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 36	82.99 91	52.71 125	85.04 110	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 44	83.35 5	93.53 1	92.72 17

MVP-Stereo		70.97 104	70.44 92	72.59 172	76.03 208	51.36 152	85.02 112	86.99 51	60.31 126	56.53 202	78.92 199	40.11 149	90.00 73	60.00 134	90.01 2	76.41 284
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 27	56.89 21	92.77 2	86.30 64	77.83 1	77.88 13	92.13 19	60.24 2	94.78 12	78.97 16	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
HPM-MVS++		80.50 7	80.71 7	79.88 25	87.34 24	55.20 49	89.93 20	87.55 45	66.04 48	79.46 9	93.00 12	53.10 17	91.76 41	80.40 10	89.56 4	92.68 18

MVS		76.91 33	75.48 42	81.23 10	84.56 59	55.21 48	80.23 208	91.64 1	58.65 163	65.37 94	91.48 36	45.72 68	95.05 8	72.11 57	89.52 5	93.44 4

SMA-MVS		78.93 14	78.55 14	80.10 22	84.42 60	55.81 34	87.58 49	86.47 59	61.29 112	79.34 10	93.10 7	46.02 65	92.41 31	79.97 11	88.72 6	92.08 24

3Dnovator	64.70 6	74.46 58	72.48 66	80.41 15	82.84 97	55.40 43	83.08 153	88.61 20	67.61 31	59.85 153	88.66 82	34.57 212	93.97 15	58.42 141	88.70 7	91.85 29

PHI-MVS		77.49 28	77.00 28	78.95 37	85.33 50	50.69 161	88.57 35	88.59 22	58.14 173	73.60 30	93.31 5	43.14 106	93.79 18	73.81 44	88.53 8	92.37 20

CSCG		80.41 9	79.72 9	82.49 4	89.12 11	57.67 12	89.29 30	91.54 2	59.19 144	71.82 50	90.05 64	59.72 3	96.04 1	78.37 19	88.40 9	93.75 2

MS-PatchMatch		72.34 85	71.26 85	75.61 107	82.38 107	55.55 36	88.00 38	89.95 11	65.38 56	56.51 203	80.74 187	32.28 234	92.89 24	57.95 147	88.10 10	78.39 259

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 15	93.87 2	52.58 19	93.91 17	84.17 2	87.92 11	92.39 19

GG-mvs-BLEND				77.77 65	86.68 28	50.61 162	68.67 301	88.45 24		68.73 65	87.45 102	59.15 4	90.67 56	54.83 173	87.67 12	92.03 26

ACMMP_Plus		76.43 41	75.66 40	78.73 41	81.92 117	54.67 67	84.06 131	85.35 78	61.10 115	72.99 36	91.50 35	40.25 145	91.00 49	76.84 28	86.98 13	90.51 59

PAPM		76.76 36	76.07 38	78.81 39	80.20 156	59.11 5	86.86 65	86.23 65	68.60 21	70.18 60	88.84 81	51.57 24	87.16 162	65.48 90	86.68 14	90.15 70

gg-mvs-nofinetune		67.43 168	64.53 189	76.13 98	85.95 33	47.79 217	64.38 309	88.28 31	39.34 309	66.62 80	41.27 337	58.69 5	89.00 99	49.64 206	86.62 15	91.59 33

test_part1								88.42 25				58.18 6			86.59 16	91.53 36

ESAPD		80.50 7	80.42 8	80.74 12	89.33 9	55.48 38	89.59 26	88.42 25	56.02 210	82.27 2	93.65 3	58.18 6	95.22 6	79.73 12	86.59 16	91.53 36

MAR-MVS		76.76 36	75.60 41	80.21 18	90.87 3	54.68 66	89.14 31	89.11 14	62.95 92	70.54 58	92.33 17	41.05 139	94.95 9	57.90 148	86.55 18	91.00 48
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
TSAR-MVS + MP.		78.31 20	78.26 16	78.48 48	81.33 134	56.31 29	81.59 186	86.41 61	69.61 18	81.72 4	88.16 90	55.09 10	88.04 141	74.12 43	86.31 19	91.09 46

PS-MVSNAJ		80.06 10	79.52 11	81.68 7	85.58 43	60.97 3	91.69 4	87.02 50	70.62 12	80.75 6	93.22 6	37.77 168	92.50 30	82.75 6	86.25 20	91.57 34

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 64	85.46 48	49.56 183	90.99 11	86.66 57	70.58 13	80.07 7	95.30 1	56.18 9	90.97 51	82.57 8	86.22 21	93.28 7

test12				79.24 31	86.89 26	56.08 32		85.16 85		72.27 48		47.15 48	91.10 48		85.93 22	90.54 58

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 16	93.09 9	54.15 13	95.57 3	85.80 1	85.87 23	93.31 6

MVS_0304		79.84 12	79.71 10	80.25 17	85.64 40	54.62 68	90.58 14	84.48 100	72.51 8	79.22 11	93.09 9	42.01 129	93.28 21	84.00 4	85.84 24	92.87 15

xiu_mvs_v2_base		79.86 11	79.31 12	81.53 8	85.03 54	60.73 4	91.65 5	86.86 53	70.30 15	80.77 5	93.07 11	37.63 173	92.28 34	82.73 7	85.71 25	91.57 34

test_prior3		77.59 27	77.33 26	78.39 52	86.35 31	54.91 59	89.04 32	85.45 73	61.88 104	73.55 31	91.46 37	48.01 41	89.70 81	74.73 38	85.46 26	90.55 55

test_prior2							89.04 32		61.88 104	73.55 31	91.46 37	48.01 41		74.73 38	85.46 26

test9_res														78.72 18	85.44 28	91.39 41

train_agg		76.91 33	76.40 33	78.45 50	85.68 37	55.42 40	87.59 47	84.00 119	57.84 179	72.99 36	90.98 41	44.99 74	88.58 117	78.19 20	85.32 29	91.34 44

agg_prior3		76.73 38	76.15 37	78.48 48	85.66 39	55.59 35	87.54 51	83.95 123	57.78 181	71.78 51	90.81 49	44.33 82	88.52 122	78.19 20	85.32 29	91.34 44

DeepC-MVS_fast	67.50 3	78.00 23	77.63 22	79.13 34	88.52 13	55.12 51	89.95 19	85.98 67	68.31 23	71.33 54	92.75 13	45.52 70	90.37 64	71.15 60	85.14 31	91.91 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior2														75.65 32	85.11 32	91.01 47

原ACMM1				76.13 98	84.89 56	54.59 69		85.26 81	51.98 250	66.70 78	87.07 107	40.15 148	89.70 81	51.23 198	85.06 33	84.10 165

MP-MVS-pluss		75.54 51	75.03 47	77.04 82	81.37 133	52.65 127	84.34 124	84.46 101	61.16 113	69.14 62	91.76 29	39.98 152	88.99 101	78.19 20	84.89 34	89.48 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CANet		80.90 5	81.17 6	80.09 23	87.62 22	54.21 75	91.60 6	86.47 59	73.13 5	79.89 8	93.10 7	49.88 34	92.98 23	84.09 3	84.75 35	93.08 11

agg_prior1		76.68 39	76.24 36	78.00 61	85.64 40	54.92 57	87.55 50	83.61 130	57.99 176	72.53 43	91.05 39	45.36 71	88.10 139	77.76 25	84.68 36	90.99 49

MG-MVS		78.42 17	76.99 29	82.73 2	93.17 1	64.46 1	89.93 20	88.51 23	64.83 62	73.52 33	88.09 91	48.07 39	92.19 35	62.24 114	84.53 37	91.53 36

CDPH-MVS		76.05 47	75.19 46	78.62 45	86.51 30	54.98 56	87.32 54	84.59 98	58.62 164	70.75 56	90.85 47	43.10 110	90.63 58	70.50 62	84.51 38	90.24 66

DeepC-MVS	67.15 4	76.90 35	76.27 35	78.80 40	80.70 143	55.02 54	86.39 69	86.71 55	66.96 36	67.91 71	89.97 66	48.03 40	91.41 45	75.60 33	84.14 39	89.96 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
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 29	92.75 13	46.88 51	93.28 21	78.79 17	84.07 40	91.50 39

OpenMVS	61.00 11	69.99 121	67.55 139	77.30 76	78.37 183	54.07 79	84.36 123	85.76 69	57.22 188	56.71 200	87.67 98	30.79 246	92.83 25	43.04 240	84.06 41	85.01 155

SteuartSystems-ACMMP		77.08 31	76.33 34	79.34 30	80.98 137	55.31 44	89.76 24	86.91 52	62.94 93	71.65 52	91.56 34	42.33 121	92.56 29	77.14 27	83.69 42	90.15 70
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		78.44 16	78.20 17	79.19 32	88.56 12	54.55 70	89.76 24	87.77 40	55.91 212	78.56 12	92.49 16	48.20 38	92.65 28	79.49 14	83.04 43	90.39 63

EPNet		78.36 19	78.49 15	77.97 63	85.49 45	52.04 138	89.36 29	84.07 117	73.22 4	77.03 17	91.72 30	49.32 36	90.17 72	73.46 49	82.77 44	91.69 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
API-MVS		74.17 62	72.07 75	80.49 14	90.02 6	58.55 7	87.30 56	84.27 105	57.51 185	65.77 91	87.77 97	41.61 136	95.97 2	51.71 196	82.63 45	86.94 123

MSLP-MVS++		74.21 61	72.25 72	80.11 21	81.45 131	56.47 26	86.32 71	79.65 198	58.19 172	66.36 83	92.29 18	36.11 196	90.66 57	67.39 76	82.49 46	93.18 10

zzz-MVS		74.15 64	73.11 62	77.27 78	81.54 127	53.57 85	84.02 133	81.31 171	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

MTAPA		72.73 79	71.22 86	77.27 78	81.54 127	53.57 85	67.06 304	81.31 171	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

MP-MVS		74.99 56	74.33 52	76.95 85	82.89 95	53.05 118	85.63 85	83.50 133	57.86 178	67.25 75	90.24 59	43.38 103	88.85 110	76.03 30	82.23 49	88.96 92

3Dnovator+	62.71 7	72.29 87	70.50 91	77.65 68	83.40 78	51.29 155	87.32 54	86.40 62	59.01 154	58.49 178	88.32 86	32.40 232	91.27 46	57.04 155	82.15 50	90.38 64

CHOSEN 1792x2688		76.24 43	74.03 54	82.88 1	83.09 86	62.84 2	85.73 83	85.39 76	69.79 16	64.87 102	83.49 148	41.52 137	93.69 19	70.55 61	81.82 51	92.12 23

APD-MVS		76.15 45	75.68 39	77.54 70	88.52 13	53.44 90	87.26 58	85.03 88	53.79 227	74.91 23	91.68 32	43.80 91	90.31 65	74.36 41	81.82 51	88.87 94
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
QAPM		71.88 92	69.33 109	79.52 27	82.20 109	54.30 72	86.30 72	88.77 17	56.61 199	59.72 155	87.48 101	33.90 219	95.36 4	47.48 218	81.49 53	88.90 93

PVSNet_Blended		76.53 40	76.54 31	76.50 91	85.91 34	51.83 143	88.89 34	84.24 108	67.82 27	69.09 63	89.33 76	46.70 53	88.13 137	75.43 34	81.48 54	89.55 78

HFP-MVS		74.37 59	73.13 61	78.10 59	84.30 62	53.68 82	85.58 86	84.36 102	56.82 193	65.78 89	90.56 53	40.70 142	90.90 52	69.18 68	80.88 55	89.71 74

#test#		74.86 57	73.78 56	78.10 59	84.30 62	53.68 82	86.95 62	84.36 102	59.00 155	65.78 89	90.56 53	40.70 142	90.90 52	71.48 58	80.88 55	89.71 74

ACMMPR		73.76 68	72.61 63	77.24 81	83.92 71	52.96 122	85.58 86	84.29 104	56.82 193	65.12 96	90.45 55	37.24 183	90.18 71	69.18 68	80.84 57	88.58 101

region2R		73.75 69	72.55 65	77.33 75	83.90 72	52.98 121	85.54 89	84.09 110	56.83 192	65.10 97	90.45 55	37.34 181	90.24 69	68.89 70	80.83 58	88.77 97

MVS_Test		75.85 49	74.93 49	78.62 45	84.08 67	55.20 49	83.99 134	85.17 84	68.07 25	73.38 34	82.76 157	50.44 29	89.00 99	65.90 86	80.61 59	91.64 31

Vis-MVSNet		70.61 110	69.34 108	74.42 136	80.95 139	48.49 205	86.03 77	77.51 240	58.74 162	65.55 93	87.78 96	34.37 213	85.95 196	52.53 192	80.61 59	88.80 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVS		72.92 77	71.62 79	76.81 87	83.41 75	52.48 128	84.88 115	83.20 140	58.03 174	63.91 114	89.63 71	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

X-MVStestdata		65.85 194	62.20 203	76.81 87	83.41 75	52.48 128	84.88 115	83.20 140	58.03 174	63.91 114	4.82 355	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

1121		68.79 145	66.77 151	74.82 129	83.08 87	53.46 88	80.23 208	71.53 296	45.47 286	66.31 84	87.19 104	34.02 216	85.13 208	52.78 188	80.36 63	85.87 144

新几何1				73.30 161	83.10 84	53.48 87		71.43 297	45.55 284	66.14 85	87.17 105	33.88 220	80.54 261	48.50 211	80.33 64	85.88 143

PGM-MVS		72.60 81	71.20 87	76.80 89	82.95 92	52.82 124	83.07 154	82.14 153	56.51 205	63.18 123	89.81 68	35.68 204	89.76 80	67.30 77	80.19 65	87.83 111

MVSFormer		73.53 71	72.19 73	77.57 69	83.02 89	55.24 46	81.63 183	81.44 168	50.28 260	76.67 18	90.91 45	44.82 78	86.11 188	60.83 123	80.09 66	91.36 42

lupinMVS		78.38 18	78.11 18	79.19 32	83.02 89	55.24 46	91.57 7	84.82 92	69.12 19	76.67 18	92.02 23	44.82 78	90.23 70	80.83 9	80.09 66	92.08 24

HPM-MVS		72.60 81	71.50 81	75.89 104	82.02 114	51.42 151	80.70 201	83.05 142	56.12 209	64.03 113	89.53 72	37.55 175	88.37 126	70.48 63	80.04 68	87.88 110
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		76.39 42	75.38 45	79.42 29	85.33 50	56.47 26	88.15 37	84.97 89	65.15 61	66.06 86	89.88 67	43.79 92	92.16 36	75.03 37	80.03 69	89.64 77

TSAR-MVS + GP.		77.82 25	77.59 23	78.49 47	85.25 52	50.27 174	90.02 17	90.57 5	56.58 200	74.26 27	91.60 33	54.26 11	92.16 36	75.87 31	79.91 70	93.05 12

LFMVS		78.52 15	77.14 27	82.67 3	89.58 7	58.90 6	91.27 9	88.05 33	63.22 89	74.63 25	90.83 48	41.38 138	94.40 13	75.42 36	79.90 71	94.72 1

Effi-MVS+		75.24 53	73.61 57	80.16 20	81.92 117	57.42 14	85.21 94	76.71 249	60.68 122	73.32 35	89.34 74	47.30 46	91.63 42	68.28 72	79.72 72	91.42 40

PAPM_NR		71.80 93	69.98 101	77.26 80	81.54 127	53.34 98	78.60 232	85.25 82	53.46 230	60.53 149	88.66 82	45.69 69	89.24 88	56.49 157	79.62 73	89.19 87

jason		77.01 32	76.45 32	78.69 42	79.69 158	54.74 62	90.56 15	83.99 121	68.26 24	74.10 28	90.91 45	42.14 125	89.99 74	79.30 15	79.12 74	91.36 42
jason: jason.
CANet_DTU		73.71 70	73.14 59	75.40 112	82.61 104	50.05 176	84.67 120	79.36 204	69.72 17	75.39 21	90.03 65	29.41 253	85.93 197	67.99 74	79.11 75	90.22 67

TESTMET0.1,1		72.86 78	72.33 68	74.46 135	81.98 115	50.77 159	85.13 97	85.47 72	66.09 44	67.30 73	83.69 140	37.27 182	83.57 236	65.06 98	78.97 76	89.05 90

SD-MVS		76.18 44	74.85 50	80.18 19	85.39 49	56.90 20	85.75 82	82.45 152	56.79 195	74.48 26	91.81 26	43.72 97	90.75 55	74.61 40	78.65 77	92.91 13

VNet		77.99 24	77.92 20	78.19 57	87.43 23	50.12 175	90.93 12	91.41 3	67.48 32	75.12 22	90.15 63	46.77 52	91.00 49	73.52 48	78.46 78	93.44 4

旧先验1					81.57 126	47.48 219		71.83 291			88.66 82	36.94 187			78.34 79	88.67 98

mPP-MVS		71.79 94	70.38 93	76.04 101	82.65 103	52.06 137	84.45 122	81.78 164	55.59 216	62.05 133	89.68 70	33.48 222	88.28 134	65.45 93	78.24 80	87.77 113

test_normal		71.31 98	68.95 115	78.39 52	72.30 266	54.25 73	81.67 179	84.05 118	65.94 50	51.31 241	78.09 212	36.06 199	90.43 62	73.00 54	78.09 81	90.50 60

CP-MVS		72.59 83	71.46 82	76.00 103	82.93 94	52.32 135	86.93 64	82.48 151	55.15 219	63.65 119	90.44 57	35.03 209	88.53 121	68.69 71	77.83 82	87.15 122

DI_MVS_plusplus_test		71.30 99	68.98 114	78.26 56	72.76 248	54.08 78	81.72 178	83.22 138	65.75 51	51.94 237	78.47 206	36.01 202	90.31 65	73.33 50	77.60 83	90.40 62

PVSNet_Blended_VisFu		73.40 73	72.44 67	76.30 93	81.32 135	54.70 65	85.81 78	78.82 211	63.70 81	64.53 106	85.38 122	47.11 49	87.38 159	67.75 75	77.55 84	86.81 129

canonicalmvs		78.17 21	77.86 21	79.12 35	84.30 62	54.22 74	87.71 42	84.57 99	67.70 30	77.70 14	92.11 22	50.90 28	89.95 75	78.18 23	77.54 85	93.20 9

Test4		68.64 150	65.68 170	77.53 71	67.78 298	53.34 98	79.42 223	82.84 147	65.96 49	46.54 282	76.15 238	25.16 279	88.83 111	69.74 66	77.53 86	90.43 61

1314		71.11 102	69.41 106	76.22 96	79.32 162	50.49 166	80.23 208	85.14 87	59.44 136	58.93 168	88.89 80	33.83 221	89.60 85	61.49 119	77.42 87	88.57 102

Regformer-1		77.80 26	77.44 25	78.88 38	87.78 20	52.44 130	87.60 44	90.08 8	68.86 20	72.49 45	91.79 27	47.69 43	94.90 10	73.57 47	77.05 88	89.31 82

Regformer-2		77.15 30	76.82 30	78.14 58	87.78 20	51.84 142	87.60 44	89.12 13	67.23 33	71.93 49	91.79 27	46.03 64	93.53 20	72.85 55	77.05 88	89.05 90

PAPR		75.20 54	74.13 53	78.41 51	88.31 17	55.10 53	84.31 125	85.66 70	63.76 80	67.55 72	90.73 50	43.48 102	89.40 86	66.36 83	77.03 90	90.73 53

alignmvs		78.08 22	77.98 19	78.39 52	83.53 74	53.22 108	89.77 23	85.45 73	66.11 43	76.59 20	91.99 25	54.07 14	89.05 92	77.34 26	77.00 91	92.89 14

test222					79.36 160	50.97 158	77.99 236	67.84 309	42.54 303	62.84 127	86.53 113	30.26 249			76.91 92	85.23 152

PMMVS		72.98 76	72.05 76	75.78 106	83.57 73	48.60 200	84.08 129	82.85 146	61.62 108	68.24 69	90.33 58	28.35 258	87.78 150	72.71 56	76.69 93	90.95 50

UGNet		68.71 148	67.11 147	73.50 159	80.55 154	47.61 218	84.08 129	78.51 219	59.45 135	65.68 92	82.73 160	23.78 285	85.08 210	52.80 187	76.40 94	87.80 112
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
xiu_mvs_v1_base_debu		71.60 95	70.29 95	75.55 108	77.26 196	53.15 111	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 295	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base		71.60 95	70.29 95	75.55 108	77.26 196	53.15 111	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 295	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base_debi		71.60 95	70.29 95	75.55 108	77.26 196	53.15 111	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 295	86.66 176	73.28 51	76.39 95	86.85 126

Fast-Effi-MVS+		72.73 79	71.15 88	77.48 72	82.75 99	54.76 61	86.77 66	80.64 184	63.05 91	65.93 87	84.01 133	44.42 81	89.03 96	56.45 160	76.36 98	88.64 99

VDD-MVS		76.08 46	74.97 48	79.44 28	84.27 65	53.33 100	91.13 10	85.88 68	65.33 58	72.37 46	89.34 74	32.52 231	92.76 26	77.90 24	75.96 99	92.22 22

testdata				67.08 256	77.59 190	45.46 242		69.20 308	44.47 291	71.50 53	88.34 85	31.21 244	70.76 323	52.20 193	75.88 100	85.03 154

mvs_anonymous		72.29 87	70.74 89	76.94 86	82.85 96	54.72 64	78.43 234	81.54 167	63.77 79	61.69 135	79.32 194	51.11 26	85.31 205	62.15 116	75.79 101	90.79 52

VDDNet		74.37 59	72.13 74	81.09 11	79.58 159	56.52 25	90.02 17	86.70 56	52.61 244	71.23 55	87.20 103	31.75 241	93.96 16	74.30 42	75.77 102	92.79 16

IS-MVSNet		68.80 144	67.55 139	72.54 173	78.50 180	43.43 259	81.03 195	79.35 205	59.12 150	57.27 198	86.71 110	46.05 63	87.70 152	44.32 235	75.60 103	86.49 133

WTY-MVS		77.47 29	77.52 24	77.30 76	88.33 16	46.25 235	88.46 36	90.32 6	71.40 10	72.32 47	91.72 30	53.44 15	92.37 32	66.28 84	75.42 104	93.28 7

Vis-MVSNet (Re-imp)		65.52 195	65.63 171	65.17 277	77.49 192	30.54 323	75.49 258	77.73 237	59.34 140	52.26 235	86.69 111	49.38 35	80.53 262	37.07 258	75.28 105	84.42 162

test-LLR		69.65 126	69.01 113	71.60 197	78.67 174	48.17 213	85.13 97	79.72 195	59.18 146	63.13 124	82.58 162	36.91 188	80.24 266	60.56 126	75.17 106	86.39 137

test-mter		68.36 152	67.29 143	71.60 197	78.67 174	48.17 213	85.13 97	79.72 195	53.38 231	63.13 124	82.58 162	27.23 266	80.24 266	60.56 126	75.17 106	86.39 137

PVSNet	62.49 8	69.27 136	67.81 129	73.64 155	84.41 61	51.85 141	84.63 121	77.80 233	66.42 38	59.80 154	84.95 126	22.14 299	80.44 263	55.03 171	75.11 108	88.62 100

BH-w/o		70.02 117	68.51 117	74.56 134	82.77 98	50.39 168	86.60 67	78.14 225	59.77 130	59.65 156	85.57 120	39.27 157	87.30 160	49.86 204	74.94 109	85.99 140

UA-Net		67.32 170	66.23 158	70.59 215	78.85 170	41.23 279	73.60 267	75.45 266	61.54 109	66.61 81	84.53 128	38.73 161	86.57 181	42.48 244	74.24 110	83.98 170

CDS-MVSNet		70.48 112	69.43 105	73.64 155	77.56 191	48.83 196	83.51 144	77.45 241	63.27 88	62.33 129	85.54 121	43.85 89	83.29 239	57.38 154	74.00 111	88.79 96
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
BH-RMVSNet		70.08 115	68.01 124	76.27 94	84.21 66	51.22 157	87.29 57	79.33 206	58.96 157	63.63 120	86.77 109	33.29 224	90.30 68	44.63 234	73.96 112	87.30 121

CLD-MVS		75.60 50	75.39 44	76.24 95	80.69 144	52.40 131	90.69 13	86.20 66	74.40 3	65.01 100	88.93 78	42.05 128	90.58 59	76.57 29	73.96 112	85.73 145
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
APD-MVS_3200maxsize		69.62 127	68.23 122	73.80 152	81.58 125	48.22 212	81.91 172	79.50 200	48.21 269	64.24 112	89.75 69	31.91 240	87.55 155	63.08 104	73.85 114	85.64 148

HPM-MVS_fast		67.86 158	66.28 157	72.61 171	80.67 145	48.34 210	81.18 192	75.95 260	50.81 259	59.55 160	88.05 93	27.86 261	85.98 193	58.83 137	73.58 115	83.51 179

ACMMP		70.81 107	69.29 110	75.39 113	81.52 130	51.92 140	83.43 145	83.03 143	56.67 198	58.80 173	88.91 79	31.92 239	88.58 117	65.89 87	73.39 116	85.67 146
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
Regformer-3		76.02 48	75.47 43	77.70 66	85.49 45	51.47 149	85.12 100	90.19 7	68.52 22	69.36 61	90.66 51	46.45 61	94.81 11	70.25 64	73.16 117	86.81 129

Regformer-4		75.06 55	74.59 51	76.47 92	85.49 45	50.33 170	85.12 100	88.61 20	66.42 38	68.48 66	90.66 51	44.15 87	92.68 27	69.24 67	73.16 117	86.39 137

HQP3-MVS								83.68 127							73.12 119

HQP-MVS		72.34 85	71.44 83	75.03 118	79.02 167	51.56 146	88.00 38	83.68 127	65.45 52	64.48 107	85.13 123	37.35 179	88.62 114	66.70 80	73.12 119	84.91 157

TAMVS		69.51 129	68.16 123	73.56 158	76.30 204	48.71 197	82.57 162	77.17 245	62.10 101	61.32 137	84.23 131	41.90 130	83.46 237	54.80 175	73.09 121	88.50 103

BH-untuned		68.28 154	66.40 155	73.91 147	81.62 122	50.01 177	85.56 88	77.39 242	57.63 184	57.47 195	83.69 140	36.36 194	87.08 164	44.81 233	73.08 122	84.65 159

plane_prior						49.57 181	87.43 52		64.57 66						72.84 123

PCF-MVS	61.03 10	70.10 114	68.40 119	75.22 117	77.15 200	51.99 139	79.30 228	82.12 157	56.47 206	61.88 134	86.48 115	43.98 88	87.24 161	55.37 168	72.79 124	86.43 136
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DWT-MVSNet_test		75.47 52	73.87 55	80.29 16	87.33 25	57.05 18	82.86 159	87.96 35	72.59 6	67.29 74	87.79 95	51.61 23	91.52 43	54.75 176	72.63 125	92.29 21

HY-MVS	67.03 5	73.90 66	73.14 59	76.18 97	84.70 58	47.36 220	75.56 255	86.36 63	66.27 41	70.66 57	83.91 135	51.05 27	89.31 87	67.10 78	72.61 126	91.88 28

DP-MVS Recon		71.99 91	70.31 94	77.01 84	90.65 4	53.44 90	89.37 28	82.97 144	56.33 207	63.56 121	89.47 73	34.02 216	92.15 38	54.05 179	72.41 127	85.43 151

diffmvs		70.02 117	68.35 120	75.03 118	79.19 165	51.48 148	78.50 233	76.65 251	59.71 131	67.10 77	80.32 189	42.81 117	87.12 163	58.48 139	72.37 128	86.49 133

HQP_MVS		70.96 105	69.91 102	74.12 142	77.95 185	49.57 181	85.76 80	82.59 149	63.60 84	62.15 130	83.28 152	36.04 200	88.30 132	65.46 91	72.34 129	84.49 160

plane_prior5								82.59 149					88.30 132	65.46 91	72.34 129	84.49 160

MVS_111021_LR		69.07 137	67.91 125	72.54 173	77.27 195	49.56 183	79.77 216	73.96 278	59.33 142	60.73 147	87.82 94	30.19 250	81.53 253	69.94 65	72.19 131	86.53 132

EPMVS		68.45 151	65.44 177	77.47 73	84.91 55	56.17 30	71.89 286	81.91 162	61.72 107	60.85 145	72.49 266	36.21 195	87.06 165	47.32 219	71.62 132	89.17 88

TR-MVS		69.71 124	67.85 128	75.27 115	82.94 93	48.48 206	87.40 53	80.86 181	57.15 189	64.61 105	87.08 106	32.67 230	89.64 84	46.38 226	71.55 133	87.68 115

abl_6		68.03 156	66.15 160	73.66 154	78.54 179	48.48 206	79.77 216	78.04 229	47.39 273	63.70 118	88.25 88	28.21 259	89.06 90	60.17 133	71.25 134	83.45 180

OPM-MVS		70.75 108	69.58 104	74.26 140	75.55 213	51.34 153	86.05 76	83.29 137	61.94 103	62.95 126	85.77 118	34.15 215	88.44 124	65.44 94	71.07 135	82.99 189

114514_t		69.87 123	67.88 127	75.85 105	88.38 15	52.35 134	86.94 63	83.68 127	53.70 228	55.68 211	85.60 119	30.07 251	91.20 47	55.84 163	71.02 136	83.99 169

sss		70.49 111	70.13 99	71.58 199	81.59 124	39.02 289	80.78 200	84.71 97	59.34 140	66.61 81	88.09 91	37.17 184	85.52 201	61.82 118	71.02 136	90.20 68

cascas		69.01 140	66.13 161	77.66 67	79.36 160	55.41 42	86.99 60	83.75 126	56.69 197	58.92 169	81.35 178	24.31 283	92.10 39	53.23 182	70.61 138	85.46 150

LCM-MVSNet-Re		58.82 256	56.54 254	65.68 271	79.31 163	29.09 329	61.39 319	45.79 340	60.73 121	37.65 312	72.47 267	31.42 243	81.08 256	49.66 205	70.41 139	86.87 124

AdaColmap		67.86 158	65.48 174	75.00 120	88.15 19	54.99 55	86.10 75	76.63 252	49.30 266	57.80 185	86.65 112	29.39 254	88.94 107	45.10 232	70.21 140	81.06 221

CPTT-MVS		67.15 174	65.84 165	71.07 206	80.96 138	50.32 171	81.94 171	74.10 275	46.18 282	57.91 183	87.64 99	29.57 252	81.31 255	64.10 100	70.18 141	81.56 211

PatchmatchNet		67.07 177	63.63 194	77.40 74	83.10 84	58.03 8	72.11 283	77.77 235	58.85 160	59.37 162	70.83 277	37.84 167	84.93 215	42.96 241	69.83 142	89.26 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPP-MVSNet		71.14 100	70.07 100	74.33 138	79.18 166	46.52 229	83.81 136	86.49 58	56.32 208	57.95 182	84.90 127	54.23 12	89.14 89	58.14 145	69.65 143	87.33 119

EPNet_dtu		66.25 190	66.71 153	64.87 279	78.66 176	34.12 310	82.80 160	75.51 264	61.75 106	64.47 110	86.90 108	37.06 185	72.46 316	43.65 238	69.63 144	88.02 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MIMVSNet		63.12 221	60.29 232	71.61 196	75.92 210	46.65 227	65.15 305	81.94 159	59.14 148	54.65 214	69.47 283	25.74 275	80.63 260	41.03 246	69.56 145	87.55 116

EI-MVSNet-Vis-set		73.19 75	72.60 64	74.99 121	82.56 105	49.80 179	82.55 163	89.00 15	66.17 42	65.89 88	88.98 77	43.83 90	92.29 33	65.38 97	69.01 146	82.87 192

FIs		70.00 120	70.24 98	69.30 232	77.93 187	38.55 291	83.99 134	87.72 42	66.86 37	57.66 189	84.17 132	52.28 21	85.31 205	52.72 191	68.80 147	84.02 167

CostFormer		73.89 67	72.30 70	78.66 43	82.36 108	56.58 22	75.56 255	85.30 79	66.06 46	70.50 59	76.88 226	57.02 8	89.06 90	68.27 73	68.74 148	90.33 65

HyFIR lowres test		69.94 122	67.58 137	77.04 82	77.11 201	57.29 15	81.49 189	79.11 209	58.27 170	58.86 171	80.41 188	42.33 121	86.96 168	61.91 117	68.68 149	86.87 124

PatchFormer-LS_test		74.17 62	72.30 70	79.77 26	86.61 29	57.26 16	82.02 169	84.80 94	71.85 9	64.73 103	87.52 100	50.33 31	90.40 63	54.23 178	68.63 150	91.64 31

1112_ss		70.05 116	69.37 107	72.10 178	80.77 142	42.78 266	85.12 100	76.75 248	59.69 132	61.19 138	92.12 20	47.48 44	83.84 232	53.04 185	68.21 151	89.66 76

ab-mvs		70.65 109	69.11 112	75.29 114	80.87 140	46.23 236	73.48 268	85.24 83	59.99 129	66.65 79	80.94 185	43.13 107	88.69 112	63.58 102	68.07 152	90.95 50

tpm2		70.82 106	68.44 118	77.98 62	80.78 141	56.11 31	74.21 265	81.28 174	60.24 127	68.04 70	75.27 244	52.26 22	88.50 123	55.82 164	68.03 153	89.33 81

EI-MVSNet-UG-set		72.37 84	71.73 78	74.29 139	81.60 123	49.29 188	81.85 174	88.64 19	65.29 60	65.05 98	88.29 87	43.18 104	91.83 40	63.74 101	67.97 154	81.75 209

thres200		68.71 148	67.27 144	73.02 163	84.73 57	46.76 226	85.03 111	87.73 41	62.34 98	59.87 152	83.45 149	43.15 105	88.32 131	31.25 282	67.91 155	83.98 170

tpmrst		71.04 103	69.77 103	74.86 128	83.19 83	55.86 33	75.64 254	78.73 214	67.88 26	64.99 101	73.73 253	49.96 33	79.56 274	65.92 85	67.85 156	89.14 89

EG-PatchMatch MVS		62.40 236	59.59 235	70.81 211	73.29 235	49.05 190	85.81 78	84.78 95	51.85 253	44.19 288	73.48 259	15.52 327	89.85 76	40.16 248	67.24 157	73.54 306

OMC-MVS		65.97 193	65.06 183	68.71 244	72.97 239	42.58 270	78.61 231	75.35 267	54.72 222	59.31 163	86.25 116	33.30 223	77.88 286	57.99 146	67.05 158	85.66 147

TAPA-MVS	56.12 14	61.82 239	60.18 233	66.71 260	78.48 181	37.97 295	75.19 260	76.41 255	46.82 277	57.04 199	86.52 114	27.67 264	77.03 292	26.50 302	67.02 159	85.14 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CMPMVS	40.41 21	55.34 277	52.64 278	63.46 285	60.88 326	43.84 256	61.58 318	71.06 299	30.43 334	36.33 314	74.63 248	24.14 284	75.44 298	48.05 215	66.62 160	71.12 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FC-MVSNet-test		67.49 166	67.91 125	66.21 264	76.06 206	33.06 315	80.82 199	87.18 47	64.44 67	54.81 213	82.87 155	50.40 30	82.60 248	48.05 215	66.55 161	82.98 190

GA-MVS		69.04 138	66.70 154	76.06 100	75.11 216	52.36 133	83.12 152	80.23 188	63.32 87	60.65 148	79.22 196	30.98 245	88.37 126	61.25 120	66.41 162	87.46 117

conf200view11		66.80 182	65.42 178	70.95 209	83.29 79	43.15 261	81.67 179	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 288	66.36 163	81.89 203

thres100view900		66.87 180	65.42 178	71.24 203	83.29 79	43.15 261	81.67 179	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 288	66.36 163	82.78 193

tfpn200view9		67.57 164	66.13 161	71.89 191	84.05 68	45.07 245	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 288	66.36 163	82.78 193

thres400		67.40 169	66.13 161	71.19 205	84.05 68	45.07 245	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 288	66.36 163	80.71 227

tpmp4_e23		70.01 119	67.13 146	78.65 44	81.93 116	57.90 10	73.99 266	81.35 170	60.61 123	65.28 95	73.78 252	52.48 20	88.60 116	48.40 213	66.35 167	89.44 80

Test_1112_low_res		67.18 173	66.23 158	70.02 229	78.75 172	41.02 280	83.43 145	73.69 281	57.29 187	58.45 179	82.39 166	45.30 72	80.88 258	50.50 201	66.26 168	88.16 104

mvs-test1		69.04 138	67.57 138	73.44 160	75.17 214	51.68 145	86.57 68	74.48 271	62.15 99	62.07 132	85.79 117	30.59 247	87.48 156	65.40 95	65.94 169	81.18 220

PVSNet_BlendedMVS		73.42 72	73.30 58	73.76 153	85.91 34	51.83 143	86.18 74	84.24 108	65.40 55	69.09 63	80.86 186	46.70 53	88.13 137	75.43 34	65.92 170	81.33 216

XVG-OURS		61.88 238	59.34 238	69.49 230	65.37 305	46.27 234	64.80 308	73.49 284	47.04 275	57.41 197	82.85 156	25.15 280	78.18 280	53.00 186	64.98 171	84.01 168

thres600view7		66.46 186	65.12 182	70.47 216	83.41 75	43.80 257	82.15 168	87.78 36	59.37 139	56.02 206	82.21 168	43.73 93	86.90 169	26.51 301	64.94 172	80.71 227

tfpn111		66.40 188	64.99 184	70.63 214	83.29 79	43.15 261	81.67 179	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	86.83 171	26.34 303	64.92 173	81.89 203

LPG-MVS_test		66.44 187	64.58 188	72.02 182	74.42 224	48.60 200	83.07 154	80.64 184	54.69 223	53.75 222	83.83 136	25.73 276	86.98 166	60.33 131	64.71 174	80.48 233

LGP-MVS_train				72.02 182	74.42 224	48.60 200		80.64 184	54.69 223	53.75 222	83.83 136	25.73 276	86.98 166	60.33 131	64.71 174	80.48 233

MVSTER		73.25 74	72.33 68	76.01 102	85.54 44	53.76 81	83.52 140	87.16 48	67.06 35	63.88 116	81.66 176	52.77 18	90.44 60	64.66 99	64.69 176	83.84 175

EI-MVSNet		69.70 125	68.70 116	72.68 170	75.00 218	48.90 194	79.54 220	87.16 48	61.05 116	63.88 116	83.74 138	45.87 66	90.44 60	57.42 153	64.68 177	78.70 249

tpm cat1		66.28 189	62.78 200	76.77 90	81.40 132	57.14 17	70.03 295	77.19 244	53.00 235	58.76 174	70.73 279	46.17 62	86.73 174	43.27 239	64.46 178	86.44 135

testing_2		63.60 213	59.86 234	74.82 129	61.87 320	52.39 132	73.06 274	82.76 148	61.49 111	39.96 306	67.39 300	21.06 304	88.34 128	67.07 79	64.10 179	83.72 177

XVG-OURS-SEG-HR		62.02 237	59.54 236	69.46 231	65.30 306	45.88 238	65.06 306	73.57 283	46.45 280	57.42 196	83.35 151	26.95 267	78.09 282	53.77 181	64.03 180	84.42 162

LS3D		56.40 272	53.82 269	64.12 281	81.12 136	45.69 241	73.42 269	66.14 314	35.30 327	43.24 296	79.88 191	22.18 298	79.62 273	19.10 336	64.00 181	67.05 324

ACMP	61.11 9	66.24 191	64.33 190	72.00 184	74.89 220	49.12 189	83.18 151	79.83 193	55.41 218	52.29 233	82.68 161	25.83 274	86.10 190	60.89 122	63.94 182	80.78 225
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tpm		68.36 152	67.48 141	70.97 208	79.93 157	51.34 153	76.58 244	78.75 213	67.73 28	63.54 122	74.86 246	48.33 37	72.36 317	53.93 180	63.71 183	89.21 86

XXY-MVS		70.18 113	69.28 111	72.89 167	77.64 189	42.88 265	85.06 109	87.50 46	62.58 96	62.66 128	82.34 167	43.64 99	89.83 77	58.42 141	63.70 184	85.96 142

GBi-Net		67.09 175	65.47 175	71.96 185	82.71 100	46.36 231	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

test1		67.09 175	65.47 175	71.96 185	82.71 100	46.36 231	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

FMVSNet3		68.84 141	67.40 142	73.19 162	85.05 53	48.53 203	85.71 84	85.36 77	60.90 118	57.58 190	79.15 197	42.16 124	86.77 172	47.25 220	63.40 185	84.27 164

VPA-MVSNet		71.12 101	70.66 90	72.49 175	78.75 172	44.43 251	87.64 43	90.02 9	63.97 75	65.02 99	81.58 177	42.14 125	87.42 158	63.42 103	63.38 188	85.63 149

pcd1.5k->3k		27.74 321	27.68 321	27.93 339	73.75 232	0.00 361	0.00 353	85.50 71	0.00 355	0.00 358	0.00 359	26.52 270	0.00 358	0.00 357	63.37 189	83.79 176

Fast-Effi-MVS+-dtu		66.53 185	64.10 193	73.84 150	72.41 262	52.30 136	84.73 117	75.66 263	59.51 134	56.34 204	79.11 198	28.11 260	85.85 198	57.74 150	63.29 190	83.35 181

CVMVSNet		60.85 244	60.44 231	62.07 290	75.00 218	32.73 317	79.54 220	73.49 284	36.98 317	56.28 205	83.74 138	29.28 255	69.53 326	46.48 225	63.23 191	83.94 173

ACMMP++_ref															63.20 192

ACMM	58.35 12	64.35 205	62.01 205	71.38 201	74.21 227	48.51 204	82.25 167	79.66 197	47.61 271	54.54 215	80.11 190	25.26 278	86.00 192	51.26 197	63.16 193	79.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		57.53 266	56.38 258	60.97 299	74.01 228	48.10 215	46.30 335	54.31 334	48.18 270	50.88 246	77.43 220	38.37 164	59.16 338	54.83 173	63.14 194	75.66 290

PS-MVSNAJss		68.78 146	67.17 145	73.62 157	73.01 238	48.33 211	84.95 113	84.81 93	59.30 143	58.91 170	79.84 192	37.77 168	88.86 109	62.83 106	63.12 195	83.67 178

tfpn_ndepth		64.50 202	63.34 199	67.99 248	81.84 119	38.30 293	79.26 229	83.57 132	53.69 229	52.86 231	84.51 129	46.96 50	84.79 216	24.28 308	63.09 196	80.87 224

MDTV_nov1_ep13			61.56 218		81.68 121	55.12 51	72.41 279	78.18 223	59.19 144	58.85 172	69.29 284	34.69 211	86.16 187	36.76 262	62.96 197

FMVSNet2		67.57 164	65.79 166	72.90 165	82.71 100	47.97 216	85.15 96	84.93 90	58.55 165	56.71 200	78.26 207	36.72 191	86.67 175	46.15 228	62.94 198	84.07 166

view600		64.79 197	63.45 195	68.82 238	82.13 110	40.75 282	79.41 224	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 313	62.85 199	80.71 227

view800		64.79 197	63.45 195	68.82 238	82.13 110	40.75 282	79.41 224	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 313	62.85 199	80.71 227

conf0.05thres1000		64.79 197	63.45 195	68.82 238	82.13 110	40.75 282	79.41 224	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 313	62.85 199	80.71 227

tfpn		64.79 197	63.45 195	68.82 238	82.13 110	40.75 282	79.41 224	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 313	62.85 199	80.71 227

MVS-HIRNet		49.01 296	44.71 301	61.92 293	76.06 206	46.61 228	63.23 312	54.90 333	24.77 338	33.56 327	36.60 340	21.28 303	75.88 297	29.49 285	62.54 203	63.26 335

IB-MVS	68.87 2	74.01 65	72.03 77	79.94 24	83.04 88	55.50 37	90.24 16	88.65 18	67.14 34	61.38 136	81.74 175	53.21 16	94.28 14	60.45 128	62.41 204	90.03 72
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
nrg030		72.27 89	71.56 80	74.42 136	75.93 209	50.60 163	86.97 61	83.21 139	62.75 95	67.15 76	84.38 130	50.07 32	86.66 176	71.19 59	62.37 205	85.99 140

OpenMVS_ROB	53.19 17	59.20 253	56.00 260	68.83 237	71.13 278	44.30 252	83.64 139	75.02 269	46.42 281	46.48 283	73.03 262	18.69 313	88.14 136	27.74 297	61.80 206	74.05 302

dp		64.41 203	61.58 216	72.90 165	82.40 106	54.09 77	72.53 277	76.59 253	60.39 125	55.68 211	70.39 280	35.18 208	76.90 294	39.34 250	61.71 207	87.73 114

FMVSNet1		64.57 201	62.11 204	71.96 185	77.32 194	46.36 231	83.52 140	83.31 134	52.43 248	54.42 216	76.23 234	27.80 262	86.20 184	42.59 243	61.34 208	83.32 182

tfpn1000		62.79 231	61.74 207	65.95 270	80.50 155	35.93 300	76.53 246	83.99 121	51.24 256	49.82 255	83.44 150	47.32 45	83.02 247	21.84 325	60.99 209	78.89 247

VPNet		72.07 90	71.42 84	74.04 144	78.64 177	47.17 224	89.91 22	87.97 34	72.56 7	64.66 104	85.04 125	41.83 132	88.33 130	61.17 121	60.97 210	86.62 131

Effi-MVS+-dtu		66.24 191	64.96 186	70.08 224	75.17 214	49.64 180	82.01 170	74.48 271	62.15 99	57.83 184	76.08 239	30.59 247	83.79 233	65.40 95	60.93 211	76.81 279

PLC	52.38 18	60.89 243	58.97 243	66.68 262	81.77 120	45.70 240	78.96 230	74.04 277	43.66 297	47.63 266	83.19 154	23.52 290	77.78 289	37.47 253	60.46 212	76.55 283
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		68.76 147	66.79 150	74.68 132	72.60 255	53.37 96	84.72 118	80.88 180	63.80 78	60.43 150	78.21 208	40.05 151	88.89 108	60.34 130	60.07 213	81.77 208

v1neww		69.43 133	67.62 134	74.89 125	72.90 243	53.31 101	85.12 100	81.11 175	64.29 69	61.00 139	78.53 202	42.88 114	88.98 102	62.66 108	60.06 214	82.37 199

v7new		69.43 133	67.62 134	74.89 125	72.90 243	53.31 101	85.12 100	81.11 175	64.29 69	61.00 139	78.53 202	42.88 114	88.98 102	62.66 108	60.06 214	82.37 199

v6		69.43 133	67.61 136	74.88 127	72.87 247	53.30 105	85.12 100	81.10 177	64.29 69	60.99 141	78.52 204	42.88 114	88.98 102	62.67 107	60.06 214	82.37 199

CR-MVSNet		62.47 234	59.04 242	72.77 168	73.97 230	56.57 23	60.52 320	71.72 292	60.04 128	57.49 193	65.86 305	38.94 158	80.31 264	42.86 242	59.93 217	81.42 213

RPMNet		58.49 262	53.74 271	72.77 168	73.97 230	56.57 23	60.52 320	72.39 288	35.72 322	57.49 193	58.87 325	37.73 171	80.31 264	27.01 300	59.93 217	81.42 213

divwei89l23v2f112		69.50 130	67.67 130	74.98 122	72.72 251	53.41 93	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.11 108	89.04 93	62.51 110	59.62 219	82.48 196

v1141		69.50 130	67.67 130	74.98 122	72.73 250	53.41 93	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.09 111	89.04 93	62.51 110	59.61 220	82.47 197

v1		69.49 132	67.67 130	74.98 122	72.69 252	53.41 93	85.08 106	82.13 156	64.80 63	60.87 144	78.19 209	43.11 108	89.04 93	62.51 110	59.61 220	82.49 195

v1144		68.81 143	66.82 148	74.80 131	72.34 264	53.46 88	84.68 119	81.77 165	64.25 72	60.28 151	77.91 213	40.23 146	88.95 106	60.37 129	59.52 222	81.97 202

v2v482		69.55 128	67.64 133	75.26 116	72.32 265	53.83 80	84.93 114	81.94 159	65.37 57	60.80 146	79.25 195	41.62 135	88.98 102	63.03 105	59.51 223	82.98 190

CNLPA		60.59 245	58.44 244	67.05 257	79.21 164	47.26 223	79.75 218	64.34 320	42.46 304	51.90 238	83.94 134	27.79 263	75.41 299	37.12 256	59.49 224	78.47 253

ACMMP++															59.38 225

PatchMatch-RL		56.66 268	53.75 270	65.37 276	77.91 188	45.28 243	69.78 297	60.38 327	41.35 305	47.57 267	73.73 253	16.83 321	76.91 293	36.99 259	59.21 226	73.92 303

test0.0.03 1		62.54 232	62.44 202	62.86 289	72.28 267	29.51 326	82.93 157	78.78 212	59.18 146	53.07 230	82.41 165	36.91 188	77.39 290	37.45 254	58.96 227	81.66 210

v1192		67.96 157	65.74 168	74.63 133	71.79 269	53.43 92	84.06 131	80.99 179	63.19 90	59.56 159	77.46 219	37.50 178	88.65 113	58.20 144	58.93 228	81.79 207

conf0.01		63.04 222	61.74 207	66.95 258	80.60 146	35.92 301	76.01 247	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 318	58.77 229	81.89 203

conf0.002		63.04 222	61.74 207	66.95 258	80.60 146	35.92 301	76.01 247	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 318	58.77 229	81.89 203

thresconf0.02		62.84 225	61.74 207	66.14 265	80.60 146	35.92 301	76.01 247	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 318	58.77 229	79.44 242

tfpn_n400		62.84 225	61.74 207	66.14 265	80.60 146	35.92 301	76.01 247	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 318	58.77 229	79.44 242

tfpnconf		62.84 225	61.74 207	66.14 265	80.60 146	35.92 301	76.01 247	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 318	58.77 229	79.44 242

tfpnview11		62.84 225	61.74 207	66.14 265	80.60 146	35.92 301	76.01 247	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 318	58.77 229	79.44 242

V42		67.66 162	65.60 173	73.86 149	70.69 280	53.63 84	81.50 187	78.61 217	63.85 77	59.49 161	77.49 218	37.98 165	87.65 153	62.33 113	58.43 235	80.29 236

tpmvs		62.45 235	59.42 237	71.53 200	83.93 70	54.32 71	70.03 295	77.61 238	51.91 251	53.48 225	68.29 291	37.91 166	86.66 176	33.36 272	58.27 236	73.62 305

XVG-ACMP-BASELINE		56.03 274	52.85 276	65.58 272	61.91 319	40.95 281	63.36 310	72.43 287	45.20 287	46.02 285	74.09 249	9.20 338	78.12 281	45.13 231	58.27 236	77.66 274

pmmvs5		62.80 230	61.18 220	67.66 251	69.53 287	42.37 273	82.65 161	75.19 268	54.30 226	52.03 236	78.51 205	31.64 242	80.67 259	48.60 210	58.15 238	79.95 239

v1240		66.99 178	64.68 187	73.93 146	71.38 276	52.66 126	83.39 149	79.98 191	61.97 102	58.44 180	77.11 224	35.25 207	87.81 145	56.46 159	58.15 238	81.33 216

v1921920		67.45 167	65.23 181	74.10 143	71.51 274	52.90 123	83.75 138	80.44 187	62.48 97	59.12 166	77.13 223	36.98 186	87.90 143	57.53 151	58.14 240	81.49 212

jajsoiax		63.21 220	60.84 224	70.32 220	68.33 294	44.45 250	81.23 191	81.05 178	53.37 232	50.96 245	77.81 215	17.49 319	85.49 203	59.31 135	58.05 241	81.02 222

Anonymous20231206		59.08 254	57.59 248	63.55 284	68.77 290	32.14 320	80.26 206	79.78 194	50.00 263	49.39 256	72.39 269	26.64 269	78.36 278	33.12 275	57.94 242	80.14 237

mvs_tets		62.96 224	60.55 229	70.19 221	68.22 296	44.24 254	80.90 198	80.74 183	52.99 236	50.82 253	77.56 216	16.74 322	85.44 204	59.04 136	57.94 242	80.89 223

LTVRE_ROB	45.45 19	52.73 289	49.74 289	61.69 294	69.78 285	34.99 307	44.52 337	67.60 311	43.11 301	43.79 291	74.03 250	18.54 314	81.45 254	28.39 295	57.94 242	68.62 322
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
v144192		67.86 158	65.76 167	74.16 141	71.68 271	53.09 115	84.14 128	80.83 182	62.85 94	59.21 165	77.28 222	39.30 156	88.00 142	58.67 138	57.88 245	81.40 215

IterMVS-LS		66.63 183	65.36 180	70.42 218	75.10 217	48.90 194	81.45 190	76.69 250	61.05 116	55.71 210	77.10 225	45.86 67	83.65 235	57.44 152	57.88 245	78.70 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	53.70 16	59.78 249	55.94 261	71.28 202	76.59 203	48.35 209	80.15 211	76.11 256	49.74 264	41.91 299	73.45 260	16.50 324	90.31 65	31.42 280	57.63 247	75.17 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 251	55.14 265	72.32 176	74.69 221	50.71 160	74.39 264	73.58 282	44.44 292	43.40 294	77.52 217	19.45 309	90.87 54	31.31 281	57.49 248	75.38 292

pmmvs4		63.34 218	61.07 222	70.16 222	70.14 282	50.53 165	79.97 213	71.41 298	55.08 220	54.12 219	78.58 201	32.79 229	82.09 251	50.33 202	57.22 249	77.86 271

UniMVSNet (Re)		67.71 161	66.80 149	70.45 217	74.44 223	42.93 264	82.42 165	84.90 91	63.69 82	59.63 157	80.99 184	47.18 47	85.23 207	51.17 199	56.75 250	83.19 187

Patchmatch-test1		63.23 219	59.16 240	75.43 111	78.58 178	57.92 9	61.61 317	77.53 239	56.71 196	57.75 188	70.98 276	31.97 238	78.19 279	40.97 247	56.36 251	90.18 69

v8		67.25 171	64.99 184	74.04 144	72.89 245	53.31 101	82.37 166	80.11 190	61.54 109	54.29 218	76.02 240	42.89 113	88.41 125	58.43 140	56.36 251	80.39 235

DP-MVS		59.24 252	56.12 259	68.63 245	88.24 18	50.35 169	82.51 164	64.43 319	41.10 306	46.70 279	78.77 200	24.75 282	88.57 120	22.26 324	56.29 253	66.96 325

NR-MVSNet		67.25 171	65.99 164	71.04 207	73.27 236	43.91 255	85.32 93	84.75 96	66.05 47	53.65 224	82.11 172	45.05 73	85.97 195	47.55 217	56.18 254	83.24 185

v10		66.61 184	64.20 192	73.83 151	72.59 257	53.37 96	81.88 173	79.91 192	61.11 114	54.09 220	75.60 242	40.06 150	88.26 135	56.47 158	56.10 255	79.86 240

UniMVSNet_NR-MVSNet		68.82 142	68.29 121	70.40 219	75.71 212	42.59 268	84.23 126	86.78 54	66.31 40	58.51 175	82.45 164	51.57 24	84.64 219	53.11 183	55.96 256	83.96 172

DU-MVS		66.84 181	65.74 168	70.16 222	73.27 236	42.59 268	81.50 187	82.92 145	63.53 86	58.51 175	82.11 172	40.75 140	84.64 219	53.11 183	55.96 256	83.24 185

v148		68.24 155	66.35 156	73.88 148	71.76 270	51.47 149	84.23 126	81.90 163	63.69 82	58.94 167	76.44 230	43.72 97	87.78 150	60.63 125	55.86 258	82.39 198

test_djsdf		63.84 209	61.56 218	70.70 212	68.78 289	44.69 249	81.63 183	81.44 168	50.28 260	52.27 234	76.26 233	26.72 268	86.11 188	60.83 123	55.84 259	81.29 219

tfpnnormal		61.47 240	59.09 241	68.62 246	76.29 205	41.69 274	81.14 194	85.16 85	54.48 225	51.32 240	73.63 257	32.32 233	86.89 170	21.78 327	55.71 260	77.29 277

WR-MVS		67.58 163	66.76 152	70.04 228	75.92 210	45.06 248	86.23 73	85.28 80	64.31 68	58.50 177	81.00 183	44.80 80	82.00 252	49.21 208	55.57 261	83.06 188

Baseline_NR-MVSNet		65.49 196	64.27 191	69.13 233	74.37 226	41.65 275	83.39 149	78.85 210	59.56 133	59.62 158	76.88 226	40.75 140	87.44 157	49.99 203	55.05 262	78.28 266

v7n		62.50 233	59.27 239	72.20 177	67.25 300	49.83 178	77.87 237	80.12 189	52.50 247	48.80 259	73.07 261	32.10 236	87.90 143	46.83 223	54.92 263	78.86 248

TranMVSNet+NR-MVSNet		66.94 179	65.61 172	70.93 210	73.45 233	43.38 260	83.02 156	84.25 106	65.31 59	58.33 181	81.90 174	39.92 153	85.52 201	49.43 207	54.89 264	83.89 174

FMVSNet5		58.61 258	56.45 255	65.10 278	77.20 199	39.74 287	74.77 261	77.12 246	50.27 262	43.28 295	67.71 298	26.15 273	76.90 294	36.78 261	54.78 265	78.65 251

ACMH+	54.58 15	58.55 260	55.24 263	68.50 247	74.68 222	45.80 239	80.27 205	70.21 305	47.15 274	42.77 297	75.48 243	16.73 323	85.98 193	35.10 269	54.78 265	73.72 304

IterMVS		63.77 211	61.67 214	70.08 224	72.68 253	51.24 156	80.44 203	75.51 264	60.51 124	51.41 239	73.70 256	32.08 237	78.91 275	54.30 177	54.35 267	80.08 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		60.46 246	57.65 247	68.88 235	63.63 314	45.09 244	72.93 275	78.63 216	46.52 279	51.12 242	72.80 265	21.46 302	83.07 240	57.79 149	53.97 268	78.47 253

F-COLMAP		55.96 276	53.65 272	62.87 288	72.76 248	42.77 267	74.70 263	70.37 303	40.03 307	41.11 303	79.36 193	17.77 317	73.70 309	32.80 276	53.96 269	72.15 312

ADS-MVSNet2		55.21 279	51.44 282	66.51 263	80.60 146	49.56 183	55.03 328	65.44 316	44.72 289	51.00 243	61.19 316	22.83 291	75.41 299	28.54 293	53.63 270	74.57 297

ADS-MVSNet		56.17 273	51.95 281	68.84 236	80.60 146	53.07 117	55.03 328	70.02 306	44.72 289	51.00 243	61.19 316	22.83 291	78.88 276	28.54 293	53.63 270	74.57 297

semantic-postprocess				60.08 301	70.68 281	45.07 245		74.25 274	43.54 298	50.02 254	73.73 253	32.22 235	56.74 339	51.06 200	53.60 272	78.42 258

pm-mvs1		64.12 208	62.56 201	68.78 243	71.68 271	38.87 290	82.89 158	81.57 166	55.54 217	53.89 221	77.82 214	37.73 171	86.74 173	48.46 212	53.49 273	80.72 226

PatchT		56.60 269	52.97 274	67.48 252	72.94 241	46.16 237	57.30 326	73.78 280	38.77 311	54.37 217	57.26 328	37.52 176	78.06 283	32.02 277	52.79 274	78.23 269

test2356		53.94 284	52.37 280	58.64 306	61.58 321	27.53 334	78.20 235	74.33 273	46.92 276	44.01 289	66.04 304	18.91 312	74.11 304	28.80 288	52.55 275	74.28 299

JIA-IIPM		52.33 292	47.77 295	66.03 269	71.20 277	46.92 225	40.00 343	76.48 254	37.10 316	46.73 278	37.02 339	32.96 226	77.88 286	35.97 263	52.45 276	73.29 309

Patchmatch-test		53.33 288	48.17 292	68.81 242	73.31 234	42.38 272	42.98 339	58.23 329	32.53 330	38.79 311	70.77 278	39.66 154	73.51 310	25.18 305	52.06 277	90.55 55

testgi		54.25 282	52.57 279	59.29 303	62.76 317	21.65 341	72.21 282	70.47 301	53.25 233	41.94 298	77.33 221	14.28 328	77.95 285	29.18 287	51.72 278	78.28 266

V4		59.82 247	56.41 256	70.05 227	61.49 323	48.67 198	69.46 299	75.79 262	52.55 245	47.49 271	68.83 287	28.60 256	85.70 199	52.13 194	51.35 279	75.80 287

v52		59.82 247	56.41 256	70.06 226	61.49 323	48.67 198	69.46 299	75.80 261	52.55 245	47.49 271	68.82 288	28.60 256	85.70 199	52.13 194	51.34 280	75.80 287

v748		61.35 241	58.24 245	70.69 213	66.28 301	47.35 221	76.58 244	79.17 208	53.09 234	46.37 284	71.50 274	33.18 225	86.33 183	46.78 224	51.19 281	78.39 259

test_0402		56.45 271	53.03 273	66.69 261	76.78 202	50.31 172	81.76 176	69.61 307	42.79 302	43.88 290	72.13 271	22.82 293	86.46 182	16.57 341	50.94 282	63.31 334

COLMAP_ROB	43.60 20	50.90 294	48.05 293	59.47 302	67.81 297	40.57 286	71.25 288	62.72 325	36.49 321	36.19 315	73.51 258	13.48 329	73.92 307	20.71 331	50.26 283	63.92 332
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs6		59.64 250	57.15 251	67.09 255	66.01 302	36.86 299	80.50 202	78.64 215	45.05 288	49.05 258	73.94 251	27.28 265	86.10 190	43.96 237	49.94 284	78.31 264

USDC		54.36 281	51.23 283	63.76 283	64.29 312	37.71 296	62.84 315	73.48 286	56.85 191	35.47 318	71.94 273	9.23 337	78.43 277	38.43 252	48.57 285	75.13 294

v18		64.36 204	61.80 206	72.05 179	72.97 239	53.31 101	81.16 193	77.76 236	59.14 148	48.50 260	68.97 286	42.91 112	84.38 221	56.62 156	48.17 286	78.47 253

v16		64.25 206	61.66 215	72.03 180	72.91 242	53.28 106	80.93 196	77.81 232	58.86 159	48.30 261	68.80 289	42.70 118	84.37 222	56.44 161	48.14 287	78.44 256

WR-MVS_H		58.91 255	58.04 246	61.54 295	69.07 288	33.83 312	76.91 241	81.99 158	51.40 255	48.17 263	74.67 247	40.23 146	74.15 303	31.78 279	48.10 288	76.64 281

ITE_SJBPF				51.84 319	58.03 328	31.94 321		53.57 337	36.67 320	41.32 302	75.23 245	11.17 333	51.57 343	25.81 304	48.04 289	72.02 313

v17		64.19 207	61.58 216	72.03 180	72.89 245	53.28 106	80.91 197	77.80 233	58.87 158	48.22 262	68.77 290	42.69 119	84.37 222	56.43 162	47.66 290	78.43 257

CP-MVSNet		58.54 261	57.57 249	61.46 297	68.50 292	33.96 311	76.90 242	78.60 218	51.67 254	47.83 264	76.60 229	34.99 210	72.79 314	35.45 265	47.58 291	77.64 275

MIMVSNet1		50.35 295	47.81 294	57.96 308	61.53 322	27.80 333	67.40 303	74.06 276	43.25 300	33.31 330	65.38 308	16.03 325	71.34 321	21.80 326	47.55 292	74.75 295

PS-CasMVS		58.12 264	57.03 253	61.37 298	68.24 295	33.80 313	76.73 243	78.01 230	51.20 257	47.54 269	76.20 237	32.85 227	72.76 315	35.17 267	47.37 293	77.55 276

Patchmatch-RL test		58.72 257	54.32 267	71.92 189	63.91 313	44.25 253	61.73 316	55.19 332	57.38 186	49.31 257	54.24 331	37.60 174	80.89 257	62.19 115	47.28 294	90.63 54

PEN-MVS		58.35 263	57.15 251	61.94 292	67.55 299	34.39 309	77.01 240	78.35 220	51.87 252	47.72 265	76.73 228	33.91 218	73.75 308	34.03 271	47.17 295	77.68 273

FPMVS		35.40 316	33.67 316	40.57 329	46.34 342	28.74 330	41.05 341	57.05 331	20.37 341	22.27 340	53.38 332	6.87 342	44.94 349	8.62 347	47.11 296	48.01 342

test20.03		55.22 278	54.07 268	58.68 305	63.14 316	25.00 335	77.69 238	74.78 270	52.64 237	43.43 293	72.39 269	26.21 272	74.76 302	29.31 286	47.05 297	76.28 285

DSMNet-mixed		38.35 313	35.36 314	47.33 324	48.11 341	14.91 352	37.87 344	36.60 348	19.18 342	34.37 322	59.56 321	15.53 326	53.01 342	20.14 333	46.89 298	74.07 301

Patchmtry		56.56 270	52.95 275	67.42 253	72.53 259	50.59 164	59.05 322	71.72 292	37.86 315	46.92 276	65.86 305	38.94 158	80.06 269	36.94 260	46.72 299	71.60 315

v11		63.44 216	60.66 228	71.79 195	72.61 254	53.02 120	79.80 215	78.08 228	58.30 169	47.27 274	67.91 295	40.67 144	84.14 229	54.93 172	46.39 300	78.23 269

EU-MVSNet		52.63 290	50.72 284	58.37 307	62.69 318	28.13 331	72.60 276	75.97 259	30.94 333	40.76 305	72.11 272	20.16 307	70.80 322	35.11 268	46.11 301	76.19 286

v15		63.83 210	61.13 221	71.93 188	72.60 255	53.21 109	80.44 203	78.22 221	58.80 161	47.57 267	68.22 292	42.50 120	84.18 224	55.82 164	46.02 302	78.39 259

V14		63.72 212	60.99 223	71.91 190	72.58 258	53.18 110	80.24 207	78.19 222	58.53 168	47.35 273	68.10 293	42.28 123	84.18 224	55.68 166	45.97 303	78.36 262

RPSCF		45.77 307	44.13 304	50.68 320	57.67 331	29.66 325	54.92 330	45.25 342	26.69 337	45.92 286	75.92 241	17.43 320	45.70 348	27.44 298	45.95 304	76.67 280

V9		63.60 213	60.84 224	71.87 192	72.51 260	53.12 114	80.04 212	78.15 224	58.25 171	47.14 275	67.98 294	42.08 127	84.18 224	55.47 167	45.92 305	78.32 263

v12		63.47 215	60.68 227	71.85 193	72.45 261	53.08 116	79.83 214	78.13 226	57.95 177	46.89 277	67.87 296	41.81 133	84.17 227	55.30 169	45.87 306	78.29 265

v13		63.36 217	60.54 230	71.82 194	72.41 262	53.03 119	79.64 219	78.10 227	57.66 183	46.67 280	67.75 297	41.68 134	84.17 227	55.11 170	45.82 307	78.25 268

testus		48.97 297	46.53 298	56.31 313	57.39 332	24.08 337	73.40 270	70.45 302	43.37 299	35.52 317	63.95 310	4.77 349	71.36 320	24.88 306	45.02 308	73.50 307

DTE-MVSNet		57.03 267	55.73 262	60.95 300	65.94 303	32.57 318	75.71 253	77.09 247	51.16 258	46.65 281	76.34 232	32.84 228	73.22 312	30.94 283	44.87 309	77.06 278

testpf		45.92 306	45.81 300	46.27 325	69.56 286	27.86 332	23.18 348	73.91 279	44.10 295	36.99 313	57.16 329	20.56 305	71.77 318	42.17 245	44.64 310	39.18 344

pmmvs-eth3d		55.97 275	52.78 277	65.54 273	61.02 325	46.44 230	75.36 259	67.72 310	49.61 265	43.65 292	67.58 299	21.63 301	77.04 291	44.11 236	44.33 311	73.15 311

AllTest		47.32 301	44.66 302	55.32 314	65.08 308	37.50 297	62.96 314	54.25 335	35.45 325	33.42 328	72.82 263	9.98 334	59.33 336	24.13 309	43.84 312	69.13 320

TestCases				55.32 314	65.08 308	37.50 297		54.25 335	35.45 325	33.42 328	72.82 263	9.98 334	59.33 336	24.13 309	43.84 312	69.13 320

ppachtmachnet_test		58.56 259	54.34 266	71.24 203	71.42 275	54.74 62	81.84 175	72.27 289	49.02 268	45.86 287	68.99 285	26.27 271	83.30 238	30.12 284	43.23 314	75.69 289

PM-MVS		46.92 304	43.76 306	56.41 312	52.18 337	32.26 319	63.21 313	38.18 346	37.99 314	40.78 304	66.20 303	5.09 347	65.42 332	48.19 214	41.99 315	71.54 316

TinyColmap		48.15 299	44.49 303	59.13 304	65.73 304	38.04 294	63.34 311	62.86 324	38.78 310	29.48 335	67.23 302	6.46 344	73.30 311	24.59 307	41.90 316	66.04 326

N_pmnet		41.25 309	39.77 311	45.66 327	68.50 292	0.82 359	72.51 278	0.38 360	35.61 323	35.26 319	61.51 315	20.07 308	67.74 329	23.51 311	40.63 317	68.42 323

TransMVSNet (Re)		62.82 229	60.76 226	69.02 234	73.98 229	41.61 276	86.36 70	79.30 207	56.90 190	52.53 232	76.44 230	41.85 131	87.60 154	38.83 251	40.61 318	77.86 271

OurMVSNet-221017-0		52.39 291	48.73 291	63.35 286	65.21 307	38.42 292	68.54 302	64.95 317	38.19 312	39.57 307	71.43 275	13.23 330	79.92 270	37.16 255	40.32 319	71.72 314

YYNet1		53.82 286	49.96 287	65.41 275	70.09 284	48.95 192	72.30 280	71.66 294	44.25 293	31.89 331	63.07 313	23.73 286	73.95 306	33.26 273	39.40 320	73.34 308

MDA-MVSNet_test_wron		53.82 286	49.95 288	65.43 274	70.13 283	49.05 190	72.30 280	71.65 295	44.23 294	31.85 332	63.13 312	23.68 289	74.01 305	33.25 274	39.35 321	73.23 310

test1235678		47.09 302	43.82 305	56.91 311	53.18 336	24.90 336	71.93 284	70.31 304	39.54 308	31.44 333	56.59 330	9.50 336	71.55 319	22.63 317	39.24 322	74.28 299

1111		48.00 300	46.30 299	53.08 318	55.68 333	20.86 344	70.41 291	76.03 257	36.88 318	34.86 320	59.55 322	23.72 287	68.13 327	20.82 329	38.76 323	70.25 319

ambc				62.06 291	53.98 335	29.38 327	35.08 345	79.65 198		41.37 301	59.96 319	6.27 345	82.15 250	35.34 266	38.22 324	74.65 296

LP		47.05 303	42.23 308	61.53 296	72.04 268	49.37 187	49.48 332	65.50 315	34.57 328	34.29 324	52.30 333	17.73 318	75.32 301	17.56 339	36.57 325	59.91 336

Gipuma		27.47 322	24.26 323	37.12 332	60.55 327	29.17 328	11.68 351	60.00 328	14.18 345	10.52 348	15.12 351	2.20 355	63.01 334	8.39 348	35.65 326	19.18 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		57.56 265	55.15 264	64.79 280	64.57 311	33.12 314	73.17 272	83.87 125	58.98 156	41.75 300	70.03 281	22.54 294	79.92 270	46.12 229	35.31 327	81.32 218

TDRefinement		40.91 310	38.37 312	48.55 323	50.45 338	33.03 316	58.98 323	50.97 338	28.50 335	29.89 334	67.39 300	6.21 346	54.51 340	17.67 338	35.25 328	58.11 337

Anonymous20231211		46.87 305	43.27 307	57.67 309	57.88 329	30.12 324	73.14 273	64.16 321	33.43 329	34.34 323	59.42 324	12.15 331	77.99 284	19.64 335	35.23 329	64.90 330

LF4IMVS		33.04 319	32.55 317	34.52 334	40.96 345	22.03 340	44.45 338	35.62 349	20.42 340	28.12 336	62.35 314	5.03 348	31.88 354	21.61 328	34.42 330	49.63 341

new-patchmatchnet		48.21 298	46.55 297	53.18 317	57.73 330	18.19 350	70.24 293	71.02 300	45.70 283	33.70 326	60.23 318	18.00 316	69.86 325	27.97 296	34.35 331	71.49 317

pmmvs3		45.53 308	41.55 310	57.44 310	48.97 340	39.68 288	70.06 294	57.66 330	28.32 336	34.06 325	57.29 327	8.50 339	66.85 331	34.86 270	34.26 332	65.80 327

SixPastTwentyTwo		54.37 280	50.10 286	67.21 254	70.70 279	41.46 277	74.73 262	64.69 318	47.56 272	39.12 309	69.49 282	18.49 315	84.69 218	31.87 278	34.20 333	75.48 291

UnsupCasMVSNet_bld		53.86 285	50.53 285	63.84 282	63.52 315	34.75 308	71.38 287	81.92 161	46.53 278	38.95 310	57.93 326	20.55 306	80.20 268	39.91 249	34.09 334	76.57 282

MDA-MVSNet-bldmvs		51.56 293	47.75 296	63.00 287	71.60 273	47.32 222	69.70 298	72.12 290	43.81 296	27.65 337	63.38 311	21.97 300	75.96 296	27.30 299	32.19 335	65.70 328

PMVS	19.57 22	25.07 325	22.43 327	32.99 335	23.12 355	22.98 338	40.98 342	35.19 350	15.99 344	11.95 347	35.87 343	1.47 358	49.29 344	5.41 352	31.90 336	26.70 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		33.56 318	31.89 318	38.59 330	49.01 339	20.42 346	51.01 331	37.92 347	20.58 339	23.45 338	46.79 336	6.66 343	49.28 345	20.00 334	31.57 337	46.09 343

testmv		39.64 311	36.01 313	50.55 321	42.18 344	21.56 342	64.81 307	66.88 313	32.22 331	22.25 341	47.47 335	4.33 351	64.81 333	17.71 337	26.22 338	65.29 329

K. test v3		54.04 283	49.42 290	67.92 250	68.55 291	42.57 271	75.51 257	63.07 323	52.07 249	39.21 308	64.59 309	19.34 310	82.21 249	37.11 257	25.31 339	78.97 246

LCM-MVSNet		28.07 320	23.85 324	40.71 328	27.46 354	18.93 349	30.82 346	46.19 339	12.76 347	16.40 342	34.70 344	1.90 356	48.69 346	20.25 332	24.22 340	54.51 339

test12356		37.84 314	35.07 315	46.18 326	45.03 343	8.02 357	57.70 325	62.67 326	31.83 332	22.78 339	50.25 334	4.46 350	66.95 330	17.25 340	23.62 341	63.57 333

lessismore_v0				67.98 249	64.76 310	41.25 278		45.75 341		36.03 316	65.63 307	19.29 311	84.11 230	35.67 264	21.24 342	78.59 252

PVSNet_0	57.04 13	61.19 242	57.24 250	73.02 163	77.45 193	50.31 172	79.43 222	77.36 243	63.96 76	47.51 270	72.45 268	25.03 281	83.78 234	52.76 190	19.22 343	84.96 156

no-one		37.21 315	31.48 319	54.40 316	39.62 348	31.91 322	45.68 336	67.42 312	35.54 324	14.59 344	35.91 342	7.35 340	73.20 313	22.98 312	14.23 344	58.09 338

PMMVS2		26.71 323	22.98 326	37.87 331	36.89 349	8.51 356	42.51 340	29.32 354	19.09 343	13.01 345	37.54 338	2.23 354	53.11 341	14.54 342	11.71 345	51.99 340

MVE	16.60 23	17.34 330	13.39 331	29.16 338	28.43 353	19.72 348	13.73 350	23.63 355	7.23 352	7.96 350	21.41 347	0.80 359	36.08 353	6.97 349	10.39 346	31.69 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		25.11 324	23.09 325	31.17 336	40.18 346	21.30 343	57.99 324	33.28 351	13.77 346	9.94 349	30.29 346	0.45 360	43.74 350	13.61 345	8.28 347	28.46 347

E-PMN		19.16 327	18.40 328	21.44 340	36.19 350	13.63 353	47.59 333	30.89 352	10.73 348	5.91 352	16.59 349	3.66 353	39.77 351	5.95 351	8.14 348	10.92 351

wuykxyi23d		19.94 326	14.87 330	35.13 333	22.47 356	19.80 347	25.80 347	38.64 345	7.61 351	4.88 354	13.58 354	0.23 361	48.42 347	13.11 346	7.53 349	37.18 345

DeepMVS_CX				13.10 342	21.34 357	8.99 355		10.02 358	10.59 349	7.53 351	30.55 345	1.82 357	14.55 355	6.83 350	7.52 350	15.75 350

EMVS		18.42 328	17.66 329	20.71 341	34.13 351	12.64 354	46.94 334	29.94 353	10.46 350	5.58 353	14.93 352	4.23 352	38.83 352	5.24 353	7.51 351	10.67 352

wuyk23d		9.11 332	8.77 334	10.15 343	40.18 346	16.76 351	20.28 349	1.01 359	2.58 353	2.66 355	0.98 356	0.23 361	12.49 356	4.08 354	6.90 352	1.19 354

tmp_tt		9.44 331	10.68 332	5.73 344	2.49 358	4.21 358	10.48 352	18.04 356	0.34 354	12.59 346	20.49 348	11.39 332	7.03 357	13.84 344	6.46 353	5.95 353

ANet_high		34.39 317	29.59 320	48.78 322	30.34 352	22.28 339	55.53 327	63.79 322	38.11 313	15.47 343	36.56 341	6.94 341	59.98 335	13.93 343	5.64 354	64.08 331

cdsmvs_eth3d_5k		18.33 329	24.44 322	0.00 347	0.00 360	0.00 361	0.00 353	89.40 12	0.00 355	0.00 358	92.02 23	38.55 162	0.00 358	0.00 357	0.00 355	0.00 358

pcd_1.5k_mvsjas		3.15 336	4.20 337	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	0.00 359	37.77 168	0.00 358	0.00 357	0.00 355	0.00 358

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	0.00 359	0.00 363	0.00 358	0.00 357	0.00 355	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	0.00 359	0.00 363	0.00 358	0.00 357	0.00 355	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	0.00 359	0.00 363	0.00 358	0.00 357	0.00 355	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	0.00 359	0.00 363	0.00 358	0.00 357	0.00 355	0.00 358

.test1245		38.91 312	41.99 309	29.67 337	55.68 333	20.86 344	70.41 291	76.03 257	36.88 318	34.86 320	59.55 322	23.72 287	68.13 327	20.82 329	0.00 355	0.02 355

testmvs		6.14 334	8.18 335	0.01 345	0.01 359	0.00 361	73.40 270	0.00 361	0.00 355	0.02 356	0.15 357	0.00 363	0.00 358	0.02 355	0.00 355	0.02 355

test123		6.01 335	8.01 336	0.01 345	0.00 360	0.01 360	71.93 284	0.00 361	0.00 355	0.02 356	0.11 358	0.00 363	0.00 358	0.02 355	0.00 355	0.02 355

ab-mvs-re		7.68 333	10.24 333	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	92.12 20	0.00 363	0.00 358	0.00 357	0.00 355	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 360	0.00 361	0.00 353	0.00 361	0.00 355	0.00 358	0.00 359	0.00 363	0.00 358	0.00 357	0.00 355	0.00 358

GSMVS																88.13 107

test_part3							89.59 26		56.02 210		93.65 3		95.22 6	79.73 12

test_part2					89.33 9	55.48 38				82.27 2

sam_mvs1												38.86 160				88.13 107

sam_mvs												35.99 203

MTGPA								81.31 171

test_post1							70.84 290				14.72 353	34.33 214	83.86 231	48.80 209

test_post											16.22 350	37.52 176	84.72 217

patchmatchnet-post											59.74 320	38.41 163	79.91 272

MTMP								15.34 357

gm-plane-assit					83.24 82	54.21 75			70.91 11		88.23 89		95.25 5	66.37 82

TEST9					85.68 37	55.42 40	87.59 47	84.00 119	57.72 182	72.99 36	90.98 41	44.87 77	88.58 117

test_8					85.72 36	55.31 44	87.60 44	83.88 124	57.84 179	72.84 39	90.99 40	44.99 74	88.34 128

agg_prior					85.64 40	54.92 57		83.61 130		72.53 43			88.10 139

test_prior4						56.39 28	87.15 59

test_prior				78.39 52	86.35 31	54.91 59		85.45 73					89.70 81			90.55 55

旧先验2							81.73 177		45.53 285	74.66 24			70.48 324	58.31 143

新几何2							81.61 185

无先验							85.19 95	78.00 231	49.08 267				85.13 208	52.78 188		87.45 118

原ACMM2							83.77 137

testdata2													77.81 288	45.64 230

segment_acmp												44.97 76

testdata1							77.55 239		64.14 73

plane_prior7					77.95 185	48.46 208

plane_prior6					78.42 182	49.39 186						36.04 200

plane_prior4											83.28 152

plane_prior3						48.95 192			64.01 74	62.15 130

plane_prior2							85.76 80		63.60 84

plane_prior1					78.31 184

n2								0.00 361

nn								0.00 361

door-mid								41.31 344

test11								84.25 106

door								43.27 343

HQP5-MVS						51.56 146

HQP-NCC					79.02 167		88.00 38		65.45 52	64.48 107

ACMP_Plane					79.02 167		88.00 38		65.45 52	64.48 107

BP-MVS														66.70 80

HQP4-MVS										64.47 110			88.61 115			84.91 157

HQP2-MVS												37.35 179

NP-MVS					78.76 171	50.43 167					85.12 124

MDTV_nov1_ep13_2view						43.62 258	71.13 289		54.95 221	59.29 164		36.76 190		46.33 227		87.32 120

Test By Simon												39.38 155

High-resolution multi-view benchmark