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.

PVSNet_BlendedMVS		90.74 48	90.66 51	90.82 48	94.75 52	98.54 36	91.30 73	86.53 48	95.43 34	85.75 30	78.66 62	70.67 108	87.60 84	96.37 20	95.08 31	98.98 15	99.90 1

PVSNet_Blended		90.74 48	90.66 51	90.82 48	94.75 52	98.54 36	91.30 73	86.53 48	95.43 34	85.75 30	78.66 62	70.67 108	87.60 84	96.37 20	95.08 31	98.98 15	99.90 1

DeepPCF-MVS	91.00 2	94.15 28	96.87 15	90.97 46	96.82 37	99.33 8	89.40 89	92.76 25	98.76 1	82.36 43	88.74 36	95.49 21	90.58 63	98.13 3	97.80 3	93.88 194	99.88 3

ESAPD		97.61 1	98.19 2	96.94 3	99.03 2	99.49 2	99.00 1	95.35 1	97.97 5	92.21 10	97.50 3	99.73 1	96.95 3	97.13 10	95.61 22	99.11 6	99.87 4

DELS-MVS		91.09 46	90.56 55	91.71 40	95.82 47	98.59 31	95.74 36	86.68 47	85.86 92	85.12 35	72.71 91	81.36 73	88.06 83	97.31 7	98.27 1	98.86 22	99.82 5
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
GG-mvs-BLEND		65.67 211	93.78 34	32.89 231	0.47 239	99.35 4	96.92 26	0.22 238	93.28 51	0.51 241	84.07 48	92.50 33	0.62 238	93.59 54	93.86 48	98.59 32	99.79 6

APDe-MVS		97.31 4	97.51 8	97.08 1	98.95 6	99.29 9	98.58 4	95.11 3	97.69 12	94.16 1	96.91 8	96.81 13	96.57 5	96.71 16	95.39 25	99.08 10	99.79 6

TSAR-MVS + MP.		96.50 11	97.08 12	95.82 13	96.12 46	98.97 18	98.00 8	94.13 15	97.89 7	91.49 12	95.11 21	97.52 8	96.26 14	96.27 22	94.07 46	98.91 19	99.74 8

SD-MVS		96.87 8	97.69 7	95.92 10	96.38 42	99.25 10	97.76 16	94.75 8	97.72 10	92.46 9	95.94 13	99.09 2	96.48 8	96.01 24	96.08 16	97.68 87	99.73 9

TSAR-MVS + GP.		94.59 25	96.60 17	92.25 36	90.25 90	98.17 50	96.22 31	86.53 48	97.49 13	87.26 25	95.21 20	97.06 11	94.07 30	94.34 50	94.20 44	99.18 5	99.71 10

TSAR-MVS + ACMM		94.99 22	97.02 13	92.61 35	97.19 32	98.71 27	97.74 18	93.21 21	96.97 16	79.27 59	94.09 24	97.14 10	90.84 57	96.64 17	95.94 18	97.42 101	99.67 11

train_agg		95.72 16	97.37 9	93.80 24	97.82 26	98.92 19	97.84 14	93.50 19	96.86 19	81.35 47	97.10 6	97.71 6	94.19 26	96.02 23	95.37 26	98.07 59	99.64 12

PVSNet_Blended_VisFu		87.44 67	88.72 71	85.95 85	92.02 66	97.26 58	86.88 113	82.66 76	83.86 115	79.16 60	66.96 116	84.91 68	77.26 147	94.97 38	93.48 54	97.73 84	99.64 12

MCST-MVS		96.93 6	98.07 4	95.61 15	98.98 4	99.44 3	98.04 7	95.04 4	98.10 3	86.55 28	97.65 2	97.56 7	95.60 19	97.67 5	96.45 11	99.43 1	99.61 14

CNVR-MVS		97.60 3	98.08 3	97.03 2	99.14 1	99.55 1	98.67 2	95.32 2	97.91 6	92.55 7	97.11 5	97.23 9	97.49 1	98.16 2	97.05 4	99.04 11	99.55 15

CHOSEN 1792x2688		84.59 102	84.30 108	84.93 91	93.71 56	98.23 48	89.91 85	77.96 108	84.81 101	65.93 110	45.19 205	71.76 105	83.13 111	95.46 29	95.13 30	98.94 18	99.53 16

APD-MVS		96.79 9	96.99 14	96.56 5	98.76 9	98.87 21	98.42 5	94.93 6	97.70 11	91.83 11	95.52 16	95.94 17	96.63 4	95.94 25	95.47 23	98.80 24	99.47 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CANet		93.23 32	93.72 35	92.65 34	95.48 49	99.09 15	96.55 29	86.74 46	95.28 36	85.22 34	77.30 67	91.25 41	92.60 39	97.06 12	96.63 7	99.31 2	99.45 18

abl_6				93.25 30	97.12 33	98.71 27	94.40 46	87.81 41	97.86 8	87.19 26	91.07 32	95.80 18	94.18 27			98.78 26	99.36 19

SMA-MVS		96.67 10	97.36 10	95.86 11	98.90 7	99.34 7	97.86 13	94.75 8	97.31 14	89.22 19	92.39 28	99.04 3	96.29 13	97.23 9	96.87 5	98.15 53	99.34 20

thres100view900		86.48 78	85.08 97	88.12 64	90.54 76	96.90 64	92.39 58	84.82 59	84.16 109	71.65 87	70.86 99	60.49 137	91.23 49	93.65 53	90.19 87	98.10 55	99.32 21

MVS_111021_HR		92.73 37	94.83 30	90.28 51	96.27 43	99.10 14	92.77 57	86.15 51	93.41 50	77.11 72	93.82 25	87.39 51	90.61 61	95.60 27	95.15 29	98.79 25	99.32 21

IB-MVS	79.58 12	83.83 106	84.81 98	82.68 104	91.85 68	97.35 57	75.75 195	82.57 77	86.55 89	84.01 39	70.90 98	65.43 127	63.18 203	84.19 156	89.92 94	98.74 29	99.31 23
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
HyFIR lowres test		83.43 109	82.94 120	84.01 96	93.41 59	97.10 61	87.21 110	74.04 138	80.15 128	64.98 113	41.09 213	76.61 88	86.51 92	93.31 58	93.01 60	97.91 80	99.30 24

conf0.002		87.85 62	87.85 76	87.84 65	90.63 74	96.81 65	91.35 68	83.36 64	84.16 109	72.61 84	78.06 64	71.90 103	90.91 52	93.29 59	91.47 68	98.20 48	99.28 25

conf0.01		87.22 69	86.71 86	87.81 66	90.61 75	96.75 67	91.35 68	83.33 65	84.16 109	72.45 85	75.61 74	68.65 114	90.91 52	93.23 60	89.34 98	98.17 50	99.27 26

SteuartSystems-ACMMP		96.20 12	97.22 11	95.01 19	98.40 17	99.11 13	97.93 11	93.62 18	96.28 23	87.45 24	97.05 7	96.00 16	94.23 25	96.83 15	95.97 17	98.40 39	99.27 26
Skip Steuart: Steuart Systems R&D Blog.
thres200		85.80 86	84.38 106	87.46 71	90.51 80	96.39 71	91.64 63	83.15 66	81.59 119	71.54 91	70.24 104	60.41 141	89.88 69	92.89 69	89.85 95	98.06 60	99.26 28

3Dnovator+	86.26 7	92.90 36	92.45 41	93.42 29	97.25 31	98.45 43	95.82 34	85.71 54	93.83 48	89.55 18	72.31 95	92.28 36	94.01 31	95.10 36	95.92 19	98.17 50	99.23 29

HPM-MVS++		96.91 7	97.70 6	96.00 9	98.97 5	99.16 12	97.82 15	94.81 7	98.04 4	89.61 17	96.56 12	98.60 4	96.39 9	97.09 11	95.22 27	98.39 40	99.22 30

DeepC-MVS	88.77 4	92.39 40	91.74 45	93.14 32	96.21 44	98.55 35	96.30 30	93.84 16	93.06 57	81.09 51	74.69 82	85.20 67	93.48 34	95.41 30	96.13 15	97.92 78	99.18 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tfpn111		87.30 68	87.03 83	87.61 68	90.54 76	96.39 71	91.35 68	83.15 66	84.16 109	71.65 87	86.75 40	60.49 137	90.91 52	92.89 69	89.34 98	98.05 62	99.17 32

conf200view11		86.07 82	84.76 99	87.61 68	90.54 76	96.39 71	91.35 68	83.15 66	84.16 109	71.65 87	70.86 99	60.49 137	90.91 52	92.89 69	89.34 98	98.05 62	99.17 32

tfpn200view9		86.07 82	84.76 99	87.61 68	90.54 76	96.39 71	91.35 68	83.15 66	84.16 109	71.65 87	70.86 99	60.49 137	90.91 52	92.89 69	89.34 98	98.05 62	99.17 32

thres400		85.59 88	84.08 109	87.36 72	90.45 82	96.60 68	90.95 80	83.67 62	80.99 122	71.17 93	69.08 109	60.25 142	89.88 69	93.14 63	89.34 98	98.02 66	99.17 32

NCCC		97.01 5	97.74 5	96.16 7	99.02 3	99.35 4	98.63 3	95.04 4	97.84 9	88.95 21	96.83 10	97.02 12	96.39 9	97.44 6	96.51 9	98.90 20	99.16 36

QAPM		91.68 44	91.97 42	91.34 42	97.86 25	98.72 26	95.60 37	85.72 53	90.86 67	77.14 71	76.06 71	90.35 42	92.69 38	94.10 51	94.60 37	99.04 11	99.09 37

view600		85.15 91	83.59 115	86.96 76	90.38 85	96.39 71	90.33 81	83.15 66	80.46 123	70.61 96	67.96 112	60.04 143	89.22 74	92.89 69	88.30 119	98.10 55	99.08 38

thres600view7		85.14 92	83.58 116	86.96 76	90.37 87	96.39 71	90.33 81	83.15 66	80.46 123	70.60 97	67.96 112	60.04 143	89.22 74	92.89 69	88.28 120	98.06 60	99.08 38

DI_MVS_plusplus_trai		87.63 66	87.13 81	88.22 63	88.61 111	95.92 84	94.09 50	81.41 84	87.00 87	78.38 67	59.70 139	80.52 78	89.08 76	94.37 48	93.34 56	97.73 84	99.05 40

CLD-MVS		88.99 58	88.07 73	90.07 53	89.61 101	94.94 96	93.82 51	85.70 55	92.73 60	82.73 42	79.97 59	69.59 111	90.44 65	90.32 109	89.93 92	98.10 55	99.04 41
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		87.83 63	90.66 51	84.53 93	90.08 92	96.79 66	88.16 98	79.89 92	85.44 95	72.20 86	75.50 77	87.14 52	80.21 126	95.53 28	95.22 27	96.65 133	99.02 42

DeepC-MVS_fast	91.53 1	95.57 18	95.67 23	95.45 16	98.57 14	99.00 17	97.76 16	94.41 12	97.06 15	86.84 27	86.39 42	92.27 37	96.38 11	97.89 4	98.06 2	98.73 30	99.01 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HSP-MVS		97.61 1	98.30 1	96.81 4	98.66 10	99.35 4	98.00 8	94.75 8	98.45 2	92.78 5	97.99 1	98.58 5	97.41 2	98.24 1	96.48 10	99.27 4	98.99 44

MSLP-MVS++		95.49 19	94.84 29	96.25 6	98.64 11	98.63 29	98.35 6	92.37 28	95.04 41	92.62 6	87.12 39	93.79 26	96.55 6	93.53 55	96.78 6	98.98 15	98.99 44

CSCG		93.16 34	92.65 40	93.76 25	98.32 19	99.09 15	96.12 32	89.91 34	93.15 53	89.64 16	83.62 49	88.91 47	92.40 41	91.09 97	93.70 51	96.14 168	98.99 44

PHI-MVS		94.49 26	96.72 16	91.88 38	97.06 34	98.88 20	94.99 42	89.13 36	96.15 26	79.70 56	96.91 8	95.78 19	91.87 45	94.65 45	95.68 20	98.53 34	98.98 47

MVS_0304		91.90 43	92.93 39	90.69 50	93.66 57	98.78 24	96.73 28	85.43 58	93.13 54	78.11 68	77.02 70	89.09 45	91.10 50	96.98 13	96.54 8	99.11 6	98.96 48

view800		84.86 98	83.35 118	86.63 79	90.31 89	96.17 79	89.86 86	82.67 75	79.95 129	70.04 98	67.25 115	59.75 145	88.72 77	92.64 77	88.72 116	98.19 49	98.95 49

tfpn		85.32 90	84.47 104	86.31 83	90.24 91	95.99 82	89.39 90	82.28 79	79.44 130	69.50 99	66.59 118	67.71 116	88.20 82	92.47 81	90.22 85	98.26 46	98.89 50

PCF-MVS	88.14 5	90.42 52	89.56 64	91.41 41	94.44 54	98.18 49	94.35 47	94.33 13	84.55 105	76.61 76	75.84 73	88.47 48	91.29 48	90.37 108	90.66 80	97.46 95	98.88 51
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDPH-MVS		93.22 33	95.08 28	91.04 45	97.57 29	98.49 40	96.74 27	89.35 35	95.19 38	73.57 82	90.26 33	91.59 40	90.68 60	95.09 37	96.15 14	98.31 45	98.81 52

HQP-MVS		89.57 56	90.57 54	88.41 61	92.77 62	94.71 99	94.24 48	87.97 40	93.44 49	68.18 104	91.75 30	71.54 106	89.90 68	92.31 85	91.43 69	97.39 102	98.80 53

DWT-MVSNet_training		87.65 65	88.45 72	86.71 78	90.32 88	95.64 88	87.91 101	75.69 127	93.27 52	81.43 46	74.99 80	76.48 89	86.92 88	87.74 129	92.29 63	98.00 68	98.74 54

CP-MVS		95.43 20	95.67 23	95.14 18	98.24 22	98.60 30	97.45 22	92.80 24	95.98 28	89.21 20	95.22 19	93.60 27	95.43 20	94.37 48	93.22 58	97.68 87	98.72 55

CostFormer		85.47 89	86.98 84	83.71 98	88.70 110	94.02 108	88.07 100	62.72 203	89.78 71	78.68 63	72.69 92	78.37 84	87.35 86	85.96 142	89.32 107	96.73 124	98.72 55

X-MVS		94.70 23	95.71 22	93.52 28	98.38 18	98.56 32	96.99 25	92.62 27	95.58 32	81.00 53	94.57 23	93.49 28	94.16 28	94.82 40	94.29 42	97.99 71	98.68 57

MP-MVS		95.24 21	95.96 20	94.40 22	98.32 19	98.38 44	97.12 24	92.87 23	95.17 39	85.50 33	95.68 14	94.91 23	94.58 23	95.11 35	93.76 50	98.05 62	98.68 57
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EPNet		93.69 30	95.34 27	91.76 39	96.98 36	98.47 41	95.40 39	86.79 45	95.47 33	82.84 41	95.66 15	89.17 44	90.47 64	95.25 33	94.69 36	98.10 55	98.68 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gg-mvs-nofinetune		77.08 146	79.79 135	73.92 183	85.95 127	97.23 59	92.18 60	52.65 226	46.19 225	27.79 232	38.27 217	85.63 61	85.67 95	96.95 14	95.62 21	99.30 3	98.67 60

test-LLR		85.11 93	89.49 66	80.00 123	85.32 131	94.49 101	82.27 165	74.18 136	87.83 82	56.70 141	75.55 75	86.26 55	82.75 113	93.06 65	90.60 81	98.77 27	98.65 61

TESTMET0.1,1		84.62 101	89.49 66	78.94 132	82.18 144	94.49 101	82.27 165	70.94 161	87.83 82	56.70 141	75.55 75	86.26 55	82.75 113	93.06 65	90.60 81	98.77 27	98.65 61

HFP-MVS		96.09 13	96.41 19	95.72 14	98.58 13	98.84 22	97.95 10	93.08 22	96.96 17	90.24 15	96.60 11	94.40 25	96.52 7	95.13 34	94.33 40	97.93 75	98.59 63

3Dnovator	85.78 8	92.53 39	91.96 43	93.20 31	97.99 23	98.47 41	95.78 35	85.94 52	93.07 56	86.40 29	73.43 88	89.00 46	94.08 29	94.74 43	96.44 12	99.01 14	98.57 64

canonicalmvs		89.62 54	89.87 58	89.33 57	90.47 81	97.02 63	93.46 52	79.67 96	92.45 62	81.05 52	82.84 50	73.00 94	93.71 33	90.38 107	94.85 34	97.65 90	98.54 65

ACMMP_Plus		95.81 15	96.50 18	95.01 19	98.79 8	99.17 11	97.52 21	94.20 14	96.19 25	85.71 32	93.80 26	96.20 15	95.89 15	96.62 18	94.98 33	97.93 75	98.52 66

UGNet		87.04 73	89.59 63	84.07 95	90.94 72	95.95 83	86.02 119	81.65 81	85.94 91	78.54 65	78.00 65	85.40 66	69.62 185	91.83 89	91.53 67	97.63 91	98.51 67
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
test-mter		84.06 104	89.00 69	78.29 137	81.92 146	94.23 105	81.07 176	70.38 165	87.12 86	56.10 151	74.75 81	85.80 60	81.81 118	92.52 79	90.10 89	98.43 37	98.49 68

zzz-MVS		95.87 14	95.63 25	96.15 8	98.60 12	98.83 23	97.89 12	93.65 17	96.24 24	93.08 4	91.13 31	95.46 22	95.72 18	95.64 26	93.67 53	97.97 72	98.46 69

MVS_111021_LR		93.05 35	94.53 31	91.32 43	96.43 41	98.38 44	92.81 56	87.20 44	95.94 30	81.45 45	94.75 22	86.08 58	92.12 44	94.83 39	93.34 56	97.89 81	98.42 70

CPTT-MVS		94.11 29	93.99 33	94.25 23	96.58 39	97.66 54	97.31 23	91.94 29	94.84 42	88.72 23	92.51 27	93.04 31	95.78 17	91.51 91	89.97 90	95.15 185	98.37 71

OpenMVS	83.41 11	89.84 53	88.89 70	90.95 47	97.63 28	98.51 38	94.64 44	85.47 57	88.14 80	78.39 66	65.06 122	85.42 65	91.04 51	93.06 65	93.70 51	98.53 34	98.37 71

tpm cat1		82.39 119	82.32 127	82.47 108	88.13 115	92.42 122	87.43 106	62.79 202	85.30 96	78.05 69	60.14 137	72.10 98	83.20 110	82.26 187	85.67 146	95.23 183	98.35 73

ACMMPR		95.59 17	95.89 21	95.25 17	98.41 16	98.74 25	97.69 19	92.73 26	96.88 18	88.95 21	95.33 18	92.91 32	95.79 16	94.73 44	94.33 40	97.92 78	98.32 74

diffmvs		88.92 59	90.30 56	87.32 73	89.46 105	96.38 77	91.21 75	77.89 109	93.11 55	79.09 62	74.17 85	87.41 50	88.55 81	90.20 110	92.70 61	97.71 86	98.13 75

Effi-MVS+		84.80 99	85.71 92	83.73 97	87.94 116	95.76 85	90.08 83	73.45 141	85.12 99	62.66 120	72.39 94	64.97 130	90.59 62	92.95 68	90.69 79	97.67 89	98.12 76

UniMVSNet (Re)		78.00 142	77.52 144	78.57 135	79.66 160	90.36 135	82.09 171	77.86 110	76.38 139	60.26 124	54.63 155	52.07 159	75.31 170	84.97 151	86.10 141	96.22 166	98.11 77

tpmrst		81.71 122	83.87 113	79.20 130	89.01 109	93.67 110	84.22 128	60.14 208	87.45 84	59.49 127	64.97 123	71.86 104	85.30 98	84.72 152	86.30 138	97.04 114	98.09 78

CHOSEN 280x420		90.61 50	94.27 32	86.35 80	93.12 61	98.16 51	89.99 84	69.62 172	92.48 61	76.89 75	87.28 38	96.72 14	90.31 66	94.81 41	92.33 62	98.17 50	98.08 79

EPP-MVSNet		87.72 64	89.74 59	85.37 89	89.11 108	95.57 91	86.31 117	79.44 97	85.83 93	75.73 78	77.23 68	90.05 43	84.78 100	91.22 95	90.25 84	96.83 118	98.04 80

ACMMP		93.32 31	93.59 36	93.00 33	97.03 35	98.24 47	95.27 40	91.66 32	95.20 37	83.25 40	95.39 17	85.52 62	92.80 37	92.60 78	90.21 86	98.01 67	97.99 81
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
conf0.05thres1000		81.86 121	79.55 136	84.56 92	89.39 106	94.15 107	87.57 104	81.36 85	69.95 163	65.78 111	56.38 150	59.38 146	86.04 94	90.58 102	88.49 118	97.22 108	97.97 82

PGM-MVS		94.64 24	95.49 26	93.66 26	98.55 15	98.51 38	97.63 20	87.77 42	94.45 45	84.92 36	97.23 4	91.90 39	95.22 21	94.56 46	93.80 49	97.87 82	97.97 82

FC-MVSNet-train		84.88 96	84.08 109	85.82 87	89.21 107	91.74 129	85.87 120	81.20 88	81.71 118	74.66 81	73.38 89	64.99 129	86.60 91	90.75 99	88.08 123	97.36 103	97.90 84

RPMNet		81.47 123	86.24 88	75.90 175	86.72 123	92.12 125	82.82 156	55.76 221	85.21 97	53.73 174	63.45 129	83.16 71	80.13 127	92.34 84	89.52 97	96.23 165	97.90 84

AdaColmap		94.28 27	92.94 38	95.84 12	98.32 19	98.33 46	96.06 33	94.62 11	96.29 22	91.22 13	89.89 35	85.50 64	96.38 11	91.85 88	90.89 74	98.44 36	97.81 86

LGP-MVS_train		86.95 74	87.65 77	86.12 84	91.77 69	93.84 109	93.04 54	82.77 74	88.04 81	65.33 112	87.69 37	67.09 120	86.79 89	90.20 110	88.99 111	97.05 113	97.71 87

MAR-MVS		90.44 51	91.17 48	89.59 55	97.48 30	97.92 53	90.96 79	79.80 93	95.07 40	77.03 73	80.83 53	79.10 81	94.68 22	93.16 62	94.46 39	97.59 93	97.63 88
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
UniMVSNet_NR-MVSNet		78.89 138	78.04 142	79.88 124	79.40 161	89.70 142	82.92 152	80.17 90	76.37 140	58.56 132	57.10 147	54.92 156	81.44 119	83.51 162	87.12 133	96.76 121	97.60 89

DU-MVS		77.98 143	76.71 150	79.46 127	78.68 178	89.26 156	82.92 152	79.06 102	76.52 137	58.56 132	54.89 153	48.35 193	81.44 119	83.16 177	87.21 132	96.08 171	97.60 89

CR-MVSNet		81.44 124	85.29 96	76.94 159	86.53 124	92.12 125	83.86 129	58.37 215	85.21 97	56.28 146	59.60 140	80.39 79	80.50 123	92.77 75	89.32 107	96.12 169	97.59 91

PatchT		79.28 134	83.88 112	73.93 182	85.54 129	90.95 132	66.14 214	56.53 219	83.21 116	56.28 146	56.50 149	76.80 87	80.50 123	92.77 75	89.32 107	98.57 33	97.59 91

tpm		78.87 139	81.33 133	76.00 172	85.57 128	90.19 138	82.81 157	59.66 211	78.35 134	51.40 185	66.30 119	67.92 115	80.94 121	83.28 174	85.73 144	95.65 175	97.56 93

ACMP	85.16 9	87.15 72	87.04 82	87.27 74	90.80 73	94.45 103	89.41 88	83.09 72	89.15 73	76.98 74	86.35 43	65.80 125	86.94 87	88.45 123	87.52 129	96.42 158	97.56 93
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tpmp4_e23		83.72 107	84.45 105	82.86 102	88.25 113	92.54 119	88.95 92	63.01 201	88.20 79	74.83 80	68.07 111	71.99 102	86.65 90	84.11 158	88.74 115	95.47 178	97.51 95

MVSTER		91.91 42	93.43 37	90.14 52	89.81 98	92.32 123	94.53 45	81.32 86	96.00 27	84.77 37	85.41 47	92.39 35	91.32 47	96.41 19	94.01 47	99.11 6	97.45 96

CNLPA		91.53 45	89.74 59	93.63 27	96.75 38	97.63 56	91.16 76	91.70 30	96.38 21	90.82 14	69.66 107	85.52 62	93.76 32	90.44 106	91.14 73	97.55 94	97.40 97

NR-MVSNet		77.21 145	76.41 151	78.14 141	80.18 156	89.26 156	83.38 134	79.06 102	76.52 137	56.59 144	54.89 153	45.32 206	72.89 175	85.39 147	86.12 140	96.71 125	97.36 98

MVS_Test		89.02 57	90.20 57	87.64 67	89.83 97	97.05 62	92.30 59	77.59 112	92.89 58	75.01 79	77.36 66	76.10 90	92.27 42	95.30 32	95.42 24	98.83 23	97.30 99

anonymousdsp		75.14 171	77.25 146	72.69 190	76.68 201	89.26 156	75.26 199	68.44 179	65.53 194	46.65 205	58.16 145	56.67 152	73.96 173	87.84 128	86.05 142	95.13 186	97.22 100

Fast-Effi-MVS+-dtu		80.57 127	83.44 117	77.22 152	83.98 138	91.52 131	85.78 123	64.54 199	80.38 125	50.28 191	74.06 86	62.89 135	82.00 117	89.10 119	88.91 113	96.75 122	97.21 101

Vis-MVSNet (Re-imp)		85.89 84	89.62 62	81.55 115	89.85 96	96.08 81	87.55 105	79.80 93	84.80 102	66.55 108	73.70 87	86.71 53	68.25 192	94.40 47	94.53 38	97.32 105	97.09 102

gm-plane-assit		71.33 199	75.18 154	66.83 208	79.06 170	75.57 219	48.05 228	60.33 205	48.28 222	34.67 226	44.34 209	67.70 117	79.78 128	97.25 8	96.21 13	99.10 9	96.92 103

CDS-MVSNet		83.13 113	83.73 114	82.43 110	84.52 136	92.92 114	88.26 96	77.67 111	72.08 149	69.08 102	66.96 116	74.66 92	78.61 132	90.70 100	91.96 64	96.46 157	96.86 104
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		88.56 60	91.22 47	85.47 88	90.04 94	95.60 90	86.62 115	78.49 105	93.86 47	70.62 95	90.00 34	80.08 80	91.64 46	92.36 83	89.80 96	95.40 180	96.84 105

Baseline_NR-MVSNet		76.71 149	74.56 167	79.23 129	78.68 178	84.15 197	82.45 161	78.87 104	75.83 141	60.05 125	47.92 197	50.18 174	79.06 131	83.16 177	83.86 183	96.26 163	96.80 106

v1144		75.54 164	74.55 168	76.69 161	78.33 191	88.77 165	82.89 154	72.76 145	67.18 186	51.73 182	49.34 190	48.37 191	78.10 140	86.22 139	85.24 154	96.35 160	96.74 107

v7		76.00 158	75.01 160	77.15 154	78.73 175	88.87 163	83.15 144	72.40 148	69.20 169	53.57 175	49.73 183	49.23 184	78.49 134	86.15 141	85.17 157	96.53 149	96.73 108

v1192		74.96 173	73.92 171	76.17 166	77.76 194	88.19 171	82.54 160	71.94 153	66.84 187	50.07 193	48.10 196	46.14 200	78.28 137	86.30 137	85.23 155	96.41 159	96.67 109

tfpnnormal		75.27 169	72.12 192	78.94 132	82.30 143	88.52 167	82.41 162	79.41 98	58.03 211	55.59 157	43.83 211	44.71 208	77.35 144	87.70 130	85.45 152	96.60 141	96.61 110

TranMVSNet+NR-MVSNet		77.02 147	75.76 153	78.49 136	78.46 189	88.24 169	83.03 149	79.97 91	73.49 145	54.73 163	54.00 158	48.74 188	78.15 139	82.36 186	86.90 135	96.59 142	96.55 111

v144192		74.76 174	73.64 174	76.06 169	77.58 195	88.23 170	81.87 172	71.63 155	66.03 190	51.08 186	48.63 194	46.77 198	77.59 143	84.53 155	84.76 172	96.64 135	96.54 112

V42		76.21 154	75.04 159	77.58 149	78.68 178	89.33 152	82.93 151	74.64 133	69.84 164	56.13 150	50.42 175	50.93 166	76.30 164	83.32 169	84.89 171	96.83 118	96.54 112

v148		74.98 172	73.52 177	76.69 161	78.84 171	89.02 160	78.78 184	76.82 113	67.22 185	59.61 126	49.18 192	47.94 195	70.57 184	80.76 194	83.99 181	95.52 176	96.52 114

v1141		76.03 156	74.64 165	77.66 146	78.78 172	89.32 155	83.14 146	76.22 118	68.27 173	54.56 167	50.06 179	49.84 180	76.78 157	83.40 163	85.07 158	96.50 152	96.51 115

divwei89l23v2f112		76.03 156	74.64 165	77.65 148	78.78 172	89.33 152	83.15 144	76.21 120	68.26 174	54.55 168	50.08 178	49.86 178	76.73 158	83.39 164	85.06 160	96.51 151	96.51 115

v1		76.04 155	74.65 164	77.66 146	78.77 174	89.33 152	83.18 142	76.22 118	68.17 175	54.58 166	50.10 177	49.99 175	76.70 159	83.38 165	85.05 161	96.50 152	96.51 115

v1921920		74.60 176	73.56 176	75.81 177	77.43 197	87.94 173	82.18 169	71.33 159	66.48 189	49.23 197	47.84 198	45.56 204	78.03 141	85.70 145	84.92 169	96.65 133	96.50 118

v6		76.41 150	75.11 156	77.93 143	79.08 167	89.48 149	83.25 137	75.62 128	70.21 158	55.94 154	50.48 171	50.81 169	77.01 154	83.32 169	84.97 166	96.66 130	96.50 118

v2v482		76.25 153	74.78 161	77.96 142	78.50 187	89.14 159	83.05 148	76.02 122	68.78 172	54.11 170	51.36 164	48.59 190	79.49 129	83.53 161	85.60 150	96.59 142	96.49 120

v1neww		76.39 151	75.09 157	77.91 144	79.08 167	89.49 147	83.21 139	75.62 128	70.20 159	55.81 155	50.43 172	50.74 170	77.05 152	83.33 167	84.99 163	96.66 130	96.48 121

v7new		76.39 151	75.09 157	77.91 144	79.08 167	89.49 147	83.21 139	75.62 128	70.20 159	55.81 155	50.43 172	50.74 170	77.05 152	83.33 167	84.99 163	96.66 130	96.48 121

CANet_DTU		87.91 61	91.57 46	83.64 100	90.96 71	97.12 60	91.90 62	75.97 123	92.83 59	53.16 178	86.02 44	79.02 82	90.80 58	95.40 31	94.15 45	99.03 13	96.47 123

Fast-Effi-MVS+		82.61 118	82.51 126	82.72 103	85.49 130	93.06 113	87.17 112	71.39 158	84.18 108	64.59 115	63.03 131	58.89 148	90.22 67	91.39 92	90.83 75	97.44 98	96.21 124

Vis-MVSNet		82.88 115	86.04 89	79.20 130	87.77 119	96.42 70	86.10 118	76.70 114	74.82 142	61.38 123	70.70 102	77.91 85	64.83 198	93.22 61	93.19 59	98.43 37	96.01 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1240		74.04 182	73.04 185	75.20 179	77.19 199	87.69 176	80.93 177	70.72 164	65.08 196	48.47 198	47.31 199	44.71 208	77.33 145	85.50 146	85.07 158	96.59 142	95.94 126

OMC-MVS		92.05 41	91.88 44	92.25 36	96.51 40	97.94 52	93.18 53	88.97 38	96.53 20	84.47 38	80.79 57	87.85 49	93.25 36	92.48 80	91.81 65	97.12 111	95.73 127

Effi-MVS+-dtu		81.18 125	82.77 122	79.33 128	84.70 135	92.54 119	85.81 121	71.55 156	78.84 132	57.06 139	71.98 96	63.77 133	85.09 99	88.94 120	87.62 128	91.79 212	95.68 128

IterMVS		78.85 141	81.36 132	75.93 173	84.27 137	85.74 190	83.83 131	66.35 193	76.82 136	50.48 188	63.48 128	68.82 113	73.99 172	89.68 118	89.34 98	96.63 137	95.67 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		73.19 184	72.37 191	74.15 181	77.54 196	86.77 187	76.34 191	72.05 151	65.66 193	51.47 184	50.49 170	43.66 213	70.90 178	80.93 193	83.40 187	96.59 142	95.66 130

v10		75.57 163	74.67 163	76.62 163	78.73 175	87.46 182	83.14 146	69.41 173	69.27 168	53.44 176	49.73 183	49.21 185	78.44 136	86.17 140	85.18 156	96.53 149	95.65 131

IterMVS-LS		82.62 117	82.75 123	82.48 107	87.09 122	87.48 181	87.19 111	72.85 144	79.09 131	66.63 107	65.22 120	72.14 97	84.06 105	88.33 126	91.39 70	97.03 115	95.60 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		82.63 116	82.64 124	82.62 106	87.81 118	92.81 116	84.39 127	61.96 204	86.43 90	81.63 44	69.72 106	67.60 118	84.42 102	82.51 184	83.90 182	95.52 176	95.50 133

v8		75.89 159	74.74 162	77.23 151	79.09 166	88.00 172	83.19 141	71.08 160	70.03 162	56.29 145	50.50 169	50.88 168	77.06 151	83.32 169	84.99 163	96.68 129	95.49 134

v7n		72.11 193	71.66 194	72.63 191	75.26 207	86.85 183	76.74 190	68.77 175	62.70 203	49.40 194	45.92 204	43.51 214	70.63 183	84.16 157	83.21 189	94.99 187	95.25 135

PS-CasMVS		72.37 190	71.47 196	73.43 186	77.32 198	86.43 188	75.99 194	71.94 153	63.37 200	49.24 196	49.07 193	42.42 219	69.60 186	80.59 196	83.18 190	96.48 156	95.23 136

WR-MVS		72.93 185	73.57 175	72.19 195	78.14 192	87.71 175	76.21 193	73.02 143	67.78 181	50.09 192	50.35 176	50.53 172	61.27 208	80.42 197	83.10 191	94.43 192	95.11 137

ACMH	78.51 14	79.27 135	78.08 140	80.65 120	89.52 102	90.40 134	80.45 178	79.77 95	69.54 167	54.85 160	64.83 124	56.16 154	83.94 106	84.58 154	86.01 143	95.41 179	95.03 138
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		78.86 140	80.42 134	77.05 156	83.27 139	92.17 124	83.24 138	75.73 126	73.75 143	46.27 206	62.43 132	57.12 150	76.94 155	93.14 63	89.34 98	96.83 118	95.00 139

WR-MVS_H		72.69 187	72.80 189	72.56 192	77.94 193	87.83 174	75.26 199	71.53 157	64.75 197	52.19 181	49.83 180	48.62 189	61.96 207	81.12 192	82.44 194	96.50 152	95.00 139

PatchmatchNet		83.28 112	87.57 79	78.29 137	87.46 121	94.95 95	83.36 135	59.43 213	90.20 70	58.10 134	74.29 84	86.20 57	84.13 103	85.27 148	87.39 130	97.25 107	94.67 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpn_ndepth		86.61 77	87.92 75	85.08 90	90.39 84	95.45 93	88.21 97	82.30 78	90.79 68	71.22 92	82.59 51	72.09 100	80.42 125	91.37 93	88.61 117	97.93 75	94.56 142

v52		71.67 196	71.16 198	72.26 194	73.90 213	86.80 186	75.72 197	68.04 182	62.53 205	50.43 190	46.15 203	47.83 196	70.73 179	78.53 204	81.76 198	94.75 190	94.53 143

V4		71.67 196	71.15 199	72.27 193	73.91 212	86.82 184	75.73 196	68.04 182	62.49 206	50.47 189	46.20 201	47.74 197	70.70 180	78.54 202	81.76 198	94.76 189	94.52 144

FMVSNet3		87.19 70	87.32 80	87.04 75	82.82 140	90.21 137	92.88 55	76.53 115	91.69 63	81.31 48	64.81 125	80.64 75	89.79 72	94.80 42	94.76 35	98.88 21	94.32 145

v748		70.94 202	70.25 202	71.75 200	75.58 205	86.28 189	72.12 202	70.25 169	60.25 208	54.08 171	46.18 202	44.41 212	64.61 199	77.92 209	82.49 193	93.87 195	94.19 146

GBi-Net		86.16 80	86.00 90	86.35 80	81.81 148	89.52 144	91.40 65	76.53 115	91.69 63	81.31 48	64.81 125	80.64 75	88.72 77	90.54 103	90.72 76	98.34 41	94.08 147

test1		86.16 80	86.00 90	86.35 80	81.81 148	89.52 144	91.40 65	76.53 115	91.69 63	81.31 48	64.81 125	80.64 75	88.72 77	90.54 103	90.72 76	98.34 41	94.08 147

FMVSNet2		84.89 95	84.02 111	85.91 86	81.81 148	89.52 144	91.40 65	75.79 124	84.45 106	79.39 58	58.75 142	74.35 93	88.72 77	93.51 57	93.46 55	98.34 41	94.08 147

PLC	89.12 3	92.67 38	90.84 49	94.81 21	97.69 27	96.10 80	95.42 38	91.70 30	95.82 31	92.52 8	81.24 52	86.01 59	94.36 24	92.44 82	90.27 83	97.19 110	93.99 150
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	71.82 16	72.62 189	71.77 193	73.62 185	80.74 153	87.59 179	80.42 179	70.37 166	49.73 221	37.12 221	59.76 138	42.52 218	80.92 122	83.20 175	85.61 149	92.13 209	93.95 151
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
OPM-MVS		85.69 87	82.79 121	89.06 59	93.42 58	94.21 106	94.21 49	87.61 43	72.68 147	70.79 94	71.09 97	67.27 119	90.74 59	91.29 94	89.05 110	97.61 92	93.94 152

v18		75.49 166	74.04 170	77.18 153	79.31 163	82.47 200	83.66 132	68.68 176	71.77 152	57.43 138	50.71 167	51.01 164	77.31 146	83.35 166	85.03 162	96.70 127	93.91 153

pmmvs5		75.46 167	75.12 155	75.87 176	79.39 162	89.44 150	78.12 188	72.27 150	65.98 191	51.54 183	55.83 151	46.23 199	76.80 156	88.77 121	85.73 144	97.07 112	93.84 154

TAPA-MVS	87.40 6	90.98 47	90.71 50	91.30 44	96.14 45	97.66 54	94.80 43	89.00 37	94.74 44	77.42 70	80.22 58	86.70 54	92.27 42	91.65 90	90.17 88	98.15 53	93.83 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v16		75.32 168	73.90 172	76.98 158	79.23 164	82.37 203	83.27 136	68.48 177	71.54 153	57.06 139	50.43 172	50.93 166	77.18 149	83.30 172	84.92 169	96.70 127	93.79 156

PatchMatch-RL		86.75 76	85.43 94	88.29 62	94.06 55	96.37 78	86.82 114	82.94 73	88.94 74	79.59 57	79.83 60	59.17 147	89.46 73	91.12 96	88.81 114	96.88 117	93.78 157

v17		75.24 170	73.83 173	76.89 160	79.15 165	82.38 202	83.16 143	68.48 177	70.93 156	56.69 143	50.53 168	50.98 165	77.13 150	83.29 173	84.93 168	96.71 125	93.77 158

PEN-MVS		72.24 192	71.30 197	73.33 188	77.08 200	85.57 191	76.75 189	72.52 147	63.89 199	48.12 199	50.79 165	43.09 216	69.03 190	78.54 202	83.46 186	96.50 152	93.76 159

TSAR-MVS + COLMAP		89.59 55	89.64 61	89.53 56	93.32 60	96.51 69	95.03 41	88.53 39	95.98 28	69.10 101	91.81 29	64.53 131	93.40 35	93.53 55	91.35 71	97.77 83	93.75 160

MDTV_nov1_ep13		84.17 103	88.03 74	79.66 125	86.00 126	94.41 104	85.05 125	66.01 194	90.36 69	64.34 117	77.13 69	84.56 69	82.71 115	87.12 133	88.92 112	93.84 197	93.69 161

v15		74.54 177	73.06 184	76.26 164	78.70 177	82.14 204	82.89 154	68.05 181	68.07 177	54.77 161	49.76 182	49.88 177	76.56 160	83.19 176	84.76 172	96.59 142	93.60 162

v11		74.62 175	73.41 179	76.03 170	78.54 185	81.97 207	82.34 163	67.33 190	68.08 176	53.39 177	49.73 183	48.87 187	78.01 142	86.66 135	84.97 166	96.56 147	93.58 163

V14		74.48 178	73.00 186	76.20 165	78.65 181	82.09 205	82.79 158	67.88 184	68.04 178	54.75 162	49.68 186	49.92 176	76.51 161	83.12 179	84.67 174	96.63 137	93.44 164

tfpn1000		84.98 94	86.47 87	83.24 101	89.93 95	94.98 94	86.58 116	81.22 87	88.54 78	67.35 105	79.39 61	70.93 107	76.07 167	90.70 100	87.37 131	98.32 44	93.37 165

EPMVS		83.71 108	86.76 85	80.16 121	89.72 99	95.64 88	84.68 126	59.73 210	89.61 72	62.67 119	72.65 93	81.80 72	86.22 93	86.23 138	88.03 125	97.96 73	93.35 166

V9		74.37 179	72.87 187	76.11 168	78.58 182	82.02 206	82.68 159	67.75 186	67.80 180	54.63 164	49.50 188	49.86 178	76.40 162	83.05 180	84.59 175	96.63 137	93.30 167

v12		74.29 180	72.82 188	76.02 171	78.52 186	81.96 208	82.27 165	67.65 187	67.88 179	54.63 164	49.40 189	49.74 182	76.40 162	82.99 181	84.52 176	96.64 135	93.23 168

FMVSNet1		80.18 130	78.07 141	82.65 105	78.55 183	87.57 180	88.41 95	73.93 139	70.16 161	73.57 82	49.80 181	64.45 132	85.35 96	90.54 103	90.72 76	96.10 170	93.21 169

pm-mvs1		75.61 162	74.19 169	77.26 150	80.16 158	88.79 164	81.49 175	75.49 132	59.49 210	58.09 135	48.32 195	55.53 155	72.35 176	88.61 122	85.48 151	95.99 172	93.12 170

v13		74.20 181	72.72 190	75.92 174	78.49 188	81.90 209	82.28 164	67.55 188	67.64 182	54.29 169	49.25 191	49.75 181	76.30 164	82.92 183	84.47 177	96.63 137	93.08 171

ADS-MVSNet		80.25 129	82.96 119	77.08 155	87.86 117	92.60 118	81.82 173	56.19 220	86.95 88	56.16 149	68.19 110	72.42 96	83.70 108	82.05 188	85.45 152	96.75 122	93.08 171

ACMM	84.23 10	86.40 79	84.64 101	88.46 60	91.90 67	91.93 128	88.11 99	85.59 56	88.61 77	79.13 61	75.31 78	66.25 124	89.86 71	89.88 116	87.64 127	96.16 167	92.86 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		79.32 133	77.78 143	81.11 118	80.18 156	88.96 162	83.39 133	76.07 121	81.27 121	69.35 100	58.66 143	51.19 163	82.01 116	87.16 131	84.39 179	95.66 174	92.82 174

thresconf0.02		86.84 75	89.56 64	83.67 99	90.08 92	95.66 87	89.03 91	83.62 63	87.45 84	62.19 121	86.75 40	80.81 74	78.48 135	92.24 86	91.27 72	98.60 31	92.72 175

ACMH+	79.09 13	79.12 137	77.22 147	81.35 116	88.50 112	90.36 135	82.14 170	79.38 100	72.78 146	58.59 131	62.31 134	56.44 153	84.10 104	82.03 189	84.05 180	95.40 180	92.55 176

testpf		71.11 201	76.92 148	64.33 210	81.95 145	78.78 216	61.99 216	43.97 233	84.31 107	46.81 204	61.76 135	63.32 134	62.03 206	77.13 212	80.68 202	89.25 216	92.50 177

DTE-MVSNet		71.19 200	70.45 201	72.06 197	76.61 202	84.59 196	75.61 198	72.32 149	63.12 202	45.70 208	50.72 166	43.02 217	65.89 196	77.53 211	82.23 196	96.26 163	91.93 178

LS3D		87.19 70	85.48 93	89.18 58	94.96 51	95.47 92	92.02 61	93.36 20	88.69 76	67.01 106	70.56 103	72.10 98	92.47 40	89.96 115	89.93 92	95.25 182	91.68 179

test0.0.03 1		80.99 126	84.37 107	77.05 156	85.32 131	89.79 141	78.43 186	74.18 136	84.78 103	57.98 137	76.06 71	72.88 95	69.14 189	88.02 127	87.70 126	97.27 106	91.37 180

pmmvs6		70.29 203	67.90 209	73.07 189	76.17 203	85.31 193	76.29 192	70.75 163	47.39 224	55.33 158	37.15 221	50.49 173	69.55 187	82.96 182	80.85 200	90.34 215	91.18 181

TAMVS		79.23 136	78.95 139	79.56 126	81.89 147	92.52 121	82.97 150	73.70 140	67.27 184	64.97 114	61.66 136	65.06 128	78.61 132	87.12 133	88.07 124	95.23 183	90.95 182

tfpnview11		83.86 105	85.36 95	82.10 112	89.66 100	94.55 100	87.73 102	81.81 80	85.72 94	58.99 128	80.80 54	66.64 121	76.13 166	90.79 98	88.15 122	98.26 46	90.90 183

FC-MVSNet-test		77.95 144	81.85 131	73.39 187	82.31 142	88.99 161	79.33 182	74.24 135	78.75 133	47.40 203	70.22 105	72.09 100	60.78 209	86.66 135	85.62 147	96.30 162	90.61 184

tfpn_n400		83.32 110	84.61 102	81.81 113	89.50 103	94.81 97	87.41 107	81.65 81	80.24 126	58.99 128	80.80 54	66.64 121	75.84 168	90.09 112	89.33 105	97.46 95	90.37 185

tfpnconf		83.32 110	84.61 102	81.81 113	89.50 103	94.81 97	87.41 107	81.65 81	80.24 126	58.99 128	80.80 54	66.64 121	75.84 168	90.09 112	89.33 105	97.46 95	90.37 185

CVMVSNet		76.86 148	79.09 138	74.26 180	85.29 133	89.44 150	79.91 181	78.47 106	68.94 171	44.45 211	62.35 133	69.70 110	64.50 200	85.82 143	87.03 134	92.94 205	90.33 187

PM-MVS		70.17 204	69.42 205	71.04 201	70.82 216	81.26 213	71.25 205	67.80 185	69.16 170	51.04 187	53.15 161	34.93 223	72.19 177	80.30 198	76.95 213	93.16 204	90.21 188

EPNet_dtu		84.87 97	89.01 68	80.05 122	95.25 50	92.88 115	88.84 93	84.11 60	91.69 63	49.28 195	85.69 45	78.95 83	65.39 197	92.22 87	91.66 66	97.43 100	89.95 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net		84.69 100	87.64 78	81.25 117	90.38 85	95.67 86	87.33 109	79.41 98	72.07 150	66.48 109	75.09 79	92.48 34	66.88 194	94.03 52	94.25 43	97.01 116	89.88 190

SixPastTwentyTwo		72.65 188	73.22 180	71.98 198	78.40 190	87.64 178	70.09 207	70.37 166	66.49 188	47.60 201	65.09 121	45.94 201	73.09 174	78.94 200	78.66 209	92.33 208	89.82 191

MSDG		85.81 85	82.29 128	89.93 54	95.52 48	92.61 117	91.51 64	91.46 33	85.12 99	78.56 64	63.25 130	69.01 112	85.31 97	88.45 123	88.23 121	97.21 109	89.33 192

MDTV_nov1_ep13_2view		71.65 198	73.08 183	69.97 204	75.22 208	86.81 185	73.98 201	59.61 212	69.75 165	48.01 200	54.21 157	53.06 157	69.19 188	78.50 205	80.43 204	93.84 197	88.79 193

pmmvs-eth3d		69.59 205	67.57 211	71.95 199	70.04 218	80.05 214	71.48 203	70.00 171	62.57 204	55.99 153	44.92 206	35.73 222	70.64 182	81.56 191	79.69 205	93.55 200	88.43 194

TransMVSNet (Re)		72.90 186	70.51 200	75.69 178	80.88 152	85.26 194	79.25 183	78.43 107	56.13 217	52.81 179	46.81 200	48.20 194	66.77 195	85.18 150	83.70 185	95.98 173	88.28 195

MS-PatchMatch		82.16 120	82.18 129	82.12 111	91.65 70	93.50 111	89.51 87	71.95 152	81.48 120	64.45 116	59.58 141	77.54 86	77.23 148	89.88 116	85.62 147	97.94 74	87.68 196

Anonymous20231206		68.09 208	68.68 206	67.39 207	75.16 209	82.55 199	69.33 209	70.06 170	63.34 201	42.28 213	37.91 219	43.12 215	52.67 214	83.56 160	82.71 192	94.84 188	87.59 197

EU-MVSNet		68.07 209	70.25 202	65.52 209	74.68 211	81.30 212	68.53 210	70.31 168	62.40 207	37.43 220	54.62 156	48.36 192	51.34 218	78.32 206	79.27 206	90.84 213	87.47 198

EG-PatchMatch MVS		71.81 194	71.54 195	72.12 196	80.53 155	89.94 140	78.51 185	66.56 192	57.38 213	47.46 202	44.28 210	52.22 158	63.10 204	85.22 149	84.42 178	96.56 147	87.35 199

USDC		80.10 131	79.33 137	81.00 119	86.36 125	91.71 130	88.74 94	75.77 125	81.90 117	54.90 159	67.67 114	52.05 160	83.94 106	88.44 125	86.25 139	96.31 161	87.28 200

MIMVSNet		75.71 161	77.26 145	73.90 184	70.93 215	88.71 166	79.98 180	57.67 218	73.58 144	58.08 136	53.93 159	58.56 149	79.41 130	90.04 114	89.97 90	97.34 104	86.04 201

TDRefinement		75.54 164	73.22 180	78.25 140	87.65 120	89.65 143	85.81 121	79.28 101	71.14 154	56.06 152	52.17 162	51.96 161	68.74 191	81.60 190	80.58 203	91.94 210	85.45 202

ambc			57.08 219		58.68 231	67.71 226	60.07 221		57.13 215	42.79 212	30.00 225	11.64 237	50.18 219	78.89 201	69.14 224	82.64 227	85.02 203

MVS-HIRNet		72.32 191	73.45 178	71.00 202	80.58 154	89.97 139	68.51 211	55.28 222	70.89 157	52.27 180	39.09 215	57.11 151	75.02 171	85.76 144	86.33 137	94.36 193	85.00 204

N_pmnet		68.54 206	67.83 210	69.38 205	75.77 204	81.90 209	66.21 213	72.53 146	65.91 192	46.09 207	44.67 207	45.48 205	63.82 202	74.66 214	77.39 212	91.87 211	84.77 205

testgi		73.22 183	75.84 152	70.16 203	81.67 151	85.50 192	71.45 204	70.81 162	69.56 166	44.74 210	74.52 83	49.25 183	58.45 210	84.10 159	83.37 188	93.86 196	84.56 206

TinyColmap		75.75 160	73.19 182	78.74 134	84.82 134	87.69 176	81.59 174	74.62 134	71.81 151	54.01 172	55.79 152	44.42 211	82.89 112	84.61 153	83.76 184	94.50 191	84.22 207

COLMAP_ROB	75.69 15	79.47 132	76.90 149	82.46 109	92.20 63	90.53 133	85.30 124	83.69 61	78.27 135	61.47 122	58.26 144	62.75 136	78.28 137	82.41 185	82.13 197	93.83 199	83.98 208
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20231211		56.40 220	54.23 221	58.92 220	64.68 223	71.87 223	59.09 224	64.63 198	34.66 232	35.73 223	21.99 230	29.42 230	45.81 222	67.46 223	70.30 222	83.57 225	83.94 209

RPSCF		82.91 114	81.86 130	84.13 94	88.25 113	88.32 168	87.67 103	80.86 89	84.78 103	76.57 77	85.56 46	76.00 91	84.61 101	78.20 207	76.52 214	86.81 223	83.63 210

pmmvs3		60.52 217	60.87 217	60.12 218	61.38 225	71.62 224	57.42 226	53.94 224	48.09 223	35.95 222	38.62 216	32.19 228	64.12 201	75.33 213	77.99 211	87.89 219	82.28 211

MDA-MVSNet-bldmvs		62.23 214	61.13 216	63.52 211	58.94 230	82.44 201	60.71 220	73.28 142	57.22 214	38.42 218	49.63 187	27.64 231	62.83 205	54.98 228	74.16 217	86.96 222	81.83 212

test2356		66.34 210	69.50 204	62.65 212	70.77 217	74.02 221	61.29 217	64.23 200	67.61 183	33.88 229	56.51 148	44.92 207	53.09 213	80.01 199	82.24 195	92.66 207	81.22 213

LP		68.35 207	68.20 207	68.53 206	82.61 141	82.93 198	69.42 208	53.36 225	71.06 155	45.32 209	41.19 212	49.10 186	67.20 193	73.89 216	78.16 210	93.25 202	81.04 214

testus		64.41 213	66.39 213	62.10 214	70.01 219	72.88 222	59.74 222	64.99 197	65.18 195	33.49 230	57.35 146	30.48 229	51.71 217	78.09 208	80.75 201	92.69 206	79.97 215

FMVSNet5		80.56 128	82.53 125	78.26 139	73.80 214	81.52 211	82.26 168	68.36 180	88.85 75	64.21 118	69.09 108	84.38 70	83.49 109	87.13 132	86.76 136	97.44 98	79.95 216

new-patchmatchnet		60.74 216	59.78 218	61.87 215	69.52 220	76.67 218	57.99 225	65.78 195	52.63 219	38.47 217	38.08 218	32.92 226	48.88 220	68.50 220	69.87 223	90.56 214	79.75 217

test20.03		65.17 212	67.41 212	62.55 213	75.35 206	79.31 215	62.22 215	68.83 174	56.50 216	35.35 225	51.97 163	44.70 210	40.01 224	80.69 195	79.25 207	93.55 200	79.47 218

CMPMVS	54.54 17	71.74 195	67.94 208	76.16 167	90.41 83	93.25 112	78.32 187	75.60 131	59.81 209	53.95 173	44.64 208	51.22 162	70.70 180	74.59 215	75.88 215	88.01 217	76.23 219
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		60.51 218	61.43 215	59.44 219	48.75 234	77.21 217	60.98 218	66.84 191	52.09 220	38.74 216	29.29 228	39.40 221	48.08 221	77.60 210	78.87 208	93.22 203	75.56 220

new_pmnet		61.60 215	62.68 214	60.35 217	63.02 224	74.93 220	60.97 219	58.86 214	64.21 198	35.38 224	39.51 214	39.89 220	57.37 211	72.78 217	72.56 218	86.49 224	74.85 221

testmv		53.23 222	53.37 222	53.06 223	64.78 221	63.76 230	42.27 231	60.18 206	38.40 228	24.60 233	33.04 222	23.85 232	39.28 225	68.05 221	72.53 219	87.23 220	73.98 222

test1235678		53.22 223	53.36 223	53.05 224	64.78 221	63.75 231	42.27 231	60.17 207	38.36 229	24.60 233	33.03 223	23.84 233	39.28 225	68.04 222	72.52 220	87.23 220	73.96 223

test12356		48.96 224	49.54 225	48.28 226	59.74 229	57.59 233	42.10 233	58.32 217	36.65 231	23.11 235	31.44 224	19.22 234	23.46 232	61.17 226	71.98 221	82.97 226	68.75 224

DeepMVS_CX						70.68 225	59.61 223	67.36 189	72.12 148	38.41 219	53.88 160	32.44 227	55.15 212	50.88 230		74.35 231	68.42 225

PMMVS2		41.25 227	42.55 228	39.74 228	43.25 235	55.05 234	38.15 234	47.11 230	31.78 233	11.83 238	21.16 231	19.12 235	20.98 234	49.95 231	56.09 227	77.09 228	64.68 226

FPMVS		56.54 219	52.82 224	60.87 216	74.90 210	67.58 227	67.69 212	65.38 196	57.86 212	41.51 214	37.83 220	34.19 224	41.21 223	55.88 227	53.09 229	74.55 230	63.31 227

1111		54.82 221	55.44 220	54.10 222	61.33 227	64.37 228	42.52 229	46.65 231	42.29 226	34.21 227	29.57 226	45.65 202	51.95 215	71.47 218	74.60 216	87.95 218	60.10 228

PMVS	42.57 18	45.71 225	42.61 227	49.32 225	61.35 226	37.82 237	36.96 235	60.10 209	37.20 230	41.50 215	28.53 229	33.11 225	28.82 231	53.45 229	48.70 231	67.22 233	59.42 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		36.24 229	35.28 230	37.36 230	49.42 233	52.08 235	23.67 237	54.16 223	20.93 236	12.98 237	13.94 232	12.99 236	16.68 235	34.98 233	55.52 228	67.24 232	56.51 230

Gipuma		43.95 226	42.62 226	45.50 227	50.79 232	41.20 236	35.55 236	52.51 227	52.95 218	29.09 231	12.92 233	11.48 238	38.15 227	62.01 225	66.62 225	66.89 234	51.17 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	32.98 19	27.61 231	29.89 231	24.94 234	21.97 238	37.22 238	15.56 241	38.83 234	17.49 237	14.72 236	11.64 237	5.62 241	21.26 233	35.20 232	50.95 230	37.29 237	51.13 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		27.87 230	24.36 232	31.97 232	41.27 237	25.56 240	16.62 239	49.16 228	22.00 235	9.90 239	11.75 235	7.86 240	29.57 230	22.22 234	34.70 232	45.27 235	46.41 233

EMVS		26.96 232	22.96 233	31.63 233	41.91 236	25.73 239	16.30 240	49.10 229	22.38 234	9.03 240	11.22 238	8.12 239	29.93 229	20.16 235	31.04 233	43.49 236	42.04 234

test123		4.39 234	7.11 235	1.21 236	0.11 241	1.16 242	1.67 243	0.35 237	5.91 239	0.16 243	11.65 236	0.16 243	4.45 237	1.72 237	4.92 236	0.51 240	24.28 235

.test1245		40.04 228	40.41 229	39.60 229	61.33 227	64.37 228	42.52 229	46.65 231	42.29 226	34.21 227	29.57 226	45.65 202	51.95 215	71.47 218	5.65 234	0.92 238	23.86 236

testmvs		5.16 233	8.14 234	1.69 235	0.36 240	1.65 241	3.02 242	0.66 236	7.17 238	0.50 242	12.58 234	0.69 242	4.67 236	5.42 236	5.65 234	0.92 238	23.86 236

sosnet-low-res		0.00 235	0.00 236	0.00 237	0.00 242	0.00 243	0.00 244	0.00 239	0.00 240	0.00 244	0.00 239	0.00 244	0.00 239	0.00 238	0.00 237	0.00 241	0.00 238

sosnet		0.00 235	0.00 236	0.00 237	0.00 242	0.00 243	0.00 244	0.00 239	0.00 240	0.00 244	0.00 239	0.00 244	0.00 239	0.00 238	0.00 237	0.00 241	0.00 238

our_test_3					78.55 183	84.98 195	70.12 206

MTAPA										93.37 3		95.71 20

MTMP										93.84 2		94.86 24

Patchmatch-RL test							19.65 238

tmp_tt				57.89 221	79.94 159	59.29 232	52.84 227	36.65 235	94.77 43	68.22 103	72.96 90	65.62 126	33.65 228	66.20 224	58.02 226	76.06 229

XVS					92.16 64	98.56 32	91.04 77			81.00 53		93.49 28				98.00 68

X-MVStestdata					92.16 64	98.56 32	91.04 77			81.00 53		93.49 28				98.00 68

mPP-MVS					97.95 24							92.24 38

NP-MVS									94.12 46

Patchmtry						92.08 127	83.86 129	58.37 215		56.28 146

High-resolution multi-view benchmark