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.

ESAPD		95.11 1	95.65 1	94.48 1	97.96 3	98.62 1	96.45 1	92.82 2	96.24 3	90.25 5	96.16 2	93.09 1	93.32 3	93.93 13	92.02 19	96.07 19	99.50 3

HSP-MVS		94.69 2	95.39 2	93.88 4	96.78 14	98.11 5	94.75 7	90.91 9	96.89 2	89.12 10	96.98 1	89.47 9	94.76 1	95.24 2	93.29 10	96.98 7	97.73 30

CNVR-MVS		94.53 3	94.85 4	94.15 3	98.03 2	98.59 2	95.56 3	92.91 1	94.86 8	88.46 11	91.32 17	90.83 5	94.03 2	95.20 3	94.16 4	95.89 24	99.01 12

APDe-MVS		94.31 4	94.30 7	94.33 2	97.57 7	98.06 7	95.79 2	91.98 5	95.50 6	92.19 1	95.25 3	87.97 14	92.93 4	93.01 20	91.02 35	95.52 28	99.29 5

MCST-MVS		94.10 5	94.77 5	93.31 6	98.31 1	98.34 3	95.43 4	92.54 3	94.41 13	83.05 27	91.38 15	90.97 4	92.24 9	95.05 5	94.02 5	98.31 1	99.20 7

HPM-MVS++		94.04 6	94.96 3	92.96 8	97.93 4	97.71 13	94.65 9	91.01 8	95.91 4	87.43 13	93.52 8	92.63 2	92.29 8	94.22 12	92.34 16	94.47 47	98.37 22

NCCC		93.59 7	94.00 9	93.10 7	97.90 5	97.93 9	95.40 5	92.39 4	94.47 12	84.94 18	91.21 18	89.32 10	92.53 6	93.90 14	92.98 12	95.44 30	98.22 24

APD-MVS		93.47 8	93.44 13	93.50 5	97.06 10	97.09 22	95.27 6	91.47 6	95.71 5	89.57 7	93.66 6	86.28 19	92.81 5	92.06 27	90.70 37	94.83 43	98.60 18
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 9	94.33 6	92.22 10	94.68 38	97.89 11	94.56 10	90.89 10	94.80 9	90.04 6	93.53 7	90.14 7	89.78 17	92.74 22	92.17 17	93.35 98	99.07 10

SMA-MVS		93.14 10	93.96 10	92.17 11	97.64 6	97.82 12	94.28 14	90.32 11	94.72 10	85.70 17	87.64 25	90.68 6	91.15 13	94.28 11	93.86 7	93.97 56	98.72 16

TSAR-MVS + MP.		93.07 11	93.53 12	92.53 9	94.23 41	97.54 17	94.75 7	89.87 13	95.26 7	89.20 9	93.16 9	88.19 13	92.15 10	91.79 31	89.65 49	94.99 39	99.16 8

SteuartSystems-ACMMP		92.31 12	93.31 14	91.15 18	96.88 12	97.36 18	93.95 16	89.44 15	92.62 22	83.20 24	94.34 5	85.55 21	88.95 24	93.07 19	91.90 23	94.51 45	98.30 23
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_Plus		92.16 13	92.91 17	91.28 17	96.95 11	97.36 18	93.66 17	89.23 17	93.33 17	83.71 22	90.53 19	86.84 16	90.39 14	93.30 18	91.56 28	93.74 65	97.43 37

HFP-MVS		92.02 14	92.13 19	91.89 15	97.16 9	96.46 35	93.57 18	87.60 21	93.79 15	88.17 12	93.15 10	83.94 32	91.19 12	90.81 40	89.83 44	93.66 71	96.94 54

train_agg		91.99 15	93.71 11	89.98 23	96.42 23	97.03 24	94.31 13	89.05 18	93.33 17	77.75 41	95.06 4	88.27 12	88.38 30	92.02 28	91.41 30	94.00 54	98.84 15

DeepC-MVS_fast	86.59 2	91.69 16	91.39 22	92.05 14	97.43 8	96.92 27	94.05 15	90.23 12	93.31 20	83.19 25	77.91 39	84.23 28	92.42 7	94.62 8	94.83 2	95.00 38	97.88 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		91.59 17	91.12 23	92.13 12	96.76 15	96.68 30	93.39 19	88.00 20	93.63 16	90.76 4	83.97 32	85.33 23	89.89 16	91.60 33	89.65 49	94.00 54	96.97 52

TSAR-MVS + GP.		91.29 18	93.11 16	89.18 29	87.81 86	96.21 41	92.51 28	83.83 40	94.24 14	83.77 21	91.87 14	89.62 8	90.07 15	90.40 43	90.31 39	97.09 6	99.10 9

ACMMPR		91.15 19	91.44 21	90.81 19	96.61 17	96.25 39	93.09 20	87.08 23	93.32 19	84.78 19	92.08 13	82.10 38	89.71 18	90.24 44	89.82 45	93.61 76	96.30 68

DeepPCF-MVS	86.71 1	91.00 20	94.05 8	87.43 40	95.58 32	98.17 4	86.22 68	88.59 19	97.01 1	76.77 46	85.11 30	88.90 11	87.29 36	95.02 6	94.69 3	90.15 187	99.48 4

TSAR-MVS + ACMM		90.98 21	93.18 15	88.42 34	95.69 30	96.73 29	94.52 11	86.97 26	92.99 21	76.32 47	92.31 12	86.64 17	84.40 58	92.97 21	92.02 19	92.62 139	98.59 19

MP-MVS		90.81 22	91.45 20	90.06 22	96.59 18	96.33 38	92.46 29	87.19 22	90.27 34	82.54 31	91.38 15	84.88 25	88.27 31	90.58 42	89.30 54	93.30 100	97.44 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		90.57 23	90.68 25	90.44 20	96.13 25	95.90 46	92.77 25	86.86 27	92.12 25	84.19 20	89.18 23	82.37 36	89.43 22	89.65 52	88.43 58	93.27 102	97.13 46

MSLP-MVS++		90.33 24	88.82 34	92.10 13	96.52 21	95.93 42	94.35 12	86.26 28	88.37 47	89.24 8	75.94 44	82.60 35	89.71 18	89.45 54	92.17 17	96.51 14	97.24 42

CANet		89.98 25	90.42 29	89.47 28	94.13 42	98.05 8	91.76 34	83.27 43	90.87 31	81.90 34	72.32 51	84.82 26	88.42 28	94.52 9	93.78 8	97.34 4	98.58 20

PGM-MVS		89.97 26	90.64 27	89.18 29	96.53 20	95.90 46	93.06 21	82.48 52	90.04 36	80.37 36	92.75 11	80.96 43	88.93 25	89.88 49	89.08 55	93.69 70	95.86 75

PHI-MVS		89.88 27	92.75 18	86.52 50	94.97 35	97.57 16	89.99 45	84.56 36	92.52 23	69.72 77	90.35 20	87.11 15	84.89 51	91.82 30	92.37 15	95.02 37	97.51 33

CSCG		89.81 28	89.69 30	89.96 24	96.55 19	97.90 10	92.89 23	87.06 24	88.74 45	86.17 14	78.24 38	86.53 18	84.75 54	87.82 76	90.59 38	92.32 145	98.01 26

X-MVS		89.73 29	90.65 26	88.66 32	96.44 22	95.93 42	92.26 31	86.98 25	90.73 32	76.32 47	89.56 22	82.05 39	86.51 42	89.98 47	89.60 51	93.43 93	96.72 62

EPNet		89.30 30	90.89 24	87.44 39	95.67 31	96.81 28	91.13 37	83.12 45	91.14 28	76.31 51	87.60 26	80.40 46	84.45 56	92.13 26	91.12 34	93.96 58	97.01 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 31	88.03 38	89.71 26	94.83 36	96.56 31	92.57 27	89.38 16	89.25 42	79.59 38	70.02 60	77.05 56	88.24 32	92.44 24	92.79 13	93.65 74	98.10 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		88.76 32	90.43 28	86.81 46	96.04 27	96.53 34	92.95 22	85.95 30	90.36 33	67.93 82	85.80 29	80.69 44	83.82 59	90.81 40	91.85 26	94.18 50	96.99 51

3Dnovator+	81.14 5	88.59 33	87.49 40	89.88 25	95.83 29	96.45 37	91.94 33	82.41 53	87.09 51	85.94 16	62.80 88	85.37 22	89.46 20	91.51 34	91.89 25	93.72 67	97.30 40

ACMMP		88.48 34	88.71 35	88.22 36	94.61 39	95.53 50	90.64 41	85.60 32	90.97 29	78.62 40	89.88 21	74.20 67	86.29 43	88.16 74	86.37 78	93.57 78	95.86 75
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
AdaColmap		88.46 35	85.75 53	91.62 16	96.25 24	95.35 53	90.71 39	91.08 7	90.22 35	86.17 14	74.33 48	73.67 70	92.00 11	86.31 95	85.82 86	93.52 81	94.53 92

MVS_0304		88.43 36	89.46 31	87.21 41	91.85 54	97.60 14	92.62 26	81.10 59	87.16 50	73.80 57	72.19 53	83.36 34	87.03 37	94.64 7	93.67 9	96.88 8	97.64 32

3Dnovator	80.58 8	88.20 37	86.53 46	90.15 21	96.86 13	96.46 35	91.97 32	83.06 46	85.16 57	83.66 23	62.28 91	82.15 37	88.98 23	90.99 38	92.65 14	96.38 18	96.03 72

CPTT-MVS		88.17 38	87.84 39	88.55 33	93.33 44	93.75 64	92.33 30	84.75 35	89.87 38	81.72 35	83.93 33	81.12 42	88.45 27	85.42 105	84.07 105	90.72 179	96.72 62

MVS_111021_HR		87.82 39	88.84 33	86.62 48	94.42 40	97.36 18	88.21 55	83.26 44	83.42 60	72.52 65	82.63 34	76.93 57	84.95 50	91.93 29	91.15 33	96.39 17	98.49 21

DELS-MVS		87.75 40	86.92 44	88.71 31	94.69 37	97.34 21	92.78 24	84.50 37	77.87 77	81.94 33	67.17 65	75.49 62	82.84 64	95.38 1	95.93 1	95.55 27	99.27 6
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
MVSTER		87.68 41	89.12 32	86.01 52	88.11 83	90.05 107	89.28 48	77.05 81	91.37 26	79.97 37	76.70 42	85.25 24	84.89 51	93.53 15	91.41 30	96.73 10	95.55 80

MVS_111021_LR		87.58 42	88.67 36	86.31 51	92.58 48	95.89 48	86.20 69	82.49 51	89.08 44	77.47 43	86.20 28	74.22 66	85.49 47	90.03 46	88.52 56	93.66 71	96.74 61

QAPM		87.06 43	86.46 47	87.75 37	96.63 16	97.09 22	91.71 35	82.62 50	80.58 69	71.28 71	66.04 70	84.24 27	87.01 38	89.93 48	89.91 43	97.26 5	97.44 35

PVSNet_BlendedMVS		86.98 44	87.05 42	86.90 43	93.03 45	96.98 25	86.57 64	81.82 55	89.78 39	82.78 29	71.54 54	66.07 94	80.73 79	93.46 16	91.97 21	96.45 15	99.53 1

PVSNet_Blended		86.98 44	87.05 42	86.90 43	93.03 45	96.98 25	86.57 64	81.82 55	89.78 39	82.78 29	71.54 54	66.07 94	80.73 79	93.46 16	91.97 21	96.45 15	99.53 1

OMC-MVS		86.38 46	86.21 50	86.57 49	92.30 50	94.35 59	87.60 58	83.51 42	92.32 24	77.37 44	72.27 52	77.83 51	86.59 41	87.62 79	85.95 83	92.08 149	93.11 121

HQP-MVS		86.17 47	87.35 41	84.80 58	91.41 57	92.37 86	91.05 38	84.35 39	88.52 46	64.21 86	87.05 27	68.91 87	84.80 53	89.12 57	88.16 62	92.96 123	97.31 39

canonicalmvs		85.93 48	86.26 49	85.54 54	88.94 68	95.44 51	89.56 46	76.01 88	87.83 48	77.70 42	76.43 43	68.66 89	87.80 34	87.02 84	91.51 29	93.25 106	96.95 53

MAR-MVS		85.65 49	86.30 48	84.88 57	95.51 34	95.89 48	86.50 66	76.71 82	89.23 43	68.59 79	70.93 58	74.49 64	88.55 26	89.40 55	90.30 40	93.42 94	93.88 111
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
PCF-MVS	82.38 4	85.52 50	84.41 58	86.81 46	91.51 56	96.23 40	90.27 42	89.81 14	77.87 77	70.67 72	69.20 62	77.86 50	85.55 46	85.92 100	86.38 77	93.03 120	97.43 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 51	84.24 59	86.83 45	87.69 89	93.16 73	90.01 44	82.72 48	87.17 49	79.28 39	71.43 57	65.81 96	86.02 44	87.33 82	86.96 71	95.25 34	97.83 29
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OpenMVS	77.91 11	85.09 52	83.42 62	87.03 42	96.12 26	96.55 33	89.36 47	81.59 57	79.19 72	75.20 53	55.84 119	79.04 49	84.45 56	88.47 66	89.35 53	95.48 29	95.48 81

TSAR-MVS + COLMAP		84.93 53	85.79 52	83.92 61	90.90 59	93.57 68	89.25 49	82.00 54	91.29 27	61.66 91	88.25 24	59.46 115	86.71 40	89.79 50	87.09 68	93.01 121	91.09 137

TAPA-MVS	80.99 7	84.83 54	84.42 57	85.31 55	91.89 53	93.73 65	88.53 54	82.80 47	89.99 37	69.78 76	71.53 56	75.03 63	85.47 48	86.26 96	84.54 100	93.39 96	89.90 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	81.02 6	84.81 55	81.81 76	88.31 35	93.77 43	90.35 102	88.80 52	84.47 38	86.76 52	82.17 32	66.56 67	71.01 80	88.41 29	85.48 103	84.28 103	92.26 147	88.21 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		84.72 56	82.14 72	87.73 38	92.85 47	93.83 63	84.70 86	85.07 33	90.90 30	83.16 26	56.28 115	71.53 76	88.14 33	84.19 113	84.00 108	92.48 142	94.26 97

MVS_Test		84.60 57	85.13 55	83.99 60	88.17 81	95.27 54	88.21 55	73.15 108	84.31 59	70.55 74	68.67 63	68.78 88	86.99 39	91.71 32	91.90 23	96.84 9	95.27 85

diffmvs		83.81 58	84.78 56	82.69 65	86.06 110	94.03 60	86.46 67	72.43 115	85.71 55	75.29 52	65.48 75	79.49 48	81.39 69	85.55 102	86.98 69	94.48 46	96.20 70

CANet_DTU		83.33 59	86.59 45	79.53 88	88.88 69	94.87 57	86.63 63	68.85 145	85.45 56	50.54 157	77.86 40	69.94 84	85.62 45	92.63 23	90.88 36	96.63 11	94.46 93

DI_MVS_plusplus_trai		83.32 60	82.53 70	84.25 59	86.26 106	93.66 66	90.23 43	77.16 80	77.05 84	74.06 55	53.74 128	74.33 65	83.61 61	91.40 36	89.82 45	94.17 51	97.73 30

DWT-MVSNet_training		82.66 61	83.34 65	81.87 69	88.71 70	92.63 78	82.07 101	72.21 117	86.37 53	72.64 60	64.51 79	71.44 78	80.35 82	84.43 111	87.73 64	95.27 31	96.25 69

PVSNet_Blended_VisFu		82.55 62	83.70 61	81.21 75	89.66 63	95.15 56	82.41 99	77.36 78	72.53 105	73.64 58	61.15 96	77.19 55	70.35 149	91.31 37	89.72 48	93.84 61	98.85 14

PMMVS		82.26 63	85.48 54	78.51 97	85.92 111	91.92 90	78.30 144	70.77 131	86.30 54	61.11 96	82.46 35	70.88 81	84.70 55	88.05 75	84.78 98	90.24 186	93.98 103

ACMP	79.58 9	82.23 64	81.82 75	82.71 64	88.15 82	90.95 99	85.23 78	78.52 64	81.70 66	72.52 65	78.41 37	60.63 110	80.48 81	82.88 123	83.44 113	91.37 167	94.70 89
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CHOSEN 280x420		82.15 65	85.87 51	77.80 100	86.54 100	93.42 70	81.74 102	59.96 199	78.99 74	63.99 87	74.50 47	83.95 31	80.99 75	89.53 53	85.01 91	93.56 80	95.71 79

LGP-MVS_train		82.12 66	82.57 69	81.59 70	89.26 67	90.23 104	88.76 53	78.05 68	81.26 67	61.64 92	79.52 36	62.11 105	79.59 86	85.20 106	84.68 99	92.27 146	95.02 87

FMVSNet3		81.93 67	81.98 73	81.88 68	79.49 141	87.02 130	88.15 57	72.57 111	83.02 62	72.63 62	56.55 111	73.48 71	82.34 67	91.49 35	91.20 32	96.07 19	91.13 136

Anonymous20240521		81.52 68	78.08 98	85.55 53	88.50 74	93.58 67	88.93 51	82.63 49	72.75 103	71.71 69	51.37 136	55.20 129	87.78 35	87.46 80	90.17 41	93.72 67	95.99 73

OPM-MVS		81.34 69	78.18 96	85.02 56	91.27 58	91.78 92	90.66 40	83.62 41	62.39 142	65.91 83	63.35 85	64.33 101	85.03 49	87.77 77	85.88 85	93.66 71	91.75 134

IS_MVSNet		80.92 70	84.14 60	77.16 105	87.43 90	93.90 62	80.44 107	74.64 100	75.05 90	61.10 97	65.59 72	76.89 58	67.39 160	90.88 39	90.05 42	91.95 153	96.62 65

ACMM	78.09 10	80.91 71	78.39 94	83.86 62	89.61 66	87.71 124	85.16 79	80.67 60	79.04 73	74.18 54	63.82 83	60.84 109	82.59 65	84.33 112	83.59 111	90.96 174	89.39 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 72	82.79 67	78.52 95	86.31 105	92.37 86	79.83 115	74.51 101	73.79 99	64.46 85	67.01 66	80.63 45	74.33 110	85.63 101	84.35 102	91.68 159	95.79 78

conf0.002		80.80 73	80.30 82	81.38 73	88.59 71	93.19 72	85.12 80	78.10 66	70.15 111	61.55 93	63.30 86	62.66 104	81.11 70	88.74 63	86.94 72	93.79 63	97.15 44

CostFormer		80.72 74	81.81 76	79.44 90	86.50 101	91.65 94	84.31 88	59.84 200	80.86 68	72.69 59	62.46 90	73.74 68	79.93 84	82.58 126	84.50 101	93.37 97	96.90 57

GBi-Net		80.72 74	80.49 80	81.00 80	78.18 145	86.19 148	86.73 60	72.57 111	83.02 62	72.63 62	56.55 111	73.48 71	80.99 75	86.57 89	86.83 73	94.89 40	90.77 138

test1		80.72 74	80.49 80	81.00 80	78.18 145	86.19 148	86.73 60	72.57 111	83.02 62	72.63 62	56.55 111	73.48 71	80.99 75	86.57 89	86.83 73	94.89 40	90.77 138

UGNet		80.71 77	83.09 66	77.93 99	87.02 94	92.71 76	80.28 111	76.53 84	73.83 98	71.35 70	70.07 59	73.71 69	58.93 187	87.39 81	86.97 70	93.48 90	96.94 54
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
tfpn111		80.42 78	79.77 87	81.18 76	88.42 75	92.55 82	85.12 80	77.94 70	70.15 111	61.00 100	74.56 45	51.22 137	81.11 70	88.23 68	84.80 94	93.50 86	96.90 57

CHOSEN 1792x2688		80.23 79	79.16 88	81.48 71	91.97 51	96.56 31	86.18 70	75.40 96	76.17 87	61.32 95	37.43 207	61.08 108	76.52 100	92.35 25	91.64 27	97.46 3	98.86 13

conf0.01		80.10 80	79.04 90	81.34 74	88.56 72	93.09 74	85.12 80	78.08 67	70.15 111	61.43 94	60.90 97	58.54 118	81.11 70	88.66 64	84.80 94	93.74 65	97.14 45

thres100view900		79.83 81	77.79 101	82.21 66	88.42 75	93.54 69	87.07 59	81.11 58	70.15 111	61.01 98	56.65 108	51.22 137	81.78 68	89.77 51	85.95 83	93.84 61	97.26 41

Effi-MVS+		79.80 82	80.04 83	79.52 89	85.53 112	93.31 71	85.28 76	70.68 133	74.15 94	58.79 110	62.03 93	60.51 111	83.37 62	88.41 67	86.09 82	93.49 89	95.80 77

FC-MVSNet-train		79.54 83	78.20 95	81.09 79	86.55 99	88.63 120	79.96 113	78.53 63	70.90 109	68.24 80	65.87 71	56.45 127	80.29 83	86.20 98	84.08 104	92.97 122	95.31 84

test-LLR		79.52 84	83.42 62	74.97 115	81.79 126	91.26 95	76.17 168	70.57 134	77.71 79	52.14 135	66.26 68	77.47 53	73.10 116	87.02 84	87.16 66	96.05 22	97.02 48

FMVSNet2		79.24 85	78.14 97	80.53 84	78.18 145	86.19 148	86.73 60	71.91 121	72.97 101	70.48 75	50.63 139	66.56 93	80.99 75	90.10 45	89.77 47	94.89 40	90.77 138

TESTMET0.1,1		79.15 86	83.42 62	74.18 125	79.81 139	91.26 95	76.17 168	67.83 156	77.71 79	52.14 135	66.26 68	77.47 53	73.10 116	87.02 84	87.16 66	96.05 22	97.02 48

tfpn200view9		79.05 87	77.21 103	81.18 76	88.42 75	92.55 82	85.12 80	77.94 70	70.15 111	61.01 98	56.65 108	51.22 137	81.11 70	88.23 68	84.80 94	93.50 86	96.90 57

conf200view11		79.04 88	77.21 103	81.18 76	88.42 75	92.55 82	85.12 80	77.94 70	70.15 111	61.00 100	56.65 108	51.22 137	81.11 70	88.23 68	84.80 94	93.50 86	96.90 57

thresconf0.02		78.87 89	80.50 79	76.96 106	87.88 85	91.71 93	82.90 98	78.51 65	67.91 120	50.85 150	74.56 45	69.93 85	67.32 161	86.86 87	85.65 87	94.32 49	86.89 177

PatchMatch-RL		78.75 90	76.47 111	81.41 72	88.53 73	91.10 97	78.09 148	77.51 77	77.33 81	71.98 67	64.38 81	48.10 153	82.55 66	84.06 114	82.35 127	89.78 189	87.97 171

LS3D		78.72 91	75.79 117	82.15 67	91.91 52	89.39 116	83.66 91	85.88 31	76.81 85	59.22 109	57.67 105	58.53 119	83.72 60	82.07 131	81.63 140	88.50 198	84.39 185

thres200		78.69 92	76.71 107	80.99 82	88.35 79	92.56 80	86.03 71	77.94 70	66.27 123	60.66 102	56.08 116	51.11 141	79.45 87	88.23 68	85.54 89	93.52 81	97.20 43

tpmp4_e23		78.57 93	78.48 93	78.68 93	85.38 114	89.14 118	84.69 87	60.32 198	78.81 75	70.65 73	57.89 103	65.54 97	79.63 85	80.09 150	83.24 116	91.41 166	94.63 91

IB-MVS	74.10 12	78.52 94	78.51 92	78.52 95	90.15 61	95.39 52	71.95 187	77.53 76	74.95 91	77.25 45	58.93 101	55.92 128	58.37 189	79.01 170	87.89 63	95.88 25	97.47 34
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
EPNet_dtu		78.49 95	81.96 74	74.45 122	92.57 49	88.74 119	82.98 93	78.83 62	83.28 61	44.64 195	77.40 41	67.73 90	53.98 199	85.44 104	84.91 92	93.71 69	86.22 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 96	76.39 112	80.73 83	88.02 84	92.94 75	84.77 85	78.88 61	65.20 131	59.70 106	55.20 121	50.85 142	79.45 87	88.81 60	84.81 93	93.57 78	96.91 56

UA-Net		78.30 97	80.92 78	75.25 114	87.42 91	92.48 85	79.54 127	75.49 95	60.47 148	60.52 103	68.44 64	84.08 30	57.54 190	88.54 65	88.45 57	90.96 174	83.97 190

Vis-MVSNet (Re-imp)		78.28 98	82.68 68	73.16 145	86.64 97	92.68 77	78.07 149	74.48 102	74.05 95	53.47 126	64.22 82	76.52 59	54.28 195	88.96 59	88.29 60	92.03 151	94.00 102

tfpn_ndepth		78.22 99	78.84 91	77.49 102	88.32 80	90.95 99	80.79 106	76.31 86	74.24 93	59.50 108	69.52 61	60.02 114	67.11 162	85.06 107	82.95 122	92.94 128	89.18 157

MSDG		78.11 100	73.17 135	83.86 62	91.78 55	86.83 135	85.25 77	86.02 29	72.84 102	69.69 78	51.43 135	54.00 134	77.61 92	81.95 135	82.27 129	92.83 135	82.91 195

HyFIR lowres test		78.08 101	76.81 105	79.56 87	90.77 60	94.64 58	82.97 94	69.85 138	69.81 117	59.53 107	33.52 212	64.66 98	78.97 89	88.77 62	88.38 59	95.27 31	97.86 28

test-mter		77.90 102	82.44 71	72.60 150	78.52 143	90.24 103	73.85 180	65.31 174	76.37 86	51.29 139	65.58 73	75.94 61	71.36 129	85.98 99	86.26 79	95.26 33	96.71 64

view600		77.68 103	75.68 118	80.01 85	87.72 87	92.57 79	83.79 89	77.95 69	64.41 134	58.72 111	54.32 126	50.54 143	78.25 90	88.23 68	83.13 118	93.64 75	96.59 66

thres600view7		77.66 104	75.67 119	79.98 86	87.71 88	92.56 80	83.79 89	77.94 70	64.41 134	58.69 112	54.32 126	50.54 143	78.23 91	88.23 68	83.06 120	93.52 81	96.55 67

MS-PatchMatch		77.47 105	76.48 110	78.63 94	89.89 62	90.42 101	85.42 75	69.53 140	70.79 110	60.43 104	50.05 141	70.62 83	70.66 142	86.71 88	82.54 124	95.86 26	84.23 186

tfpn		77.45 106	76.23 114	78.87 92	87.15 93	91.90 91	82.17 100	76.59 83	62.98 140	56.93 115	53.08 132	57.31 124	76.41 102	87.26 83	85.20 90	93.95 59	95.89 74

Fast-Effi-MVS+		77.37 107	76.68 108	78.17 98	82.84 123	89.94 108	81.47 104	68.01 153	72.99 100	60.26 105	55.07 122	53.20 135	82.99 63	86.47 94	86.12 81	93.46 91	92.98 124

Vis-MVSNet		77.24 108	79.99 86	74.02 129	84.62 117	93.92 61	80.33 110	72.55 114	62.58 141	55.25 120	64.45 80	69.49 86	57.00 191	88.78 61	88.21 61	94.36 48	92.54 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
view800		77.22 109	75.35 120	79.41 91	87.42 91	92.21 88	82.94 96	77.19 79	63.67 138	57.78 113	53.68 129	50.19 145	77.32 93	87.70 78	83.84 109	93.79 63	96.19 71

MDTV_nov1_ep13		77.20 110	80.04 83	73.90 131	82.22 124	90.14 105	79.25 133	61.52 192	78.63 76	56.98 114	65.52 74	72.80 74	73.05 118	80.93 143	83.20 117	90.36 183	89.05 159

EPMVS		77.16 111	79.08 89	74.92 116	86.73 95	91.98 89	78.62 140	55.44 210	79.43 70	56.59 117	61.24 95	70.73 82	76.97 97	80.59 146	81.43 152	95.15 36	88.17 170

tpm cat1		76.93 112	76.19 115	77.79 101	85.08 116	88.58 121	82.96 95	59.33 201	75.72 89	72.64 60	51.25 137	64.41 100	75.74 105	77.90 179	80.10 175	90.97 173	95.35 82

PatchmatchNet		76.85 113	80.03 85	73.15 146	84.08 119	91.04 98	77.76 153	55.85 209	79.43 70	52.74 131	62.08 92	76.02 60	74.56 108	79.92 151	81.41 153	93.92 60	90.29 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 114	76.33 113	77.35 104	84.07 120	84.11 169	81.54 103	68.52 147	66.17 124	61.74 90	57.84 104	64.31 102	74.88 106	83.48 121	86.21 80	93.34 99	92.16 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		76.57 115	76.78 106	76.32 109	80.94 133	89.75 112	82.94 96	72.64 110	59.01 159	62.95 89	58.60 102	62.67 103	66.91 164	86.26 96	87.20 65	91.57 161	93.97 104
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpmrst		76.27 116	77.65 102	74.66 118	86.13 108	89.53 115	79.31 132	54.91 211	77.19 83	56.27 118	55.87 118	64.58 99	77.25 94	80.85 144	80.21 172	94.07 52	95.32 83

dps		75.76 117	75.02 123	76.63 108	84.51 118	88.12 122	77.51 155	58.33 203	75.91 88	71.98 67	57.37 106	57.85 121	76.81 99	77.89 180	78.40 184	90.63 180	89.63 147

tfpn1000		75.39 118	76.18 116	74.47 121	86.71 96	90.10 106	77.57 154	74.78 98	68.76 119	53.33 127	63.57 84	58.37 120	60.84 183	83.80 117	81.24 157	93.58 77	87.42 173

tfpnview11		74.85 119	75.06 122	74.61 119	86.58 98	89.54 114	79.98 112	75.81 90	64.95 133	47.47 174	64.85 76	54.72 130	63.86 171	84.54 110	82.20 131	93.97 56	84.64 182

CR-MVSNet		74.84 120	77.91 99	71.26 172	81.77 128	85.52 157	78.32 142	54.14 213	74.05 95	51.09 143	50.00 142	71.38 79	70.77 139	86.48 92	84.03 106	91.46 165	93.92 107

Effi-MVS+-dtu		74.57 121	74.60 127	74.53 120	81.38 130	86.74 137	80.39 109	67.70 157	67.36 122	53.06 128	59.86 99	57.50 122	75.84 104	80.19 148	78.62 182	88.79 197	91.95 133

tfpn_n400		74.36 122	74.39 129	74.32 123	86.37 103	89.86 109	79.71 117	75.69 92	60.00 150	47.47 174	64.85 76	54.72 130	63.70 174	83.80 117	83.35 114	92.96 123	84.16 187

tfpnconf		74.36 122	74.39 129	74.32 123	86.37 103	89.86 109	79.71 117	75.69 92	60.00 150	47.47 174	64.85 76	54.72 130	63.70 174	83.80 117	83.35 114	92.96 123	84.16 187

RPSCF		74.27 124	73.24 134	75.48 113	81.01 132	80.18 193	76.24 167	72.37 116	74.84 92	68.24 80	72.47 50	67.39 91	73.89 111	71.05 207	69.38 216	81.14 223	77.37 206

FMVSNet1		74.26 125	71.95 140	76.95 107	74.28 193	83.94 171	83.61 92	69.99 136	57.08 164	65.08 84	42.39 188	57.41 123	76.98 96	86.57 89	86.83 73	91.77 158	89.42 150

conf0.05thres1000		74.20 126	71.44 143	77.43 103	86.09 109	89.85 111	80.82 105	75.79 91	53.51 188	54.71 121	44.37 165	49.78 146	74.67 107	85.02 108	83.47 112	92.49 141	94.10 100

GA-MVS		73.62 127	74.52 128	72.58 151	79.93 137	89.29 117	78.02 150	71.67 127	60.79 147	42.68 199	54.41 125	49.07 149	70.07 152	89.39 56	86.55 76	93.13 117	92.12 130

Fast-Effi-MVS+-dtu		73.56 128	75.32 121	71.50 168	80.35 135	86.83 135	79.72 116	58.07 204	67.64 121	44.83 192	60.28 98	54.07 133	73.59 115	81.90 137	82.30 128	92.46 143	94.18 98

tpm		73.50 129	74.85 124	71.93 162	83.19 122	86.84 134	78.61 141	55.91 208	65.64 126	48.90 166	56.30 114	61.09 107	72.31 120	79.10 169	80.61 171	92.68 137	94.35 96

RPMNet		73.46 130	77.85 100	68.34 180	81.71 129	85.52 157	73.83 181	50.54 221	74.05 95	46.10 184	53.03 133	71.91 75	66.31 166	83.55 120	82.18 132	91.55 163	94.71 88

USDC		73.43 131	72.31 138	74.73 117	80.86 134	86.21 146	80.42 108	71.83 123	71.69 107	46.94 178	59.60 100	42.58 190	76.47 101	82.66 125	81.22 159	91.88 155	82.24 200

pmmvs4		73.38 132	71.53 142	75.55 112	75.95 173	85.24 161	77.25 158	71.59 128	71.03 108	63.10 88	49.09 147	44.22 178	73.73 114	82.04 132	80.18 173	91.68 159	88.89 163

UniMVSNet_NR-MVSNet		73.11 133	72.59 136	73.71 133	76.90 154	86.58 141	77.01 159	75.82 89	65.59 127	48.82 167	50.97 138	48.42 151	71.61 126	79.19 167	83.03 121	92.11 148	94.37 94

FMVSNet5		72.83 134	73.89 132	71.59 166	67.42 213	76.28 209	75.88 172	63.74 184	77.27 82	54.59 123	53.32 130	71.48 77	73.85 112	81.95 135	81.69 138	94.06 53	75.20 212

PatchT		72.66 135	76.58 109	68.09 182	79.02 142	86.09 152	59.81 211	51.78 219	72.00 106	51.09 143	46.84 152	66.70 92	70.77 139	86.48 92	84.03 106	96.07 19	93.92 107

ACMH	71.22 14	72.65 136	70.13 151	75.59 111	86.19 107	86.14 151	75.76 173	77.63 75	54.79 181	46.16 183	53.28 131	47.28 155	77.24 95	78.91 172	81.18 161	90.57 181	89.33 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 137	74.05 131	70.55 176	80.34 136	81.17 188	77.44 156	61.00 194	63.57 139	46.82 180	55.88 117	59.09 117	65.03 168	83.15 122	83.83 110	92.67 138	91.65 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 138	69.34 162	75.93 110	85.21 115	84.89 164	76.96 162	76.04 87	59.76 152	51.63 138	50.37 140	48.69 150	76.90 98	76.06 188	78.69 180	88.85 196	86.90 176

DU-MVS		72.19 139	71.35 144	73.17 144	75.95 173	86.02 153	77.01 159	74.42 103	65.39 129	48.82 167	49.10 145	42.81 188	71.61 126	78.67 173	83.10 119	91.22 169	94.37 94

UniMVSNet (Re)		72.12 140	72.28 139	71.93 162	76.77 155	87.38 126	75.73 174	73.51 107	65.76 125	50.24 159	48.65 148	46.49 156	63.85 172	80.10 149	82.47 125	91.49 164	95.13 86

ADS-MVSNet		72.11 141	73.72 133	70.24 177	81.24 131	86.59 140	74.75 177	50.56 220	72.58 104	49.17 164	55.40 120	61.46 106	73.80 113	76.01 189	78.14 185	91.93 154	85.86 180

gg-mvs-nofinetune		72.10 142	74.79 125	68.97 179	83.31 121	95.22 55	85.66 74	48.77 223	35.68 223	22.17 232	30.49 216	77.73 52	76.37 103	94.30 10	93.03 11	97.55 2	97.05 47

TAMVS		72.06 143	71.76 141	72.41 155	76.68 157	88.12 122	74.82 176	68.09 152	53.52 187	56.91 116	52.94 134	56.93 126	66.91 164	81.37 140	82.44 126	91.07 171	86.99 175

v6		72.04 144	70.26 147	74.11 126	76.46 161	87.06 127	79.60 119	71.75 124	59.48 154	52.69 132	44.61 158	45.79 161	71.01 137	79.57 157	81.45 150	93.16 112	93.85 114

v1neww		72.02 145	70.23 149	74.10 127	76.45 162	87.06 127	79.59 122	71.75 124	59.35 155	52.60 133	44.59 160	45.74 162	71.06 134	79.57 157	81.46 148	93.16 112	93.84 115

v7new		72.02 145	70.23 149	74.10 127	76.45 162	87.06 127	79.59 122	71.75 124	59.35 155	52.60 133	44.59 160	45.74 162	71.06 134	79.57 157	81.46 148	93.16 112	93.84 115

v2v482		71.73 147	69.80 154	73.99 130	75.88 180	86.66 139	79.58 125	71.90 122	57.58 163	50.41 158	45.35 155	43.24 186	73.05 118	79.69 152	82.18 132	93.08 119	93.87 112

test0.0.03 1		71.70 148	74.68 126	68.23 181	81.79 126	83.81 172	68.64 193	70.57 134	68.81 118	43.47 196	62.77 89	60.09 113	51.77 205	82.48 127	81.67 139	93.16 112	83.13 193

V42		71.58 149	70.11 152	73.30 142	75.66 186	86.68 138	79.17 135	69.92 137	59.29 158	52.80 130	44.36 166	45.66 164	68.83 154	79.48 163	81.49 147	93.44 92	93.82 117

v1		71.54 150	69.71 155	73.66 136	76.08 167	86.88 131	79.60 119	72.06 120	57.00 166	50.75 154	44.23 169	44.79 167	70.61 144	79.62 153	81.52 143	92.88 132	93.93 105

v1141		71.53 151	69.69 156	73.68 134	76.08 167	86.86 132	79.59 122	72.07 119	57.01 165	50.78 152	44.23 169	44.70 170	70.68 141	79.61 155	81.52 143	92.89 129	93.92 107

divwei89l23v2f112		71.53 151	69.69 156	73.68 134	76.09 166	86.86 132	79.60 119	72.08 118	56.96 167	50.78 152	44.24 168	44.70 170	70.65 143	79.62 153	81.53 141	92.89 129	93.93 105

v7		71.49 153	69.98 153	73.25 143	75.89 178	86.45 142	79.44 130	69.29 143	58.07 161	50.08 160	43.87 176	43.67 180	71.94 122	82.03 134	81.70 136	92.88 132	94.04 101

NR-MVSNet		71.47 154	71.11 145	71.90 164	77.73 150	86.02 153	76.88 163	74.42 103	65.39 129	46.09 185	49.10 145	39.87 202	64.27 170	81.40 139	82.24 130	91.99 152	93.75 118

v8		71.42 155	69.69 156	73.43 140	76.45 162	85.12 163	79.53 128	67.47 160	59.34 157	52.90 129	44.60 159	45.82 160	71.05 136	79.56 160	81.45 150	93.17 110	91.96 132

v18		71.13 156	68.98 164	73.63 137	76.66 158	79.78 195	79.95 114	65.98 168	61.34 144	54.71 121	44.75 157	46.06 157	71.27 130	79.59 156	81.51 146	93.21 108	89.81 145

TranMVSNet+NR-MVSNet		71.12 157	70.24 148	72.15 159	76.01 171	84.80 166	76.55 165	75.65 94	61.99 143	45.29 188	48.42 149	43.07 187	67.55 158	78.28 176	82.83 123	91.85 156	92.29 127

v10		70.97 158	69.44 159	72.75 147	75.90 177	84.58 168	79.43 131	66.45 165	58.07 161	49.93 161	43.87 176	43.68 179	71.91 123	82.04 132	81.70 136	92.89 129	92.11 131

v1144		70.93 159	69.42 161	72.70 148	75.48 187	86.26 144	79.22 134	69.39 142	55.61 178	48.05 172	43.47 182	42.55 191	71.51 128	82.11 130	81.74 135	92.56 140	94.17 99

v16		70.93 159	68.76 168	73.47 139	76.60 159	79.66 197	79.57 126	65.81 171	60.85 145	54.44 124	44.50 164	45.90 159	71.15 131	79.50 161	81.39 154	93.27 102	89.51 149

v17		70.82 161	68.69 169	73.31 141	76.53 160	79.67 196	79.45 129	65.80 172	60.32 149	53.75 125	44.51 163	45.92 158	71.09 133	79.49 162	81.38 155	93.26 105	89.54 148

Baseline_NR-MVSNet		70.61 162	68.87 166	72.65 149	75.95 173	80.49 191	75.92 171	74.75 99	65.10 132	48.78 169	41.28 196	44.28 177	68.45 155	78.67 173	79.64 176	92.04 150	92.62 125

v148		70.34 163	68.46 171	72.54 153	76.04 170	86.38 143	74.83 175	72.73 109	55.88 177	55.26 119	43.32 185	43.49 181	64.52 169	76.93 186	80.11 174	91.85 156	93.11 121

v1192		70.32 164	68.77 167	72.12 161	74.76 189	85.62 156	78.73 138	68.53 146	55.08 180	46.34 182	42.39 188	40.67 198	71.90 124	82.27 128	81.53 141	92.43 144	93.86 113

v144192		70.10 165	68.55 170	71.90 164	74.55 190	85.67 155	77.81 151	68.22 151	54.65 182	46.91 179	42.76 186	41.27 197	70.95 138	80.48 147	81.11 166	92.96 123	93.90 110

pmmvs5		70.01 166	69.31 163	70.82 175	75.80 183	86.26 144	72.94 182	67.91 154	53.84 186	47.22 177	47.31 151	41.47 196	67.61 157	83.93 116	81.93 134	93.42 94	90.42 142

v15		70.00 167	67.82 176	72.55 152	76.06 169	79.37 200	79.10 136	65.30 175	56.89 168	51.18 141	43.96 175	44.76 168	70.52 146	79.40 164	81.22 159	93.13 117	89.14 158

V14		69.91 168	67.71 178	72.47 154	76.01 171	79.30 201	78.92 137	65.17 176	56.74 169	51.08 146	43.82 178	44.73 169	70.44 148	79.31 165	81.14 164	93.20 109	88.91 162

COLMAP_ROB	66.31 15	69.91 168	66.61 182	73.76 132	86.44 102	82.76 176	76.59 164	76.46 85	63.82 137	50.92 149	45.60 154	49.13 148	65.87 167	74.96 193	74.45 204	86.30 210	75.57 210
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 170	68.38 172	71.58 167	74.35 191	85.39 159	77.78 152	67.88 155	54.64 183	45.39 187	42.11 191	39.97 201	71.10 132	81.68 138	81.17 163	92.96 123	93.69 120

v11		69.84 171	67.85 175	72.17 158	75.78 184	79.15 203	78.20 147	64.76 182	56.10 175	49.50 162	43.54 180	43.36 184	71.62 125	82.21 129	81.52 143	93.17 110	89.05 159

V9		69.79 172	67.57 179	72.38 156	75.95 173	79.21 202	78.72 139	65.06 177	56.51 171	51.06 147	43.66 179	44.70 170	70.28 150	79.22 166	81.06 167	93.24 107	88.67 166

v12		69.66 173	67.45 180	72.23 157	75.89 178	79.13 204	78.29 145	64.96 180	56.40 172	50.75 154	43.53 181	44.60 173	70.21 151	79.11 168	80.99 168	93.27 102	88.41 167

pm-mvs1		69.62 174	68.07 174	71.44 169	77.21 152	85.32 160	76.11 170	71.05 129	46.55 211	51.17 142	41.83 193	48.20 152	61.81 180	84.00 115	81.14 164	91.28 168	89.42 150

v13		69.55 175	67.33 181	72.14 160	75.83 181	79.04 205	78.22 146	64.85 181	56.16 174	50.60 156	43.43 183	44.56 174	70.05 153	79.01 170	80.92 170	93.28 101	88.22 168

tfpnnormal		69.29 176	65.58 184	73.62 138	79.87 138	84.82 165	76.97 161	75.12 97	45.29 213	49.03 165	35.57 210	37.20 211	68.02 156	82.70 124	81.24 157	92.69 136	92.20 128

v1240		69.28 177	67.82 176	71.00 174	74.09 194	85.13 162	76.54 166	67.28 162	53.17 189	44.70 193	41.55 195	39.38 203	70.51 147	81.29 141	81.18 161	92.88 132	93.02 123

CVMVSNet		68.95 178	70.79 146	66.79 188	79.69 140	83.75 173	72.05 186	70.90 130	56.20 173	36.30 210	54.94 124	59.22 116	54.03 198	78.33 175	78.65 181	87.77 204	84.44 184

MIMVSNet		68.66 179	69.43 160	67.76 183	64.92 218	84.68 167	74.16 178	54.10 215	60.85 145	51.27 140	39.47 200	49.48 147	67.48 159	84.86 109	85.57 88	94.63 44	81.10 201

TDRefinement		67.82 180	64.91 190	71.22 173	82.08 125	81.45 184	77.42 157	73.79 106	59.62 153	48.35 171	42.35 190	42.40 192	60.87 182	74.69 194	74.64 203	84.83 214	79.20 204

anonymousdsp		67.61 181	68.94 165	66.04 192	71.44 207	83.97 170	66.45 198	63.53 186	50.54 198	42.42 200	49.39 143	45.63 165	62.84 177	77.99 178	81.34 156	89.59 192	93.75 118

TinyColmap		67.16 182	63.51 200	71.42 170	77.94 148	79.54 199	72.80 183	69.78 139	56.58 170	45.52 186	44.53 162	33.53 220	74.45 109	76.91 187	77.06 192	88.03 203	76.41 207

FC-MVSNet-test		67.04 183	72.47 137	60.70 209	76.92 153	81.41 185	61.52 207	69.45 141	65.58 128	26.74 228	61.79 94	60.40 112	41.17 218	77.60 182	77.78 187	88.41 199	82.70 197

TransMVSNet (Re)		66.87 184	64.30 195	69.88 178	78.32 144	81.35 187	73.88 179	74.34 105	43.19 217	45.20 190	40.12 197	42.37 193	55.97 193	80.85 144	79.15 177	91.56 162	83.06 194

CMPMVS	50.59 17	66.74 185	62.72 204	71.42 170	85.40 113	89.72 113	72.69 184	70.72 132	51.24 194	51.75 137	38.91 203	44.40 175	63.74 173	70.84 208	71.52 208	84.19 215	72.45 217
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 186	65.51 185	67.51 184	71.63 206	83.10 174	70.89 190	65.02 178	50.13 201	44.68 194	39.59 199	38.77 205	62.57 178	77.59 183	78.91 178	90.29 185	90.44 141

EG-PatchMatch MVS		66.23 187	65.20 187	67.43 185	77.74 149	86.20 147	72.51 185	63.68 185	43.95 215	43.44 197	36.22 209	45.43 166	54.04 197	81.00 142	80.95 169	93.15 116	82.67 198

v52		65.34 188	64.59 192	66.21 190	69.63 211	82.41 180	69.22 191	62.80 188	49.63 202	45.15 191	39.31 202	41.85 194	60.68 184	72.61 198	77.02 194	89.75 191	89.33 153

V4		65.34 188	64.59 192	66.21 190	69.64 210	82.42 179	69.22 191	62.80 188	49.60 203	45.21 189	39.33 201	41.82 195	60.66 185	72.61 198	77.03 193	89.76 190	89.32 155

v748		65.00 190	63.86 199	66.33 189	71.85 204	82.15 182	66.80 196	65.64 173	48.50 207	47.98 173	39.62 198	39.20 204	56.44 192	71.25 205	77.53 189	89.29 193	88.74 165

WR-MVS		64.98 191	66.59 183	63.09 201	74.34 192	82.68 177	64.98 204	69.17 144	54.42 184	36.18 211	44.32 167	44.35 176	44.65 208	73.60 195	77.83 186	89.21 195	88.96 161

gm-plane-assit		64.86 192	68.15 173	61.02 208	76.44 165	68.29 219	41.60 228	53.37 216	34.68 225	26.19 230	33.22 213	57.09 125	71.97 121	95.12 4	93.97 6	96.54 13	94.66 90

CP-MVSNet		64.84 193	64.97 188	64.69 196	72.09 201	81.04 189	66.66 197	67.53 159	52.45 191	37.40 206	44.00 174	38.37 207	53.54 200	72.26 202	76.93 195	90.94 176	89.75 146

MDTV_nov1_ep13_2view		64.72 194	64.94 189	64.46 197	71.14 208	81.94 183	67.53 194	54.54 212	55.92 176	43.29 198	44.02 173	43.27 185	59.87 186	71.85 204	74.77 202	90.36 183	82.82 196

MVS-HIRNet		64.63 195	64.03 198	65.33 194	75.01 188	82.84 175	58.54 215	52.10 218	55.42 179	49.29 163	29.83 219	43.48 182	66.97 163	78.28 176	78.81 179	90.07 188	79.52 203

LTVRE_ROB	63.07 16	64.49 196	63.16 203	66.04 192	77.47 151	82.64 178	70.98 189	65.02 178	34.01 226	29.61 220	49.12 144	35.58 216	70.57 145	75.10 192	78.45 183	82.60 218	87.24 174
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
PEN-MVS		64.35 197	64.29 196	64.42 198	72.67 197	79.83 194	66.97 195	68.24 150	51.21 195	35.29 213	44.09 171	38.51 206	52.36 203	71.06 206	77.65 188	90.99 172	87.68 172

pmmvs6		64.24 198	61.77 208	67.12 186	72.39 200	81.39 186	71.33 188	65.95 170	36.05 222	48.48 170	30.55 215	43.45 183	58.75 188	77.88 181	76.36 198	85.83 211	86.70 178

pmmvs-eth3d		64.24 198	61.96 206	66.90 187	66.35 215	76.04 211	66.09 200	66.31 166	52.59 190	50.94 148	37.61 205	32.79 222	62.43 179	75.78 190	75.48 200	89.27 194	83.39 192

PS-CasMVS		64.22 200	64.19 197	64.25 199	71.86 203	80.67 190	66.42 199	67.43 161	50.64 197	36.48 208	42.60 187	37.46 210	52.56 202	71.98 203	76.69 197	90.76 177	89.29 156

WR-MVS_H		64.14 201	65.36 186	62.71 203	72.47 199	82.33 181	65.13 201	66.99 163	51.81 193	36.47 209	43.33 184	42.77 189	43.99 210	72.41 201	75.99 199	91.20 170	88.86 164

SixPastTwentyTwo		63.75 202	63.42 201	64.13 200	72.91 196	80.34 192	61.29 208	63.90 183	49.58 204	40.42 203	54.99 123	37.13 212	60.90 181	68.46 212	70.80 211	85.37 213	82.65 199

PM-MVS		63.52 203	62.51 205	64.70 195	64.79 220	76.08 210	65.07 202	62.08 190	58.13 160	46.56 181	44.98 156	31.31 223	62.89 176	72.58 200	69.93 215	86.81 208	84.55 183

DTE-MVSNet		63.26 204	63.41 202	63.08 202	72.59 198	78.56 206	65.03 203	68.28 149	50.53 199	32.38 217	44.03 172	37.79 209	49.48 206	70.83 209	76.73 196	90.73 178	85.42 181

testgi		63.11 205	64.88 191	61.05 207	75.83 181	78.51 207	60.42 210	66.20 167	48.77 206	34.56 215	56.96 107	40.35 199	40.95 219	77.46 184	77.22 191	88.37 201	74.86 214

GG-mvs-BLEND		62.08 206	88.31 37	31.46 231	0.16 240	98.10 6	91.57 36	0.09 237	85.07 58	0.21 242	73.90 49	83.74 33	0.19 239	88.98 58	89.39 52	96.58 12	99.02 11

Anonymous20231206		62.05 207	61.83 207	62.30 205	72.09 201	77.84 208	63.10 206	67.62 158	50.20 200	36.68 207	29.59 220	37.05 213	43.90 211	77.33 185	77.31 190	90.41 182	83.49 191

N_pmnet		60.52 208	58.83 212	62.50 204	68.97 212	75.61 212	59.72 213	66.47 164	51.90 192	41.26 201	35.42 211	35.63 215	52.25 204	67.07 216	70.08 214	86.35 209	76.10 208

LP		59.72 209	58.23 213	61.44 206	75.67 185	74.97 213	61.05 209	48.34 224	54.02 185	40.82 202	31.61 214	36.92 214	54.69 194	67.52 214	71.18 210	88.08 202	71.42 220

testpf		59.38 210	64.51 194	53.40 218	76.71 156	66.40 221	50.18 222	38.98 234	64.13 136	35.10 214	47.91 150	51.41 136	43.16 212	66.37 217	71.23 209	76.25 226	84.14 189

EU-MVSNet		58.73 211	60.92 209	56.17 214	66.17 216	72.39 216	58.85 214	61.24 193	48.47 208	27.91 225	46.70 153	40.06 200	39.07 220	68.27 213	70.34 213	83.77 216	80.23 202

test2356		58.43 212	59.52 210	57.16 212	66.71 214	68.00 220	54.69 217	60.91 196	49.22 205	28.63 223	41.86 192	33.68 219	44.36 209	72.98 196	75.47 201	87.69 205	75.40 211

test20.03		57.93 213	59.22 211	56.44 213	71.84 205	73.78 215	53.55 219	65.96 169	43.02 218	28.46 224	37.50 206	38.17 208	30.41 227	75.25 191	74.42 205	88.41 199	72.37 218

testus		55.91 214	56.38 214	55.37 216	65.15 217	65.88 223	50.07 223	60.92 195	45.62 212	26.99 227	41.74 194	24.43 229	42.08 215	69.50 211	73.60 206	86.97 207	73.91 215

MDA-MVSNet-bldmvs		54.99 215	52.66 217	57.71 211	52.74 231	74.87 214	55.61 216	68.41 148	43.65 216	32.54 216	37.93 204	22.11 231	54.11 196	48.85 229	67.34 218	82.85 217	73.88 216

new-patchmatchnet		53.91 216	52.69 216	55.33 217	64.83 219	70.90 217	52.24 221	61.75 191	41.09 219	30.82 218	29.90 218	28.22 225	36.69 222	61.52 222	65.08 221	85.64 212	72.14 219

Anonymous20231211		53.48 217	47.45 222	60.52 210	60.54 223	66.16 222	52.47 220	60.87 197	25.32 232	74.00 56	15.98 231	23.66 230	37.00 221	61.01 224	64.41 223	78.25 224	75.60 209

MIMVSNet1		52.76 218	53.95 215	51.38 220	41.96 235	70.79 218	53.56 218	63.03 187	39.36 220	27.83 226	22.73 229	33.07 221	34.47 224	70.49 210	72.69 207	87.41 206	68.51 221

pmmvs3		52.59 219	52.43 218	52.78 219	54.53 229	64.49 225	50.07 223	46.89 227	35.31 224	30.19 219	27.27 222	26.96 227	53.02 201	67.28 215	70.54 212	81.96 219	75.20 212

new_pmnet		50.32 220	51.36 219	49.11 221	49.19 232	64.89 224	48.66 226	47.99 226	47.55 209	26.27 229	29.51 221	28.66 224	44.89 207	61.12 223	62.74 225	77.66 225	65.03 224

FPMVS		50.25 221	45.67 225	55.58 215	70.48 209	60.12 226	59.78 212	59.33 201	46.66 210	37.94 204	30.22 217	27.51 226	35.94 223	50.98 228	47.90 228	70.02 229	56.31 226

1111		48.34 222	47.93 221	48.83 222	58.14 225	59.33 228	37.54 229	43.85 228	31.76 227	29.36 221	23.26 226	34.58 217	42.20 213	65.15 218	68.72 217	81.86 220	52.66 229

testmv		46.89 223	46.37 223	47.48 223	60.96 221	58.36 230	36.71 231	56.94 205	27.16 230	17.93 234	23.94 224	18.84 233	31.06 225	61.55 220	66.72 219	81.28 221	68.05 222

test1235678		46.88 224	46.36 224	47.48 223	60.96 221	58.35 231	36.71 231	56.94 205	27.15 231	17.93 234	23.93 225	18.82 234	31.06 225	61.55 220	66.71 220	81.27 222	68.04 223

test12356		41.15 225	41.46 226	40.78 226	53.10 230	49.87 232	33.37 234	52.25 217	25.12 233	15.64 236	22.76 228	15.01 235	15.81 232	52.97 226	64.54 222	74.50 228	59.96 225

PMVS	36.83 18	40.62 226	36.39 227	45.56 225	58.40 224	33.20 236	32.62 235	56.02 207	28.25 229	37.92 205	22.29 230	26.15 228	25.29 229	48.49 230	43.82 231	63.13 232	52.53 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		35.20 227	33.96 228	36.65 228	43.30 234	32.51 237	26.96 237	48.31 225	38.87 221	20.08 233	8.08 234	7.41 239	26.44 228	53.60 225	58.43 226	54.81 234	38.79 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		33.05 228	31.21 230	35.20 229	58.14 225	59.33 228	37.54 229	43.85 228	31.76 227	29.36 221	23.26 226	34.58 217	42.20 213	65.15 218	0.77 235	0.11 239	3.62 237

PMMVS2		32.52 229	33.92 229	30.88 232	34.15 238	44.70 235	27.79 236	39.69 233	22.21 234	4.31 241	15.73 232	14.13 236	12.45 236	40.11 231	47.00 229	66.88 230	53.54 227

no-one		32.08 230	31.09 231	33.23 230	46.10 233	46.90 234	20.80 238	49.13 222	16.27 235	7.85 238	10.62 233	10.68 237	13.65 235	31.50 233	51.31 227	61.83 233	50.38 231

E-PMN		21.42 231	17.56 233	25.94 233	36.25 237	19.02 240	11.56 239	43.72 231	15.25 237	6.99 239	8.04 235	4.53 241	21.77 231	16.13 235	26.16 233	35.34 236	33.77 234

MVE	25.07 19	21.25 232	23.51 232	18.62 235	15.07 239	29.77 239	10.67 241	34.60 235	12.51 238	9.46 237	7.84 236	3.82 242	14.38 234	27.45 234	42.42 232	27.56 238	40.74 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 233	16.32 234	25.62 234	36.41 236	18.93 241	11.51 240	43.75 230	15.65 236	6.53 240	7.56 237	4.68 240	22.03 230	14.56 236	23.10 234	33.51 237	29.77 235

testmvs		0.76 234	1.23 235	0.21 236	0.05 241	0.21 242	0.38 243	0.09 237	0.94 239	0.05 243	2.13 239	0.08 243	0.60 238	0.82 237	0.77 235	0.11 239	3.62 237

test123		0.67 235	1.11 236	0.16 237	0.01 242	0.14 243	0.20 244	0.04 239	0.77 240	0.02 244	2.15 238	0.02 244	0.61 237	0.23 238	0.72 237	0.07 241	3.76 236

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					73.80 195	79.57 198	64.47 205

ambc			50.35 220		55.61 228	59.93 227	48.73 225		44.08 214	35.81 212	24.01 223	10.64 238	41.57 217	72.83 197	63.35 224	74.99 227	77.61 205

MTAPA										91.14 2		85.84 20

MTMP										90.95 3		84.13 29

Patchmatch-RL test							8.17 242

tmp_tt				39.78 227	56.31 227	31.71 238	35.84 233	15.08 236	82.57 65	50.83 151	63.07 87	47.51 154	15.28 233	52.23 227	44.24 230	65.35 231

XVS					89.65 64	95.93 42	85.97 72			76.32 47		82.05 39				93.51 84

X-MVStestdata					89.65 64	95.93 42	85.97 72			76.32 47		82.05 39				93.51 84

abl_6				89.54 27	95.55 33	97.59 15	89.01 50	85.00 34	94.67 11	83.04 28	84.70 31	91.47 3	89.46 20			95.20 35	98.63 17

mPP-MVS					95.90 28							80.22 47

NP-MVS									89.55 41

Patchmtry						87.41 125	78.32 142	54.14 213		51.09 143

DeepMVS_CX						48.96 233	43.77 227	40.58 232	50.93 196	24.67 231	36.95 208	20.18 232	41.60 216	38.92 232		52.37 235	53.31 228

High-resolution multi-view benchmark