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		90.38 2	91.89 1	85.84 70	92.83 58	64.03 173	93.06 77	94.52 32	82.19 19	93.65 1	96.15 13	85.89 1	97.19 60	91.02 10	97.75 1	96.29 16

TSAR-MVS + MP.		88.11 12	88.64 10	86.54 49	91.73 87	68.04 64	90.36 177	93.55 59	82.89 14	91.29 2	92.89 90	72.27 12	96.03 103	87.99 23	94.77 16	95.54 34

SMA-MVS		87.99 13	88.11 14	87.62 24	93.21 49	68.55 50	93.85 57	93.82 47	74.24 106	90.84 3	96.67 5	65.20 51	98.42 21	89.24 15	95.96 3	95.88 28

test_part2					96.29 7	68.16 61				90.78 4

ESAPD		89.08 8	89.53 8	87.72 20	96.29 7	68.16 61	94.96 31	94.26 41	68.52 213	90.78 4	97.23 2	77.03 4	98.90 7	91.52 6	95.18 9	96.11 19

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 105	93.00 55	58.16 262	96.72 3	94.41 37	86.50 5	90.25 6	97.83 1	75.46 7	98.67 14	92.78 2	95.49 8	97.32 1

CANet		89.61 6	89.99 6	88.46 13	94.39 26	69.71 32	96.53 5	93.78 48	86.89 4	89.68 7	95.78 18	65.94 44	99.10 2	92.99 1	93.91 28	96.58 11

xiu_mvs_v2_base		87.92 16	87.38 24	89.55 7	91.41 101	76.43 2	95.74 12	93.12 87	83.53 13	89.55 8	95.95 15	53.45 186	97.68 35	91.07 9	92.62 45	94.54 71

PS-MVSNAJ		88.14 11	87.61 19	89.71 4	92.06 75	76.72 1	95.75 11	93.26 78	83.86 11	89.55 8	96.06 14	53.55 181	97.89 33	91.10 8	93.31 38	94.54 71

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 24	84.83 9	89.07 10	96.80 4	70.86 16	99.06 3	92.64 4	95.71 6	96.12 18

HPM-MVS++		89.37 7	89.95 7	87.64 21	95.10 19	68.23 60	95.24 22	94.49 34	82.43 17	88.90 11	96.35 8	71.89 15	98.63 15	88.76 21	96.40 2	96.06 21

MVS_0304		88.39 10	88.35 13	88.50 12	93.01 54	70.11 23	95.90 10	92.20 120	86.27 6	88.70 12	95.92 16	56.76 133	99.02 4	92.68 3	93.76 31	96.37 15

APDe-MVS		87.54 19	87.84 16	86.65 44	96.07 11	66.30 124	94.84 34	93.78 48	69.35 201	88.39 13	96.34 9	67.74 30	97.66 39	90.62 11	93.44 37	96.01 24

EPNet		87.84 17	88.38 11	86.23 63	93.30 46	66.05 128	95.26 21	94.84 23	87.09 3	88.06 14	94.53 55	66.79 36	97.34 53	83.89 51	91.68 57	95.29 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SD-MVS		87.49 20	87.49 21	87.50 26	93.60 41	68.82 46	93.90 55	92.63 105	76.86 71	87.90 15	95.76 19	66.17 40	97.63 41	89.06 18	91.48 61	96.05 22

canonicalmvs		86.85 30	86.25 33	88.66 10	91.80 86	71.92 10	93.54 66	91.71 138	80.26 31	87.55 16	95.25 35	63.59 74	96.93 80	88.18 22	84.34 114	97.11 3

旧先验2							92.00 115		59.37 290	87.54 17			93.47 200	75.39 102

MVSFormer		83.75 67	82.88 67	86.37 58	89.24 142	71.18 14	89.07 205	90.69 170	65.80 237	87.13 18	94.34 63	64.99 60	92.67 220	72.83 116	91.80 55	95.27 43

lupinMVS		87.74 18	87.77 17	87.63 23	89.24 142	71.18 14	96.57 4	92.90 95	82.70 16	87.13 18	95.27 33	64.99 60	95.80 109	89.34 14	91.80 55	95.93 26

alignmvs		87.28 22	86.97 27	88.24 15	91.30 102	71.14 16	95.61 16	93.56 58	79.30 38	87.07 20	95.25 35	68.43 21	96.93 80	87.87 24	84.33 115	96.65 8

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 40	96.38 7	94.64 31	84.42 10	86.74 21	96.20 11	66.56 39	98.76 13	89.03 19	94.56 21	95.92 27

APD-MVS		85.93 40	85.99 35	85.76 75	95.98 13	65.21 144	93.59 64	92.58 107	66.54 231	86.17 22	95.88 17	63.83 69	97.00 70	86.39 35	92.94 41	95.06 52
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CANet_DTU		84.09 60	83.52 55	85.81 71	90.30 116	66.82 96	91.87 125	89.01 233	85.27 7	86.09 23	93.74 73	47.71 233	96.98 74	77.90 90	89.78 76	93.65 102

VNet		86.20 37	85.65 42	87.84 18	93.92 36	69.99 26	95.73 14	95.94 12	78.43 53	86.00 24	93.07 84	58.22 117	97.00 70	85.22 42	84.33 115	96.52 13

TSAR-MVS + GP.		87.96 14	88.37 12	86.70 43	93.51 44	65.32 142	95.15 25	93.84 46	78.17 55	85.93 25	94.80 51	75.80 6	98.21 24	89.38 13	88.78 80	96.59 10

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 13	89.33 1	85.77 26	96.26 10	72.84 11	99.38 1	92.64 4	95.93 5	97.08 4

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 52	73.88 6	97.01 2	94.40 38	88.32 2	85.71 27	94.91 48	74.11 9	98.91 6	87.26 29	95.94 4	97.03 5
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
PHI-MVS		86.83 31	86.85 30	86.78 42	93.47 45	65.55 139	95.39 20	95.10 20	71.77 166	85.69 28	96.52 6	62.07 85	98.77 12	86.06 37	95.60 7	96.03 23

TEST9					94.18 29	67.28 80	94.16 38	93.51 60	71.75 167	85.52 29	95.33 29	68.01 25	97.27 57

train_agg		87.21 24	87.42 23	86.60 46	94.18 29	67.28 80	94.16 38	93.51 60	71.87 159	85.52 29	95.33 29	68.19 23	97.27 57	89.09 16	94.90 13	95.25 46

test_8					94.19 28	67.19 82	94.15 40	93.42 71	71.87 159	85.38 31	95.35 28	68.19 23	96.95 77

testdata				81.34 184	89.02 146	57.72 266		89.84 203	58.65 293	85.32 32	94.09 67	57.03 128	93.28 202	69.34 150	90.56 72	93.03 119

test_prior3		87.38 21	87.70 18	86.42 55	94.71 23	67.35 78	95.10 27	93.10 88	75.40 89	85.25 33	95.61 24	67.94 26	96.84 82	87.47 26	94.77 16	95.05 53

test_prior2							95.10 27		75.40 89	85.25 33	95.61 24	67.94 26		87.47 26	94.77 16

agg_prior3		86.93 28	87.08 26	86.48 52	94.21 27	66.95 91	94.14 41	93.40 72	71.80 164	84.86 35	95.13 39	66.16 41	97.25 59	89.09 16	94.90 13	95.25 46

ACMMP_Plus		86.05 39	85.80 39	86.80 41	91.58 90	67.53 75	91.79 129	93.49 62	74.93 96	84.61 36	95.30 31	59.42 108	97.92 31	86.13 36	94.92 12	94.94 59

jason		86.40 34	86.17 34	87.11 35	86.16 192	70.54 20	95.71 15	92.19 122	82.00 24	84.58 37	94.34 63	61.86 87	95.53 125	87.76 25	90.89 67	95.27 43
jason: jason.
agg_prior1		87.02 26	87.26 25	86.28 62	94.16 33	66.97 89	94.08 44	93.31 76	71.85 161	84.49 38	95.39 27	68.91 19	96.75 86	88.84 20	94.32 23	95.13 50

agg_prior					94.16 33	66.97 89		93.31 76		84.49 38			96.75 86

Regformer-1		87.24 23	87.60 20	86.15 64	95.14 17	65.83 135	93.95 51	95.12 18	82.11 21	84.25 40	95.73 20	67.88 29	98.35 22	85.60 39	88.64 81	94.26 78

xiu_mvs_v1_base_debu		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 176	73.12 132	84.20 41	94.36 58	38.04 283	95.73 113	84.12 48	86.81 92	91.33 147

xiu_mvs_v1_base		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 176	73.12 132	84.20 41	94.36 58	38.04 283	95.73 113	84.12 48	86.81 92	91.33 147

xiu_mvs_v1_base_debi		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 176	73.12 132	84.20 41	94.36 58	38.04 283	95.73 113	84.12 48	86.81 92	91.33 147

Regformer-2		87.00 27	87.43 22	85.71 78	95.14 17	64.73 155	93.95 51	94.95 21	81.69 26	84.03 44	95.73 20	67.35 33	98.19 26	85.40 41	88.64 81	94.20 80

SteuartSystems-ACMMP		86.82 32	86.90 28	86.58 48	90.42 113	66.38 121	96.09 9	93.87 45	77.73 60	84.01 45	95.66 22	63.39 75	97.94 30	87.40 28	93.55 36	95.42 35
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		87.11 25	86.27 31	89.62 5	97.79 1	76.27 3	94.96 31	94.49 34	78.74 51	83.87 46	92.94 87	64.34 65	96.94 78	75.19 103	94.09 25	95.66 30

DeepC-MVS_fast	79.48 2	87.95 15	88.00 15	87.79 19	95.86 14	68.32 56	95.74 12	94.11 43	83.82 12	83.49 47	96.19 12	64.53 64	98.44 19	83.42 54	94.88 15	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Effi-MVS+		83.82 65	82.76 70	86.99 39	89.56 135	69.40 36	91.35 148	86.12 280	72.59 139	83.22 48	92.81 92	59.60 107	96.01 105	81.76 63	87.80 87	95.56 33

CDPH-MVS		85.71 43	85.46 43	86.46 53	94.75 22	67.19 82	93.89 56	92.83 97	70.90 180	83.09 49	95.28 32	63.62 72	97.36 51	80.63 71	94.18 24	94.84 61

MVS_Test		84.16 59	83.20 64	87.05 37	91.56 91	69.82 29	89.99 186	92.05 125	77.77 59	82.84 50	86.57 176	63.93 68	96.09 100	74.91 109	89.18 78	95.25 46

test12				87.09 36	94.60 25	68.86 44		92.91 94		82.67 51		65.44 49	97.55 44		93.69 34	94.84 61

HY-MVS	76.49 5	84.28 57	83.36 63	87.02 38	92.22 72	67.74 69	84.65 263	94.50 33	79.15 42	82.23 52	87.93 157	66.88 35	96.94 78	80.53 72	82.20 128	96.39 14

LFMVS		84.34 56	82.73 71	89.18 8	94.76 21	73.25 8	94.99 30	91.89 131	71.90 157	82.16 53	93.49 77	47.98 230	97.05 65	82.55 58	84.82 108	97.25 2

WTY-MVS		86.32 35	85.81 38	87.85 17	92.82 60	69.37 37	95.20 23	95.25 16	82.71 15	81.91 54	94.73 52	67.93 28	97.63 41	79.55 76	82.25 127	96.54 12

VDD-MVS		83.06 74	81.81 80	86.81 40	90.86 110	67.70 70	95.40 19	91.50 146	75.46 86	81.78 55	92.34 100	40.09 272	97.13 64	86.85 33	82.04 129	95.60 32

Regformer-3		85.80 42	85.92 36	85.46 82	94.17 31	65.09 150	92.95 81	95.11 19	81.13 27	81.68 56	95.04 40	65.82 46	98.32 23	83.02 55	84.36 112	92.97 121

MSLP-MVS++		86.27 36	85.91 37	87.35 29	92.01 76	68.97 43	95.04 29	92.70 100	79.04 46	81.50 57	96.50 7	58.98 114	96.78 84	83.49 53	93.93 27	96.29 16

Regformer-4		85.45 45	85.69 41	84.73 103	94.17 31	63.23 188	92.95 81	94.83 24	80.66 29	81.29 58	95.04 40	65.12 52	98.08 29	82.74 56	84.36 112	92.88 125

UA-Net		80.02 116	79.65 104	81.11 192	89.33 138	57.72 266	86.33 255	89.00 234	77.44 65	81.01 59	89.15 141	59.33 109	95.90 106	61.01 218	84.28 117	89.73 166

PVSNet_BlendedMVS		83.38 69	83.43 59	83.22 133	93.76 37	67.53 75	94.06 45	93.61 56	79.13 43	81.00 60	85.14 190	63.19 78	97.29 55	87.08 30	73.91 186	84.83 253

PVSNet_Blended		86.73 33	86.86 29	86.31 61	93.76 37	67.53 75	96.33 8	93.61 56	82.34 18	81.00 60	93.08 82	63.19 78	97.29 55	87.08 30	91.38 62	94.13 86

MP-MVS-pluss		85.24 46	85.13 46	85.56 79	91.42 99	65.59 138	91.54 141	92.51 109	74.56 99	80.62 62	95.64 23	59.15 111	97.00 70	86.94 32	93.80 29	94.07 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		84.73 51	84.47 49	85.50 80	91.89 81	65.16 145	91.55 140	92.23 115	75.32 91	80.53 63	95.21 37	56.06 145	97.16 62	84.86 45	92.55 47	94.18 81

MTAPA		83.91 63	83.38 62	85.50 80	91.89 81	65.16 145	81.75 284	92.23 115	75.32 91	80.53 63	95.21 37	56.06 145	97.16 62	84.86 45	92.55 47	94.18 81

PAPM		85.89 41	85.46 43	87.18 32	88.20 164	72.42 9	92.41 100	92.77 98	82.11 21	80.34 65	93.07 84	68.27 22	95.02 133	78.39 86	93.59 35	94.09 89

CostFormer		82.33 83	81.15 86	85.86 69	89.01 147	68.46 52	82.39 281	93.01 90	75.59 84	80.25 66	81.57 231	72.03 14	94.96 135	79.06 81	77.48 162	94.16 84

PMMVS		81.98 91	82.04 77	81.78 176	89.76 126	56.17 283	91.13 160	90.69 170	77.96 57	80.09 67	93.57 75	46.33 243	94.99 134	81.41 67	87.46 89	94.17 83

1121		81.25 97	80.05 97	84.87 100	92.30 69	64.31 167	87.91 226	91.39 150	59.44 289	79.94 68	92.91 88	57.09 126	97.01 68	66.63 169	92.81 44	93.29 111

sss		82.71 79	82.38 74	83.73 124	89.25 140	59.58 248	92.24 103	94.89 22	77.96 57	79.86 69	92.38 98	56.70 136	97.05 65	77.26 93	80.86 135	94.55 69

新几何1				84.73 103	92.32 68	64.28 169		91.46 148	59.56 288	79.77 70	92.90 89	56.95 131	96.57 91	63.40 198	92.91 42	93.34 108

APD-MVS_3200maxsize		81.64 93	81.32 85	82.59 146	92.36 66	58.74 259	91.39 144	91.01 164	63.35 261	79.72 71	94.62 54	51.82 197	96.14 98	79.71 74	87.93 86	92.89 124

MP-MVS		85.02 48	84.97 47	85.17 94	92.60 64	64.27 170	93.24 72	92.27 114	73.13 131	79.63 72	94.43 56	61.90 86	97.17 61	85.00 43	92.56 46	94.06 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
原ACMM1				84.42 112	93.21 49	64.27 170		93.40 72	65.39 240	79.51 73	92.50 94	58.11 119	96.69 88	65.27 185	93.96 26	92.32 135

VDDNet		80.50 106	78.26 125	87.21 31	86.19 191	69.79 30	94.48 36	91.31 153	60.42 281	79.34 74	90.91 114	38.48 279	96.56 92	82.16 59	81.05 133	95.27 43

HFP-MVS		84.73 51	84.40 51	85.72 76	93.75 39	65.01 151	93.50 67	93.19 83	72.19 151	79.22 75	94.93 45	59.04 112	97.67 36	81.55 65	92.21 49	94.49 75

#test#		84.98 49	84.74 48	85.72 76	93.75 39	65.01 151	94.09 43	93.19 83	73.55 125	79.22 75	94.93 45	59.04 112	97.67 36	82.66 57	92.21 49	94.49 75

MAR-MVS		84.18 58	83.43 59	86.44 54	96.25 9	65.93 132	94.28 37	94.27 40	74.41 100	79.16 77	95.61 24	53.99 176	98.88 11	69.62 147	93.26 39	94.50 74
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
PAPR		85.15 47	84.47 49	87.18 32	96.02 12	68.29 57	91.85 127	93.00 92	76.59 76	79.03 78	95.00 42	61.59 88	97.61 43	78.16 87	89.00 79	95.63 31

MVS_111021_HR		86.19 38	85.80 39	87.37 28	93.17 51	69.79 30	93.99 49	93.76 51	79.08 45	78.88 79	93.99 69	62.25 84	98.15 27	85.93 38	91.15 65	94.15 85

region2R		84.36 55	84.03 53	85.36 88	93.54 43	64.31 167	93.43 70	92.95 93	72.16 154	78.86 80	94.84 50	56.97 130	97.53 45	81.38 68	92.11 53	94.24 79

ACMMPR		84.37 54	84.06 52	85.28 90	93.56 42	64.37 165	93.50 67	93.15 86	72.19 151	78.85 81	94.86 49	56.69 137	97.45 47	81.55 65	92.20 51	94.02 94

UGNet		79.87 119	78.68 119	83.45 132	89.96 121	61.51 218	92.13 105	90.79 167	76.83 72	78.85 81	86.33 179	38.16 281	96.17 97	67.93 159	87.17 90	92.67 127
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
diffmvs		80.18 111	78.55 122	85.07 95	88.56 154	66.93 92	86.70 253	88.62 241	70.42 190	78.69 83	85.26 188	56.93 132	94.77 141	68.68 156	83.09 121	93.51 105

GG-mvs-BLEND				86.53 51	91.91 80	69.67 34	75.02 318	94.75 27		78.67 84	90.85 115	77.91 2	94.56 149	72.25 122	93.74 32	95.36 37

XVS		83.87 64	83.47 57	85.05 96	93.22 47	63.78 176	92.92 83	92.66 103	73.99 112	78.18 85	94.31 65	55.25 150	97.41 48	79.16 79	91.58 59	93.95 96

X-MVStestdata		76.86 176	74.13 190	85.05 96	93.22 47	63.78 176	92.92 83	92.66 103	73.99 112	78.18 85	10.19 357	55.25 150	97.41 48	79.16 79	91.58 59	93.95 96

EI-MVSNet-Vis-set		83.77 66	83.67 54	84.06 117	92.79 62	63.56 185	91.76 132	94.81 26	79.65 36	77.87 87	94.09 67	63.35 76	97.90 32	79.35 77	79.36 141	90.74 155

Vis-MVSNet		80.92 102	79.98 100	83.74 122	88.48 157	61.80 215	93.44 69	88.26 250	73.96 115	77.73 88	91.76 107	49.94 212	94.76 142	65.84 180	90.37 73	94.65 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
abl_6		79.82 120	79.20 115	81.70 180	89.85 123	58.34 261	88.47 215	90.07 196	62.56 268	77.71 89	93.08 82	47.65 234	96.78 84	77.94 89	85.45 106	89.99 163

DWT-MVSNet_test		83.95 62	82.80 69	87.41 27	92.90 57	70.07 25	89.12 204	94.42 36	82.15 20	77.64 90	91.77 106	70.81 17	96.22 95	65.03 186	81.36 131	95.94 25

CSCG		86.87 29	86.26 32	88.72 9	95.05 20	70.79 17	93.83 59	95.33 15	68.48 216	77.63 91	94.35 62	73.04 10	98.45 18	84.92 44	93.71 33	96.92 6

TESTMET0.1,1		82.41 82	81.98 78	83.72 125	88.08 165	63.74 178	92.70 89	93.77 50	79.30 38	77.61 92	87.57 164	58.19 118	94.08 178	73.91 112	86.68 95	93.33 110

tpm2		79.80 121	77.95 130	85.34 89	88.28 162	68.26 59	81.56 289	91.42 149	70.11 194	77.59 93	80.50 248	67.40 31	94.26 169	67.34 164	77.35 163	93.51 105

CP-MVS		83.71 68	83.40 61	84.65 106	93.14 52	63.84 174	94.59 35	92.28 113	71.03 178	77.41 94	94.92 47	55.21 153	96.19 96	81.32 69	90.70 69	93.91 98

ab-mvs		80.18 111	78.31 124	85.80 72	88.44 159	65.49 141	83.00 278	92.67 102	71.82 163	77.36 95	85.01 191	54.50 168	96.59 89	76.35 98	75.63 173	95.32 40

test222					89.77 125	61.60 217	89.55 195	89.42 217	56.83 302	77.28 96	92.43 97	52.76 190			91.14 66	93.09 117

PGM-MVS		83.25 70	82.70 72	84.92 98	92.81 61	64.07 172	90.44 174	92.20 120	71.28 176	77.23 97	94.43 56	55.17 154	97.31 54	79.33 78	91.38 62	93.37 107

gg-mvs-nofinetune		77.18 170	74.31 187	85.80 72	91.42 99	68.36 55	71.78 321	94.72 28	49.61 319	77.12 98	45.92 342	77.41 3	93.98 185	67.62 162	93.16 40	95.05 53

HPM-MVS		83.25 70	82.95 66	84.17 115	92.25 71	62.88 199	90.91 163	91.86 132	70.30 193	77.12 98	93.96 70	56.75 135	96.28 94	82.04 61	91.34 64	93.34 108
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PVSNet_Blended_VisFu		83.97 61	83.50 56	85.39 87	90.02 120	66.59 114	93.77 60	91.73 136	77.43 66	77.08 100	89.81 137	63.77 71	96.97 75	79.67 75	88.21 84	92.60 129

DeepC-MVS	77.85 3	85.52 44	85.24 45	86.37 58	88.80 151	66.64 111	92.15 104	93.68 54	81.07 28	76.91 101	93.64 74	62.59 83	98.44 19	85.50 40	92.84 43	94.03 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-UG-set		83.14 72	82.96 65	83.67 127	92.28 70	63.19 192	91.38 146	94.68 29	79.22 40	76.60 102	93.75 72	62.64 82	97.76 34	78.07 88	78.01 152	90.05 162

EPNet_dtu		78.80 138	79.26 114	77.43 264	88.06 166	49.71 314	91.96 117	91.95 130	77.67 61	76.56 103	91.28 112	58.51 116	90.20 270	56.37 237	80.95 134	92.39 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DP-MVS Recon		82.73 77	81.65 82	85.98 66	97.31 3	67.06 86	95.15 25	91.99 127	69.08 204	76.50 104	93.89 71	54.48 170	98.20 25	70.76 139	85.66 104	92.69 126

tpmrst		80.57 104	79.14 117	84.84 101	90.10 119	68.28 58	81.70 285	89.72 210	77.63 62	75.96 105	79.54 262	64.94 62	92.71 218	75.43 101	77.28 165	93.55 104

CHOSEN 1792x2688		84.98 49	83.45 58	89.57 6	89.94 122	75.14 4	92.07 110	92.32 112	81.87 25	75.68 106	88.27 151	60.18 102	98.60 16	80.46 73	90.27 74	94.96 58

test-LLR		80.10 114	79.56 106	81.72 178	86.93 183	61.17 220	92.70 89	91.54 143	71.51 174	75.62 107	86.94 173	53.83 177	92.38 228	72.21 123	84.76 110	91.60 144

test-mter		79.96 117	79.38 112	81.72 178	86.93 183	61.17 220	92.70 89	91.54 143	73.85 117	75.62 107	86.94 173	49.84 214	92.38 228	72.21 123	84.76 110	91.60 144

mPP-MVS		82.96 76	82.44 73	84.52 110	92.83 58	62.92 197	92.76 86	91.85 133	71.52 173	75.61 109	94.24 66	53.48 185	96.99 73	78.97 82	90.73 68	93.64 103

MVS_111021_LR		82.02 90	81.52 83	83.51 130	88.42 160	62.88 199	89.77 193	88.93 235	76.78 73	75.55 110	93.10 80	50.31 208	95.38 127	83.82 52	87.02 91	92.26 138

API-MVS		82.28 84	80.53 95	87.54 25	96.13 10	70.59 19	93.63 62	91.04 163	65.72 239	75.45 111	92.83 91	56.11 144	98.89 10	64.10 194	89.75 77	93.15 115

Fast-Effi-MVS+		81.14 98	80.01 98	84.51 111	90.24 118	65.86 133	94.12 42	89.15 227	73.81 119	75.37 112	88.26 152	57.26 125	94.53 152	66.97 168	84.92 107	93.15 115

tpmp4_e23		78.85 136	76.55 153	85.77 74	89.25 140	68.39 54	81.63 288	91.38 151	70.40 191	75.21 113	79.22 264	67.37 32	94.79 140	58.98 230	75.51 174	94.13 86

nrg030		80.93 101	79.86 101	84.13 116	83.69 222	68.83 45	93.23 73	91.20 156	75.55 85	75.06 114	88.22 155	63.04 81	94.74 144	81.88 62	66.88 235	88.82 174

PatchFormer-LS_test		83.14 72	81.81 80	87.12 34	92.34 67	69.92 28	88.64 212	93.32 75	82.07 23	74.87 115	91.62 110	68.91 19	96.08 102	66.07 177	78.45 151	95.37 36

HPM-MVS_fast		80.25 110	79.55 108	82.33 158	91.55 92	59.95 243	91.32 150	89.16 226	65.23 243	74.71 116	93.07 84	47.81 232	95.74 112	74.87 111	88.23 83	91.31 151

TR-MVS		78.77 140	77.37 142	82.95 136	90.49 112	60.88 223	93.67 61	90.07 196	70.08 195	74.51 117	91.37 111	45.69 246	95.70 118	60.12 223	80.32 136	92.29 136

HQP-NCC					87.54 173		94.06 45		79.80 33	74.18 118

ACMP_Plane					87.54 173		94.06 45		79.80 33	74.18 118

HQP4-MVS										74.18 118			95.61 119			88.63 176

HQP-MVS		81.14 98	80.64 93	82.64 144	87.54 173	63.66 183	94.06 45	91.70 139	79.80 33	74.18 118	90.30 124	51.63 201	95.61 119	77.63 91	78.90 145	88.63 176

PAPM_NR		82.97 75	81.84 79	86.37 58	94.10 35	66.76 103	87.66 237	92.84 96	69.96 196	74.07 122	93.57 75	63.10 80	97.50 46	70.66 140	90.58 71	94.85 60

VPA-MVSNet		79.03 132	78.00 129	82.11 172	85.95 195	64.48 159	93.22 74	94.66 30	75.05 95	74.04 123	84.95 192	52.17 196	93.52 198	74.90 110	67.04 234	88.32 183

CDS-MVSNet		81.43 95	80.74 91	83.52 129	86.26 190	64.45 160	92.09 108	90.65 173	75.83 83	73.95 124	89.81 137	63.97 67	92.91 212	71.27 132	82.82 124	93.20 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		78.58 145	77.03 145	83.22 133	85.94 197	64.56 156	83.21 276	91.14 159	78.31 54	73.67 125	79.68 259	64.01 66	92.09 237	66.07 177	71.26 205	93.03 119

BH-RMVSNet		79.46 128	77.65 134	84.89 99	91.68 89	65.66 137	93.55 65	88.09 251	72.93 135	73.37 126	91.12 113	46.20 245	96.12 99	56.28 238	85.61 105	92.91 123

thres200		79.66 123	78.33 123	83.66 128	92.54 65	65.82 136	93.06 77	96.31 9	74.90 97	73.30 127	88.66 144	59.67 106	95.61 119	47.84 267	78.67 148	89.56 168

CPTT-MVS		79.59 125	79.16 116	80.89 199	91.54 93	59.80 245	92.10 107	88.54 244	60.42 281	72.96 128	93.28 79	48.27 226	92.80 215	78.89 83	86.50 99	90.06 161

HyFIR lowres test		81.03 100	79.56 106	85.43 85	87.81 171	68.11 63	90.18 181	90.01 199	70.65 188	72.95 129	86.06 182	63.61 73	94.50 153	75.01 107	79.75 139	93.67 101

EPP-MVSNet		81.79 92	81.52 83	82.61 145	88.77 152	60.21 238	93.02 79	93.66 55	68.52 213	72.90 130	90.39 123	72.19 13	94.96 135	74.93 108	79.29 143	92.67 127

MDTV_nov1_ep13_2view						59.90 244	80.13 300		67.65 224	72.79 131		54.33 172		59.83 224		92.58 130

mvs-test1		78.74 141	77.95 130	81.14 191	83.22 227	57.13 273	93.96 50	87.78 255	75.42 87	72.68 132	90.80 116	45.08 250	94.54 151	75.08 105	77.49 161	91.74 143

TAMVS		80.37 108	79.45 109	83.13 135	85.14 203	63.37 186	91.23 153	90.76 169	74.81 98	72.65 133	88.49 146	60.63 97	92.95 208	69.41 149	81.95 130	93.08 118

VPNet		78.82 137	77.53 137	82.70 141	84.52 210	66.44 120	93.93 53	92.23 115	80.46 30	72.60 134	88.38 149	49.18 219	93.13 204	72.47 121	63.97 261	88.55 178

CLD-MVS		82.73 77	82.35 75	83.86 120	87.90 170	67.65 72	95.45 18	92.18 123	85.06 8	72.58 135	92.27 101	52.46 194	95.78 110	84.18 47	79.06 144	88.16 188
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		80.34 109	79.75 103	82.12 169	86.94 181	62.42 204	93.13 75	91.31 153	78.81 49	72.53 136	89.14 142	50.66 205	95.55 123	76.74 94	78.53 149	88.39 181

plane_prior3						61.95 214			79.09 44	72.53 136

EPMVS		78.49 147	75.98 160	86.02 65	91.21 103	69.68 33	80.23 298	91.20 156	75.25 93	72.48 138	78.11 269	54.65 167	93.69 195	57.66 235	83.04 122	94.69 64

1112_ss		80.56 105	79.83 102	82.77 139	88.65 153	60.78 225	92.29 101	88.36 246	72.58 140	72.46 139	94.95 43	65.09 53	93.42 201	66.38 173	77.71 154	94.10 88

PVSNet	73.49 8	80.05 115	78.63 120	84.31 113	90.92 108	64.97 153	92.47 99	91.05 162	79.18 41	72.43 140	90.51 122	37.05 295	94.06 179	68.06 157	86.00 102	93.90 99

OMC-MVS		78.67 144	77.91 132	80.95 198	85.76 199	57.40 271	88.49 214	88.67 239	73.85 117	72.43 140	92.10 102	49.29 218	94.55 150	72.73 118	77.89 153	90.91 154

MVS		84.66 53	82.86 68	90.06 1	90.93 107	74.56 5	87.91 226	95.54 14	68.55 212	72.35 142	94.71 53	59.78 105	98.90 7	81.29 70	94.69 20	96.74 7

EI-MVSNet		78.97 134	78.22 126	81.25 185	85.33 200	62.73 202	89.53 197	93.21 80	72.39 144	72.14 143	90.13 127	60.99 90	94.72 145	67.73 161	72.49 196	86.29 224

MVSTER		82.47 81	82.05 76	83.74 122	92.68 63	69.01 41	91.90 124	93.21 80	79.83 32	72.14 143	85.71 186	74.72 8	94.72 145	75.72 99	72.49 196	87.50 198

OPM-MVS		79.00 133	78.09 127	81.73 177	83.52 225	63.83 175	91.64 139	90.30 186	76.36 79	71.97 145	89.93 136	46.30 244	95.17 132	75.10 104	77.70 155	86.19 226

Test_1112_low_res		79.56 126	78.60 121	82.43 151	88.24 163	60.39 234	92.09 108	87.99 253	72.10 155	71.84 146	87.42 166	64.62 63	93.04 205	65.80 181	77.30 164	93.85 100

MDTV_nov1_ep13			72.61 214		89.06 145	68.48 51	80.33 296	90.11 195	71.84 162	71.81 147	75.92 288	53.01 188	93.92 188	48.04 266	73.38 187

tfpn200view9		78.79 139	77.43 139	82.88 137	92.21 73	64.49 157	92.05 111	96.28 10	73.48 126	71.75 148	88.26 152	60.07 103	95.32 128	45.16 277	77.58 157	88.83 172

thres400		78.68 142	77.43 139	82.43 151	92.21 73	64.49 157	92.05 111	96.28 10	73.48 126	71.75 148	88.26 152	60.07 103	95.32 128	45.16 277	77.58 157	87.48 199

ACMMP		81.49 94	80.67 92	83.93 119	91.71 88	62.90 198	92.13 105	92.22 119	71.79 165	71.68 150	93.49 77	50.32 207	96.96 76	78.47 84	84.22 119	91.93 141
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
CHOSEN 280x420		77.35 165	76.95 149	78.55 248	87.07 180	62.68 203	69.71 327	82.95 307	68.80 207	71.48 151	87.27 172	66.03 43	84.00 316	76.47 97	82.81 125	88.95 171

IS-MVSNet		80.14 113	79.41 110	82.33 158	87.91 169	60.08 242	91.97 116	88.27 249	72.90 136	71.44 152	91.73 109	61.44 89	93.66 196	62.47 211	86.53 98	93.24 112

PatchmatchNet		77.46 160	74.63 181	85.96 67	89.55 136	70.35 21	79.97 302	89.55 213	72.23 149	70.94 153	76.91 281	57.03 128	92.79 216	54.27 244	81.17 132	94.74 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AdaColmap		78.94 135	77.00 148	84.76 102	96.34 6	65.86 133	92.66 93	87.97 254	62.18 270	70.56 154	92.37 99	43.53 257	97.35 52	64.50 191	82.86 123	91.05 153

cascas		78.18 151	75.77 163	85.41 86	87.14 179	69.11 39	92.96 80	91.15 158	66.71 230	70.47 155	86.07 181	37.49 289	96.48 93	70.15 143	79.80 138	90.65 156

thres600view7		78.00 153	76.66 151	82.03 174	91.93 78	63.69 181	91.30 151	96.33 5	72.43 142	70.46 156	87.89 158	60.31 98	94.92 138	42.64 290	76.64 167	87.48 199

tfpn111		78.00 153	76.62 152	82.13 168	91.89 81	63.21 189	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	94.91 139	42.61 291	76.64 167	88.27 184

conf200view11		78.32 150	77.01 146	82.27 161	91.89 81	63.21 189	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	95.32 128	45.16 277	77.58 157	88.27 184

thres100view900		78.37 148	77.01 146	82.46 147	91.89 81	63.21 189	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	95.32 128	45.16 277	77.58 157	88.83 172

CVMVSNet		74.04 217	74.27 188	73.33 291	85.33 200	43.94 328	89.53 197	88.39 245	54.33 309	70.37 160	90.13 127	49.17 220	84.05 313	61.83 215	79.36 141	91.99 140

GA-MVS		78.33 149	76.23 157	84.65 106	83.65 223	66.30 124	91.44 142	90.14 194	76.01 81	70.32 161	84.02 200	42.50 260	94.72 145	70.98 136	77.00 166	92.94 122

mvs_anonymous		81.36 96	79.99 99	85.46 82	90.39 115	68.40 53	86.88 250	90.61 174	74.41 100	70.31 162	84.67 195	63.79 70	92.32 231	73.13 113	85.70 103	95.67 29

IB-MVS	77.80 4	82.18 85	80.46 96	87.35 29	89.14 144	70.28 22	95.59 17	95.17 17	78.85 47	70.19 163	85.82 184	70.66 18	97.67 36	72.19 125	66.52 238	94.09 89
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
TAPA-MVS	70.22 12	74.94 207	73.53 204	79.17 235	90.40 114	52.07 301	89.19 202	89.61 212	62.69 267	70.07 164	92.67 93	48.89 224	94.32 164	38.26 310	79.97 137	91.12 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Patchmatch-test1		75.00 204	71.80 223	84.58 108	86.63 185	70.08 24	81.06 291	89.19 224	71.60 170	70.01 165	77.16 279	45.53 247	88.63 290	51.79 253	73.27 188	95.02 57

XXY-MVS		77.94 156	76.44 155	82.43 151	82.60 233	64.44 161	92.01 113	91.83 134	73.59 124	70.00 166	85.82 184	54.43 171	94.76 142	69.63 146	68.02 229	88.10 189

CR-MVSNet		73.79 220	70.82 229	82.70 141	83.15 229	67.96 65	70.25 324	84.00 297	73.67 123	69.97 167	72.41 303	57.82 121	89.48 286	52.99 251	73.13 189	90.64 157

RPMNet		69.58 257	65.21 267	82.70 141	83.15 229	67.96 65	70.25 324	86.15 279	46.83 327	69.97 167	65.10 328	56.48 141	89.48 286	35.79 317	73.13 189	90.64 157

UniMVSNet (Re)		77.58 159	76.78 150	79.98 211	84.11 218	60.80 224	91.76 132	93.17 85	76.56 77	69.93 169	84.78 194	63.32 77	92.36 230	64.89 187	62.51 267	86.78 218

PCF-MVS	73.15 9	79.29 129	77.63 135	84.29 114	86.06 193	65.96 131	87.03 245	91.10 160	69.86 197	69.79 170	90.64 117	57.54 124	96.59 89	64.37 193	82.29 126	90.32 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1neww		77.39 162	75.71 165	82.44 148	80.69 252	66.83 94	91.94 121	90.18 191	74.19 108	69.60 171	82.51 212	54.99 161	94.44 154	71.68 128	65.60 241	86.05 231

v7new		77.39 162	75.71 165	82.44 148	80.69 252	66.83 94	91.94 121	90.18 191	74.19 108	69.60 171	82.51 212	54.99 161	94.44 154	71.68 128	65.60 241	86.05 231

v6		77.39 162	75.71 165	82.44 148	80.67 254	66.82 96	91.94 121	90.18 191	74.19 108	69.60 171	82.50 215	55.00 160	94.44 154	71.68 128	65.60 241	86.05 231

v1141		77.28 167	75.57 169	82.42 154	80.63 258	66.73 104	91.96 117	90.42 179	74.41 100	69.46 174	82.12 222	55.09 157	94.40 159	70.99 135	65.05 248	86.12 228

divwei89l23v2f112		77.28 167	75.57 169	82.42 154	80.62 259	66.72 106	91.96 117	90.42 179	74.41 100	69.46 174	82.12 222	55.11 156	94.40 159	71.00 133	65.04 249	86.12 228

v1		77.29 166	75.57 169	82.42 154	80.61 262	66.73 104	91.96 117	90.42 179	74.41 100	69.46 174	82.12 222	55.14 155	94.40 159	71.00 133	65.04 249	86.13 227

v2v482		77.42 161	75.65 168	82.73 140	80.38 264	67.13 85	91.85 127	90.23 188	75.09 94	69.37 177	83.39 206	53.79 179	94.44 154	71.77 126	65.00 252	86.63 222

PatchT		69.11 261	65.37 266	80.32 203	82.07 237	63.68 182	67.96 333	87.62 257	50.86 317	69.37 177	65.18 327	57.09 126	88.53 294	41.59 294	66.60 237	88.74 175

v7		76.83 178	75.01 179	82.29 160	80.35 265	66.70 108	91.68 136	89.97 200	73.47 128	69.22 179	82.22 219	52.52 192	94.43 158	69.73 145	65.96 240	85.74 242

Vis-MVSNet (Re-imp)		79.24 130	79.57 105	78.24 254	88.46 158	52.29 300	90.41 176	89.12 228	74.24 106	69.13 180	91.91 104	65.77 47	90.09 275	59.00 229	88.09 85	92.33 134

BH-w/o		80.49 107	79.30 113	84.05 118	90.83 111	64.36 166	93.60 63	89.42 217	74.35 105	69.09 181	90.15 126	55.23 152	95.61 119	64.61 189	86.43 100	92.17 139

v1144		76.73 180	74.88 180	82.27 161	80.23 272	66.60 112	91.68 136	90.21 190	73.69 121	69.06 182	81.89 227	52.73 191	94.40 159	69.21 151	65.23 245	85.80 238

Baseline_NR-MVSNet		73.99 218	72.83 211	77.48 263	80.78 248	59.29 253	91.79 129	84.55 292	68.85 206	68.99 183	80.70 245	56.16 142	92.04 238	62.67 209	60.98 280	81.11 299

FIs		79.47 127	79.41 110	79.67 219	85.95 195	59.40 250	91.68 136	93.94 44	78.06 56	68.96 184	88.28 150	66.61 38	91.77 242	66.20 176	74.99 179	87.82 195

UniMVSNet_NR-MVSNet		78.15 152	77.55 136	79.98 211	84.46 212	60.26 236	92.25 102	93.20 82	77.50 64	68.88 185	86.61 175	66.10 42	92.13 235	66.38 173	62.55 265	87.54 197

DU-MVS		76.86 176	75.84 162	79.91 213	82.96 231	60.26 236	91.26 152	91.54 143	76.46 78	68.88 185	86.35 177	56.16 142	92.13 235	66.38 173	62.55 265	87.35 208

XVG-OURS-SEG-HR		74.70 213	73.08 209	79.57 222	78.25 296	57.33 272	80.49 294	87.32 261	63.22 263	68.76 187	90.12 129	44.89 253	91.59 251	70.55 141	74.09 184	89.79 164

XVG-OURS		74.25 216	72.46 217	79.63 220	78.45 295	57.59 269	80.33 296	87.39 258	63.86 259	68.76 187	89.62 140	40.50 271	91.72 243	69.00 152	74.25 182	89.58 167

V42		76.46 182	74.55 184	82.19 166	79.14 286	67.82 67	90.26 180	89.42 217	73.75 120	68.63 189	81.89 227	51.31 203	94.09 177	71.69 127	64.84 253	84.66 254

PS-MVSNAJss		77.26 169	76.31 156	80.13 208	80.64 257	59.16 254	90.63 173	91.06 161	72.80 137	68.58 190	84.57 197	53.55 181	93.96 186	72.97 114	71.96 199	87.27 211

view600		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 305	75.31 175	87.48 199

view800		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 305	75.31 175	87.48 199

conf0.05thres1000		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 305	75.31 175	87.48 199

tfpn		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 305	75.31 175	87.48 199

v1192		75.98 190	73.92 194	82.15 167	79.73 277	66.24 126	91.22 154	89.75 205	72.67 138	68.49 195	81.42 233	49.86 213	94.27 167	67.08 166	65.02 251	85.95 235

tpm cat1		75.30 198	72.21 219	84.58 108	88.52 155	67.77 68	78.16 313	88.02 252	61.88 274	68.45 196	76.37 282	60.65 96	94.03 183	53.77 247	74.11 183	91.93 141

v144192		76.05 188	74.03 191	82.12 169	79.50 281	66.55 116	91.39 144	89.71 211	72.30 145	68.17 197	81.33 235	51.75 199	94.03 183	67.94 158	64.19 258	85.77 239

v1921920		75.63 195	73.49 205	82.06 173	79.38 282	66.35 122	91.07 162	89.48 214	71.98 156	67.99 198	81.22 239	49.16 221	93.90 189	66.56 171	64.56 257	85.92 237

Effi-MVS+-dtu		76.14 186	75.28 176	78.72 247	83.22 227	55.17 289	89.87 191	87.78 255	75.42 87	67.98 199	81.43 232	45.08 250	92.52 224	75.08 105	71.63 200	88.48 179

114514_t		79.17 131	77.67 133	83.68 126	95.32 16	65.53 140	92.85 85	91.60 142	63.49 260	67.92 200	90.63 119	46.65 240	95.72 117	67.01 167	83.54 120	89.79 164

3Dnovator	73.91 6	82.69 80	80.82 90	88.31 14	89.57 134	71.26 13	92.60 94	94.39 39	78.84 48	67.89 201	92.48 96	48.42 225	98.52 17	68.80 155	94.40 22	95.15 49

WR-MVS		76.76 179	75.74 164	79.82 216	84.60 208	62.27 208	92.60 94	92.51 109	76.06 80	67.87 202	85.34 187	56.76 133	90.24 268	62.20 212	63.69 263	86.94 216

dp		75.01 203	72.09 220	83.76 121	89.28 139	66.22 127	79.96 303	89.75 205	71.16 177	67.80 203	77.19 277	51.81 198	92.54 223	50.39 257	71.44 204	92.51 132

TranMVSNet+NR-MVSNet		75.86 191	74.52 185	79.89 214	82.44 234	60.64 231	91.37 147	91.37 152	76.63 75	67.65 204	86.21 180	52.37 195	91.55 252	61.84 214	60.81 281	87.48 199

1314		80.70 103	78.95 118	85.94 68	87.77 172	67.56 74	87.91 226	92.55 108	72.17 153	67.44 205	93.09 81	50.27 209	97.04 67	71.68 128	87.64 88	93.23 113

3Dnovator+	73.60 7	82.10 89	80.60 94	86.60 46	90.89 109	66.80 102	95.20 23	93.44 70	74.05 111	67.42 206	92.49 95	49.46 216	97.65 40	70.80 138	91.68 57	95.33 38

v1240		75.21 201	72.98 210	81.88 175	79.20 284	66.00 130	90.75 168	89.11 229	71.63 169	67.41 207	81.22 239	47.36 235	93.87 190	65.46 184	64.72 255	85.77 239

QAPM		79.95 118	77.39 141	87.64 21	89.63 133	71.41 12	93.30 71	93.70 53	65.34 242	67.39 208	91.75 108	47.83 231	98.96 5	57.71 234	89.81 75	92.54 131

tfpn_ndepth		76.45 183	75.22 177	80.14 206	90.97 106	58.92 256	90.11 182	93.24 79	65.96 236	67.37 209	90.52 121	66.67 37	92.29 232	37.71 311	74.44 181	89.21 170

v148		76.19 185	74.47 186	81.36 183	80.05 276	64.44 161	91.75 134	90.23 188	73.68 122	67.13 210	80.84 244	55.92 148	93.86 192	68.95 153	61.73 274	85.76 241

GBi-Net		75.65 193	73.83 195	81.10 193	88.85 148	65.11 147	90.01 183	90.32 182	70.84 181	67.04 211	80.25 253	48.03 227	91.54 253	59.80 225	69.34 218	86.64 219

test1		75.65 193	73.83 195	81.10 193	88.85 148	65.11 147	90.01 183	90.32 182	70.84 181	67.04 211	80.25 253	48.03 227	91.54 253	59.80 225	69.34 218	86.64 219

FMVSNet3		77.73 158	76.04 159	82.80 138	91.20 104	68.99 42	91.87 125	91.99 127	73.35 129	67.04 211	83.19 208	56.62 138	92.14 234	59.80 225	69.34 218	87.28 210

BH-untuned		78.68 142	77.08 144	83.48 131	89.84 124	63.74 178	92.70 89	88.59 242	71.57 171	66.83 214	88.65 145	51.75 199	95.39 126	59.03 228	84.77 109	91.32 150

FC-MVSNet-test		77.99 155	78.08 128	77.70 259	84.89 206	55.51 287	90.27 179	93.75 52	76.87 70	66.80 215	87.59 163	65.71 48	90.23 269	62.89 206	73.94 185	87.37 206

FMVSNet2		76.07 187	74.01 192	82.26 164	88.85 148	67.66 71	91.33 149	91.61 141	70.84 181	65.98 216	82.25 218	48.03 227	92.00 239	58.46 231	68.73 224	87.10 212

conf0.01		74.95 205	73.61 198	78.96 240	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 318	70.61 207	88.27 184

conf0.002		74.95 205	73.61 198	78.96 240	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 318	70.61 207	88.27 184

thresconf0.02		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 318	70.61 207	87.94 191

tfpn_n400		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 318	70.61 207	87.94 191

tfpnconf		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 318	70.61 207	87.94 191

tfpnview11		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 318	70.61 207	87.94 191

ACMM	69.62 13	74.34 214	72.73 212	79.17 235	84.25 217	57.87 264	90.36 177	89.93 201	63.17 264	65.64 223	86.04 183	37.79 287	94.10 176	65.89 179	71.52 202	85.55 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+-dtu		75.04 202	73.37 207	80.07 209	80.86 247	59.52 249	91.20 156	85.38 287	71.90 157	65.20 224	84.84 193	41.46 267	92.97 207	66.50 172	72.96 192	87.73 196

tfpn1000		75.25 199	74.00 193	79.03 239	90.30 116	57.56 270	88.55 213	93.36 74	64.14 256	65.17 225	89.76 139	67.06 34	91.46 258	34.54 326	73.09 191	88.06 190

IterMVS-LS		76.49 181	75.18 178	80.43 202	84.49 211	62.74 201	90.64 171	88.80 237	72.40 143	65.16 226	81.72 230	60.98 91	92.27 233	67.74 160	64.65 256	86.29 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LPG-MVS_test		75.82 192	74.58 183	79.56 223	84.31 215	59.37 251	90.44 174	89.73 208	69.49 199	64.86 227	88.42 147	38.65 277	94.30 165	72.56 119	72.76 193	85.01 251

LGP-MVS_train				79.56 223	84.31 215	59.37 251		89.73 208	69.49 199	64.86 227	88.42 147	38.65 277	94.30 165	72.56 119	72.76 193	85.01 251

MIMVSNet		71.64 236	68.44 245	81.23 186	81.97 239	64.44 161	73.05 320	88.80 237	69.67 198	64.59 229	74.79 292	32.79 306	87.82 299	53.99 245	76.35 170	91.42 146

OpenMVS	70.45 11	78.54 146	75.92 161	86.41 57	85.93 198	71.68 11	92.74 87	92.51 109	66.49 232	64.56 230	91.96 103	43.88 256	98.10 28	54.61 242	90.65 70	89.44 169

ADS-MVSNet2		66.90 276	63.44 279	77.26 268	88.06 166	60.70 229	68.01 331	75.56 328	57.57 295	64.48 231	69.87 318	38.68 275	84.10 312	40.87 296	67.89 230	86.97 213

ADS-MVSNet		68.54 264	64.38 275	81.03 196	88.06 166	66.90 93	68.01 331	84.02 296	57.57 295	64.48 231	69.87 318	38.68 275	89.21 289	40.87 296	67.89 230	86.97 213

PLC	68.80 14	75.23 200	73.68 197	79.86 215	92.93 56	58.68 260	90.64 171	88.30 247	60.90 278	64.43 233	90.53 120	42.38 261	94.57 148	56.52 236	76.54 169	86.33 223
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpmvs		72.88 226	69.76 234	82.22 165	90.98 105	67.05 87	78.22 312	88.30 247	63.10 265	64.35 234	74.98 291	55.09 157	94.27 167	43.25 284	69.57 217	85.34 249

test_djsdf		73.76 221	72.56 215	77.39 265	77.00 304	53.93 294	89.07 205	90.69 170	65.80 237	63.92 235	82.03 225	43.14 259	92.67 220	72.83 116	68.53 225	85.57 244

JIA-IIPM		66.06 280	62.45 286	76.88 270	81.42 244	54.45 293	57.49 345	88.67 239	49.36 320	63.86 236	46.86 341	56.06 145	90.25 266	49.53 261	68.83 222	85.95 235

CNLPA		74.31 215	72.30 218	80.32 203	91.49 94	61.66 216	90.85 164	80.72 315	56.67 303	63.85 237	90.64 117	46.75 238	90.84 261	53.79 246	75.99 172	88.47 180

PatchMatch-RL		72.06 231	69.98 230	78.28 251	89.51 137	55.70 286	83.49 270	83.39 303	61.24 277	63.72 238	82.76 210	34.77 302	93.03 206	53.37 250	77.59 156	86.12 228

FMVSNet1		72.71 228	69.91 232	81.10 193	83.60 224	65.11 147	90.01 183	90.32 182	63.92 257	63.56 239	80.25 253	36.35 297	91.54 253	54.46 243	66.75 236	86.64 219

pmmvs4		73.92 219	71.81 222	80.25 205	79.17 285	65.24 143	87.43 240	87.26 263	67.64 225	63.46 240	83.91 201	48.96 223	91.53 256	62.94 205	65.49 244	83.96 258

pmmvs5		73.35 222	71.52 224	78.86 242	78.64 294	60.61 232	91.08 161	86.90 264	67.69 222	63.32 241	83.64 202	44.33 255	90.53 263	62.04 213	66.02 239	85.46 246

v8		75.35 197	73.26 208	81.61 181	80.67 254	66.82 96	89.54 196	89.27 221	71.65 168	63.30 242	80.30 252	54.99 161	94.06 179	67.33 165	62.33 268	83.94 259

v10		74.77 212	72.54 216	81.46 182	80.33 269	66.71 107	89.15 203	89.08 230	70.94 179	63.08 243	79.86 257	52.52 192	94.04 182	65.70 182	62.17 269	83.64 261

ACMP	71.68 10	75.58 196	74.23 189	79.62 221	84.97 205	59.64 246	90.80 166	89.07 231	70.39 192	62.95 244	87.30 171	38.28 280	93.87 190	72.89 115	71.45 203	85.36 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pm-mvs1		72.89 225	71.09 227	78.26 253	79.10 288	57.62 268	90.80 166	89.30 220	67.66 223	62.91 245	81.78 229	49.11 222	92.95 208	60.29 222	58.89 290	84.22 257

jajsoiax		73.05 223	71.51 225	77.67 260	77.46 301	54.83 290	88.81 208	90.04 198	69.13 203	62.85 246	83.51 204	31.16 315	92.75 217	70.83 137	69.80 214	85.43 247

mvs_tets		72.71 228	71.11 226	77.52 261	77.41 302	54.52 292	88.45 216	89.76 204	68.76 208	62.70 247	83.26 207	29.49 319	92.71 218	70.51 142	69.62 216	85.34 249

MS-PatchMatch		77.90 157	76.50 154	82.12 169	85.99 194	69.95 27	91.75 134	92.70 100	73.97 114	62.58 248	84.44 198	41.11 268	95.78 110	63.76 195	92.17 52	80.62 305

test0.0.03 1		72.76 227	72.71 213	72.88 295	80.25 271	47.99 319	91.22 154	89.45 215	71.51 174	62.51 249	87.66 162	53.83 177	85.06 309	50.16 258	67.84 232	85.58 243

anonymousdsp		71.14 241	69.37 236	76.45 272	72.95 316	54.71 291	84.19 265	88.88 236	61.92 273	62.15 250	79.77 258	38.14 282	91.44 259	68.90 154	67.45 233	83.21 271

MVP-Stereo		77.12 171	76.23 157	79.79 217	81.72 240	66.34 123	89.29 199	90.88 165	70.56 189	62.01 251	82.88 209	49.34 217	94.13 175	65.55 183	93.80 29	78.88 318
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
NR-MVSNet		76.05 188	74.59 182	80.44 201	82.96 231	62.18 209	90.83 165	91.73 136	77.12 67	60.96 252	86.35 177	59.28 110	91.80 241	60.74 219	61.34 278	87.35 208

tfpnnormal		70.10 250	67.36 254	78.32 250	83.45 226	60.97 222	88.85 207	92.77 98	64.85 251	60.83 253	78.53 267	43.52 258	93.48 199	31.73 334	61.70 275	80.52 306

IterMVS		72.65 230	70.83 228	78.09 257	82.17 235	62.96 194	87.64 238	86.28 276	71.56 172	60.44 254	78.85 266	45.42 249	86.66 304	63.30 199	61.83 271	84.65 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS_H		70.59 247	69.94 231	72.53 297	81.03 245	51.43 304	87.35 242	92.03 126	67.38 227	60.23 255	80.70 245	55.84 149	83.45 320	46.33 273	58.58 291	82.72 278

TransMVSNet (Re)		70.07 251	67.66 253	77.31 267	80.62 259	59.13 255	91.78 131	84.94 290	65.97 235	60.08 256	80.44 249	50.78 204	91.87 240	48.84 263	45.46 329	80.94 301

CP-MVSNet		70.50 249	69.91 232	72.26 300	80.71 251	51.00 307	87.23 243	90.30 186	67.84 221	59.64 257	82.69 211	50.23 210	82.30 327	51.28 254	59.28 285	83.46 266

semantic-postprocess				76.32 273	81.48 241	60.67 230		85.99 282	66.17 234	59.50 258	78.88 265	45.51 248	83.65 318	62.58 210	61.93 270	84.63 256

Patchmtry		67.53 273	63.93 276	78.34 249	82.12 236	64.38 164	68.72 328	84.00 297	48.23 324	59.24 259	72.41 303	57.82 121	89.27 288	46.10 274	56.68 297	81.36 293

PS-CasMVS		69.86 253	69.13 239	72.07 303	80.35 265	50.57 309	87.02 246	89.75 205	67.27 228	59.19 260	82.28 217	46.58 241	82.24 328	50.69 256	59.02 288	83.39 268

PEN-MVS		69.46 259	68.56 243	72.17 302	79.27 283	49.71 314	86.90 249	89.24 223	67.24 229	59.08 261	82.51 212	47.23 236	83.54 319	48.42 265	57.12 293	83.25 270

DI_MVS_plusplus_test		79.78 122	77.50 138	86.62 45	80.90 246	69.46 35	90.69 169	91.97 129	77.00 68	59.07 262	82.34 216	46.82 237	95.88 107	82.14 60	86.59 97	94.53 73

RPSCF		64.24 288	61.98 289	71.01 307	76.10 308	45.00 325	75.83 317	75.94 325	46.94 326	58.96 263	84.59 196	31.40 314	82.00 329	47.76 268	60.33 284	86.04 234

XVG-ACMP-BASELINE		68.04 268	65.53 264	75.56 277	74.06 315	52.37 299	78.43 309	85.88 284	62.03 271	58.91 264	81.21 241	20.38 336	91.15 260	60.69 220	68.18 227	83.16 272

v7n		71.31 238	68.65 241	79.28 230	76.40 306	60.77 226	86.71 252	89.45 215	64.17 255	58.77 265	78.24 268	44.59 254	93.54 197	57.76 233	61.75 273	83.52 264

test_normal		79.66 123	77.36 143	86.54 49	80.72 250	69.21 38	90.68 170	92.16 124	76.99 69	58.63 266	82.03 225	46.70 239	95.86 108	81.74 64	86.63 96	94.56 68

DTE-MVSNet		68.46 265	67.33 255	71.87 306	77.94 299	49.00 317	86.16 257	88.58 243	66.36 233	58.19 267	82.21 220	46.36 242	83.87 317	44.97 281	55.17 301	82.73 277

Anonymous20231206		67.53 273	65.78 261	72.79 296	74.95 312	47.59 321	88.23 219	87.32 261	61.75 276	58.07 268	77.29 275	37.79 287	87.29 302	42.91 286	63.71 262	83.48 265

PVSNet_0	68.08 15	71.81 233	68.32 247	82.27 161	84.68 207	62.31 207	88.68 210	90.31 185	75.84 82	57.93 269	80.65 247	37.85 286	94.19 170	69.94 144	29.05 345	90.31 160

v52		69.80 254	67.01 258	78.15 255	71.84 320	60.10 240	82.02 282	87.39 258	64.48 252	57.80 270	75.97 286	41.47 266	92.90 213	63.00 203	59.13 287	81.45 290

V4		69.80 254	67.02 257	78.15 255	71.86 319	60.10 240	82.02 282	87.39 258	64.48 252	57.78 271	75.98 285	41.49 265	92.90 213	63.00 203	59.16 286	81.44 291

DP-MVS		69.90 252	66.48 259	80.14 206	95.36 15	62.93 195	89.56 194	76.11 323	50.27 318	57.69 272	85.23 189	39.68 273	95.73 113	33.35 328	71.05 206	81.78 286

Anonymous20240521		69.68 256	68.15 250	74.28 286	82.04 238	49.91 312	85.92 258	90.52 175	63.87 258	57.61 273	81.33 235	41.82 262	89.57 285	46.86 271	63.36 264	83.37 269

pmmvs6		67.57 272	64.76 269	76.00 276	72.82 318	53.37 296	88.71 209	86.78 265	53.19 310	57.58 274	78.03 270	35.33 300	92.41 227	55.56 240	54.88 303	82.21 283

F-COLMAP		70.66 246	68.44 245	77.32 266	86.37 189	55.91 285	88.00 222	86.32 275	56.94 301	57.28 275	88.07 156	33.58 304	92.49 225	51.02 255	68.37 226	83.55 262

Patchmatch-RL test		68.17 267	64.49 273	79.19 234	71.22 322	53.93 294	70.07 326	71.54 339	69.22 202	56.79 276	62.89 331	56.58 139	88.61 291	69.53 148	52.61 307	95.03 56

v748		70.55 248	67.97 252	78.27 252	75.75 311	58.78 258	86.29 256	89.25 222	65.12 250	56.66 277	77.17 278	45.05 252	92.95 208	58.13 232	58.33 292	83.10 274

LS3D		69.17 260	66.40 260	77.50 262	91.92 79	56.12 284	85.12 260	80.37 316	46.96 325	56.50 278	87.51 165	37.25 290	93.71 194	32.52 333	79.40 140	82.68 280

v18		71.94 232	69.43 235	79.50 225	80.74 249	66.82 96	88.16 220	86.66 266	68.95 205	55.55 279	72.66 298	55.03 159	90.15 271	64.78 188	52.30 309	81.54 287

ppachtmachnet_test		67.72 270	63.70 277	79.77 218	78.92 289	66.04 129	88.68 210	82.90 308	60.11 285	55.45 280	75.96 287	39.19 274	90.55 262	39.53 301	52.55 308	82.71 279

v16		71.81 233	69.26 237	79.47 226	80.66 256	66.81 100	87.93 224	86.63 268	68.70 210	55.35 281	72.51 299	54.75 165	90.12 273	64.51 190	52.28 310	81.47 288

v17		71.77 235	69.20 238	79.46 227	80.62 259	66.81 100	87.93 224	86.63 268	68.71 209	55.25 282	72.49 300	54.72 166	90.11 274	64.50 191	51.97 311	81.47 288

LTVRE_ROB	59.60 19	66.27 279	63.54 278	74.45 283	84.00 220	51.55 303	67.08 334	83.53 300	58.78 292	54.94 283	80.31 251	34.54 303	93.23 203	40.64 298	68.03 228	78.58 321
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
v15		71.40 237	68.75 240	79.35 228	80.39 263	66.70 108	87.57 239	86.64 267	68.66 211	54.68 284	72.00 307	54.50 168	89.98 276	63.69 196	50.66 316	81.38 292

MSDG		69.54 258	65.73 262	80.96 197	85.11 204	63.71 180	84.19 265	83.28 304	56.95 300	54.50 285	84.03 199	31.50 313	96.03 103	42.87 288	69.13 221	83.14 273

V14		71.29 239	68.61 242	79.31 229	80.34 267	66.65 110	87.39 241	86.61 270	68.41 217	54.49 286	71.91 308	54.25 173	89.96 277	63.50 197	50.62 317	81.33 294

v11		71.05 243	68.32 247	79.23 232	80.34 267	66.57 115	87.01 247	86.55 272	68.11 219	54.40 287	71.66 312	52.94 189	89.91 279	62.71 208	51.12 314	81.21 296

EU-MVSNet		64.01 290	63.01 282	67.02 316	74.40 314	38.86 340	83.27 274	86.19 278	45.11 331	54.27 288	81.15 242	36.91 296	80.01 333	48.79 264	57.02 294	82.19 284

V9		71.16 240	68.46 244	79.27 231	80.26 270	66.60 112	87.21 244	86.56 271	68.17 218	54.26 289	71.81 310	54.00 175	89.93 278	63.28 200	50.57 318	81.27 295

testgi		64.48 287	62.87 284	69.31 310	71.24 321	40.62 335	85.49 259	79.92 317	65.36 241	54.18 290	83.49 205	23.74 331	84.55 310	41.60 293	60.79 282	82.77 276

ITE_SJBPF				70.43 308	74.44 313	47.06 323		77.32 321	60.16 284	54.04 291	83.53 203	23.30 332	84.01 315	43.07 285	61.58 277	80.21 310

v12		71.02 244	68.29 249	79.22 233	80.18 273	66.53 117	87.01 247	86.54 273	67.90 220	54.00 292	71.70 311	53.66 180	89.91 279	63.09 202	50.51 319	81.21 296

OpenMVS_ROB	61.12 18	66.39 278	62.92 283	76.80 271	76.51 305	57.77 265	89.22 200	83.41 302	55.48 307	53.86 293	77.84 271	26.28 328	93.95 187	34.90 325	68.76 223	78.68 320

v13		70.90 245	68.15 250	79.15 237	80.08 274	66.45 119	86.83 251	86.50 274	67.62 226	53.78 294	71.61 313	53.51 184	89.87 281	62.89 206	50.50 320	81.14 298

FMVSNet5		68.04 268	65.66 263	75.18 279	84.43 213	57.89 263	83.54 269	86.26 277	61.83 275	53.64 295	73.30 295	37.15 293	85.08 308	48.99 262	61.77 272	82.56 282

test2356		64.16 289	63.28 280	66.81 317	69.37 330	39.86 338	87.76 229	86.02 281	59.83 287	53.54 296	73.23 296	34.94 301	80.67 332	39.66 300	65.20 246	79.89 311

Test4		76.45 183	73.45 206	85.45 84	76.07 310	67.61 73	88.38 218	90.83 166	76.71 74	53.06 297	79.65 261	31.61 312	94.35 163	78.47 84	86.22 101	94.40 77

ACMH+	65.35 16	67.65 271	64.55 271	76.96 269	84.59 209	57.10 274	88.08 221	80.79 314	58.59 294	53.00 298	81.09 243	26.63 327	92.95 208	46.51 272	61.69 276	80.82 302

our_test_3		68.29 266	64.69 270	79.11 238	78.92 289	64.85 154	88.40 217	85.06 288	60.32 283	52.68 299	76.12 284	40.81 270	89.80 284	44.25 283	55.65 299	82.67 281

test_0402		64.54 286	61.09 291	74.92 280	84.10 219	60.75 227	87.95 223	79.71 318	52.03 313	52.41 300	77.20 276	32.21 310	91.64 250	23.14 344	61.03 279	72.36 333

LCM-MVSNet-Re		72.93 224	71.84 221	76.18 275	88.49 156	48.02 318	80.07 301	70.17 340	73.96 115	52.25 301	80.09 256	49.98 211	88.24 296	67.35 163	84.23 118	92.28 137

test20.03		63.83 291	62.65 285	67.38 315	70.58 326	39.94 336	86.57 254	84.17 294	63.29 262	51.86 302	77.30 274	37.09 294	82.47 325	38.87 309	54.13 305	79.73 313

OurMVSNet-221017-0		64.68 285	62.17 288	72.21 301	76.08 309	47.35 322	80.67 293	81.02 313	56.19 304	51.60 303	79.66 260	27.05 326	88.56 293	53.60 248	53.63 306	80.71 304

ACMH	63.93 17	68.62 262	64.81 268	80.03 210	85.22 202	63.25 187	87.72 230	84.66 291	60.83 279	51.57 304	79.43 263	27.29 325	94.96 135	41.76 292	64.84 253	81.88 285
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DSMNet-mixed		56.78 306	54.44 308	63.79 322	63.21 336	29.44 349	64.43 337	64.10 347	42.12 339	51.32 305	71.60 314	31.76 311	75.04 342	36.23 314	65.20 246	86.87 217

pmmvs-eth3d		65.53 283	62.32 287	75.19 278	69.39 329	59.59 247	82.80 279	83.43 301	62.52 269	51.30 306	72.49 300	32.86 305	87.16 303	55.32 241	50.73 315	78.83 319

PM-MVS		59.40 302	56.59 303	67.84 312	63.63 335	41.86 332	76.76 315	63.22 348	59.01 291	51.07 307	72.27 306	11.72 346	83.25 322	61.34 216	50.28 321	78.39 322

Patchmatch-test		65.86 281	60.94 292	80.62 200	83.75 221	58.83 257	58.91 344	75.26 330	44.50 334	50.95 308	77.09 280	58.81 115	87.90 298	35.13 324	64.03 259	95.12 51

SixPastTwentyTwo		64.92 284	61.78 290	74.34 285	78.74 292	49.76 313	83.42 273	79.51 319	62.86 266	50.27 309	77.35 273	30.92 317	90.49 264	45.89 275	47.06 326	82.78 275

EG-PatchMatch MVS		68.55 263	65.41 265	77.96 258	78.69 293	62.93 195	89.86 192	89.17 225	60.55 280	50.27 309	77.73 272	22.60 333	94.06 179	47.18 270	72.65 195	76.88 326

ambc				69.61 309	61.38 341	41.35 333	49.07 348	85.86 285		50.18 311	66.40 323	10.16 349	88.14 297	45.73 276	44.20 330	79.32 317

UnsupCasMVSNet_eth		65.79 282	63.10 281	73.88 287	70.71 324	50.29 311	81.09 290	89.88 202	72.58 140	49.25 312	74.77 293	32.57 308	87.43 301	55.96 239	41.04 335	83.90 260

COLMAP_ROB	57.96 20	62.98 295	59.65 295	72.98 294	81.44 243	53.00 298	83.75 267	75.53 329	48.34 323	48.81 313	81.40 234	24.14 329	90.30 265	32.95 330	60.52 283	75.65 329
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
USDC		67.43 275	64.51 272	76.19 274	77.94 299	55.29 288	78.38 310	85.00 289	73.17 130	48.36 314	80.37 250	21.23 335	92.48 226	52.15 252	64.02 260	80.81 303

K. test v3		63.09 294	59.61 296	73.53 290	76.26 307	49.38 316	83.27 274	77.15 322	64.35 254	47.77 315	72.32 305	28.73 320	87.79 300	49.93 260	36.69 340	83.41 267

testus		59.36 303	57.51 300	64.90 320	66.72 332	37.56 341	84.98 261	81.09 312	57.46 298	47.72 316	72.76 297	11.43 348	78.78 339	36.56 312	58.91 289	78.36 323

testpf		57.17 305	56.93 302	57.88 327	79.13 287	42.40 329	34.23 351	85.97 283	52.64 311	47.66 317	66.50 322	36.33 298	79.65 335	53.60 248	56.31 298	51.60 346

UnsupCasMVSNet_bld		61.60 297	57.71 298	73.29 292	68.73 331	51.64 302	78.61 308	89.05 232	57.20 299	46.11 318	61.96 333	28.70 321	88.60 292	50.08 259	38.90 338	79.63 314

AllTest		61.66 296	58.06 297	72.46 298	79.57 278	51.42 305	80.17 299	68.61 342	51.25 315	45.88 319	81.23 237	19.86 337	86.58 305	38.98 303	57.01 295	79.39 315

TestCases				72.46 298	79.57 278	51.42 305		68.61 342	51.25 315	45.88 319	81.23 237	19.86 337	86.58 305	38.98 303	57.01 295	79.39 315

lessismore_v0				73.72 289	72.93 317	47.83 320		61.72 350		45.86 321	73.76 294	28.63 322	89.81 282	47.75 269	31.37 344	83.53 263

N_pmnet		50.55 314	49.11 316	54.88 331	77.17 303	4.02 361	84.36 264	2.00 361	48.59 321	45.86 321	68.82 320	32.22 309	82.80 324	31.58 335	51.38 313	77.81 324

MVS-HIRNet		60.25 300	55.55 306	74.35 284	84.37 214	56.57 282	71.64 322	74.11 332	34.44 343	45.54 323	42.24 345	31.11 316	89.81 282	40.36 299	76.10 171	76.67 327

testing_2		71.09 242	67.32 256	82.40 157	69.82 327	66.52 118	83.64 268	90.77 168	72.21 150	45.12 324	71.07 317	27.60 324	93.74 193	75.71 100	69.96 213	86.95 215

CMPMVS	48.56 21	66.77 277	64.41 274	73.84 288	70.65 325	50.31 310	77.79 314	85.73 286	45.54 330	44.76 325	82.14 221	35.40 299	90.14 272	63.18 201	74.54 180	81.07 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		60.16 301	57.33 301	68.67 311	69.71 328	44.13 327	78.92 307	84.21 293	55.05 308	44.63 326	71.85 309	23.91 330	81.54 331	32.63 332	55.03 302	80.35 307

LF4IMVS		54.01 312	52.12 310	59.69 325	62.41 338	39.91 337	68.59 329	68.28 344	42.96 337	44.55 327	75.18 290	14.09 345	68.39 346	41.36 295	51.68 312	70.78 336

pmmvs3		55.51 310	51.50 313	67.53 314	57.90 344	50.93 308	80.37 295	73.66 333	40.63 340	44.15 328	64.75 329	16.30 340	78.97 338	44.77 282	40.98 336	72.69 332

LP		56.71 307	51.64 311	71.91 305	80.08 274	60.33 235	61.72 339	75.61 327	43.87 336	43.76 329	60.30 335	30.46 318	84.05 313	22.94 345	46.06 328	71.34 335

new-patchmatchnet		59.30 304	56.48 304	67.79 313	65.86 333	44.19 326	82.47 280	81.77 309	59.94 286	43.65 330	66.20 324	27.67 323	81.68 330	39.34 302	41.40 334	77.50 325

TDRefinement		55.28 311	51.58 312	66.39 318	59.53 343	46.15 324	76.23 316	72.80 334	44.60 333	42.49 331	76.28 283	15.29 342	82.39 326	33.20 329	43.75 331	70.62 337

1111		56.66 308	54.98 307	61.69 323	61.99 339	31.38 345	79.81 304	83.17 305	45.66 328	41.94 332	65.44 325	41.50 263	79.56 336	27.64 338	47.68 325	74.14 331

.test1245		46.52 318	49.68 314	37.02 340	61.99 339	31.38 345	79.81 304	83.17 305	45.66 328	41.94 332	65.44 325	41.50 263	79.56 336	27.64 338	0.01 357	0.13 358

Anonymous20231211		53.57 313	49.43 315	66.00 319	65.01 334	42.08 330	80.95 292	72.60 335	38.46 341	41.65 334	64.48 330	15.72 341	84.23 311	25.78 341	40.24 337	71.68 334

TinyColmap		60.32 299	56.42 305	72.00 304	78.78 291	53.18 297	78.36 311	75.64 326	52.30 312	41.59 335	75.82 289	14.76 344	88.35 295	35.84 315	54.71 304	74.46 330

YYNet1		63.76 293	60.14 294	74.62 282	78.06 298	60.19 239	83.46 272	83.99 299	56.18 305	39.25 336	71.56 316	37.18 292	83.34 321	42.90 287	48.70 324	80.32 308

MDA-MVSNet_test_wron		63.78 292	60.16 293	74.64 281	78.15 297	60.41 233	83.49 270	84.03 295	56.17 306	39.17 337	71.59 315	37.22 291	83.24 323	42.87 288	48.73 323	80.26 309

test1235678		55.73 309	52.74 309	64.68 321	60.16 342	35.56 343	81.65 286	81.46 310	51.27 314	38.93 338	62.82 332	17.44 339	78.58 340	30.87 336	50.09 322	79.89 311

new_pmnet		49.31 315	46.44 317	57.93 326	62.84 337	40.74 334	68.47 330	62.96 349	36.48 342	35.09 339	57.81 337	14.97 343	72.18 343	32.86 331	46.44 327	60.88 344

MDA-MVSNet-bldmvs		61.54 298	57.70 299	73.05 293	79.53 280	57.00 275	83.08 277	81.23 311	57.57 295	34.91 340	72.45 302	32.79 306	86.26 307	35.81 316	41.95 333	75.89 328

test12356		47.51 316	44.82 318	55.56 329	52.53 345	21.09 356	71.45 323	76.03 324	44.14 335	30.69 341	58.18 336	9.01 352	76.14 341	26.95 340	34.43 343	69.46 339

testmv		46.98 317	43.53 319	57.35 328	47.75 350	30.41 348	74.99 319	77.69 320	42.84 338	28.03 342	53.36 338	8.18 353	71.18 344	24.36 343	34.55 341	70.46 338

FPMVS		45.64 319	43.10 320	53.23 333	51.42 347	36.46 342	64.97 336	71.91 337	29.13 345	27.53 343	61.55 334	9.83 350	65.01 350	16.00 350	55.58 300	58.22 345

LCM-MVSNet		40.54 321	35.79 322	54.76 332	36.92 355	30.81 347	51.41 346	69.02 341	22.07 347	24.63 344	45.37 343	4.56 358	65.81 348	33.67 327	34.50 342	67.67 340

PMMVS2		37.93 323	33.61 324	50.92 334	46.31 351	24.76 354	60.55 343	50.05 352	28.94 346	20.93 345	47.59 340	4.41 359	65.13 349	25.14 342	18.55 347	62.87 342

no-one		44.13 320	38.39 321	61.34 324	45.91 352	41.94 331	61.67 340	75.07 331	45.05 332	20.07 346	40.68 348	11.58 347	79.82 334	30.18 337	15.30 348	62.26 343

tmp_tt		22.26 332	23.75 331	17.80 345	5.23 360	12.06 360	35.26 350	39.48 356	2.82 356	18.94 347	44.20 344	22.23 334	24.64 358	36.30 313	9.31 354	16.69 353

ANet_high		40.27 322	35.20 323	55.47 330	34.74 356	34.47 344	63.84 338	71.56 338	48.42 322	18.80 348	41.08 346	9.52 351	64.45 351	20.18 347	8.66 355	67.49 341

DeepMVS_CX				34.71 341	51.45 346	24.73 355		28.48 360	31.46 344	17.49 349	52.75 339	5.80 356	42.60 357	18.18 349	19.42 346	36.81 350

Gipuma		34.91 324	31.44 326	45.30 336	70.99 323	39.64 339	19.85 354	72.56 336	20.10 350	16.16 350	21.47 353	5.08 357	71.16 345	13.07 351	43.70 332	25.08 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		32.77 325	29.98 327	41.11 338	48.05 348	29.17 350	65.82 335	50.02 353	21.42 348	14.74 351	37.19 349	1.11 362	55.11 353	19.75 348	11.77 350	39.06 348

PMVS	26.43 22	31.84 326	28.16 328	42.89 337	25.87 359	27.58 352	50.92 347	49.78 354	21.37 349	14.17 352	40.81 347	2.01 360	66.62 347	9.61 353	38.88 339	34.49 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 330	19.77 334	38.09 339	34.56 357	26.92 353	26.57 352	38.87 357	11.73 354	11.37 353	27.44 350	1.37 361	50.42 354	11.41 352	14.60 349	36.93 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 329	24.00 330	26.45 343	43.74 353	18.44 358	60.86 341	39.66 355	15.11 351	9.53 354	22.10 352	6.52 355	46.94 355	8.31 354	10.14 351	13.98 354

EMVS		23.76 331	23.20 332	25.46 344	41.52 354	16.90 359	60.56 342	38.79 358	14.62 352	8.99 355	20.24 356	7.35 354	45.82 356	7.25 355	9.46 353	13.64 355

wuykxyi23d		29.03 328	23.09 333	46.84 335	31.67 358	28.82 351	43.46 349	57.72 351	14.39 353	7.52 356	20.84 354	0.64 363	60.29 352	21.57 346	10.04 352	51.40 347

wuyk23d		11.30 334	10.95 335	12.33 346	48.05 348	19.89 357	25.89 353	1.92 362	3.58 355	3.12 357	1.37 358	0.64 363	15.77 359	6.23 356	7.77 356	1.35 356

testmvs		7.23 336	9.62 337	0.06 348	0.04 361	0.02 363	84.98 261	0.02 363	0.03 357	0.18 358	1.21 359	0.01 366	0.02 360	0.14 357	0.01 357	0.13 358

test123		6.92 337	9.21 338	0.08 347	0.03 362	0.05 362	81.65 286	0.01 364	0.02 358	0.14 359	0.85 360	0.03 365	0.02 360	0.12 358	0.00 359	0.16 357

cdsmvs_eth3d_5k		19.86 333	26.47 329	0.00 349	0.00 363	0.00 364	0.00 355	93.45 63	0.00 359	0.00 360	95.27 33	49.56 215	0.00 362	0.00 359	0.00 359	0.00 360

pcd_1.5k_mvsjas		4.46 338	5.95 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	53.55 181	0.00 362	0.00 359	0.00 359	0.00 360

pcd1.5k->3k		31.17 327	31.85 325	29.12 342	81.48 241	0.00 364	0.00 355	91.79 135	0.00 359	0.00 360	0.00 361	41.05 269	0.00 362	0.00 359	72.34 198	87.36 207

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 359	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 359	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 359	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 359	0.00 360

ab-mvs-re		7.91 335	10.55 336	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	94.95 43	0.00 367	0.00 362	0.00 359	0.00 359	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 359	0.00 360

GSMVS																94.68 65

test_part3							94.96 31		68.52 213		97.23 2		98.90 7	91.52 6

test_part1								94.26 41				77.03 4			95.18 9	96.11 19

sam_mvs1												57.85 120				94.68 65

sam_mvs												54.91 164

MTGPA								92.23 115

test_post1							78.95 306				20.70 355	53.05 187	91.50 257	60.43 221

test_post											23.01 351	56.49 140	92.67 220

patchmatchnet-post											67.62 321	57.62 123	90.25 266

MTMP								32.52 359

gm-plane-assit					88.42 160	67.04 88			78.62 52		91.83 105		97.37 50	76.57 96

test9_res														89.41 12	94.96 11	95.29 41

agg_prior2														86.41 34	94.75 19	95.33 38

test_prior4						67.18 84	93.92 54

test_prior				86.42 55	94.71 23	67.35 78		93.10 88					96.84 82			95.05 53

新几何2							91.41 143

旧先验1					91.94 77	60.74 228		91.50 146			94.36 58	65.23 50			91.84 54	94.55 69

无先验							92.71 88	92.61 106	62.03 271				97.01 68	66.63 169		93.97 95

原ACMM2							92.01 113

testdata2													96.09 100	61.26 217

segment_acmp												65.94 44

testdata1							89.21 201		77.55 63

plane_prior7					86.94 181	61.51 218

plane_prior6					87.23 177	62.32 206						50.66 205

plane_prior5								91.31 153					95.55 123	76.74 94	78.53 149	88.39 181

plane_prior4											89.14 142

plane_prior2							93.13 75		78.81 49

plane_prior1					87.15 178

plane_prior						62.42 204	93.85 57		79.38 37						78.80 147

n2								0.00 365

nn								0.00 365

door-mid								66.01 346

test11								93.01 90

door								66.57 345

HQP5-MVS						63.66 183

BP-MVS														77.63 91

HQP3-MVS								91.70 139							78.90 145

HQP2-MVS												51.63 201

NP-MVS					87.41 176	63.04 193					90.30 124

ACMMP++_ref															71.63 200

ACMMP++															69.72 215

Test By Simon												54.21 174

High-resolution multi-view benchmark