.994

0.996

0.998

Std

20.797

16.653

0.033

0.022

0.009

0.008

64.297

38.356

0.026

0.024

0.021

0.015

0.013

0.007

Scene

Mean-ICP

P-DN

Std-ICP

P-DN

Mean-ICP

S-DN

Std-ICP-

S-DN

52.8

22.1

60.8

15.7

110.2

30.1

112.2

20.0

40.0

13.8

52.8

39.9

49.2

15.9

47.4

15.4

50.9

26.1

51.9

33.1

60.1

37.9

59.4

37.0

49.6

17.1

52.4

14.5

33.4

21.0

41.92

23.5

40.4

9.8

53.7

13.1

S10

150.8

14.5

154.2

21.0

S11

134.0

22.9

128.8

26.1

S12

54.3

21.4

54.9

22.5

S13

47.0

14.3

44.67

12.0

S14

54.9

12.3

55.7

12.3

S15

27.0

16.7

29.3

16.0

Lihua Hu, Wenzhuang Yin, Siyuan Xing, Jifu Zhang, Qiulei Dong, Zhanyi Hu. 3D Reconstruction and Accuracy Evaluation of Ancient Chinese Architectural Patches Based on Depth Learning from Single Image[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415020

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