High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

HSP-MVS		87.45 2	90.22 2	84.22 6	90.00 17	91.80 2	90.59 3	75.80 3	89.93 3	78.35 14	92.54 1	89.18 2	80.89 1	87.99 10	86.29 24	89.70 34	93.85 7

APDe-MVS		88.00 1	90.50 1	85.08 1	90.95 4	91.58 3	92.03 1	75.53 7	91.15 1	80.10 8	92.27 2	88.34 5	80.80 2	88.00 9	86.99 13	91.09 4	95.16 1

APD-MVS		86.84 6	88.91 8	84.41 3	90.66 7	90.10 6	90.78 2	75.64 4	87.38 11	78.72 12	90.68 5	86.82 9	80.15 3	87.13 19	86.45 22	90.51 14	93.83 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		86.88 5	89.23 4	84.14 7	89.78 20	88.67 25	90.59 3	73.46 20	88.99 5	80.52 7	91.26 3	88.65 3	79.91 4	86.96 24	86.22 25	90.59 12	93.83 8

HPM-MVS++		87.09 3	88.92 7	84.95 2	92.61 1	87.91 33	90.23 8	76.06 2	88.85 6	81.20 4	87.33 8	87.93 6	79.47 5	88.59 4	88.23 4	90.15 27	93.60 14

CNVR-MVS		86.36 8	88.19 11	84.23 5	91.33 3	89.84 8	90.34 5	75.56 5	87.36 12	78.97 11	81.19 22	86.76 10	78.74 6	89.30 2	88.58 1	90.45 20	94.33 4

SD-MVS		86.96 4	89.45 3	84.05 9	90.13 14	89.23 16	89.77 11	74.59 8	89.17 4	80.70 5	89.93 6	89.67 1	78.47 7	87.57 14	86.79 16	90.67 11	93.76 10

MCST-MVS		85.13 17	86.62 18	83.39 12	90.55 10	89.82 10	89.29 15	73.89 17	84.38 25	76.03 22	79.01 25	85.90 14	78.47 7	87.81 11	86.11 27	92.11 1	93.29 16

HFP-MVS		86.15 9	87.95 12	84.06 8	90.80 5	89.20 17	89.62 13	74.26 10	87.52 9	80.63 6	86.82 11	84.19 22	78.22 9	87.58 13	87.19 11	90.81 6	93.13 18

MPTG		85.71 11	86.88 17	84.34 4	90.54 11	87.11 37	89.77 11	74.17 12	88.54 7	83.08 2	78.60 26	86.10 12	78.11 10	87.80 12	87.46 9	90.35 23	92.56 20

NCCC		85.34 14	86.59 19	83.88 10	91.48 2	88.88 19	89.79 10	75.54 6	86.67 15	77.94 17	76.55 29	84.99 18	78.07 11	88.04 7	87.68 7	90.46 19	93.31 15

TSAR-MVS + GP.		83.69 24	86.58 20	80.32 29	85.14 47	86.96 38	84.91 43	70.25 34	84.71 23	73.91 29	85.16 15	85.63 15	77.92 12	85.44 34	85.71 30	89.77 31	92.45 21

MSLP-MVS++		82.09 30	82.66 34	81.42 23	87.03 36	87.22 36	85.82 35	70.04 35	80.30 37	78.66 13	68.67 55	81.04 36	77.81 13	85.19 37	84.88 37	89.19 43	91.31 31

ACMMPR		85.52 12	87.53 14	83.17 16	90.13 14	89.27 14	89.30 14	73.97 15	86.89 14	77.14 19	86.09 12	83.18 25	77.74 14	87.42 15	87.20 10	90.77 7	92.63 19

DeepC-MVS_fast	78.24 3	84.27 23	85.50 25	82.85 17	90.46 12	89.24 15	87.83 26	74.24 11	84.88 20	76.23 21	75.26 32	81.05 35	77.62 15	88.02 8	87.62 8	90.69 10	92.41 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	78.47 2	84.81 20	86.03 23	83.37 13	89.29 25	90.38 5	88.61 20	76.50 1	86.25 17	77.22 18	75.12 33	80.28 37	77.59 16	88.39 5	88.17 5	91.02 5	93.66 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	79.04 1	85.30 15	88.93 6	81.06 25	88.77 28	90.48 4	85.46 39	73.08 21	90.97 2	73.77 30	84.81 16	85.95 13	77.43 17	88.22 6	87.73 6	87.85 65	94.34 3

MP-MVS		85.50 13	87.40 15	83.28 14	90.65 8	89.51 13	89.16 17	74.11 13	83.70 27	78.06 16	85.54 14	84.89 20	77.31 18	87.40 16	87.14 12	90.41 21	93.65 13

SteuartSystems-ACMMP		85.99 10	88.31 10	83.27 15	90.73 6	89.84 8	90.27 7	74.31 9	84.56 24	75.88 23	87.32 9	85.04 17	77.31 18	89.01 3	88.46 2	91.14 3	93.96 6
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		84.74 21	86.43 21	82.77 18	89.48 23	88.13 32	88.64 19	73.93 16	84.92 19	76.77 20	81.94 20	83.50 23	77.29 20	86.92 25	86.49 21	90.49 15	93.14 17

3Dnovator+	75.73 4	82.40 28	82.76 33	81.97 22	88.02 30	89.67 11	86.60 30	71.48 29	81.28 36	78.18 15	64.78 68	77.96 43	77.13 21	87.32 17	86.83 15	90.41 21	91.48 30

PGM-MVS		84.42 22	86.29 22	82.23 20	90.04 16	88.82 21	89.23 16	71.74 28	82.82 30	74.61 26	84.41 17	82.09 27	77.03 22	87.13 19	86.73 18	90.73 9	92.06 26

ACMMP_Plus		86.52 7	89.01 5	83.62 11	90.28 13	90.09 7	90.32 6	74.05 14	88.32 8	79.74 9	87.04 10	85.59 16	76.97 23	89.35 1	88.44 3	90.35 23	94.27 5

AdaColmap		79.74 40	78.62 51	81.05 26	89.23 26	86.06 45	84.95 42	71.96 26	79.39 41	75.51 24	63.16 72	68.84 78	76.51 24	83.55 48	82.85 47	88.13 57	86.46 61

CPTT-MVS		81.77 31	83.10 32	80.21 30	85.93 43	86.45 43	87.72 27	70.98 31	82.54 32	71.53 42	74.23 38	81.49 32	76.31 25	82.85 55	81.87 51	88.79 50	92.26 24

train_agg		84.86 19	87.21 16	82.11 21	90.59 9	85.47 48	89.81 9	73.55 19	83.95 26	73.30 31	89.84 7	87.23 8	75.61 26	86.47 28	85.46 32	89.78 30	92.06 26

ACMMP		83.42 25	85.27 26	81.26 24	88.47 29	88.49 28	88.31 24	72.09 25	83.42 28	72.77 34	82.65 18	78.22 41	75.18 27	86.24 31	85.76 29	90.74 8	92.13 25
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
TSAR-MVS + ACMM		85.10 18	88.81 9	80.77 28	89.55 22	88.53 27	88.59 21	72.55 23	87.39 10	71.90 36	90.95 4	87.55 7	74.57 28	87.08 21	86.54 20	87.47 70	93.67 11

CNLPA		77.20 53	77.54 56	76.80 49	82.63 57	84.31 56	79.77 60	64.64 70	85.17 18	73.18 32	56.37 106	69.81 69	74.53 29	81.12 71	78.69 89	86.04 123	87.29 58

MVS_0304		81.73 32	83.86 29	79.26 35	86.22 42	89.18 18	86.41 31	67.15 55	75.28 48	70.75 46	74.59 35	83.49 24	74.42 30	87.05 22	86.34 23	90.58 13	91.08 34

OPM-MVS		79.68 41	79.28 50	80.15 31	87.99 31	86.77 40	88.52 22	72.72 22	64.55 80	67.65 53	67.87 59	74.33 54	74.31 31	86.37 30	85.25 34	89.73 33	89.81 43

3Dnovator	73.76 5	79.75 39	80.52 44	78.84 38	84.94 52	87.35 34	84.43 45	65.54 65	78.29 42	73.97 28	63.00 74	75.62 50	74.07 32	85.00 38	85.34 33	90.11 28	89.04 46

CSCG		85.28 16	87.68 13	82.49 19	89.95 18	91.99 1	88.82 18	71.20 30	86.41 16	79.63 10	79.26 23	88.36 4	73.94 33	86.64 26	86.67 19	91.40 2	94.41 2

PLC	68.99 11	75.68 58	75.31 68	76.12 52	82.94 56	81.26 75	79.94 59	66.10 60	77.15 45	66.86 58	59.13 87	68.53 79	73.73 34	80.38 80	79.04 86	87.13 80	81.68 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMM	72.26 8	78.86 48	78.13 52	79.71 33	86.89 37	83.40 62	86.02 33	70.50 32	75.28 48	71.49 43	63.01 73	69.26 72	73.57 35	84.11 43	83.98 41	89.76 32	87.84 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OMC-MVS		80.26 35	82.59 35	77.54 45	83.04 55	85.54 47	83.25 50	65.05 68	87.32 13	72.42 35	72.04 43	78.97 39	73.30 36	83.86 44	81.60 54	88.15 56	88.83 48

MVS_111021_HR		80.13 36	81.46 38	78.58 40	85.77 44	85.17 52	83.45 49	69.28 42	74.08 54	70.31 47	74.31 37	75.26 51	73.13 37	86.46 29	85.15 35	89.53 37	89.81 43

PHI-MVS		82.36 29	85.89 24	78.24 42	86.40 40	89.52 12	85.52 37	69.52 41	82.38 33	65.67 59	81.35 21	82.36 26	73.07 38	87.31 18	86.76 17	89.24 41	91.56 29

CANet		81.62 33	83.41 30	79.53 34	87.06 35	88.59 26	85.47 38	67.96 51	76.59 46	74.05 27	74.69 34	81.98 28	72.98 39	86.14 32	85.47 31	89.68 35	90.42 40

QAPM		78.47 49	80.22 47	76.43 50	85.03 49	86.75 41	80.62 56	66.00 62	73.77 55	65.35 60	65.54 66	78.02 42	72.69 40	83.71 46	83.36 46	88.87 49	90.41 41

abl_6				79.05 36	87.27 34	88.85 20	83.62 48	68.25 47	81.68 34	72.94 33	73.79 39	84.45 21	72.55 41			89.66 36	90.64 37

MVS_111021_LR		78.13 51	79.85 49	76.13 51	81.12 64	81.50 72	80.28 57	65.25 66	76.09 47	71.32 44	76.49 30	72.87 58	72.21 42	82.79 56	81.29 56	86.59 111	87.91 52

MAR-MVS		79.21 44	80.32 46	77.92 44	87.46 32	88.15 31	83.95 46	67.48 54	74.28 52	68.25 50	64.70 69	77.04 44	72.17 43	85.42 35	85.00 36	88.22 54	87.62 55
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
ACMP	73.23 7	79.79 38	80.53 43	78.94 37	85.61 45	85.68 46	85.61 36	69.59 39	77.33 44	71.00 45	74.45 36	69.16 73	71.88 44	83.15 52	83.37 45	89.92 29	90.57 39
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS	71.42 9	77.69 52	80.05 48	74.94 56	80.68 67	84.52 55	81.36 52	63.14 80	84.77 21	64.82 63	68.72 53	75.91 49	71.86 45	81.62 60	79.55 82	87.80 66	85.24 75
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CDPH-MVS		82.64 27	85.03 28	79.86 32	89.41 24	88.31 29	88.32 23	71.84 27	80.11 38	67.47 54	82.09 19	81.44 33	71.85 46	85.89 33	86.15 26	90.24 25	91.25 32

PCF-MVS	73.28 6	79.42 42	80.41 45	78.26 41	84.88 53	88.17 30	86.08 32	69.85 36	75.23 50	68.43 49	68.03 58	78.38 40	71.76 47	81.26 69	80.65 70	88.56 53	91.18 33
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVS		83.23 26	85.20 27	80.92 27	89.71 21	88.68 22	88.21 25	73.60 18	82.57 31	71.81 39	77.07 27	81.92 29	71.72 48	86.98 23	86.86 14	90.47 16	92.36 23

HQP-MVS		81.19 34	83.27 31	78.76 39	87.40 33	85.45 49	86.95 28	70.47 33	81.31 35	66.91 57	79.24 24	76.63 45	71.67 49	84.43 41	83.78 42	89.19 43	92.05 28

TSAR-MVS + COLMAP		78.34 50	81.64 37	74.48 60	80.13 71	85.01 53	81.73 51	65.93 64	84.75 22	61.68 70	85.79 13	66.27 84	71.39 50	82.91 54	80.78 61	86.01 124	85.98 63

LGP-MVS_train		79.83 37	81.22 40	78.22 43	86.28 41	85.36 51	86.76 29	69.59 39	77.34 43	65.14 61	75.68 31	70.79 64	71.37 51	84.60 39	84.01 40	90.18 26	90.74 36

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 92	80.88 81	78.55 84	58.83 150	65.19 74	60.36 74	59.98 82	62.42 95	71.22 52	81.66 59	80.61 72	88.20 55	84.88 83

LS3D		74.08 63	73.39 73	74.88 57	85.05 48	82.62 68	79.71 61	68.66 45	72.82 56	58.80 80	57.61 99	61.31 97	71.07 53	80.32 82	78.87 88	86.00 126	80.18 135

Effi-MVS+		75.28 60	76.20 65	74.20 61	81.15 63	83.24 63	81.11 53	63.13 81	66.37 66	60.27 75	64.30 70	68.88 77	70.93 54	81.56 62	81.69 53	88.61 51	87.35 56

OpenMVS	70.44 10	76.15 57	76.82 64	75.37 54	85.01 50	84.79 54	78.99 69	62.07 103	71.27 58	67.88 52	57.91 98	72.36 59	70.15 55	82.23 58	81.41 55	88.12 58	87.78 54

DELS-MVS		79.15 46	81.07 41	76.91 48	83.54 54	87.31 35	84.45 44	64.92 69	69.98 59	69.34 48	71.62 45	76.26 46	69.84 56	86.57 27	85.90 28	89.39 39	89.88 42
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs		79.16 45	82.37 36	75.41 53	82.33 60	86.38 44	80.80 55	63.18 79	82.90 29	67.34 55	72.79 41	76.07 47	69.62 57	83.46 51	84.41 39	89.20 42	90.60 38

PatchMatch-RL		67.78 129	66.65 149	69.10 104	73.01 149	72.69 166	68.49 160	61.85 106	62.93 89	60.20 76	56.83 105	50.42 179	69.52 58	75.62 147	74.46 158	81.51 164	73.62 181

DI_MVS_plusplus_trai		75.13 61	76.12 66	73.96 62	78.18 81	81.55 71	80.97 54	62.54 97	68.59 63	65.13 62	61.43 75	74.81 52	69.32 59	81.01 73	79.59 80	87.64 68	85.89 64

MVS_Test		75.37 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 131	69.72 62	64.11 65	70.53 47	73.22 56	68.90 60	80.14 86	79.48 84	87.67 67	85.50 70

Effi-MVS+-dtu		71.82 73	71.86 81	71.78 67	78.77 77	80.47 90	78.55 84	61.67 109	60.68 103	55.49 106	58.48 91	65.48 86	68.85 61	76.92 134	75.55 149	87.35 72	85.46 71

ACMH	65.37 14	70.71 80	70.00 91	71.54 68	82.51 59	82.47 69	77.78 95	68.13 48	56.19 149	46.06 158	54.30 128	51.20 175	68.68 62	80.66 75	80.72 63	86.07 119	84.45 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	66.54 13	71.36 76	70.09 90	72.85 65	82.59 58	81.13 76	78.56 83	68.04 49	61.55 98	52.52 125	51.50 166	54.14 142	68.56 63	78.85 102	79.50 83	86.82 97	83.94 91

CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 112	80.07 40	75.35 25	72.96 40	73.21 57	68.43 64	85.41 36	84.63 38	87.41 71	85.44 72
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 101	82.93 66	76.63 104	60.17 126	59.00 115	54.03 113	40.54 197	65.23 87	67.89 65	76.54 141	78.30 94	85.03 145	80.07 136

PVSNet_Blended_VisFu		76.57 54	77.90 53	75.02 55	80.56 68	86.58 42	79.24 64	66.18 59	64.81 77	68.18 51	65.61 64	71.45 61	67.05 66	84.16 42	81.80 52	88.90 47	90.92 35

PVSNet_BlendedMVS		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

PVSNet_Blended		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

v7		70.33 87	69.87 95	70.88 69	74.79 124	81.04 77	79.22 65	60.57 116	57.70 128	56.65 101	54.23 133	55.29 134	66.95 69	78.28 108	77.47 109	87.12 83	85.05 79

v10		70.22 89	69.76 99	70.74 75	74.79 124	80.30 93	79.22 65	59.81 134	57.71 127	56.58 102	54.22 135	55.31 132	66.95 69	78.28 108	77.47 109	87.12 83	85.07 78

v1192		69.50 106	68.83 117	70.29 91	74.49 137	80.92 80	78.55 84	60.54 117	55.04 160	54.21 111	52.79 155	52.33 166	66.92 71	77.88 113	77.35 114	87.04 86	85.51 69

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 100	78.93 101	77.27 100	60.12 131	61.86 95	54.42 110	42.54 191	61.61 96	66.91 72	78.55 105	78.14 98	79.23 176	83.23 101

v1921920		69.03 112	68.32 130	69.86 97	74.03 142	80.37 91	77.55 96	60.25 125	54.62 163	53.59 117	52.36 162	51.50 174	66.75 73	77.17 131	76.69 138	86.96 89	85.56 66

v144192		69.34 109	68.68 124	70.12 94	74.06 141	80.54 86	78.08 94	60.54 117	54.99 162	54.13 112	52.92 152	52.80 162	66.73 74	77.13 132	76.72 133	87.15 76	85.63 65

v1240		68.64 117	67.89 136	69.51 101	73.89 144	80.26 94	76.73 103	59.97 133	53.43 170	53.08 120	51.82 165	50.84 177	66.62 75	76.79 136	76.77 121	86.78 103	85.34 73

v1144		69.93 98	69.36 110	70.61 80	74.89 117	80.93 78	79.11 67	60.64 114	55.97 152	55.31 108	53.85 140	54.14 142	66.54 76	78.10 110	77.44 111	87.14 79	85.09 77

v11		69.37 108	68.65 125	70.20 92	74.87 120	76.97 136	78.29 91	58.55 154	56.38 146	56.04 104	54.02 137	54.98 136	66.47 77	78.30 107	76.91 119	86.97 88	83.02 102

v2v482		70.05 93	69.46 104	70.74 75	74.62 136	80.32 92	79.00 68	60.62 115	57.41 129	56.89 94	55.43 111	55.14 135	66.39 78	77.25 130	77.14 116	86.90 90	83.57 99

v17		70.03 94	69.43 109	70.72 77	74.75 129	77.09 128	78.78 79	58.85 146	59.53 113	58.72 81	54.87 120	57.39 110	66.38 79	77.60 120	76.75 126	86.83 96	82.80 104

v16		70.07 92	69.46 104	70.79 73	74.74 130	77.08 129	78.79 77	58.86 144	59.75 110	59.15 78	54.87 120	57.33 111	66.38 79	77.61 119	76.77 121	86.81 102	82.79 106

v18		70.10 91	69.52 102	70.77 74	74.66 135	77.06 130	78.84 72	58.84 149	60.01 109	59.23 77	55.06 113	57.47 109	66.34 81	77.50 125	76.75 126	86.71 104	82.77 108

v1neww		70.34 85	69.93 93	70.82 71	74.68 132	80.61 84	78.80 75	60.17 126	58.74 118	58.10 85	55.00 115	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v7new		70.34 85	69.93 93	70.82 71	74.68 132	80.61 84	78.80 75	60.17 126	58.74 118	58.10 85	55.00 115	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v6		70.35 84	69.94 92	70.83 70	74.68 132	80.62 83	78.81 74	60.16 129	58.81 116	58.17 84	55.01 114	57.31 113	66.32 84	77.53 121	76.73 132	86.82 97	83.62 93

v8		70.23 88	69.86 97	70.67 79	74.69 131	79.82 95	78.79 77	59.18 140	58.80 117	58.20 83	55.00 115	57.33 111	66.31 85	77.51 124	76.71 136	86.82 97	83.88 92

v12		69.54 103	68.79 119	70.41 88	74.88 118	77.03 133	78.54 87	58.85 146	56.71 138	56.87 95	54.13 136	56.23 127	66.15 86	77.89 112	76.74 128	87.17 75	82.80 104

V14		69.59 101	68.86 116	70.45 87	74.83 122	77.04 131	78.70 81	58.83 150	56.95 134	57.08 92	54.41 127	56.34 121	66.15 86	77.77 115	76.76 123	87.08 85	82.74 109

V9		69.58 102	68.83 117	70.46 85	74.85 121	77.04 131	78.65 82	58.85 146	56.83 137	57.12 91	54.26 131	56.31 122	66.14 88	77.83 114	76.76 123	87.13 80	82.79 106

v15		69.61 100	68.88 115	70.46 85	74.81 123	77.03 133	78.75 80	58.83 150	57.06 131	57.18 90	54.55 126	56.37 120	66.13 89	77.70 116	76.76 123	87.03 87	82.69 111

v13		69.52 105	68.76 121	70.41 88	74.88 118	77.02 135	78.52 88	58.86 144	56.61 143	56.91 93	54.00 138	56.17 128	66.11 90	77.93 111	76.74 128	87.21 74	82.83 103

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 94	76.34 145	75.13 113	60.46 119	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 133	77.37 113	85.48 139	85.18 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1141		69.96 97	69.44 107	70.58 83	74.78 126	80.50 88	78.85 70	60.30 121	56.95 134	56.74 98	54.68 124	56.26 126	65.93 92	77.38 127	76.72 133	86.88 93	83.57 99

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 126	80.50 88	78.85 70	60.30 121	56.97 133	56.75 97	54.67 125	56.27 125	65.92 93	77.37 128	76.72 133	86.88 93	83.58 98

v1		69.97 95	69.45 106	70.59 81	74.78 126	80.51 87	78.84 72	60.30 121	56.98 132	56.81 96	54.69 123	56.29 124	65.91 94	77.37 128	76.71 136	86.89 92	83.59 96

MSDG		71.52 75	69.87 95	73.44 63	82.21 61	79.35 99	79.52 62	64.59 71	66.15 68	61.87 69	53.21 147	56.09 129	65.85 95	78.94 101	78.50 90	86.60 110	76.85 161

DWT-MVSNet_training		67.24 139	65.96 153	68.74 106	76.15 103	74.36 162	74.37 122	56.66 163	61.82 96	60.51 73	58.23 97	49.76 183	65.07 96	70.04 186	70.39 173	79.70 173	77.11 158

diffmvs		73.13 67	75.65 67	70.19 93	74.07 140	77.17 127	78.24 92	57.45 158	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 150	75.27 152	82.47 161	84.53 85

V42		68.76 116	69.63 100	67.74 116	64.93 191	78.01 113	78.30 90	56.48 164	58.65 120	56.30 103	54.26 131	57.03 117	64.85 98	77.47 126	77.01 118	85.60 137	84.96 81

EPNet		79.08 47	80.62 42	77.28 46	88.90 27	83.17 65	83.65 47	72.41 24	74.41 51	67.15 56	76.78 28	74.37 53	64.43 99	83.70 47	83.69 43	87.15 76	88.19 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		67.64 133	71.25 83	63.43 158	61.86 197	70.73 172	67.26 165	50.86 186	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 171	68.45 187	77.61 180	77.17 156

LTVRE_ROB	59.44 16	61.82 179	62.64 180	60.87 169	72.83 154	77.19 126	64.37 182	58.97 141	33.56 217	28.00 200	52.59 160	42.21 201	63.93 101	74.52 152	76.28 140	77.15 183	82.13 113
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
MVSTER		72.06 72	74.24 70	69.51 101	70.39 170	75.97 149	76.91 102	57.36 160	64.64 79	61.39 72	68.86 52	63.76 90	63.46 102	81.44 63	79.70 77	87.56 69	85.31 74

tpmp4_e23		68.32 119	67.08 144	69.76 99	77.86 85	75.22 156	78.37 89	56.17 167	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 178	68.29 188	78.46 178	77.00 160

PMMVS		65.06 153	69.17 112	60.26 172	55.25 214	63.43 197	66.71 170	43.01 212	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 154	73.54 161	82.68 160	73.99 178

EPP-MVSNet		74.00 64	77.41 59	70.02 96	80.53 69	83.91 58	74.99 115	62.68 93	65.06 75	49.77 141	68.68 54	72.09 60	63.06 105	82.49 57	80.73 62	89.12 45	88.91 47

CHOSEN 280x420		58.70 188	61.88 186	54.98 192	55.45 213	50.55 217	64.92 179	40.36 214	55.21 157	38.13 185	48.31 176	63.76 90	63.03 106	73.73 158	68.58 185	68.00 210	73.04 182

TinyColmap		62.84 165	61.03 189	64.96 147	69.61 175	71.69 169	68.48 161	59.76 135	55.41 155	47.69 150	47.33 183	34.20 210	62.76 107	74.52 152	72.59 166	81.44 165	71.47 185

Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 131	75.15 113	77.97 119	77.12 101	55.40 168	57.87 122	46.68 155	56.17 107	60.39 98	62.36 108	76.32 142	76.25 142	85.35 141	81.34 123

USDC		67.36 137	67.90 135	66.74 137	71.72 160	75.23 155	71.58 149	60.28 124	67.45 65	50.54 136	60.93 76	45.20 197	62.08 109	76.56 140	74.50 157	84.25 152	75.38 170

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 119	77.51 97	58.95 142	62.33 91	55.22 109	53.14 148	65.90 85	62.03 110	79.08 100	77.11 117	84.08 153	77.91 151

v148		67.85 127	67.53 137	68.23 111	73.25 148	77.57 125	74.26 126	57.36 160	55.70 154	57.45 89	53.53 141	55.42 131	61.96 111	75.23 149	73.92 159	85.08 144	81.32 124

pmmvs4		67.89 126	67.39 141	68.48 110	71.60 164	73.57 164	74.45 118	60.98 111	64.65 78	57.97 87	54.95 118	51.73 172	61.88 112	73.78 157	75.11 154	83.99 155	77.91 151

CostFormer		68.92 113	69.58 101	68.15 112	75.98 107	76.17 148	78.22 93	51.86 181	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 185	68.67 183	79.42 174	79.62 141

tpm cat1		65.41 147	63.81 173	67.28 126	75.61 111	72.88 165	75.32 107	52.85 175	62.97 88	63.66 67	53.24 146	53.29 158	61.83 114	65.54 197	64.14 200	74.43 194	74.60 173

CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 99	82.06 70	79.49 63	56.30 165	67.85 64	53.29 119	71.12 46	70.37 68	61.81 115	81.59 61	80.96 59	86.09 118	84.73 84

GA-MVS		68.14 120	69.17 112	66.93 133	73.77 145	78.50 110	74.45 118	58.28 155	55.11 159	48.44 145	60.08 80	53.99 145	61.50 116	78.43 106	77.57 107	85.13 143	80.54 130

anonymousdsp		65.28 148	67.98 134	62.13 162	58.73 205	73.98 163	67.10 167	50.69 188	48.41 191	47.66 151	54.27 129	52.75 163	61.45 117	76.71 138	80.20 75	87.13 80	89.53 45

v7n		67.05 141	66.94 145	67.17 127	72.35 155	78.97 100	73.26 139	58.88 143	51.16 181	50.90 132	48.21 177	50.11 181	60.96 118	77.70 116	77.38 112	86.68 108	85.05 79

CR-MVSNet		64.83 154	65.54 158	64.01 154	70.64 169	69.41 175	65.97 174	52.74 176	57.81 124	52.65 122	54.27 129	56.31 122	60.92 119	72.20 167	73.09 163	81.12 167	75.69 167

PatchT		61.97 175	64.04 171	59.55 177	60.49 199	67.40 183	56.54 200	48.65 195	56.69 139	52.65 122	51.10 169	52.14 171	60.92 119	72.20 167	73.09 163	78.03 179	75.69 167

dps		64.00 160	62.99 176	65.18 143	73.29 147	72.07 168	68.98 159	53.07 174	57.74 126	58.41 82	55.55 110	47.74 190	60.89 121	69.53 188	67.14 192	76.44 186	71.19 186

IterMVS		66.36 143	68.30 131	64.10 151	69.48 177	74.61 160	73.41 137	50.79 187	57.30 130	48.28 146	60.64 77	59.92 101	60.85 122	74.14 155	72.66 165	81.80 163	78.82 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TDRefinement		66.09 145	65.03 165	67.31 124	69.73 174	76.75 138	75.33 106	64.55 72	60.28 107	49.72 142	45.63 186	42.83 200	60.46 123	75.75 144	75.95 145	84.08 153	78.04 150

V4		65.23 149	66.23 152	64.06 152	61.94 195	76.42 142	72.05 147	54.31 169	49.91 187	50.06 137	47.42 181	52.40 165	60.24 124	75.71 145	76.22 143	85.78 132	85.56 66

v52		65.23 149	66.24 151	64.06 152	61.94 195	76.42 142	72.06 146	54.30 170	49.94 185	50.04 138	47.41 182	52.42 164	60.23 125	75.71 145	76.22 143	85.78 132	85.56 66

PatchmatchNet		64.21 159	64.65 167	63.69 155	71.29 168	68.66 179	69.63 155	51.70 183	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 192	66.06 195	75.56 189	68.08 193
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
gm-plane-assit		57.00 191	57.62 197	56.28 188	76.10 104	62.43 204	47.62 213	46.57 201	33.84 216	23.24 207	37.52 198	40.19 205	59.61 127	79.81 89	77.55 108	84.55 151	72.03 184

IB-MVS	66.94 12	71.21 77	71.66 82	70.68 78	79.18 75	82.83 67	72.61 141	61.77 107	59.66 111	63.44 68	53.26 145	59.65 102	59.16 128	76.78 137	82.11 50	87.90 62	87.33 57
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
UniMVSNet_NR-MVSNet		70.59 81	72.19 79	68.72 107	77.72 92	80.72 82	73.81 130	69.65 38	61.99 93	43.23 169	60.54 78	57.50 108	58.57 129	79.56 94	81.07 58	89.34 40	83.97 89

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 105	79.54 96	73.81 130	69.20 43	61.20 101	43.23 169	58.52 89	53.50 149	58.57 129	79.22 98	80.45 73	87.97 60	83.97 89

COLMAP_ROB	62.73 15	67.66 131	66.76 148	68.70 108	80.49 70	77.98 117	75.29 108	62.95 83	63.62 84	49.96 139	47.32 184	50.72 178	58.57 129	76.87 135	75.50 150	84.94 148	75.33 171
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PM-MVS		60.48 183	60.94 190	59.94 173	58.85 204	66.83 186	64.27 183	51.39 184	55.03 161	48.03 147	50.00 174	40.79 204	58.26 132	69.20 190	67.13 193	78.84 177	77.60 153

SixPastTwentyTwo		61.84 177	62.45 182	61.12 167	69.20 178	72.20 167	62.03 190	57.40 159	46.54 197	38.03 186	57.14 104	41.72 202	58.12 133	69.67 187	71.58 169	81.94 162	78.30 149

CMPMVS	47.78 17	62.49 169	62.52 181	62.46 161	70.01 172	70.66 173	62.97 187	51.84 182	51.98 176	56.71 100	42.87 189	53.62 146	57.80 134	72.23 165	70.37 174	75.45 191	75.91 164
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GBi-Net		70.78 78	73.37 74	67.76 114	72.95 150	78.00 114	75.15 110	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

test1		70.78 78	73.37 74	67.76 114	72.95 150	78.00 114	75.15 110	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

FMVSNet2		70.39 83	72.67 78	67.72 117	72.95 150	78.00 114	75.15 110	62.69 92	63.29 86	51.25 131	55.64 108	68.49 80	57.59 135	80.91 74	80.35 74	86.70 105	82.02 114

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 153	77.98 117	74.96 116	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.43 138	79.43 96	79.57 81	86.59 111	81.81 121

MDTV_nov1_ep13		64.37 157	65.24 160	63.37 159	68.94 179	70.81 171	72.40 144	50.29 190	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 189	67.30 190	77.47 181	69.78 189

v748		65.12 151	65.24 160	64.98 146	69.77 173	76.45 141	69.47 156	57.06 162	49.93 186	50.70 133	47.87 180	49.50 185	57.14 140	73.64 159	75.18 153	85.75 134	84.14 88

FMVSNet1		68.84 114	70.47 89	66.94 132	71.35 167	77.68 122	74.71 117	62.35 102	56.93 136	49.94 140	50.01 172	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

pmmvs-eth3d		63.52 162	62.44 183	64.77 148	66.82 185	70.12 174	69.41 157	59.48 137	54.34 167	52.71 121	46.24 185	44.35 199	56.93 142	72.37 162	73.77 160	83.30 157	75.91 164

FC-MVSNet-train		72.60 71	75.07 69	69.71 100	81.10 65	78.79 106	73.74 132	65.23 67	66.10 69	53.34 118	70.36 48	63.40 92	56.92 143	81.44 63	80.96 59	87.93 61	84.46 86

conf200view11		68.11 121	68.72 122	67.39 123	77.83 87	78.93 101	74.28 123	62.81 84	56.64 140	46.70 154	52.65 157	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.53 131

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 101	74.28 123	62.81 84	56.64 140	46.82 152	52.65 157	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.52 132

thres400		67.95 125	68.62 126	67.17 127	77.90 82	78.59 109	74.27 125	62.72 88	56.34 148	45.77 160	53.00 150	53.35 156	56.46 146	80.21 85	78.43 91	85.91 130	80.43 133

thres200		67.98 124	68.55 127	67.30 125	77.89 84	78.86 104	74.18 127	62.75 86	56.35 147	46.48 156	52.98 151	53.54 148	56.46 146	80.41 76	77.97 99	86.05 121	79.78 140

MVS-HIRNet		54.41 196	52.10 203	57.11 186	58.99 203	56.10 209	49.68 210	49.10 192	46.18 198	52.15 126	33.18 208	46.11 195	56.10 148	63.19 203	59.70 211	76.64 185	60.25 207

Baseline_NR-MVSNet		67.53 136	68.77 120	66.09 140	75.99 105	74.75 159	72.43 143	68.41 46	61.33 100	38.33 184	51.31 167	54.13 144	56.03 149	79.22 98	78.19 96	85.37 140	82.45 112

thres100view900		67.60 135	68.02 133	67.12 129	77.83 87	77.75 121	73.90 128	62.52 98	56.64 140	46.82 152	52.65 157	53.47 152	55.92 150	78.77 103	77.62 106	85.72 135	79.23 144

test-mter		60.84 182	64.62 168	56.42 187	55.99 212	64.18 192	65.39 176	34.23 220	54.39 166	46.21 157	57.40 102	59.49 103	55.86 151	71.02 177	69.65 176	80.87 169	76.20 163

tpmrst		62.00 174	62.35 184	61.58 164	71.62 163	64.14 193	69.07 158	48.22 199	62.21 92	53.93 114	58.26 96	55.30 133	55.81 152	63.22 202	62.62 203	70.85 204	70.70 187

TranMVSNet+NR-MVSNet		69.25 110	70.81 86	67.43 121	77.23 98	79.46 98	73.48 136	69.66 37	60.43 106	39.56 181	58.82 88	53.48 151	55.74 153	79.59 92	81.21 57	88.89 48	82.70 110

thres600view7		67.68 130	68.43 128	66.80 134	77.90 82	78.86 104	73.84 129	62.75 86	56.07 150	44.70 166	52.85 154	52.81 161	55.58 154	80.41 76	77.77 102	86.05 121	80.28 134

Vis-MVSNet		72.77 70	77.20 61	67.59 120	74.19 139	84.01 57	76.61 105	61.69 108	60.62 105	50.61 135	70.25 49	71.31 63	55.57 155	83.85 45	82.28 48	86.90 90	88.08 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EG-PatchMatch MVS		67.24 139	66.94 145	67.60 119	78.73 78	81.35 73	73.28 138	59.49 136	46.89 196	51.42 130	43.65 188	53.49 150	55.50 156	81.38 65	80.66 69	87.15 76	81.17 125

view600		67.63 134	68.36 129	66.77 135	77.84 86	78.66 107	73.74 132	62.62 95	56.04 151	44.98 163	52.86 153	52.83 160	55.48 157	80.36 81	77.75 103	85.95 129	80.02 137

test-LLR		64.42 156	64.36 169	64.49 150	75.02 115	63.93 194	66.61 171	61.96 104	54.41 164	47.77 148	57.46 100	60.25 99	55.20 158	70.80 179	69.33 178	80.40 170	74.38 175

TESTMET0.1,1		61.10 181	64.36 169	57.29 184	57.53 207	63.93 194	66.61 171	36.22 218	54.41 164	47.77 148	57.46 100	60.25 99	55.20 158	70.80 179	69.33 178	80.40 170	74.38 175

UA-Net		74.47 62	77.80 54	70.59 81	85.33 46	85.40 50	73.54 134	65.98 63	60.65 104	56.00 105	72.11 42	79.15 38	54.63 160	83.13 53	82.25 49	88.04 59	81.92 120

IS_MVSNet		73.33 65	77.34 60	68.65 109	81.29 62	83.47 61	74.45 118	63.58 77	65.75 72	48.49 144	67.11 63	70.61 65	54.63 160	84.51 40	83.58 44	89.48 38	86.34 62

view800		67.35 138	68.22 132	66.35 139	77.83 87	78.62 108	72.97 140	62.58 96	55.71 153	44.13 167	52.69 156	52.24 170	54.58 162	80.27 83	78.19 96	86.01 124	79.79 139

tfpn		66.58 142	67.18 142	65.88 141	77.82 91	78.45 111	72.07 145	62.52 98	55.35 156	43.21 171	52.54 161	46.12 194	53.68 163	80.02 87	78.23 95	85.99 127	79.55 142

tpm		62.41 170	63.15 175	61.55 165	72.24 156	63.79 196	71.31 150	46.12 203	57.82 123	55.33 107	59.90 83	54.74 138	53.63 164	67.24 195	64.29 198	70.65 205	74.25 177

conf0.05thres1000		66.26 144	66.77 147	65.66 142	77.45 96	78.10 112	71.85 148	62.44 101	51.47 180	43.00 172	47.92 179	51.66 173	53.40 165	79.71 90	77.97 99	85.82 131	80.56 129

MDTV_nov1_ep13_2view		60.16 184	60.51 191	59.75 174	65.39 188	69.05 178	68.00 162	48.29 197	51.99 175	45.95 159	48.01 178	49.64 184	53.39 166	68.83 191	66.52 194	77.47 181	69.55 190

UniMVSNet (Re)		69.53 104	71.90 80	66.76 136	76.42 102	80.93 78	72.59 142	68.03 50	61.75 97	41.68 177	58.34 95	57.23 116	53.27 167	79.53 95	80.62 71	88.57 52	84.90 82

RPMNet		61.71 180	62.88 177	60.34 171	69.51 176	69.41 175	63.48 185	49.23 191	57.81 124	45.64 161	50.51 170	50.12 180	53.13 168	68.17 194	68.49 186	81.07 168	75.62 169

tfpnnormal		64.27 158	63.64 174	65.02 145	75.84 108	75.61 151	71.24 151	62.52 98	47.79 193	42.97 173	42.65 190	44.49 198	52.66 169	78.77 103	76.86 120	84.88 149	79.29 143

MDA-MVSNet-bldmvs		53.37 200	53.01 202	53.79 197	43.67 222	67.95 182	59.69 196	57.92 156	43.69 200	32.41 194	41.47 192	27.89 220	52.38 170	56.97 216	65.99 196	76.68 184	67.13 194

NR-MVSNet		68.79 115	70.56 87	66.71 138	77.48 95	79.54 96	73.52 135	69.20 43	61.20 101	39.76 180	58.52 89	50.11 181	51.37 171	80.26 84	80.71 67	88.97 46	83.59 96

EPMVS		60.00 185	61.97 185	57.71 183	68.46 180	63.17 200	64.54 181	48.23 198	63.30 85	44.72 165	60.19 79	56.05 130	50.85 172	65.27 199	62.02 205	69.44 207	63.81 200

CVMVSNet		62.55 167	65.89 154	58.64 180	66.95 183	69.15 177	66.49 173	56.29 166	52.46 174	32.70 193	59.27 86	58.21 107	50.09 173	71.77 170	71.39 170	79.31 175	78.99 146

pmmvs5		62.37 173	64.04 171	60.42 170	65.03 189	71.67 170	67.17 166	52.70 178	50.30 182	44.80 164	54.23 133	51.19 176	49.37 174	72.88 161	73.48 162	83.45 156	74.55 174

UGNet		72.78 69	77.67 55	67.07 130	71.65 162	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 175	81.02 72	80.70 68	86.22 115	88.67 49
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
pm-mvs1		65.62 146	67.42 139	63.53 157	73.66 146	76.39 144	69.66 154	60.87 113	49.73 188	43.97 168	51.24 168	57.00 118	48.16 176	79.89 88	77.84 101	84.85 150	79.82 138

gg-mvs-nofinetune		62.55 167	65.05 164	59.62 176	78.72 79	77.61 123	70.83 152	53.63 171	39.71 208	22.04 213	36.36 201	64.32 89	47.53 177	81.16 70	79.03 87	85.00 146	77.17 156

pmmvs6		62.41 170	62.88 177	61.87 163	71.38 166	75.18 158	67.76 164	59.45 138	41.64 204	42.52 176	37.33 199	52.91 159	46.87 178	77.67 118	76.26 141	83.23 158	79.18 145

tfpn_ndepth		65.09 152	67.12 143	62.73 160	75.75 110	76.23 146	68.00 162	60.36 120	58.16 121	40.27 179	54.89 119	54.22 141	46.80 179	76.69 139	75.66 147	85.19 142	73.98 179

LP		53.62 199	53.43 199	53.83 196	58.51 206	62.59 203	57.31 199	46.04 204	47.86 192	42.69 175	36.08 203	36.86 208	46.53 180	64.38 200	64.25 199	71.92 201	62.00 205

ADS-MVSNet		55.94 194	58.01 194	53.54 198	62.48 194	58.48 206	59.12 198	46.20 202	59.65 112	42.88 174	52.34 163	53.31 157	46.31 181	62.00 206	60.02 210	64.23 216	60.24 208

pmmvs3		47.65 204	49.08 208	45.99 206	44.61 219	54.79 212	50.04 208	31.95 223	33.91 215	29.90 195	30.37 209	33.53 211	46.31 181	63.50 201	63.67 201	73.14 199	63.77 201

TransMVSNet (Re)		64.74 155	65.66 157	63.66 156	77.40 97	75.33 154	69.86 153	62.67 94	47.63 194	41.21 178	50.01 172	52.33 166	45.31 183	79.57 93	77.69 105	85.49 138	77.07 159

CDS-MVSNet		67.65 132	69.83 98	65.09 144	75.39 112	76.55 140	74.42 121	63.75 75	53.55 169	49.37 143	59.41 85	62.45 94	44.44 184	79.71 90	79.82 76	83.17 159	77.36 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		59.58 186	62.81 179	55.81 189	66.03 187	65.64 191	63.86 184	48.74 194	49.95 184	37.07 188	54.77 122	58.54 105	44.44 184	72.29 164	71.79 167	74.70 193	66.66 195

EPNet_dtu		68.08 123	71.00 84	64.67 149	79.64 72	68.62 180	75.05 114	63.30 78	66.36 67	45.27 162	67.40 61	66.84 83	43.64 186	75.37 148	74.98 156	81.15 166	77.44 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn1000		63.81 161	66.31 150	60.90 168	75.76 109	75.74 150	65.14 178	60.14 130	56.47 144	35.99 189	55.11 112	52.30 168	43.42 187	76.21 143	75.34 151	84.97 147	73.01 183

FMVSNet5		57.24 190	60.02 192	53.99 195	56.45 209	62.74 201	65.27 177	47.03 200	55.14 158	39.55 182	40.88 194	53.42 155	41.83 188	72.35 163	71.10 172	73.79 196	64.50 199

ambc			53.42 200		64.99 190	63.36 198	49.96 209		47.07 195	37.12 187	28.97 211	16.36 226	41.82 189	75.10 151	67.34 189	71.55 203	75.72 166

FPMVS		51.87 201	50.00 206	54.07 194	66.83 184	57.25 207	60.25 195	50.91 185	50.25 183	34.36 191	36.04 204	32.02 212	41.49 190	58.98 214	56.07 214	70.56 206	59.36 209

CP-MVSNet		62.68 166	65.49 159	59.40 178	71.84 158	75.34 153	62.87 188	67.04 56	52.64 172	27.19 201	53.38 143	48.15 188	41.40 191	71.26 172	75.68 146	86.07 119	82.00 117

PS-CasMVS		62.38 172	65.06 163	59.25 179	71.73 159	75.21 157	62.77 189	66.99 57	51.94 178	26.96 202	52.00 164	47.52 191	41.06 192	71.16 175	75.60 148	85.97 128	81.97 119

PEN-MVS		62.96 164	65.77 156	59.70 175	73.98 143	75.45 152	63.39 186	67.61 53	52.49 173	25.49 203	53.39 142	49.12 186	40.85 193	71.94 169	77.26 115	86.86 95	80.72 128

MIMVSNet		58.52 189	61.34 188	55.22 191	60.76 198	67.01 185	66.81 168	49.02 193	56.43 145	38.90 183	40.59 196	54.54 140	40.57 194	73.16 160	71.65 168	75.30 192	66.00 196

Vis-MVSNet (Re-imp)		67.83 128	73.52 72	61.19 166	78.37 80	76.72 139	66.80 169	62.96 82	65.50 73	34.17 192	67.19 62	69.68 70	39.20 195	79.39 97	79.44 85	85.68 136	76.73 162

DTE-MVSNet		61.85 176	64.96 166	58.22 181	74.32 138	74.39 161	61.01 192	67.85 52	51.76 179	21.91 214	53.28 144	48.17 187	37.74 196	72.22 166	76.44 139	86.52 113	78.49 148

EU-MVSNet		54.63 195	58.69 193	49.90 202	56.99 208	62.70 202	56.41 201	50.64 189	45.95 199	23.14 208	50.42 171	46.51 193	36.63 197	65.51 198	64.85 197	75.57 188	74.91 172

Anonymous20231211		51.46 202	50.59 204	52.46 199	67.30 181	66.70 187	55.00 202	59.22 139	29.96 219	17.62 219	19.11 221	28.74 219	35.72 198	66.42 196	69.52 177	79.92 172	73.71 180

Anonymous20231206		56.36 193	57.80 196	54.67 193	70.08 171	66.39 188	60.46 194	57.54 157	49.50 190	29.30 197	33.86 207	46.64 192	35.18 199	70.44 183	68.88 182	75.47 190	68.88 192

WR-MVS		63.03 163	67.40 140	57.92 182	75.14 114	77.60 124	60.56 193	66.10 60	54.11 168	23.88 204	53.94 139	53.58 147	34.50 200	73.93 156	77.71 104	87.35 72	80.94 126

WR-MVS_H		61.83 178	65.87 155	57.12 185	71.72 160	76.87 137	61.45 191	66.19 58	51.97 177	22.92 211	53.13 149	52.30 168	33.80 201	71.03 176	75.00 155	86.65 109	80.78 127

PMVS	39.38 18	46.06 210	43.30 215	49.28 203	62.93 192	38.75 223	41.88 216	53.50 172	33.33 218	35.46 190	28.90 212	31.01 215	33.04 202	58.61 215	54.63 217	68.86 208	57.88 212
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test0.0.03 1		58.80 187	61.58 187	55.56 190	75.02 115	68.45 181	59.58 197	61.96 104	52.74 171	29.57 196	49.75 175	54.56 139	31.46 203	71.19 173	69.77 175	75.75 187	64.57 198

testpf		47.41 205	48.47 211	46.18 205	66.30 186	50.67 216	48.15 212	42.60 213	37.10 212	28.75 198	40.97 193	39.01 207	30.82 204	52.95 219	53.74 218	60.46 217	64.87 197

N_pmnet		47.35 206	50.13 205	44.11 209	59.98 200	51.64 215	51.86 206	44.80 208	49.58 189	20.76 215	40.65 195	40.05 206	29.64 205	59.84 212	55.15 215	57.63 218	54.00 216

test2356		47.20 207	48.62 210	45.54 207	56.38 210	54.89 211	50.62 207	45.08 207	38.65 209	23.40 206	36.23 202	31.10 214	29.31 206	62.76 204	62.49 204	68.48 209	54.23 215

1111		43.08 212	44.02 214	41.98 211	59.22 201	49.27 219	41.48 217	45.63 205	35.01 213	23.06 209	28.60 213	30.15 216	27.22 207	60.42 210	57.97 212	55.27 221	46.74 218

.test1245		30.81 219	29.14 221	32.77 218	59.22 201	49.27 219	41.48 217	45.63 205	35.01 213	23.06 209	28.60 213	30.15 216	27.22 207	60.42 210	0.10 225	0.01 229	0.43 227

DeepMVS_CX						18.74 229	18.55 226	8.02 225	26.96 222	7.33 226	23.81 220	13.05 228	25.99 209	25.17 224		22.45 228	36.25 223

MIMVSNet1		49.27 203	53.25 201	44.62 208	44.61 219	61.52 205	53.61 204	52.18 179	41.62 205	18.68 216	28.14 215	41.58 203	25.50 210	68.46 193	69.04 180	73.15 198	62.37 204

Gipuma		36.38 216	35.80 219	37.07 215	45.76 218	33.90 224	29.81 223	48.47 196	39.91 207	18.02 218	8.00 227	8.14 229	25.14 211	59.29 213	61.02 208	55.19 222	40.31 220
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testgi		54.39 197	57.86 195	50.35 201	71.59 165	67.24 184	54.95 203	53.25 173	43.36 201	23.78 205	44.64 187	47.87 189	24.96 212	70.45 182	68.66 184	73.60 197	62.78 203

new_pmnet		38.40 215	42.64 216	33.44 217	37.54 225	45.00 221	36.60 221	32.72 222	40.27 206	12.72 223	29.89 210	28.90 218	24.78 213	53.17 218	52.90 219	56.31 219	48.34 217

testus		45.61 211	49.06 209	41.59 212	56.13 211	55.28 210	43.51 215	39.64 216	37.74 210	18.23 217	35.52 206	31.28 213	24.69 214	62.46 205	62.90 202	67.33 211	58.26 211

FC-MVSNet-test		56.90 192	65.20 162	47.21 204	66.98 182	63.20 199	49.11 211	58.60 153	59.38 114	11.50 224	65.60 65	56.68 119	24.66 215	71.17 174	71.36 171	72.38 200	69.02 191

test20.03		53.93 198	56.28 198	51.19 200	72.19 157	65.83 189	53.20 205	61.08 110	42.74 202	22.08 212	37.07 200	45.76 196	24.29 216	70.44 183	69.04 180	74.31 195	63.05 202

testmv		42.58 213	44.36 212	40.49 213	54.63 215	52.76 213	41.21 219	44.37 209	28.83 220	12.87 221	27.16 216	25.03 221	23.01 217	60.83 208	61.13 206	66.88 212	54.81 213

test1235678		42.57 214	44.36 212	40.49 213	54.63 215	52.75 214	41.21 219	44.37 209	28.82 221	12.87 221	27.15 217	25.01 222	23.01 217	60.83 208	61.13 206	66.88 212	54.81 213

EMVS		20.98 222	17.15 224	25.44 221	39.51 224	19.37 228	12.66 227	39.59 217	19.10 226	6.62 229	9.27 225	4.40 231	22.43 219	17.99 226	24.40 224	31.81 225	25.53 225

E-PMN		21.77 221	18.24 223	25.89 220	40.22 223	19.58 227	12.46 228	39.87 215	18.68 227	6.71 228	9.57 224	4.31 232	22.36 220	19.89 225	27.28 223	33.73 224	28.34 224

new-patchmatchnet		46.97 208	49.47 207	44.05 210	62.82 193	56.55 208	45.35 214	52.01 180	42.47 203	17.04 220	35.73 205	35.21 209	21.84 221	61.27 207	54.83 216	65.26 215	60.26 206

no-one		36.35 217	37.59 218	34.91 216	46.13 217	49.89 218	27.99 224	43.56 211	20.91 225	7.03 227	14.64 223	15.50 227	18.92 222	42.95 220	60.20 209	65.84 214	59.03 210

MVE	19.12 19	20.47 223	23.27 222	17.20 224	12.66 228	25.41 226	10.52 229	34.14 221	14.79 228	6.53 230	8.79 226	4.68 230	16.64 223	29.49 223	41.63 220	22.73 227	38.11 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test12356		35.10 218	38.50 217	31.13 219	44.14 221	43.70 222	32.27 222	34.42 219	26.51 223	9.47 225	25.22 219	20.34 223	10.86 224	53.47 217	56.15 213	55.59 220	44.11 219

PMMVS2		25.60 220	29.75 220	20.76 223	28.00 226	30.93 225	23.10 225	29.18 224	23.14 224	1.46 231	18.23 222	16.54 225	5.08 225	40.22 221	41.40 221	37.76 223	37.79 222

tmp_tt				14.50 225	14.68 227	7.17 230	10.46 230	2.21 226	37.73 211	28.71 199	25.26 218	16.98 224	4.37 226	31.49 222	29.77 222	26.56 226

GG-mvs-BLEND		46.86 209	67.51 138	22.75 222	0.05 229	76.21 147	64.69 180	0.04 227	61.90 94	0.09 232	55.57 109	71.32 62	0.08 227	70.54 181	67.19 191	71.58 202	69.86 188

test123		0.09 224	0.14 226	0.02 226	0.00 231	0.02 231	0.02 233	0.01 228	0.09 230	0.00 234	0.30 228	0.00 234	0.08 227	0.03 228	0.09 227	0.01 229	0.45 226

testmvs		0.09 224	0.15 225	0.02 226	0.01 230	0.02 231	0.05 232	0.01 228	0.11 229	0.01 233	0.26 229	0.01 233	0.06 229	0.10 227	0.10 225	0.01 229	0.43 227

test11111		0.00 226	0.00 227	0.00 228	0.00 231	0.00 233	0.00 234	0.00 230	0.00 231	0.00 234	0.00 230	0.00 234	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet-low-res		0.00 226	0.00 227	0.00 228	0.00 231	0.00 233	0.00 234	0.00 230	0.00 231	0.00 234	0.00 230	0.00 234	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet		0.00 226	0.00 227	0.00 228	0.00 231	0.00 233	0.00 234	0.00 230	0.00 231	0.00 234	0.00 230	0.00 234	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 231

XVS					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

X-MVStestdata					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

mPP-MVS					89.90 19							81.29 34

NP-MVS									80.10 39

Patchmtry						65.80 190	65.97 174	52.74 176		52.65 122

High-resolution multi-view benchmark