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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

HSP-MVS		82.45 2	83.62 1	78.96 35	82.99 89	52.71 123	85.04 109	89.99 10	66.08 45	86.77 1	92.75 12	72.05 1	91.46 43	83.35 5	93.53 1	92.72 17

MCST-MVS		83.01 1	83.30 2	82.15 5	92.84 2	57.58 13	93.77 1	91.10 4	75.95 2	77.10 15	93.09 8	54.15 13	95.57 3	85.80 1	85.87 22	93.31 6

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 27	56.89 21	92.77 2	86.30 62	77.83 1	77.88 12	92.13 18	60.24 2	94.78 12	78.97 15	89.61 3	93.69 3
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
CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 11	91.71 3	88.87 16	70.31 14	77.64 14	93.87 2	52.58 19	93.91 17	84.17 2	87.92 10	92.39 19

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 63	85.46 48	49.56 181	90.99 11	86.66 56	70.58 13	80.07 7	95.30 1	56.18 9	90.97 50	82.57 8	86.22 20	93.28 7

CANet		80.90 5	81.17 6	80.09 22	87.62 22	54.21 73	91.60 6	86.47 58	73.13 5	79.89 8	93.10 7	49.88 34	92.98 23	84.09 3	84.75 34	93.08 11

HPM-MVS++		80.50 7	80.71 7	79.88 24	87.34 24	55.20 48	89.93 20	87.55 44	66.04 48	79.46 9	93.00 11	53.10 17	91.76 40	80.40 10	89.56 4	92.68 18

ESAPD		80.50 7	80.42 8	80.74 12	89.33 9	55.48 37	89.59 26	88.42 25	56.02 208	82.27 2	93.65 3	58.18 6	95.22 6	79.73 11	86.59 15	91.53 35

CSCG		80.41 9	79.72 9	82.49 4	89.12 11	57.67 12	89.29 30	91.54 2	59.19 143	71.82 49	90.05 63	59.72 3	96.04 1	78.37 18	88.40 8	93.75 2

MVS_0304		79.84 12	79.71 10	80.25 17	85.64 40	54.62 66	90.58 14	84.48 98	72.51 8	79.22 10	93.09 8	42.01 127	93.28 21	84.00 4	85.84 23	92.87 15

PS-MVSNAJ		80.06 10	79.52 11	81.68 7	85.58 43	60.97 3	91.69 4	87.02 49	70.62 12	80.75 6	93.22 6	37.77 166	92.50 30	82.75 6	86.25 19	91.57 33

xiu_mvs_v2_base		79.86 11	79.31 12	81.53 8	85.03 54	60.73 4	91.65 5	86.86 52	70.30 15	80.77 5	93.07 10	37.63 171	92.28 33	82.73 7	85.71 24	91.57 33

NCCC		79.57 13	79.23 13	80.59 13	89.50 8	56.99 19	91.38 8	88.17 32	67.71 29	73.81 28	92.75 12	46.88 51	93.28 21	78.79 16	84.07 39	91.50 38

EPNet		78.36 18	78.49 14	77.97 62	85.49 45	52.04 136	89.36 29	84.07 115	73.22 4	77.03 16	91.72 29	49.32 36	90.17 71	73.46 48	82.77 43	91.69 29
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 19	78.26 15	78.48 47	81.33 132	56.31 29	81.59 183	86.41 59	69.61 18	81.72 4	88.16 89	55.09 10	88.04 140	74.12 42	86.31 18	91.09 45

APDe-MVS		78.44 15	78.20 16	79.19 31	88.56 12	54.55 68	89.76 24	87.77 39	55.91 210	78.56 11	92.49 15	48.20 38	92.65 28	79.49 13	83.04 42	90.39 62

lupinMVS		78.38 17	78.11 17	79.19 31	83.02 87	55.24 45	91.57 7	84.82 90	69.12 19	76.67 17	92.02 22	44.82 77	90.23 69	80.83 9	80.09 65	92.08 24

alignmvs		78.08 21	77.98 18	78.39 51	83.53 73	53.22 106	89.77 23	85.45 71	66.11 43	76.59 19	91.99 24	54.07 14	89.05 91	77.34 25	77.00 90	92.89 14

VNet		77.99 23	77.92 19	78.19 56	87.43 23	50.12 173	90.93 12	91.41 3	67.48 32	75.12 21	90.15 62	46.77 52	91.00 48	73.52 47	78.46 77	93.44 4

canonicalmvs		78.17 20	77.86 20	79.12 34	84.30 61	54.22 72	87.71 42	84.57 97	67.70 30	77.70 13	92.11 21	50.90 28	89.95 74	78.18 22	77.54 84	93.20 9

DeepC-MVS_fast	67.50 3	78.00 22	77.63 21	79.13 33	88.52 13	55.12 50	89.95 19	85.98 65	68.31 23	71.33 53	92.75 12	45.52 69	90.37 63	71.15 59	85.14 30	91.91 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		77.82 24	77.59 22	78.49 46	85.25 52	50.27 172	90.02 17	90.57 5	56.58 198	74.26 26	91.60 32	54.26 11	92.16 35	75.87 30	79.91 69	93.05 12

WTY-MVS		77.47 28	77.52 23	77.30 75	88.33 16	46.25 233	88.46 36	90.32 6	71.40 10	72.32 46	91.72 29	53.44 15	92.37 31	66.28 83	75.42 103	93.28 7

Regformer-1		77.80 25	77.44 24	78.88 37	87.78 20	52.44 128	87.60 44	90.08 8	68.86 20	72.49 44	91.79 26	47.69 43	94.90 10	73.57 46	77.05 87	89.31 81

test_prior3		77.59 26	77.33 25	78.39 51	86.35 31	54.91 58	89.04 32	85.45 71	61.88 104	73.55 30	91.46 36	48.01 41	89.70 80	74.73 37	85.46 25	90.55 54

LFMVS		78.52 14	77.14 26	82.67 3	89.58 7	58.90 6	91.27 9	88.05 33	63.22 89	74.63 24	90.83 47	41.38 136	94.40 13	75.42 35	79.90 70	94.72 1

PHI-MVS		77.49 27	77.00 27	78.95 36	85.33 50	50.69 159	88.57 35	88.59 22	58.14 171	73.60 29	93.31 5	43.14 104	93.79 18	73.81 43	88.53 7	92.37 20

MG-MVS		78.42 16	76.99 28	82.73 2	93.17 1	64.46 1	89.93 20	88.51 23	64.83 62	73.52 32	88.09 90	48.07 39	92.19 34	62.24 113	84.53 36	91.53 35

Regformer-2		77.15 29	76.82 29	78.14 57	87.78 20	51.84 140	87.60 44	89.12 13	67.23 33	71.93 48	91.79 26	46.03 64	93.53 20	72.85 54	77.05 87	89.05 89

PVSNet_Blended		76.53 39	76.54 30	76.50 90	85.91 34	51.83 141	88.89 34	84.24 106	67.82 27	69.09 62	89.33 75	46.70 53	88.13 136	75.43 33	81.48 53	89.55 77

jason		77.01 31	76.45 31	78.69 41	79.69 156	54.74 61	90.56 15	83.99 119	68.26 24	74.10 27	90.91 44	42.14 123	89.99 73	79.30 14	79.12 73	91.36 41
jason: jason.
train_agg		76.91 32	76.40 32	78.45 49	85.68 37	55.42 39	87.59 47	84.00 117	57.84 177	72.99 35	90.98 40	44.99 73	88.58 116	78.19 19	85.32 28	91.34 43

SteuartSystems-ACMMP		77.08 30	76.33 33	79.34 29	80.98 135	55.31 43	89.76 24	86.91 51	62.94 93	71.65 51	91.56 33	42.33 119	92.56 29	77.14 26	83.69 41	90.15 69
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	67.15 4	76.90 34	76.27 34	78.80 39	80.70 141	55.02 53	86.39 68	86.71 54	66.96 36	67.91 70	89.97 65	48.03 40	91.41 44	75.60 32	84.14 38	89.96 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		76.68 38	76.24 35	78.00 60	85.64 40	54.92 56	87.55 49	83.61 128	57.99 174	72.53 42	91.05 38	45.36 70	88.10 138	77.76 24	84.68 35	90.99 48

agg_prior3		76.73 37	76.15 36	78.48 47	85.66 39	55.59 34	87.54 50	83.95 121	57.78 179	71.78 50	90.81 48	44.33 81	88.52 121	78.19 19	85.32 28	91.34 43

PAPM		76.76 35	76.07 37	78.81 38	80.20 154	59.11 5	86.86 64	86.23 63	68.60 21	70.18 59	88.84 80	51.57 24	87.16 161	65.48 89	86.68 13	90.15 69

APD-MVS		76.15 44	75.68 38	77.54 69	88.52 13	53.44 88	87.26 57	85.03 86	53.79 225	74.91 22	91.68 31	43.80 90	90.31 64	74.36 40	81.82 50	88.87 93
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		76.43 40	75.66 39	78.73 40	81.92 115	54.67 65	84.06 130	85.35 76	61.10 114	72.99 35	91.50 34	40.25 143	91.00 48	76.84 27	86.98 12	90.51 58

MAR-MVS		76.76 35	75.60 40	80.21 18	90.87 3	54.68 64	89.14 31	89.11 14	62.95 92	70.54 57	92.33 16	41.05 137	94.95 9	57.90 147	86.55 17	91.00 47
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
MVS		76.91 32	75.48 41	81.23 10	84.56 59	55.21 47	80.23 205	91.64 1	58.65 161	65.37 93	91.48 35	45.72 67	95.05 8	72.11 56	89.52 5	93.44 4

Regformer-3		76.02 47	75.47 42	77.70 65	85.49 45	51.47 147	85.12 99	90.19 7	68.52 22	69.36 60	90.66 50	46.45 61	94.81 11	70.25 63	73.16 116	86.81 128

CLD-MVS		75.60 49	75.39 43	76.24 94	80.69 142	52.40 129	90.69 13	86.20 64	74.40 3	65.01 99	88.93 77	42.05 126	90.58 58	76.57 28	73.96 111	85.73 144
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		76.39 41	75.38 44	79.42 28	85.33 50	56.47 26	88.15 37	84.97 87	65.15 61	66.06 85	89.88 66	43.79 91	92.16 35	75.03 36	80.03 68	89.64 76

CDPH-MVS		76.05 46	75.19 45	78.62 44	86.51 30	54.98 55	87.32 53	84.59 96	58.62 162	70.75 55	90.85 46	43.10 108	90.63 57	70.50 61	84.51 37	90.24 65

MP-MVS-pluss		75.54 50	75.03 46	77.04 81	81.37 131	52.65 125	84.34 123	84.46 99	61.16 112	69.14 61	91.76 28	39.98 150	88.99 100	78.19 19	84.89 33	89.48 78
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		76.08 45	74.97 47	79.44 27	84.27 64	53.33 98	91.13 10	85.88 66	65.33 58	72.37 45	89.34 73	32.52 229	92.76 26	77.90 23	75.96 98	92.22 22

MVS_Test		75.85 48	74.93 48	78.62 44	84.08 66	55.20 48	83.99 133	85.17 82	68.07 25	73.38 33	82.76 156	50.44 29	89.00 98	65.90 85	80.61 58	91.64 30

SD-MVS		76.18 43	74.85 49	80.18 19	85.39 49	56.90 20	85.75 81	82.45 150	56.79 193	74.48 25	91.81 25	43.72 95	90.75 54	74.61 39	78.65 76	92.91 13

Regformer-4		75.06 54	74.59 50	76.47 91	85.49 45	50.33 168	85.12 99	88.61 20	66.42 38	68.48 65	90.66 50	44.15 86	92.68 27	69.24 66	73.16 116	86.39 136

MP-MVS		74.99 55	74.33 51	76.95 84	82.89 93	53.05 116	85.63 84	83.50 131	57.86 176	67.25 74	90.24 58	43.38 101	88.85 109	76.03 29	82.23 48	88.96 91

PAPR		75.20 53	74.13 52	78.41 50	88.31 17	55.10 52	84.31 124	85.66 68	63.76 80	67.55 71	90.73 49	43.48 100	89.40 85	66.36 82	77.03 89	90.73 52

CHOSEN 1792x2688		76.24 42	74.03 53	82.88 1	83.09 84	62.84 2	85.73 82	85.39 74	69.79 16	64.87 101	83.49 147	41.52 135	93.69 19	70.55 60	81.82 50	92.12 23

DWT-MVSNet_test		75.47 51	73.87 54	80.29 16	87.33 25	57.05 18	82.86 158	87.96 35	72.59 6	67.29 73	87.79 94	51.61 23	91.52 42	54.75 175	72.63 124	92.29 21

#test#		74.86 56	73.78 55	78.10 58	84.30 61	53.68 80	86.95 61	84.36 100	59.00 153	65.78 88	90.56 52	40.70 140	90.90 51	71.48 57	80.88 54	89.71 73

Effi-MVS+		75.24 52	73.61 56	80.16 20	81.92 115	57.42 14	85.21 93	76.71 247	60.68 121	73.32 34	89.34 73	47.30 46	91.63 41	68.28 71	79.72 71	91.42 39

PVSNet_BlendedMVS		73.42 71	73.30 57	73.76 152	85.91 34	51.83 141	86.18 73	84.24 106	65.40 55	69.09 62	80.86 184	46.70 53	88.13 136	75.43 33	65.92 169	81.33 214

CANet_DTU		73.71 69	73.14 58	75.40 111	82.61 102	50.05 174	84.67 119	79.36 202	69.72 17	75.39 20	90.03 64	29.41 251	85.93 195	67.99 73	79.11 74	90.22 66

HY-MVS	67.03 5	73.90 65	73.14 58	76.18 96	84.70 58	47.36 218	75.56 252	86.36 61	66.27 41	70.66 56	83.91 134	51.05 27	89.31 86	67.10 77	72.61 125	91.88 27

HFP-MVS		74.37 58	73.13 60	78.10 58	84.30 61	53.68 80	85.58 85	84.36 100	56.82 191	65.78 88	90.56 52	40.70 140	90.90 51	69.18 67	80.88 54	89.71 73

MPTG		74.15 63	73.11 61	77.27 77	81.54 125	53.57 83	84.02 132	81.31 169	59.41 136	68.39 66	90.96 42	36.07 195	89.01 96	73.80 44	82.45 46	89.23 83

ACMMPR		73.76 67	72.61 62	77.24 80	83.92 70	52.96 120	85.58 85	84.29 102	56.82 191	65.12 95	90.45 54	37.24 181	90.18 70	69.18 67	80.84 56	88.58 100

EI-MVSNet-Vis-set		73.19 74	72.60 63	74.99 120	82.56 103	49.80 177	82.55 162	89.00 15	66.17 42	65.89 87	88.98 76	43.83 89	92.29 32	65.38 96	69.01 145	82.87 191

region2R		73.75 68	72.55 64	77.33 74	83.90 71	52.98 119	85.54 88	84.09 108	56.83 190	65.10 96	90.45 54	37.34 179	90.24 68	68.89 69	80.83 57	88.77 96

3Dnovator	64.70 6	74.46 57	72.48 65	80.41 15	82.84 95	55.40 42	83.08 152	88.61 20	67.61 31	59.85 152	88.66 81	34.57 210	93.97 15	58.42 140	88.70 6	91.85 28

PVSNet_Blended_VisFu		73.40 72	72.44 66	76.30 92	81.32 133	54.70 63	85.81 77	78.82 209	63.70 81	64.53 105	85.38 121	47.11 49	87.38 158	67.75 74	77.55 83	86.81 128

TESTMET0.1,1		72.86 77	72.33 67	74.46 134	81.98 113	50.77 157	85.13 96	85.47 70	66.09 44	67.30 72	83.69 139	37.27 180	83.57 234	65.06 97	78.97 75	89.05 89

MVSTER		73.25 73	72.33 67	76.01 101	85.54 44	53.76 79	83.52 139	87.16 47	67.06 35	63.88 115	81.66 174	52.77 18	90.44 59	64.66 98	64.69 174	83.84 174

PatchFormer-LS_test		74.17 61	72.30 69	79.77 25	86.61 29	57.26 16	82.02 168	84.80 92	71.85 9	64.73 102	87.52 99	50.33 31	90.40 62	54.23 177	68.63 149	91.64 30

CostFormer		73.89 66	72.30 69	78.66 42	82.36 106	56.58 22	75.56 252	85.30 77	66.06 46	70.50 58	76.88 224	57.02 8	89.06 89	68.27 72	68.74 147	90.33 64

MSLP-MVS++		74.21 60	72.25 71	80.11 21	81.45 129	56.47 26	86.32 70	79.65 196	58.19 170	66.36 82	92.29 17	36.11 194	90.66 56	67.39 75	82.49 45	93.18 10

MVSFormer		73.53 70	72.19 72	77.57 68	83.02 87	55.24 45	81.63 180	81.44 166	50.28 258	76.67 17	90.91 44	44.82 77	86.11 186	60.83 122	80.09 65	91.36 41

VDDNet		74.37 58	72.13 73	81.09 11	79.58 157	56.52 25	90.02 17	86.70 55	52.61 242	71.23 54	87.20 102	31.75 239	93.96 16	74.30 41	75.77 101	92.79 16

API-MVS		74.17 61	72.07 74	80.49 14	90.02 6	58.55 7	87.30 55	84.27 103	57.51 183	65.77 90	87.77 96	41.61 134	95.97 2	51.71 195	82.63 44	86.94 122

PMMVS		72.98 75	72.05 75	75.78 105	83.57 72	48.60 198	84.08 128	82.85 144	61.62 108	68.24 68	90.33 57	28.35 256	87.78 149	72.71 55	76.69 92	90.95 49

IB-MVS	68.87 2	74.01 64	72.03 76	79.94 23	83.04 86	55.50 36	90.24 16	88.65 18	67.14 34	61.38 135	81.74 173	53.21 16	94.28 14	60.45 127	62.41 202	90.03 71
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
EI-MVSNet-UG-set		72.37 83	71.73 77	74.29 138	81.60 121	49.29 186	81.85 173	88.64 19	65.29 60	65.05 97	88.29 86	43.18 102	91.83 39	63.74 100	67.97 153	81.75 207

XVS		72.92 76	71.62 78	76.81 86	83.41 74	52.48 126	84.88 114	83.20 138	58.03 172	63.91 113	89.63 70	35.50 203	89.78 77	65.50 87	80.50 60	88.16 103

nrg030		72.27 88	71.56 79	74.42 135	75.93 207	50.60 161	86.97 60	83.21 137	62.75 95	67.15 75	84.38 129	50.07 32	86.66 174	71.19 58	62.37 203	85.99 139

HPM-MVS		72.60 80	71.50 80	75.89 103	82.02 112	51.42 149	80.70 198	83.05 140	56.12 207	64.03 112	89.53 71	37.55 173	88.37 125	70.48 62	80.04 67	87.88 109

CP-MVS		72.59 82	71.46 81	76.00 102	82.93 92	52.32 133	86.93 63	82.48 149	55.15 217	63.65 118	90.44 56	35.03 207	88.53 120	68.69 70	77.83 81	87.15 121

HQP-MVS		72.34 84	71.44 82	75.03 117	79.02 165	51.56 144	88.00 38	83.68 125	65.45 52	64.48 106	85.13 122	37.35 177	88.62 113	66.70 79	73.12 118	84.91 156

VPNet		72.07 89	71.42 83	74.04 143	78.64 175	47.17 222	89.91 22	87.97 34	72.56 7	64.66 103	85.04 124	41.83 130	88.33 129	61.17 120	60.97 208	86.62 130

MS-PatchMatch		72.34 84	71.26 84	75.61 106	82.38 105	55.55 35	88.00 38	89.95 11	65.38 56	56.51 202	80.74 185	32.28 232	92.89 24	57.95 146	88.10 9	78.39 257

MTAPA		72.73 78	71.22 85	77.27 77	81.54 125	53.57 83	67.06 301	81.31 169	59.41 136	68.39 66	90.96 42	36.07 195	89.01 96	73.80 44	82.45 46	89.23 83

PGM-MVS		72.60 80	71.20 86	76.80 88	82.95 90	52.82 122	83.07 153	82.14 151	56.51 203	63.18 122	89.81 67	35.68 202	89.76 79	67.30 76	80.19 64	87.83 110

Fast-Effi-MVS+		72.73 78	71.15 87	77.48 71	82.75 97	54.76 60	86.77 65	80.64 182	63.05 91	65.93 86	84.01 132	44.42 80	89.03 95	56.45 159	76.36 97	88.64 98

mvs_anonymous		72.29 86	70.74 88	76.94 85	82.85 94	54.72 62	78.43 231	81.54 165	63.77 79	61.69 134	79.32 192	51.11 26	85.31 203	62.15 115	75.79 100	90.79 51

VPA-MVSNet		71.12 100	70.66 89	72.49 174	78.75 170	44.43 249	87.64 43	90.02 9	63.97 75	65.02 98	81.58 175	42.14 123	87.42 157	63.42 102	63.38 186	85.63 148

3Dnovator+	62.71 7	72.29 86	70.50 90	77.65 67	83.40 77	51.29 153	87.32 53	86.40 60	59.01 152	58.49 177	88.32 85	32.40 230	91.27 45	57.04 154	82.15 49	90.38 63

MVP-Stereo		70.97 103	70.44 91	72.59 171	76.03 206	51.36 150	85.02 111	86.99 50	60.31 125	56.53 201	78.92 197	40.11 147	90.00 72	60.00 133	90.01 2	76.41 282
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mPP-MVS		71.79 93	70.38 92	76.04 100	82.65 101	52.06 135	84.45 121	81.78 162	55.59 214	62.05 132	89.68 69	33.48 220	88.28 133	65.45 92	78.24 79	87.77 112

DP-MVS Recon		71.99 90	70.31 93	77.01 83	90.65 4	53.44 88	89.37 28	82.97 142	56.33 205	63.56 120	89.47 72	34.02 214	92.15 37	54.05 178	72.41 126	85.43 150

xiu_mvs_v1_base_debu		71.60 94	70.29 94	75.55 107	77.26 194	53.15 109	85.34 89	79.37 199	55.83 211	72.54 39	90.19 59	22.38 292	86.66 174	73.28 50	76.39 94	86.85 125

xiu_mvs_v1_base		71.60 94	70.29 94	75.55 107	77.26 194	53.15 109	85.34 89	79.37 199	55.83 211	72.54 39	90.19 59	22.38 292	86.66 174	73.28 50	76.39 94	86.85 125

xiu_mvs_v1_base_debi		71.60 94	70.29 94	75.55 107	77.26 194	53.15 109	85.34 89	79.37 199	55.83 211	72.54 39	90.19 59	22.38 292	86.66 174	73.28 50	76.39 94	86.85 125

FIs		70.00 119	70.24 97	69.30 229	77.93 185	38.55 288	83.99 133	87.72 41	66.86 37	57.66 188	84.17 131	52.28 21	85.31 203	52.72 190	68.80 146	84.02 166

sss		70.49 110	70.13 98	71.58 198	81.59 122	39.02 286	80.78 197	84.71 95	59.34 139	66.61 80	88.09 90	37.17 182	85.52 199	61.82 117	71.02 135	90.20 67

EPP-MVSNet		71.14 99	70.07 99	74.33 137	79.18 164	46.52 227	83.81 135	86.49 57	56.32 206	57.95 181	84.90 126	54.23 12	89.14 88	58.14 144	69.65 142	87.33 118

PAPM_NR		71.80 92	69.98 100	77.26 79	81.54 125	53.34 96	78.60 229	85.25 80	53.46 228	60.53 148	88.66 81	45.69 68	89.24 87	56.49 156	79.62 72	89.19 86

HQP_MVS		70.96 104	69.91 101	74.12 141	77.95 183	49.57 179	85.76 79	82.59 147	63.60 84	62.15 129	83.28 151	36.04 198	88.30 131	65.46 90	72.34 128	84.49 159

tpmrst		71.04 102	69.77 102	74.86 127	83.19 81	55.86 33	75.64 251	78.73 212	67.88 26	64.99 100	73.73 251	49.96 33	79.56 271	65.92 84	67.85 155	89.14 88

OPM-MVS		70.75 107	69.58 103	74.26 139	75.55 211	51.34 151	86.05 75	83.29 135	61.94 103	62.95 125	85.77 117	34.15 213	88.44 123	65.44 93	71.07 134	82.99 188

CDS-MVSNet		70.48 111	69.43 104	73.64 154	77.56 189	48.83 194	83.51 143	77.45 239	63.27 88	62.33 128	85.54 120	43.85 88	83.29 236	57.38 153	74.00 110	88.79 95
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		71.11 101	69.41 105	76.22 95	79.32 160	50.49 164	80.23 205	85.14 85	59.44 135	58.93 167	88.89 79	33.83 219	89.60 84	61.49 118	77.42 86	88.57 101

1112_ss		70.05 115	69.37 106	72.10 177	80.77 140	42.78 263	85.12 99	76.75 246	59.69 131	61.19 137	92.12 19	47.48 44	83.84 230	53.04 184	68.21 150	89.66 75

Vis-MVSNet		70.61 109	69.34 107	74.42 135	80.95 137	48.49 203	86.03 76	77.51 238	58.74 160	65.55 92	87.78 95	34.37 211	85.95 194	52.53 191	80.61 58	88.80 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
QAPM		71.88 91	69.33 108	79.52 26	82.20 107	54.30 70	86.30 71	88.77 17	56.61 197	59.72 154	87.48 100	33.90 217	95.36 4	47.48 217	81.49 52	88.90 92

ACMMP		70.81 106	69.29 109	75.39 112	81.52 128	51.92 138	83.43 144	83.03 141	56.67 196	58.80 172	88.91 78	31.92 237	88.58 116	65.89 86	73.39 115	85.67 145
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
XXY-MVS		70.18 112	69.28 110	72.89 166	77.64 187	42.88 262	85.06 108	87.50 45	62.58 96	62.66 127	82.34 166	43.64 97	89.83 76	58.42 140	63.70 182	85.96 141

ab-mvs		70.65 108	69.11 111	75.29 113	80.87 138	46.23 234	73.48 265	85.24 81	59.99 128	66.65 78	80.94 183	43.13 105	88.69 111	63.58 101	68.07 151	90.95 49

test-LLR		69.65 125	69.01 112	71.60 196	78.67 172	48.17 211	85.13 96	79.72 193	59.18 145	63.13 123	82.58 161	36.91 186	80.24 263	60.56 125	75.17 105	86.39 136

DI_MVS_plusplus_test		71.30 98	68.98 113	78.26 55	72.76 246	54.08 76	81.72 176	83.22 136	65.75 51	51.94 235	78.47 204	36.01 200	90.31 64	73.33 49	77.60 82	90.40 61

test_normal		71.31 97	68.95 114	78.39 51	72.30 264	54.25 71	81.67 177	84.05 116	65.94 50	51.31 239	78.09 210	36.06 197	90.43 61	73.00 53	78.09 80	90.50 59

EI-MVSNet		69.70 124	68.70 115	72.68 169	75.00 216	48.90 192	79.54 217	87.16 47	61.05 115	63.88 115	83.74 137	45.87 65	90.44 59	57.42 152	64.68 175	78.70 247

BH-w/o		70.02 116	68.51 116	74.56 133	82.77 96	50.39 166	86.60 66	78.14 223	59.77 129	59.65 155	85.57 119	39.27 155	87.30 159	49.86 203	74.94 108	85.99 139

tpm2		70.82 105	68.44 117	77.98 61	80.78 139	56.11 31	74.21 262	81.28 172	60.24 126	68.04 69	75.27 242	52.26 22	88.50 122	55.82 163	68.03 152	89.33 80

PCF-MVS	61.03 10	70.10 113	68.40 118	75.22 116	77.15 198	51.99 137	79.30 225	82.12 155	56.47 204	61.88 133	86.48 114	43.98 87	87.24 160	55.37 167	72.79 123	86.43 135
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		70.02 116	68.35 119	75.03 117	79.19 163	51.48 146	78.50 230	76.65 249	59.71 130	67.10 76	80.32 187	42.81 115	87.12 162	58.48 138	72.37 127	86.49 132

UniMVSNet_NR-MVSNet		68.82 141	68.29 120	70.40 216	75.71 210	42.59 265	84.23 125	86.78 53	66.31 40	58.51 174	82.45 163	51.57 24	84.64 217	53.11 182	55.96 254	83.96 171

APD-MVS_3200maxsize		69.62 126	68.23 121	73.80 151	81.58 123	48.22 210	81.91 171	79.50 198	48.21 266	64.24 111	89.75 68	31.91 238	87.55 154	63.08 103	73.85 113	85.64 147

TAMVS		69.51 128	68.16 122	73.56 157	76.30 202	48.71 195	82.57 161	77.17 243	62.10 101	61.32 136	84.23 130	41.90 128	83.46 235	54.80 174	73.09 120	88.50 102

BH-RMVSNet		70.08 114	68.01 123	76.27 93	84.21 65	51.22 155	87.29 56	79.33 204	58.96 155	63.63 119	86.77 108	33.29 222	90.30 67	44.63 233	73.96 111	87.30 120

FC-MVSNet-test		67.49 165	67.91 124	66.21 261	76.06 204	33.06 312	80.82 196	87.18 46	64.44 67	54.81 211	82.87 154	50.40 30	82.60 245	48.05 214	66.55 160	82.98 189

MVS_111021_LR		69.07 136	67.91 124	72.54 172	77.27 193	49.56 181	79.77 213	73.96 276	59.33 141	60.73 146	87.82 93	30.19 248	81.53 250	69.94 64	72.19 130	86.53 131

114514_t		69.87 122	67.88 126	75.85 104	88.38 15	52.35 132	86.94 62	83.68 125	53.70 226	55.68 209	85.60 118	30.07 249	91.20 46	55.84 162	71.02 135	83.99 168

TR-MVS		69.71 123	67.85 127	75.27 114	82.94 91	48.48 204	87.40 52	80.86 179	57.15 187	64.61 104	87.08 105	32.67 228	89.64 83	46.38 225	71.55 132	87.68 114

PVSNet	62.49 8	69.27 135	67.81 128	73.64 154	84.41 60	51.85 139	84.63 120	77.80 231	66.42 38	59.80 153	84.95 125	22.14 296	80.44 260	55.03 170	75.11 107	88.62 99

v1141		69.50 129	67.67 129	74.98 121	72.73 248	53.41 91	85.08 105	82.14 151	64.79 64	60.88 141	78.19 207	43.09 109	89.04 92	62.51 109	59.61 218	82.47 196

divwei89l23v2f112		69.50 129	67.67 129	74.98 121	72.72 249	53.41 91	85.08 105	82.14 151	64.79 64	60.88 141	78.19 207	43.11 106	89.04 92	62.51 109	59.62 217	82.48 195

v1		69.49 131	67.67 129	74.98 121	72.69 250	53.41 91	85.08 105	82.13 154	64.80 63	60.87 143	78.19 207	43.11 106	89.04 92	62.51 109	59.61 218	82.49 194

v2v482		69.55 127	67.64 132	75.26 115	72.32 263	53.83 78	84.93 113	81.94 157	65.37 57	60.80 145	79.25 193	41.62 133	88.98 101	63.03 104	59.51 221	82.98 189

v1neww		69.43 132	67.62 133	74.89 124	72.90 241	53.31 99	85.12 99	81.11 173	64.29 69	61.00 138	78.53 200	42.88 112	88.98 101	62.66 107	60.06 212	82.37 198

v7new		69.43 132	67.62 133	74.89 124	72.90 241	53.31 99	85.12 99	81.11 173	64.29 69	61.00 138	78.53 200	42.88 112	88.98 101	62.66 107	60.06 212	82.37 198

v6		69.43 132	67.61 135	74.88 126	72.87 245	53.30 103	85.12 99	81.10 175	64.29 69	60.99 140	78.52 202	42.88 112	88.98 101	62.67 106	60.06 212	82.37 198

HyFIR lowres test		69.94 121	67.58 136	77.04 81	77.11 199	57.29 15	81.49 186	79.11 207	58.27 168	58.86 170	80.41 186	42.33 119	86.96 167	61.91 116	68.68 148	86.87 123

mvs-test1		69.04 137	67.57 137	73.44 159	75.17 212	51.68 143	86.57 67	74.48 269	62.15 99	62.07 131	85.79 116	30.59 245	87.48 155	65.40 94	65.94 168	81.18 218

IS-MVSNet		68.80 143	67.55 138	72.54 172	78.50 178	43.43 257	81.03 192	79.35 203	59.12 149	57.27 197	86.71 109	46.05 63	87.70 151	44.32 234	75.60 102	86.49 132

OpenMVS	61.00 11	69.99 120	67.55 138	77.30 75	78.37 181	54.07 77	84.36 122	85.76 67	57.22 186	56.71 199	87.67 97	30.79 244	92.83 25	43.04 239	84.06 40	85.01 154

tpm		68.36 151	67.48 140	70.97 206	79.93 155	51.34 151	76.58 241	78.75 211	67.73 28	63.54 121	74.86 244	48.33 37	72.36 314	53.93 179	63.71 181	89.21 85

FMVSNet3		68.84 140	67.40 141	73.19 161	85.05 53	48.53 201	85.71 83	85.36 75	60.90 117	57.58 189	79.15 195	42.16 122	86.77 170	47.25 219	63.40 183	84.27 163

test-mter		68.36 151	67.29 142	71.60 196	78.67 172	48.17 211	85.13 96	79.72 193	53.38 229	63.13 123	82.58 161	27.23 264	80.24 263	60.56 125	75.17 105	86.39 136

thres200		68.71 147	67.27 143	73.02 162	84.73 57	46.76 224	85.03 110	87.73 40	62.34 98	59.87 151	83.45 148	43.15 103	88.32 130	31.25 281	67.91 154	83.98 169

PS-MVSNAJss		68.78 145	67.17 144	73.62 156	73.01 236	48.33 209	84.95 112	84.81 91	59.30 142	58.91 169	79.84 190	37.77 166	88.86 108	62.83 105	63.12 193	83.67 177

tpmp4_e23		70.01 118	67.13 145	78.65 43	81.93 114	57.90 10	73.99 263	81.35 168	60.61 122	65.28 94	73.78 250	52.48 20	88.60 115	48.40 212	66.35 166	89.44 79

UGNet		68.71 147	67.11 146	73.50 158	80.55 152	47.61 216	84.08 128	78.51 217	59.45 134	65.68 91	82.73 159	23.78 282	85.08 208	52.80 186	76.40 93	87.80 111
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
v1144		68.81 142	66.82 147	74.80 130	72.34 262	53.46 86	84.68 118	81.77 163	64.25 72	60.28 150	77.91 211	40.23 144	88.95 105	60.37 128	59.52 220	81.97 201

UniMVSNet (Re)		67.71 160	66.80 148	70.45 214	74.44 221	42.93 261	82.42 164	84.90 89	63.69 82	59.63 156	80.99 182	47.18 47	85.23 205	51.17 198	56.75 248	83.19 186

v7		68.76 146	66.79 149	74.68 131	72.60 253	53.37 94	84.72 117	80.88 178	63.80 78	60.43 149	78.21 206	40.05 149	88.89 107	60.34 129	60.07 211	81.77 206

1121		68.79 144	66.77 150	74.82 128	83.08 85	53.46 86	80.23 205	71.53 293	45.47 283	66.31 83	87.19 103	34.02 214	85.13 206	52.78 187	80.36 62	85.87 143

WR-MVS		67.58 162	66.76 151	70.04 225	75.92 208	45.06 246	86.23 72	85.28 78	64.31 68	58.50 176	81.00 181	44.80 79	82.00 249	49.21 207	55.57 259	83.06 187

EPNet_dtu		66.25 188	66.71 152	64.87 276	78.66 174	34.12 307	82.80 159	75.51 262	61.75 106	64.47 109	86.90 107	37.06 183	72.46 313	43.65 237	69.63 143	88.02 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GA-MVS		69.04 137	66.70 153	76.06 99	75.11 214	52.36 131	83.12 151	80.23 186	63.32 87	60.65 147	79.22 194	30.98 243	88.37 125	61.25 119	66.41 161	87.46 116

BH-untuned		68.28 153	66.40 154	73.91 146	81.62 120	50.01 175	85.56 87	77.39 240	57.63 182	57.47 194	83.69 139	36.36 192	87.08 163	44.81 232	73.08 121	84.65 158

v148		68.24 154	66.35 155	73.88 147	71.76 268	51.47 147	84.23 125	81.90 161	63.69 82	58.94 166	76.44 228	43.72 95	87.78 149	60.63 124	55.86 256	82.39 197

HPM-MVS_fast		67.86 157	66.28 156	72.61 170	80.67 143	48.34 208	81.18 189	75.95 258	50.81 257	59.55 159	88.05 92	27.86 259	85.98 191	58.83 136	73.58 114	83.51 178

UA-Net		67.32 169	66.23 157	70.59 212	78.85 168	41.23 276	73.60 264	75.45 264	61.54 109	66.61 80	84.53 127	38.73 159	86.57 179	42.48 243	74.24 109	83.98 169

Test_1112_low_res		67.18 172	66.23 157	70.02 226	78.75 170	41.02 277	83.43 144	73.69 279	57.29 185	58.45 178	82.39 165	45.30 71	80.88 255	50.50 200	66.26 167	88.16 103

abl_6		68.03 155	66.15 159	73.66 153	78.54 177	48.48 204	79.77 213	78.04 227	47.39 270	63.70 117	88.25 87	28.21 257	89.06 89	60.17 132	71.25 133	83.45 179

tfpn200view9		67.57 163	66.13 160	71.89 190	84.05 67	45.07 243	83.40 146	87.71 42	60.79 118	57.79 185	82.76 156	43.53 98	87.80 145	28.80 286	66.36 162	82.78 192

thres400		67.40 168	66.13 160	71.19 203	84.05 67	45.07 243	83.40 146	87.71 42	60.79 118	57.79 185	82.76 156	43.53 98	87.80 145	28.80 286	66.36 162	80.71 225

cascas		69.01 139	66.13 160	77.66 66	79.36 158	55.41 41	86.99 59	83.75 124	56.69 195	58.92 168	81.35 176	24.31 280	92.10 38	53.23 181	70.61 137	85.46 149

NR-MVSNet		67.25 170	65.99 163	71.04 205	73.27 234	43.91 253	85.32 92	84.75 94	66.05 47	53.65 222	82.11 170	45.05 72	85.97 193	47.55 216	56.18 252	83.24 184

CPTT-MVS		67.15 173	65.84 164	71.07 204	80.96 136	50.32 169	81.94 170	74.10 273	46.18 279	57.91 182	87.64 98	29.57 250	81.31 252	64.10 99	70.18 140	81.56 209

FMVSNet2		67.57 163	65.79 165	72.90 164	82.71 98	47.97 214	85.15 95	84.93 88	58.55 163	56.71 199	78.26 205	36.72 189	86.67 173	46.15 227	62.94 196	84.07 165

v144192		67.86 157	65.76 166	74.16 140	71.68 269	53.09 113	84.14 127	80.83 180	62.85 94	59.21 164	77.28 220	39.30 154	88.00 141	58.67 137	57.88 243	81.40 213

v1192		67.96 156	65.74 167	74.63 132	71.79 267	53.43 90	84.06 130	80.99 177	63.19 90	59.56 158	77.46 217	37.50 176	88.65 112	58.20 143	58.93 226	81.79 205

DU-MVS		66.84 180	65.74 167	70.16 219	73.27 234	42.59 265	81.50 184	82.92 143	63.53 86	58.51 174	82.11 170	40.75 138	84.64 217	53.11 182	55.96 254	83.24 184

Test4		68.64 149	65.68 169	77.53 70	67.78 295	53.34 96	79.42 220	82.84 145	65.96 49	46.54 280	76.15 236	25.16 276	88.83 110	69.74 65	77.53 85	90.43 60

Vis-MVSNet (Re-imp)		65.52 193	65.63 170	65.17 274	77.49 190	30.54 320	75.49 255	77.73 235	59.34 139	52.26 233	86.69 110	49.38 35	80.53 259	37.07 257	75.28 104	84.42 161

TranMVSNet+NR-MVSNet		66.94 178	65.61 171	70.93 208	73.45 231	43.38 258	83.02 155	84.25 104	65.31 59	58.33 180	81.90 172	39.92 151	85.52 199	49.43 206	54.89 262	83.89 173

V42		67.66 161	65.60 172	73.86 148	70.69 277	53.63 82	81.50 184	78.61 215	63.85 77	59.49 160	77.49 216	37.98 163	87.65 152	62.33 112	58.43 233	80.29 234

AdaColmap		67.86 157	65.48 173	75.00 119	88.15 19	54.99 54	86.10 74	76.63 250	49.30 264	57.80 184	86.65 111	29.39 252	88.94 106	45.10 231	70.21 139	81.06 219

GBi-Net		67.09 174	65.47 174	71.96 184	82.71 98	46.36 229	83.52 139	83.31 132	58.55 163	57.58 189	76.23 232	36.72 189	86.20 182	47.25 219	63.40 183	83.32 181

test1		67.09 174	65.47 174	71.96 184	82.71 98	46.36 229	83.52 139	83.31 132	58.55 163	57.58 189	76.23 232	36.72 189	86.20 182	47.25 219	63.40 183	83.32 181

EPMVS		68.45 150	65.44 176	77.47 72	84.91 55	56.17 30	71.89 283	81.91 160	61.72 107	60.85 144	72.49 264	36.21 193	87.06 164	47.32 218	71.62 131	89.17 87

conf200view11		66.80 181	65.42 177	70.95 207	83.29 78	43.15 259	81.67 177	87.78 36	59.04 150	55.92 206	82.18 168	43.73 92	87.80 145	28.80 286	66.36 162	81.89 202

thres100view900		66.87 179	65.42 177	71.24 202	83.29 78	43.15 259	81.67 177	87.78 36	59.04 150	55.92 206	82.18 168	43.73 92	87.80 145	28.80 286	66.36 162	82.78 192

IterMVS-LS		66.63 182	65.36 179	70.42 215	75.10 215	48.90 192	81.45 187	76.69 248	61.05 115	55.71 208	77.10 223	45.86 66	83.65 233	57.44 151	57.88 243	78.70 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		67.45 166	65.23 180	74.10 142	71.51 272	52.90 121	83.75 137	80.44 185	62.48 97	59.12 165	77.13 221	36.98 184	87.90 142	57.53 150	58.14 238	81.49 210

thres600view7		66.46 185	65.12 181	70.47 213	83.41 74	43.80 255	82.15 167	87.78 36	59.37 138	56.02 205	82.21 167	43.73 92	86.90 168	26.51 299	64.94 171	80.71 225

OMC-MVS		65.97 191	65.06 182	68.71 241	72.97 237	42.58 267	78.61 228	75.35 265	54.72 220	59.31 162	86.25 115	33.30 221	77.88 283	57.99 145	67.05 157	85.66 146

v8		67.25 170	64.99 183	74.04 143	72.89 243	53.31 99	82.37 165	80.11 188	61.54 109	54.29 216	76.02 238	42.89 111	88.41 124	58.43 139	56.36 249	80.39 233

Effi-MVS+-dtu		66.24 189	64.96 184	70.08 221	75.17 212	49.64 178	82.01 169	74.48 269	62.15 99	57.83 183	76.08 237	30.59 245	83.79 231	65.40 94	60.93 209	76.81 277

v1240		66.99 177	64.68 185	73.93 145	71.38 273	52.66 124	83.39 148	79.98 189	61.97 102	58.44 179	77.11 222	35.25 205	87.81 144	56.46 158	58.15 236	81.33 214

LPG-MVS_test		66.44 186	64.58 186	72.02 181	74.42 222	48.60 198	83.07 153	80.64 182	54.69 221	53.75 220	83.83 135	25.73 273	86.98 165	60.33 130	64.71 172	80.48 231

gg-mvs-nofinetune		67.43 167	64.53 187	76.13 97	85.95 33	47.79 215	64.38 306	88.28 31	39.34 306	66.62 79	41.27 334	58.69 5	89.00 98	49.64 205	86.62 14	91.59 32

ACMP	61.11 9	66.24 189	64.33 188	72.00 183	74.89 218	49.12 187	83.18 150	79.83 191	55.41 216	52.29 231	82.68 160	25.83 271	86.10 188	60.89 121	63.94 180	80.78 223
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Baseline_NR-MVSNet		65.49 194	64.27 189	69.13 230	74.37 224	41.65 272	83.39 148	78.85 208	59.56 132	59.62 157	76.88 224	40.75 138	87.44 156	49.99 202	55.05 260	78.28 264

v10		66.61 183	64.20 190	73.83 150	72.59 255	53.37 94	81.88 172	79.91 190	61.11 113	54.09 218	75.60 240	40.06 148	88.26 134	56.47 157	56.10 253	79.86 238

Fast-Effi-MVS+-dtu		66.53 184	64.10 191	73.84 149	72.41 260	52.30 134	84.73 116	75.66 261	59.51 133	56.34 203	79.11 196	28.11 258	85.85 196	57.74 149	63.29 188	83.35 180

PatchmatchNet		67.07 176	63.63 192	77.40 73	83.10 82	58.03 8	72.11 280	77.77 233	58.85 158	59.37 161	70.83 275	37.84 165	84.93 213	42.96 240	69.83 141	89.26 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
view600		64.79 195	63.45 193	68.82 235	82.13 108	40.75 279	79.41 221	88.29 27	56.54 199	53.26 224	81.30 177	44.26 82	85.01 209	22.97 310	62.85 197	80.71 225

view800		64.79 195	63.45 193	68.82 235	82.13 108	40.75 279	79.41 221	88.29 27	56.54 199	53.26 224	81.30 177	44.26 82	85.01 209	22.97 310	62.85 197	80.71 225

conf0.05thres1000		64.79 195	63.45 193	68.82 235	82.13 108	40.75 279	79.41 221	88.29 27	56.54 199	53.26 224	81.30 177	44.26 82	85.01 209	22.97 310	62.85 197	80.71 225

tfpn		64.79 195	63.45 193	68.82 235	82.13 108	40.75 279	79.41 221	88.29 27	56.54 199	53.26 224	81.30 177	44.26 82	85.01 209	22.97 310	62.85 197	80.71 225

tfpn_ndepth		64.50 200	63.34 197	67.99 245	81.84 117	38.30 290	79.26 226	83.57 130	53.69 227	52.86 229	84.51 128	46.96 50	84.79 214	24.28 305	63.09 194	80.87 222

tpm cat1		66.28 187	62.78 198	76.77 89	81.40 130	57.14 17	70.03 292	77.19 242	53.00 233	58.76 173	70.73 277	46.17 62	86.73 172	43.27 238	64.46 176	86.44 134

pm-mvs1		64.12 206	62.56 199	68.78 240	71.68 269	38.87 287	82.89 157	81.57 164	55.54 215	53.89 219	77.82 212	37.73 169	86.74 171	48.46 211	53.49 271	80.72 224

test0.0.03 1		62.54 230	62.44 200	62.86 286	72.28 265	29.51 323	82.93 156	78.78 210	59.18 145	53.07 228	82.41 164	36.91 186	77.39 287	37.45 253	58.96 225	81.66 208

X-MVStestdata		65.85 192	62.20 201	76.81 86	83.41 74	52.48 126	84.88 114	83.20 138	58.03 172	63.91 113	4.82 352	35.50 203	89.78 77	65.50 87	80.50 60	88.16 103

FMVSNet1		64.57 199	62.11 202	71.96 184	77.32 192	46.36 229	83.52 139	83.31 132	52.43 246	54.42 214	76.23 232	27.80 260	86.20 182	42.59 242	61.34 206	83.32 181

ACMM	58.35 12	64.35 203	62.01 203	71.38 200	74.21 225	48.51 202	82.25 166	79.66 195	47.61 268	54.54 213	80.11 188	25.26 275	86.00 190	51.26 196	63.16 191	79.64 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v18		64.36 202	61.80 204	72.05 178	72.97 237	53.31 99	81.16 190	77.76 234	59.14 147	48.50 258	68.97 283	42.91 110	84.38 219	56.62 155	48.17 284	78.47 251

conf0.01		63.04 220	61.74 205	66.95 255	80.60 144	35.92 298	76.01 244	84.09 108	52.62 236	50.87 245	83.60 141	46.49 55	83.04 238	22.59 315	58.77 227	81.89 202

conf0.002		63.04 220	61.74 205	66.95 255	80.60 144	35.92 298	76.01 244	84.09 108	52.62 236	50.87 245	83.60 141	46.49 55	83.04 238	22.59 315	58.77 227	81.89 202

thresconf0.02		62.84 223	61.74 205	66.14 262	80.60 144	35.92 298	76.01 244	84.09 108	52.62 236	50.87 245	83.60 141	46.49 55	83.04 238	22.59 315	58.77 227	79.44 240

tfpn_n400		62.84 223	61.74 205	66.14 262	80.60 144	35.92 298	76.01 244	84.09 108	52.62 236	50.87 245	83.60 141	46.49 55	83.04 238	22.59 315	58.77 227	79.44 240

tfpnconf		62.84 223	61.74 205	66.14 262	80.60 144	35.92 298	76.01 244	84.09 108	52.62 236	50.87 245	83.60 141	46.49 55	83.04 238	22.59 315	58.77 227	79.44 240

tfpnview11		62.84 223	61.74 205	66.14 262	80.60 144	35.92 298	76.01 244	84.09 108	52.62 236	50.87 245	83.60 141	46.49 55	83.04 238	22.59 315	58.77 227	79.44 240

tfpn1000		62.79 229	61.74 205	65.95 267	80.50 153	35.93 297	76.53 243	83.99 119	51.24 254	49.82 253	83.44 149	47.32 45	83.02 244	21.84 322	60.99 207	78.89 245

IterMVS		63.77 209	61.67 212	70.08 221	72.68 251	51.24 154	80.44 200	75.51 262	60.51 123	51.41 237	73.70 254	32.08 235	78.91 272	54.30 176	54.35 265	80.08 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v16		64.25 204	61.66 213	72.03 179	72.91 240	53.28 104	80.93 193	77.81 230	58.86 157	48.30 259	68.80 286	42.70 116	84.37 220	56.44 160	48.14 285	78.44 254

v17		64.19 205	61.58 214	72.03 179	72.89 243	53.28 104	80.91 194	77.80 231	58.87 156	48.22 260	68.77 287	42.69 117	84.37 220	56.43 161	47.66 288	78.43 255

dp		64.41 201	61.58 214	72.90 164	82.40 104	54.09 75	72.53 274	76.59 251	60.39 124	55.68 209	70.39 278	35.18 206	76.90 291	39.34 249	61.71 205	87.73 113

test_djsdf		63.84 207	61.56 216	70.70 210	68.78 286	44.69 247	81.63 180	81.44 166	50.28 258	52.27 232	76.26 231	26.72 266	86.11 186	60.83 122	55.84 257	81.29 217

MDTV_nov1_ep13			61.56 216		81.68 119	55.12 50	72.41 276	78.18 221	59.19 143	58.85 171	69.29 282	34.69 209	86.16 185	36.76 261	62.96 195

pmmvs5		62.80 228	61.18 218	67.66 248	69.53 284	42.37 270	82.65 160	75.19 266	54.30 224	52.03 234	78.51 203	31.64 240	80.67 256	48.60 209	58.15 236	79.95 237

v15		63.83 208	61.13 219	71.93 187	72.60 253	53.21 107	80.44 200	78.22 219	58.80 159	47.57 265	68.22 289	42.50 118	84.18 222	55.82 163	46.02 300	78.39 257

pmmvs4		63.34 216	61.07 220	70.16 219	70.14 279	50.53 163	79.97 210	71.41 295	55.08 218	54.12 217	78.58 199	32.79 227	82.09 248	50.33 201	57.22 247	77.86 269

V14		63.72 210	60.99 221	71.91 189	72.58 256	53.18 108	80.24 204	78.19 220	58.53 166	47.35 271	68.10 290	42.28 121	84.18 222	55.68 165	45.97 301	78.36 260

jajsoiax		63.21 218	60.84 222	70.32 217	68.33 291	44.45 248	81.23 188	81.05 176	53.37 230	50.96 243	77.81 213	17.49 316	85.49 201	59.31 134	58.05 239	81.02 220

V9		63.60 211	60.84 222	71.87 191	72.51 258	53.12 112	80.04 209	78.15 222	58.25 169	47.14 273	67.98 291	42.08 125	84.18 222	55.47 166	45.92 303	78.32 261

TransMVSNet (Re)		62.82 227	60.76 224	69.02 231	73.98 227	41.61 273	86.36 69	79.30 205	56.90 188	52.53 230	76.44 228	41.85 129	87.60 153	38.83 250	40.61 315	77.86 269

v12		63.47 213	60.68 225	71.85 192	72.45 259	53.08 114	79.83 211	78.13 224	57.95 175	46.89 275	67.87 293	41.81 131	84.17 225	55.30 168	45.87 304	78.29 263

v11		63.44 214	60.66 226	71.79 194	72.61 252	53.02 118	79.80 212	78.08 226	58.30 167	47.27 272	67.91 292	40.67 142	84.14 227	54.93 171	46.39 298	78.23 267

mvs_tets		62.96 222	60.55 227	70.19 218	68.22 293	44.24 252	80.90 195	80.74 181	52.99 234	50.82 251	77.56 214	16.74 319	85.44 202	59.04 135	57.94 240	80.89 221

v13		63.36 215	60.54 228	71.82 193	72.41 260	53.03 117	79.64 216	78.10 225	57.66 181	46.67 278	67.75 294	41.68 132	84.17 225	55.11 169	45.82 305	78.25 266

CVMVSNet		60.85 242	60.44 229	62.07 287	75.00 216	32.73 314	79.54 217	73.49 282	36.98 314	56.28 204	83.74 137	29.28 253	69.53 323	46.48 224	63.23 189	83.94 172

MIMVSNet		63.12 219	60.29 230	71.61 195	75.92 208	46.65 225	65.15 302	81.94 157	59.14 147	54.65 212	69.47 281	25.74 272	80.63 257	41.03 245	69.56 144	87.55 115

TAPA-MVS	56.12 14	61.82 237	60.18 231	66.71 257	78.48 179	37.97 292	75.19 257	76.41 253	46.82 274	57.04 198	86.52 113	27.67 262	77.03 289	26.50 300	67.02 158	85.14 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
testing_2		63.60 211	59.86 232	74.82 128	61.87 317	52.39 130	73.06 271	82.76 146	61.49 111	39.96 303	67.39 297	21.06 301	88.34 127	67.07 78	64.10 177	83.72 176

EG-PatchMatch MVS		62.40 234	59.59 233	70.81 209	73.29 233	49.05 188	85.81 77	84.78 93	51.85 251	44.19 285	73.48 257	15.52 324	89.85 75	40.16 247	67.24 156	73.54 303

XVG-OURS-SEG-HR		62.02 235	59.54 234	69.46 228	65.30 303	45.88 236	65.06 303	73.57 281	46.45 277	57.42 195	83.35 150	26.95 265	78.09 279	53.77 180	64.03 178	84.42 161

tpmvs		62.45 233	59.42 235	71.53 199	83.93 69	54.32 69	70.03 292	77.61 236	51.91 249	53.48 223	68.29 288	37.91 164	86.66 174	33.36 271	58.27 234	73.62 302

XVG-OURS		61.88 236	59.34 236	69.49 227	65.37 302	46.27 232	64.80 305	73.49 282	47.04 272	57.41 196	82.85 155	25.15 277	78.18 277	53.00 185	64.98 170	84.01 167

v7n		62.50 231	59.27 237	72.20 176	67.25 297	49.83 176	77.87 234	80.12 187	52.50 245	48.80 257	73.07 259	32.10 234	87.90 142	46.83 222	54.92 261	78.86 246

Patchmatch-test1		63.23 217	59.16 238	75.43 110	78.58 176	57.92 9	61.61 314	77.53 237	56.71 194	57.75 187	70.98 274	31.97 236	78.19 276	40.97 246	56.36 249	90.18 68

tfpnnormal		61.47 238	59.09 239	68.62 243	76.29 203	41.69 271	81.14 191	85.16 83	54.48 223	51.32 238	73.63 255	32.32 231	86.89 169	21.78 324	55.71 258	77.29 275

CR-MVSNet		62.47 232	59.04 240	72.77 167	73.97 228	56.57 23	60.52 317	71.72 289	60.04 127	57.49 192	65.86 302	38.94 156	80.31 261	42.86 241	59.93 215	81.42 211

PLC	52.38 18	60.89 241	58.97 241	66.68 259	81.77 118	45.70 238	78.96 227	74.04 275	43.66 294	47.63 264	83.19 153	23.52 287	77.78 286	37.47 252	60.46 210	76.55 281
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		60.59 243	58.44 242	67.05 254	79.21 162	47.26 221	79.75 215	64.34 317	42.46 301	51.90 236	83.94 133	27.79 261	75.41 296	37.12 255	59.49 222	78.47 251

v748		61.35 239	58.24 243	70.69 211	66.28 298	47.35 219	76.58 241	79.17 206	53.09 232	46.37 282	71.50 272	33.18 223	86.33 181	46.78 223	51.19 279	78.39 257

WR-MVS_H		58.91 253	58.04 244	61.54 292	69.07 285	33.83 309	76.91 238	81.99 156	51.40 253	48.17 261	74.67 245	40.23 144	74.15 300	31.78 278	48.10 286	76.64 279

anonymousdsp		60.46 244	57.65 245	68.88 232	63.63 311	45.09 242	72.93 272	78.63 214	46.52 276	51.12 240	72.80 263	21.46 299	83.07 237	57.79 148	53.97 266	78.47 251

Anonymous20231206		59.08 252	57.59 246	63.55 281	68.77 287	32.14 317	80.26 203	79.78 192	50.00 261	49.39 254	72.39 267	26.64 267	78.36 275	33.12 274	57.94 240	80.14 235

CP-MVSNet		58.54 258	57.57 247	61.46 294	68.50 289	33.96 308	76.90 239	78.60 216	51.67 252	47.83 262	76.60 227	34.99 208	72.79 311	35.45 264	47.58 289	77.64 273

PVSNet_0	57.04 13	61.19 240	57.24 248	73.02 162	77.45 191	50.31 170	79.43 219	77.36 241	63.96 76	47.51 268	72.45 266	25.03 278	83.78 232	52.76 189	19.22 340	84.96 155

pmmvs6		59.64 248	57.15 249	67.09 252	66.01 299	36.86 296	80.50 199	78.64 213	45.05 285	49.05 256	73.94 249	27.28 263	86.10 188	43.96 236	49.94 282	78.31 262

PEN-MVS		58.35 260	57.15 249	61.94 289	67.55 296	34.39 306	77.01 237	78.35 218	51.87 250	47.72 263	76.73 226	33.91 216	73.75 305	34.03 270	47.17 293	77.68 271

PS-CasMVS		58.12 261	57.03 251	61.37 295	68.24 292	33.80 310	76.73 240	78.01 228	51.20 255	47.54 267	76.20 235	32.85 225	72.76 312	35.17 266	47.37 291	77.55 274

LCM-MVSNet-Re		58.82 254	56.54 252	65.68 268	79.31 161	29.09 326	61.39 316	45.79 337	60.73 120	37.65 309	72.47 265	31.42 241	81.08 253	49.66 204	70.41 138	86.87 123

FMVSNet5		58.61 256	56.45 253	65.10 275	77.20 197	39.74 284	74.77 258	77.12 244	50.27 260	43.28 292	67.71 295	26.15 270	76.90 291	36.78 260	54.78 263	78.65 249

v52		59.82 245	56.41 254	70.06 223	61.49 320	48.67 196	69.46 296	75.80 259	52.55 243	47.49 269	68.82 285	28.60 254	85.70 197	52.13 193	51.34 278	75.80 285

V4		59.82 245	56.41 254	70.05 224	61.49 320	48.67 196	69.46 296	75.79 260	52.55 243	47.49 269	68.83 284	28.60 254	85.70 197	52.13 193	51.35 277	75.80 285

CHOSEN 280x420		57.53 263	56.38 256	60.97 296	74.01 226	48.10 213	46.30 332	54.31 331	48.18 267	50.88 244	77.43 218	38.37 162	59.16 335	54.83 172	63.14 192	75.66 287

DP-MVS		59.24 250	56.12 257	68.63 242	88.24 18	50.35 167	82.51 163	64.43 316	41.10 303	46.70 277	78.77 198	24.75 279	88.57 119	22.26 321	56.29 251	66.96 322

OpenMVS_ROB	53.19 17	59.20 251	56.00 258	68.83 234	71.13 275	44.30 250	83.64 138	75.02 267	46.42 278	46.48 281	73.03 260	18.69 310	88.14 135	27.74 295	61.80 204	74.05 299

ACMH	53.70 16	59.78 247	55.94 259	71.28 201	76.59 201	48.35 207	80.15 208	76.11 254	49.74 262	41.91 296	73.45 258	16.50 321	90.31 64	31.42 279	57.63 245	75.17 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DTE-MVSNet		57.03 264	55.73 260	60.95 297	65.94 300	32.57 315	75.71 250	77.09 245	51.16 256	46.65 279	76.34 230	32.84 226	73.22 309	30.94 282	44.87 307	77.06 276

ACMH+	54.58 15	58.55 257	55.24 261	68.50 244	74.68 220	45.80 237	80.27 202	70.21 302	47.15 271	42.77 294	75.48 241	16.73 320	85.98 191	35.10 268	54.78 263	73.72 301

UnsupCasMVSNet_eth		57.56 262	55.15 262	64.79 277	64.57 308	33.12 311	73.17 269	83.87 123	58.98 154	41.75 297	70.03 279	22.54 291	79.92 267	46.12 228	35.31 324	81.32 216

MSDG		59.44 249	55.14 263	72.32 175	74.69 219	50.71 158	74.39 261	73.58 280	44.44 289	43.40 291	77.52 215	19.45 306	90.87 53	31.31 280	57.49 246	75.38 289

Patchmatch-RL test		58.72 255	54.32 264	71.92 188	63.91 310	44.25 251	61.73 313	55.19 329	57.38 184	49.31 255	54.24 328	37.60 172	80.89 254	62.19 114	47.28 292	90.63 53

test20.03		55.22 275	54.07 265	58.68 302	63.14 313	25.00 332	77.69 235	74.78 268	52.64 235	43.43 290	72.39 267	26.21 269	74.76 299	29.31 284	47.05 295	76.28 283

LS3D		56.40 269	53.82 266	64.12 278	81.12 134	45.69 239	73.42 266	66.14 311	35.30 324	43.24 293	79.88 189	22.18 295	79.62 270	19.10 333	64.00 179	67.05 321

PatchMatch-RL		56.66 265	53.75 267	65.37 273	77.91 186	45.28 241	69.78 294	60.38 324	41.35 302	47.57 265	73.73 251	16.83 318	76.91 290	36.99 258	59.21 224	73.92 300

RPMNet		58.49 259	53.74 268	72.77 167	73.97 228	56.57 23	60.52 317	72.39 286	35.72 319	57.49 192	58.87 322	37.73 169	80.31 261	27.01 298	59.93 215	81.42 211

F-COLMAP		55.96 273	53.65 269	62.87 285	72.76 246	42.77 264	74.70 260	70.37 300	40.03 304	41.11 300	79.36 191	17.77 314	73.70 306	32.80 275	53.96 267	72.15 309

test_0402		56.45 268	53.03 270	66.69 258	76.78 200	50.31 170	81.76 174	69.61 304	42.79 299	43.88 287	72.13 269	22.82 290	86.46 180	16.57 338	50.94 280	63.31 331

PatchT		56.60 266	52.97 271	67.48 249	72.94 239	46.16 235	57.30 323	73.78 278	38.77 308	54.37 215	57.26 325	37.52 174	78.06 280	32.02 276	52.79 272	78.23 267

Patchmtry		56.56 267	52.95 272	67.42 250	72.53 257	50.59 162	59.05 319	71.72 289	37.86 312	46.92 274	65.86 302	38.94 156	80.06 266	36.94 259	46.72 297	71.60 312

XVG-ACMP-BASELINE		56.03 271	52.85 273	65.58 269	61.91 316	40.95 278	63.36 307	72.43 285	45.20 284	46.02 283	74.09 247	9.20 335	78.12 278	45.13 230	58.27 234	77.66 272

pmmvs-eth3d		55.97 272	52.78 274	65.54 270	61.02 322	46.44 228	75.36 256	67.72 307	49.61 263	43.65 289	67.58 296	21.63 298	77.04 288	44.11 235	44.33 309	73.15 308

CMPMVS	40.41 21	55.34 274	52.64 275	63.46 282	60.88 323	43.84 254	61.58 315	71.06 296	30.43 331	36.33 311	74.63 246	24.14 281	75.44 295	48.05 214	66.62 159	71.12 315
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		54.25 279	52.57 276	59.29 300	62.76 314	21.65 338	72.21 279	70.47 298	53.25 231	41.94 295	77.33 219	14.28 325	77.95 282	29.18 285	51.72 276	78.28 264

test2356		53.94 281	52.37 277	58.64 303	61.58 318	27.53 331	78.20 232	74.33 271	46.92 273	44.01 286	66.04 301	18.91 309	74.11 301	28.80 286	52.55 273	74.28 296

ADS-MVSNet		56.17 270	51.95 278	68.84 233	80.60 144	53.07 115	55.03 325	70.02 303	44.72 286	51.00 241	61.19 313	22.83 288	78.88 273	28.54 291	53.63 268	74.57 294

ADS-MVSNet2		55.21 276	51.44 279	66.51 260	80.60 144	49.56 181	55.03 325	65.44 313	44.72 286	51.00 241	61.19 313	22.83 288	75.41 296	28.54 291	53.63 268	74.57 294

USDC		54.36 278	51.23 280	63.76 280	64.29 309	37.71 293	62.84 312	73.48 284	56.85 189	35.47 315	71.94 271	9.23 334	78.43 274	38.43 251	48.57 283	75.13 291

EU-MVSNet		52.63 287	50.72 281	58.37 304	62.69 315	28.13 328	72.60 273	75.97 257	30.94 330	40.76 302	72.11 270	20.16 304	70.80 319	35.11 267	46.11 299	76.19 284

UnsupCasMVSNet_bld		53.86 282	50.53 282	63.84 279	63.52 312	34.75 305	71.38 284	81.92 159	46.53 275	38.95 307	57.93 323	20.55 303	80.20 265	39.91 248	34.09 331	76.57 280

SixPastTwentyTwo		54.37 277	50.10 283	67.21 251	70.70 276	41.46 274	74.73 259	64.69 315	47.56 269	39.12 306	69.49 280	18.49 312	84.69 216	31.87 277	34.20 330	75.48 288

YYNet1		53.82 283	49.96 284	65.41 272	70.09 281	48.95 190	72.30 277	71.66 291	44.25 290	31.89 328	63.07 310	23.73 283	73.95 303	33.26 272	39.40 317	73.34 305

MDA-MVSNet_test_wron		53.82 283	49.95 285	65.43 271	70.13 280	49.05 188	72.30 277	71.65 292	44.23 291	31.85 329	63.13 309	23.68 286	74.01 302	33.25 273	39.35 318	73.23 307

LTVRE_ROB	45.45 19	52.73 286	49.74 286	61.69 291	69.78 282	34.99 304	44.52 334	67.60 308	43.11 298	43.79 288	74.03 248	18.54 311	81.45 251	28.39 293	57.94 240	68.62 319
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
K. test v3		54.04 280	49.42 287	67.92 247	68.55 288	42.57 268	75.51 254	63.07 320	52.07 247	39.21 305	64.59 306	19.34 307	82.21 246	37.11 256	25.31 336	78.97 244

OurMVSNet-221017-0		52.39 288	48.73 288	63.35 283	65.21 304	38.42 289	68.54 299	64.95 314	38.19 309	39.57 304	71.43 273	13.23 327	79.92 267	37.16 254	40.32 316	71.72 311

Patchmatch-test		53.33 285	48.17 289	68.81 239	73.31 232	42.38 269	42.98 336	58.23 326	32.53 327	38.79 308	70.77 276	39.66 152	73.51 307	25.18 302	52.06 275	90.55 54

COLMAP_ROB	43.60 20	50.90 291	48.05 290	59.47 299	67.81 294	40.57 283	71.25 285	62.72 322	36.49 318	36.19 312	73.51 256	13.48 326	73.92 304	20.71 328	50.26 281	63.92 329
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 292	47.81 291	57.96 305	61.53 319	27.80 330	67.40 300	74.06 274	43.25 297	33.31 327	65.38 305	16.03 322	71.34 318	21.80 323	47.55 290	74.75 292

JIA-IIPM		52.33 289	47.77 292	66.03 266	71.20 274	46.92 223	40.00 340	76.48 252	37.10 313	46.73 276	37.02 336	32.96 224	77.88 283	35.97 262	52.45 274	73.29 306

MDA-MVSNet-bldmvs		51.56 290	47.75 293	63.00 284	71.60 271	47.32 220	69.70 295	72.12 287	43.81 293	27.65 334	63.38 308	21.97 297	75.96 293	27.30 297	32.19 332	65.70 325

new-patchmatchnet		48.21 295	46.55 294	53.18 314	57.73 327	18.19 347	70.24 290	71.02 297	45.70 280	33.70 323	60.23 315	18.00 313	69.86 322	27.97 294	34.35 328	71.49 314

testus		48.97 294	46.53 295	56.31 310	57.39 329	24.08 334	73.40 267	70.45 299	43.37 296	35.52 314	63.95 307	4.77 346	71.36 317	24.88 303	45.02 306	73.50 304

1111		48.00 297	46.30 296	53.08 315	55.68 330	20.86 341	70.41 288	76.03 255	36.88 315	34.86 317	59.55 319	23.72 284	68.13 324	20.82 326	38.76 320	70.25 316

testpf		45.92 303	45.81 297	46.27 322	69.56 283	27.86 329	23.18 345	73.91 277	44.10 292	36.99 310	57.16 326	20.56 302	71.77 315	42.17 244	44.64 308	39.18 341

MVS-HIRNet		49.01 293	44.71 298	61.92 290	76.06 204	46.61 226	63.23 309	54.90 330	24.77 335	33.56 324	36.60 337	21.28 300	75.88 294	29.49 283	62.54 201	63.26 332

AllTest		47.32 298	44.66 299	55.32 311	65.08 305	37.50 294	62.96 311	54.25 332	35.45 322	33.42 325	72.82 261	9.98 331	59.33 333	24.13 306	43.84 310	69.13 317

TinyColmap		48.15 296	44.49 300	59.13 301	65.73 301	38.04 291	63.34 308	62.86 321	38.78 307	29.48 332	67.23 299	6.46 341	73.30 308	24.59 304	41.90 313	66.04 323

RPSCF		45.77 304	44.13 301	50.68 317	57.67 328	29.66 322	54.92 327	45.25 339	26.69 334	45.92 284	75.92 239	17.43 317	45.70 345	27.44 296	45.95 302	76.67 278

test1235678		47.09 299	43.82 302	56.91 308	53.18 333	24.90 333	71.93 281	70.31 301	39.54 305	31.44 330	56.59 327	9.50 333	71.55 316	22.63 314	39.24 319	74.28 296

PM-MVS		46.92 301	43.76 303	56.41 309	52.18 334	32.26 316	63.21 310	38.18 343	37.99 311	40.78 301	66.20 300	5.09 344	65.42 329	48.19 213	41.99 312	71.54 313

Anonymous20231211		46.87 302	43.27 304	57.67 306	57.88 326	30.12 321	73.14 270	64.16 318	33.43 326	34.34 320	59.42 321	12.15 328	77.99 281	19.64 332	35.23 326	64.90 327

LP		47.05 300	42.23 305	61.53 293	72.04 266	49.37 185	49.48 329	65.50 312	34.57 325	34.29 321	52.30 330	17.73 315	75.32 298	17.56 336	36.57 322	59.91 333

.test1245		38.91 309	41.99 306	29.67 334	55.68 330	20.86 341	70.41 288	76.03 255	36.88 315	34.86 317	59.55 319	23.72 284	68.13 324	20.82 326	0.00 352	0.02 352

pmmvs3		45.53 305	41.55 307	57.44 307	48.97 337	39.68 285	70.06 291	57.66 327	28.32 333	34.06 322	57.29 324	8.50 336	66.85 328	34.86 269	34.26 329	65.80 324

N_pmnet		41.25 306	39.77 308	45.66 324	68.50 289	0.82 356	72.51 275	0.38 357	35.61 320	35.26 316	61.51 312	20.07 305	67.74 326	23.51 308	40.63 314	68.42 320

TDRefinement		40.91 307	38.37 309	48.55 320	50.45 335	33.03 313	58.98 320	50.97 335	28.50 332	29.89 331	67.39 297	6.21 343	54.51 337	17.67 335	35.25 325	58.11 334

testmv		39.64 308	36.01 310	50.55 318	42.18 341	21.56 339	64.81 304	66.88 310	32.22 328	22.25 338	47.47 332	4.33 348	64.81 330	17.71 334	26.22 335	65.29 326

DSMNet-mixed		38.35 310	35.36 311	47.33 321	48.11 338	14.91 349	37.87 341	36.60 345	19.18 339	34.37 319	59.56 318	15.53 323	53.01 339	20.14 330	46.89 296	74.07 298

test12356		37.84 311	35.07 312	46.18 323	45.03 340	8.02 354	57.70 322	62.67 323	31.83 329	22.78 336	50.25 331	4.46 347	66.95 327	17.25 337	23.62 338	63.57 330

FPMVS		35.40 313	33.67 313	40.57 326	46.34 339	28.74 327	41.05 338	57.05 328	20.37 338	22.27 337	53.38 329	6.87 339	44.94 346	8.62 344	47.11 294	48.01 339

LF4IMVS		33.04 316	32.55 314	34.52 331	40.96 342	22.03 337	44.45 335	35.62 346	20.42 337	28.12 333	62.35 311	5.03 345	31.88 351	21.61 325	34.42 327	49.63 338

new_pmnet		33.56 315	31.89 315	38.59 327	49.01 336	20.42 343	51.01 328	37.92 344	20.58 336	23.45 335	46.79 333	6.66 340	49.28 342	20.00 331	31.57 334	46.09 340

no-one		37.21 312	31.48 316	54.40 313	39.62 345	31.91 319	45.68 333	67.42 309	35.54 321	14.59 341	35.91 339	7.35 337	73.20 310	22.98 309	14.23 341	58.09 335

ANet_high		34.39 314	29.59 317	48.78 319	30.34 349	22.28 336	55.53 324	63.79 319	38.11 310	15.47 340	36.56 338	6.94 338	59.98 332	13.93 340	5.64 351	64.08 328

pcd1.5k->3k		27.74 318	27.68 318	27.93 336	73.75 230	0.00 358	0.00 350	85.50 69	0.00 352	0.00 355	0.00 356	26.52 268	0.00 355	0.00 354	63.37 187	83.79 175

cdsmvs_eth3d_5k		18.33 326	24.44 319	0.00 344	0.00 357	0.00 358	0.00 350	89.40 12	0.00 352	0.00 355	92.02 22	38.55 160	0.00 355	0.00 354	0.00 352	0.00 355

Gipuma		27.47 319	24.26 320	37.12 329	60.55 324	29.17 325	11.68 348	60.00 325	14.18 342	10.52 345	15.12 348	2.20 352	63.01 331	8.39 345	35.65 323	19.18 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 317	23.85 321	40.71 325	27.46 351	18.93 346	30.82 343	46.19 336	12.76 344	16.40 339	34.70 341	1.90 353	48.69 343	20.25 329	24.22 337	54.51 336

PNet_i23d		25.11 321	23.09 322	31.17 333	40.18 343	21.30 340	57.99 321	33.28 348	13.77 343	9.94 346	30.29 343	0.45 357	43.74 347	13.61 342	8.28 344	28.46 344

PMMVS2		26.71 320	22.98 323	37.87 328	36.89 346	8.51 353	42.51 337	29.32 351	19.09 340	13.01 342	37.54 335	2.23 351	53.11 338	14.54 339	11.71 342	51.99 337

PMVS	19.57 22	25.07 322	22.43 324	32.99 332	23.12 352	22.98 335	40.98 339	35.19 347	15.99 341	11.95 344	35.87 340	1.47 355	49.29 341	5.41 349	31.90 333	26.70 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		19.16 324	18.40 325	21.44 337	36.19 347	13.63 350	47.59 330	30.89 349	10.73 345	5.91 349	16.59 346	3.66 350	39.77 348	5.95 348	8.14 345	10.92 348

EMVS		18.42 325	17.66 326	20.71 338	34.13 348	12.64 351	46.94 331	29.94 350	10.46 347	5.58 350	14.93 349	4.23 349	38.83 349	5.24 350	7.51 348	10.67 349

wuykxyi23d		19.94 323	14.87 327	35.13 330	22.47 353	19.80 344	25.80 344	38.64 342	7.61 348	4.88 351	13.58 351	0.23 358	48.42 344	13.11 343	7.53 346	37.18 342

MVE	16.60 23	17.34 327	13.39 328	29.16 335	28.43 350	19.72 345	13.73 347	23.63 352	7.23 349	7.96 347	21.41 344	0.80 356	36.08 350	6.97 346	10.39 343	31.69 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 328	10.68 329	5.73 341	2.49 355	4.21 355	10.48 349	18.04 353	0.34 351	12.59 343	20.49 345	11.39 329	7.03 354	13.84 341	6.46 350	5.95 350

ab-mvs-re		7.68 330	10.24 330	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	92.12 19	0.00 360	0.00 355	0.00 354	0.00 352	0.00 355

wuyk23d		9.11 329	8.77 331	10.15 340	40.18 343	16.76 348	20.28 346	1.01 356	2.58 350	2.66 352	0.98 353	0.23 358	12.49 353	4.08 351	6.90 349	1.19 351

testmvs		6.14 331	8.18 332	0.01 342	0.01 356	0.00 358	73.40 267	0.00 358	0.00 352	0.02 353	0.15 354	0.00 360	0.00 355	0.02 352	0.00 352	0.02 352

test123		6.01 332	8.01 333	0.01 342	0.00 357	0.01 357	71.93 281	0.00 358	0.00 352	0.02 353	0.11 355	0.00 360	0.00 355	0.02 352	0.00 352	0.02 352

pcd_1.5k_mvsjas		3.15 333	4.20 334	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	0.00 356	37.77 166	0.00 355	0.00 354	0.00 352	0.00 355

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	0.00 356	0.00 360	0.00 355	0.00 354	0.00 352	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	0.00 356	0.00 360	0.00 355	0.00 354	0.00 352	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	0.00 356	0.00 360	0.00 355	0.00 354	0.00 352	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	0.00 356	0.00 360	0.00 355	0.00 354	0.00 352	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 357	0.00 358	0.00 350	0.00 358	0.00 352	0.00 355	0.00 356	0.00 360	0.00 355	0.00 354	0.00 352	0.00 355

GSMVS																88.13 106

test_part3							89.59 26		56.02 208		93.65 3		95.22 6	79.73 11

test_part2					89.33 9	55.48 37				82.27 2

test_part1								88.42 25				58.18 6			86.59 15	91.53 35

sam_mvs1												38.86 158				88.13 106

sam_mvs												35.99 201

semantic-postprocess				60.08 298	70.68 278	45.07 243		74.25 272	43.54 295	50.02 252	73.73 251	32.22 233	56.74 336	51.06 199	53.60 270	78.42 256

ambc				62.06 288	53.98 332	29.38 324	35.08 342	79.65 196		41.37 298	59.96 316	6.27 342	82.15 247	35.34 265	38.22 321	74.65 293

MTGPA								81.31 169

test_post1							70.84 287				14.72 350	34.33 212	83.86 229	48.80 208

test_post											16.22 347	37.52 174	84.72 215

patchmatchnet-post											59.74 317	38.41 161	79.91 269

GG-mvs-BLEND				77.77 64	86.68 28	50.61 160	68.67 298	88.45 24		68.73 64	87.45 101	59.15 4	90.67 55	54.83 172	87.67 11	92.03 25

MTMP								15.34 354

gm-plane-assit					83.24 80	54.21 73			70.91 11		88.23 88		95.25 5	66.37 81

test9_res														78.72 17	85.44 27	91.39 40

TEST9					85.68 37	55.42 39	87.59 47	84.00 117	57.72 180	72.99 35	90.98 40	44.87 76	88.58 116

test_8					85.72 36	55.31 43	87.60 44	83.88 122	57.84 177	72.84 38	90.99 39	44.99 73	88.34 127

agg_prior2														75.65 31	85.11 31	91.01 46

agg_prior					85.64 40	54.92 56		83.61 128		72.53 42			88.10 138

TestCases				55.32 311	65.08 305	37.50 294		54.25 332	35.45 322	33.42 325	72.82 261	9.98 331	59.33 333	24.13 306	43.84 310	69.13 317

test_prior4						56.39 28	87.15 58

test_prior2							89.04 32		61.88 104	73.55 30	91.46 36	48.01 41		74.73 37	85.46 25

test_prior				78.39 51	86.35 31	54.91 58		85.45 71					89.70 80			90.55 54

旧先验2							81.73 175		45.53 282	74.66 23			70.48 321	58.31 142

新几何2							81.61 182

新几何1				73.30 160	83.10 82	53.48 85		71.43 294	45.55 281	66.14 84	87.17 104	33.88 218	80.54 258	48.50 210	80.33 63	85.88 142

旧先验1					81.57 124	47.48 217		71.83 288			88.66 81	36.94 185			78.34 78	88.67 97

无先验							85.19 94	78.00 229	49.08 265				85.13 206	52.78 187		87.45 117

原ACMM2							83.77 136

原ACMM1				76.13 97	84.89 56	54.59 67		85.26 79	51.98 248	66.70 77	87.07 106	40.15 146	89.70 80	51.23 197	85.06 32	84.10 164

test222					79.36 158	50.97 156	77.99 233	67.84 306	42.54 300	62.84 126	86.53 112	30.26 247			76.91 91	85.23 151

testdata2													77.81 285	45.64 229

segment_acmp												44.97 75

testdata				67.08 253	77.59 188	45.46 240		69.20 305	44.47 288	71.50 52	88.34 84	31.21 242	70.76 320	52.20 192	75.88 99	85.03 153

testdata1							77.55 236		64.14 73

test12				79.24 30	86.89 26	56.08 32		85.16 83		72.27 47		47.15 48	91.10 47		85.93 21	90.54 57

plane_prior7					77.95 183	48.46 206

plane_prior6					78.42 180	49.39 184						36.04 198

plane_prior5								82.59 147					88.30 131	65.46 90	72.34 128	84.49 159

plane_prior4											83.28 151

plane_prior3						48.95 190			64.01 74	62.15 129

plane_prior2							85.76 79		63.60 84

plane_prior1					78.31 182

plane_prior						49.57 179	87.43 51		64.57 66						72.84 122

n2								0.00 358

nn								0.00 358

door-mid								41.31 341

lessismore_v0				67.98 246	64.76 307	41.25 275		45.75 338		36.03 313	65.63 304	19.29 308	84.11 228	35.67 263	21.24 339	78.59 250

LGP-MVS_train				72.02 181	74.42 222	48.60 198		80.64 182	54.69 221	53.75 220	83.83 135	25.73 273	86.98 165	60.33 130	64.71 172	80.48 231

test11								84.25 104

door								43.27 340

HQP5-MVS						51.56 144

HQP-NCC					79.02 165		88.00 38		65.45 52	64.48 106

ACMP_Plane					79.02 165		88.00 38		65.45 52	64.48 106

BP-MVS														66.70 79

HQP4-MVS										64.47 109			88.61 114			84.91 156

HQP3-MVS								83.68 125							73.12 118

HQP2-MVS												37.35 177

NP-MVS					78.76 169	50.43 165					85.12 123

MDTV_nov1_ep13_2view						43.62 256	71.13 286		54.95 219	59.29 163		36.76 188		46.33 226		87.32 119

ACMMP++_ref															63.20 190

ACMMP++															59.38 223

Test By Simon												39.38 153

ITE_SJBPF				51.84 316	58.03 325	31.94 318		53.57 334	36.67 317	41.32 299	75.23 243	11.17 330	51.57 340	25.81 301	48.04 287	72.02 310

DeepMVS_CX				13.10 339	21.34 354	8.99 352		10.02 355	10.59 346	7.53 348	30.55 342	1.82 354	14.55 352	6.83 347	7.52 347	15.75 347

High-resolution multi-view benchmark