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 68	92.83 56	64.03 169	93.06 74	94.52 32	82.19 19	93.65 1	96.15 10	85.89 1	97.19 57	91.02 8	97.75 1	96.29 16

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 12	89.33 1	85.77 24	96.26 7	72.84 10	99.38 1	92.64 4	95.93 4	97.08 4

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 103	93.00 53	58.16 257	96.72 3	94.41 37	86.50 5	90.25 4	97.83 1	75.46 6	98.67 12	92.78 2	95.49 7	97.32 1

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 23	84.83 9	89.07 8	96.80 2	70.86 15	99.06 3	92.64 4	95.71 5	96.12 18

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 50	73.88 6	97.01 2	94.40 38	88.32 2	85.71 25	94.91 45	74.11 8	98.91 6	87.26 26	95.94 3	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
CANet		89.61 6	89.99 6	88.46 13	94.39 25	69.71 32	96.53 5	93.78 46	86.89 4	89.68 5	95.78 15	65.94 43	99.10 2	92.99 1	93.91 26	96.58 11

HPM-MVS++		89.37 7	89.95 7	87.64 20	95.10 18	68.23 59	95.24 22	94.49 34	82.43 17	88.90 9	96.35 5	71.89 14	98.63 13	88.76 18	96.40 2	96.06 20

NCCC		89.07 8	89.46 8	87.91 16	96.60 5	69.05 40	96.38 7	94.64 31	84.42 10	86.74 19	96.20 8	66.56 38	98.76 11	89.03 16	94.56 19	95.92 26

TSAR-MVS + MP.		88.11 11	88.64 9	86.54 47	91.73 84	68.04 62	90.36 173	93.55 57	82.89 14	91.29 2	92.89 87	72.27 11	96.03 100	87.99 20	94.77 14	95.54 32

EPNet		87.84 15	88.38 10	86.23 61	93.30 45	66.05 126	95.26 21	94.84 22	87.09 3	88.06 12	94.53 52	66.79 35	97.34 50	83.89 48	91.68 55	95.29 39
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		87.96 12	88.37 11	86.70 41	93.51 43	65.32 139	95.15 25	93.84 45	78.17 55	85.93 23	94.80 48	75.80 5	98.21 21	89.38 11	88.78 78	96.59 10

MVS_0304		88.39 9	88.35 12	88.50 12	93.01 52	70.11 23	95.90 10	92.20 112	86.27 6	88.70 10	95.92 13	56.76 124	99.02 4	92.68 3	93.76 29	96.37 15

DeepC-MVS_fast	79.48 2	87.95 13	88.00 13	87.79 19	95.86 13	68.32 55	95.74 12	94.11 42	83.82 12	83.49 45	96.19 9	64.53 56	98.44 17	83.42 51	94.88 13	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
APDe-MVS		87.54 17	87.84 14	86.65 42	96.07 10	66.30 122	94.84 32	93.78 46	69.35 199	88.39 11	96.34 6	67.74 29	97.66 36	90.62 9	93.44 35	96.01 23

lupinMVS		87.74 16	87.77 15	87.63 22	89.24 133	71.18 14	96.57 4	92.90 87	82.70 16	87.13 16	95.27 30	64.99 52	95.80 106	89.34 12	91.80 53	95.93 25

test_prior3		87.38 19	87.70 16	86.42 53	94.71 22	67.35 76	95.10 27	93.10 80	75.40 89	85.25 31	95.61 21	67.94 25	96.84 79	87.47 23	94.77 14	95.05 51

PS-MVSNAJ		88.14 10	87.61 17	89.71 4	92.06 73	76.72 1	95.75 11	93.26 70	83.86 11	89.55 6	96.06 11	53.55 172	97.89 30	91.10 6	93.31 36	94.54 67

Regformer-1		87.24 21	87.60 18	86.15 62	95.14 16	65.83 132	93.95 49	95.12 17	82.11 21	84.25 38	95.73 17	67.88 28	98.35 19	85.60 36	88.64 79	94.26 74

SD-MVS		87.49 18	87.49 19	87.50 24	93.60 40	68.82 46	93.90 53	92.63 97	76.86 71	87.90 13	95.76 16	66.17 39	97.63 38	89.06 15	91.48 59	96.05 21

Regformer-2		87.00 25	87.43 20	85.71 76	95.14 16	64.73 151	93.95 49	94.95 20	81.69 26	84.03 42	95.73 17	67.35 32	98.19 23	85.40 38	88.64 79	94.20 76

train_agg		87.21 22	87.42 21	86.60 44	94.18 28	67.28 78	94.16 36	93.51 58	71.87 157	85.52 27	95.33 26	68.19 22	97.27 54	89.09 13	94.90 11	95.25 44

xiu_mvs_v2_base		87.92 14	87.38 22	89.55 7	91.41 98	76.43 2	95.74 12	93.12 79	83.53 13	89.55 6	95.95 12	53.45 177	97.68 32	91.07 7	92.62 43	94.54 67

agg_prior1		87.02 24	87.26 23	86.28 60	94.16 32	66.97 87	94.08 42	93.31 68	71.85 159	84.49 36	95.39 24	68.91 18	96.75 83	88.84 17	94.32 21	95.13 48

agg_prior3		86.93 26	87.08 24	86.48 50	94.21 26	66.95 89	94.14 39	93.40 64	71.80 162	84.86 33	95.13 36	66.16 40	97.25 56	89.09 13	94.90 11	95.25 44

alignmvs		87.28 20	86.97 25	88.24 15	91.30 99	71.14 16	95.61 16	93.56 56	79.30 38	87.07 18	95.25 32	68.43 20	96.93 77	87.87 21	84.33 113	96.65 8

SteuartSystems-ACMMP		86.82 30	86.90 26	86.58 46	90.42 110	66.38 119	96.09 9	93.87 44	77.73 60	84.01 43	95.66 19	63.39 67	97.94 27	87.40 25	93.55 34	95.42 33
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 31	86.86 27	86.31 59	93.76 36	67.53 73	96.33 8	93.61 54	82.34 18	81.00 58	93.08 79	63.19 70	97.29 52	87.08 27	91.38 60	94.13 82

PHI-MVS		86.83 29	86.85 28	86.78 40	93.47 44	65.55 136	95.39 20	95.10 19	71.77 164	85.69 26	96.52 3	62.07 77	98.77 10	86.06 34	95.60 6	96.03 22

MG-MVS		87.11 23	86.27 29	89.62 5	97.79 1	76.27 3	94.96 31	94.49 34	78.74 51	83.87 44	92.94 84	64.34 57	96.94 75	75.19 100	94.09 23	95.66 28

CSCG		86.87 27	86.26 30	88.72 9	95.05 19	70.79 17	93.83 56	95.33 14	68.48 212	77.63 89	94.35 59	73.04 9	98.45 16	84.92 41	93.71 31	96.92 6

canonicalmvs		86.85 28	86.25 31	88.66 10	91.80 83	71.92 10	93.54 63	91.71 130	80.26 31	87.55 14	95.25 32	63.59 66	96.93 77	88.18 19	84.34 112	97.11 3

jason		86.40 32	86.17 32	87.11 33	86.16 183	70.54 20	95.71 15	92.19 114	82.00 24	84.58 35	94.34 60	61.86 79	95.53 122	87.76 22	90.89 65	95.27 41
jason: jason.
APD-MVS		85.93 38	85.99 33	85.76 73	95.98 12	65.21 141	93.59 61	92.58 99	66.54 227	86.17 20	95.88 14	63.83 61	97.00 67	86.39 32	92.94 39	95.06 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-3		85.80 40	85.92 34	85.46 80	94.17 30	65.09 147	92.95 78	95.11 18	81.13 27	81.68 54	95.04 37	65.82 45	98.32 20	83.02 52	84.36 110	92.97 117

MSLP-MVS++		86.27 34	85.91 35	87.35 27	92.01 74	68.97 43	95.04 29	92.70 92	79.04 46	81.50 55	96.50 4	58.98 105	96.78 81	83.49 50	93.93 25	96.29 16

WTY-MVS		86.32 33	85.81 36	87.85 17	92.82 58	69.37 37	95.20 23	95.25 15	82.71 15	81.91 52	94.73 49	67.93 27	97.63 38	79.55 73	82.25 125	96.54 12

ACMMP_Plus		86.05 37	85.80 37	86.80 39	91.58 87	67.53 73	91.79 126	93.49 60	74.93 96	84.61 34	95.30 28	59.42 99	97.92 28	86.13 33	94.92 10	94.94 57

MVS_111021_HR		86.19 36	85.80 37	87.37 26	93.17 49	69.79 30	93.99 47	93.76 49	79.08 45	78.88 77	93.99 66	62.25 76	98.15 24	85.93 35	91.15 63	94.15 81

Regformer-4		85.45 43	85.69 39	84.73 101	94.17 30	63.23 184	92.95 78	94.83 23	80.66 29	81.29 56	95.04 37	65.12 50	98.08 26	82.74 53	84.36 110	92.88 121

VNet		86.20 35	85.65 40	87.84 18	93.92 35	69.99 26	95.73 14	95.94 11	78.43 53	86.00 22	93.07 81	58.22 108	97.00 67	85.22 39	84.33 113	96.52 13

CDPH-MVS		85.71 41	85.46 41	86.46 51	94.75 21	67.19 80	93.89 54	92.83 89	70.90 178	83.09 47	95.28 29	63.62 64	97.36 48	80.63 68	94.18 22	94.84 59

PAPM		85.89 39	85.46 41	87.18 30	88.20 155	72.42 9	92.41 97	92.77 90	82.11 21	80.34 63	93.07 81	68.27 21	95.02 130	78.39 83	93.59 33	94.09 85

DeepC-MVS	77.85 3	85.52 42	85.24 43	86.37 56	88.80 142	66.64 109	92.15 101	93.68 52	81.07 28	76.91 99	93.64 71	62.59 75	98.44 17	85.50 37	92.84 41	94.03 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS-pluss		85.24 44	85.13 44	85.56 77	91.42 96	65.59 135	91.54 138	92.51 101	74.56 99	80.62 60	95.64 20	59.15 102	97.00 67	86.94 29	93.80 27	94.07 87
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		85.02 46	84.97 45	85.17 92	92.60 62	64.27 166	93.24 69	92.27 106	73.13 130	79.63 70	94.43 53	61.90 78	97.17 58	85.00 40	92.56 44	94.06 88

#test#		84.98 47	84.74 46	85.72 74	93.75 38	65.01 148	94.09 41	93.19 75	73.55 124	79.22 73	94.93 42	59.04 103	97.67 33	82.66 54	92.21 47	94.49 71

MPTG		84.73 49	84.47 47	85.50 78	91.89 79	65.16 142	91.55 137	92.23 107	75.32 91	80.53 61	95.21 34	56.06 136	97.16 59	84.86 42	92.55 45	94.18 77

PAPR		85.15 45	84.47 47	87.18 30	96.02 11	68.29 56	91.85 124	93.00 84	76.59 76	79.03 76	95.00 39	61.59 80	97.61 40	78.16 84	89.00 77	95.63 29

HFP-MVS		84.73 49	84.40 49	85.72 74	93.75 38	65.01 148	93.50 64	93.19 75	72.19 149	79.22 73	94.93 42	59.04 103	97.67 33	81.55 62	92.21 47	94.49 71

ACMMPR		84.37 52	84.06 50	85.28 88	93.56 41	64.37 161	93.50 64	93.15 78	72.19 149	78.85 79	94.86 46	56.69 128	97.45 44	81.55 62	92.20 49	94.02 90

region2R		84.36 53	84.03 51	85.36 86	93.54 42	64.31 163	93.43 67	92.95 85	72.16 152	78.86 78	94.84 47	56.97 121	97.53 42	81.38 65	92.11 51	94.24 75

EI-MVSNet-Vis-set		83.77 64	83.67 52	84.06 115	92.79 60	63.56 181	91.76 129	94.81 25	79.65 36	77.87 85	94.09 64	63.35 68	97.90 29	79.35 74	79.36 139	90.74 151

CANet_DTU		84.09 58	83.52 53	85.81 69	90.30 113	66.82 94	91.87 122	89.01 224	85.27 7	86.09 21	93.74 70	47.71 224	96.98 71	77.90 87	89.78 74	93.65 98

PVSNet_Blended_VisFu		83.97 59	83.50 54	85.39 85	90.02 117	66.59 112	93.77 57	91.73 128	77.43 66	77.08 98	89.81 128	63.77 63	96.97 72	79.67 72	88.21 82	92.60 125

XVS		83.87 62	83.47 55	85.05 94	93.22 46	63.78 172	92.92 80	92.66 95	73.99 111	78.18 83	94.31 62	55.25 141	97.41 45	79.16 76	91.58 57	93.95 92

CHOSEN 1792x2688		84.98 47	83.45 56	89.57 6	89.94 119	75.14 4	92.07 107	92.32 104	81.87 25	75.68 104	88.27 142	60.18 93	98.60 14	80.46 70	90.27 72	94.96 56

PVSNet_BlendedMVS		83.38 67	83.43 57	83.22 131	93.76 36	67.53 73	94.06 43	93.61 54	79.13 43	81.00 58	85.14 180	63.19 70	97.29 52	87.08 27	73.91 183	84.83 242

MAR-MVS		84.18 56	83.43 57	86.44 52	96.25 8	65.93 129	94.28 35	94.27 40	74.41 100	79.16 75	95.61 21	53.99 167	98.88 9	69.62 144	93.26 37	94.50 70
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
CP-MVS		83.71 66	83.40 59	84.65 104	93.14 50	63.84 170	94.59 33	92.28 105	71.03 176	77.41 92	94.92 44	55.21 144	96.19 93	81.32 66	90.70 67	93.91 94

MTAPA		83.91 61	83.38 60	85.50 78	91.89 79	65.16 142	81.75 271	92.23 107	75.32 91	80.53 61	95.21 34	56.06 136	97.16 59	84.86 42	92.55 45	94.18 77

HY-MVS	76.49 5	84.28 55	83.36 61	87.02 36	92.22 70	67.74 67	84.65 250	94.50 33	79.15 42	82.23 50	87.93 148	66.88 34	96.94 75	80.53 69	82.20 126	96.39 14

MVS_Test		84.16 57	83.20 62	87.05 35	91.56 88	69.82 29	89.99 182	92.05 117	77.77 59	82.84 48	86.57 166	63.93 60	96.09 97	74.91 106	89.18 76	95.25 44

EI-MVSNet-UG-set		83.14 70	82.96 63	83.67 125	92.28 68	63.19 187	91.38 143	94.68 28	79.22 40	76.60 100	93.75 69	62.64 74	97.76 31	78.07 85	78.01 150	90.05 158

HPM-MVS		83.25 68	82.95 64	84.17 113	92.25 69	62.88 194	90.91 159	91.86 124	70.30 191	77.12 96	93.96 67	56.75 126	96.28 91	82.04 58	91.34 62	93.34 104

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 133	71.18 14	89.07 201	90.69 162	65.80 233	87.13 16	94.34 60	64.99 52	92.67 216	72.83 113	91.80 53	95.27 41

MVS		84.66 51	82.86 66	90.06 1	90.93 104	74.56 5	87.91 220	95.54 13	68.55 210	72.35 140	94.71 50	59.78 96	98.90 7	81.29 67	94.69 18	96.74 7

DWT-MVSNet_test		83.95 60	82.80 67	87.41 25	92.90 55	70.07 25	89.12 200	94.42 36	82.15 20	77.64 88	91.77 103	70.81 16	96.22 92	65.03 183	81.36 129	95.94 24

Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 126	69.40 36	91.35 145	86.12 271	72.59 138	83.22 46	92.81 89	59.60 98	96.01 102	81.76 60	87.80 85	95.56 31

LFMVS		84.34 54	82.73 69	89.18 8	94.76 20	73.25 8	94.99 30	91.89 123	71.90 155	82.16 51	93.49 74	47.98 221	97.05 62	82.55 55	84.82 106	97.25 2

PGM-MVS		83.25 68	82.70 70	84.92 96	92.81 59	64.07 168	90.44 170	92.20 112	71.28 174	77.23 95	94.43 53	55.17 145	97.31 51	79.33 75	91.38 60	93.37 103

mPP-MVS		82.96 74	82.44 71	84.52 108	92.83 56	62.92 192	92.76 83	91.85 125	71.52 171	75.61 107	94.24 63	53.48 176	96.99 70	78.97 79	90.73 66	93.64 99

sss		82.71 77	82.38 72	83.73 122	89.25 131	59.58 243	92.24 100	94.89 21	77.96 57	79.86 67	92.38 95	56.70 127	97.05 62	77.26 90	80.86 133	94.55 65

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 161	67.65 70	95.45 18	92.18 115	85.06 8	72.58 133	92.27 98	52.46 185	95.78 107	84.18 44	79.06 142	88.16 181
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSTER		82.47 79	82.05 74	83.74 120	92.68 61	69.01 41	91.90 121	93.21 72	79.83 32	72.14 141	85.71 176	74.72 7	94.72 141	75.72 96	72.49 193	87.50 187

PMMVS		81.98 89	82.04 75	81.78 173	89.76 123	56.17 272	91.13 156	90.69 162	77.96 57	80.09 65	93.57 72	46.33 234	94.99 131	81.41 64	87.46 87	94.17 79

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 156	63.74 174	92.70 86	93.77 48	79.30 38	77.61 90	87.57 154	58.19 109	94.08 174	73.91 109	86.68 93	93.33 106

PAPM_NR		82.97 73	81.84 77	86.37 56	94.10 34	66.76 101	87.66 225	92.84 88	69.96 194	74.07 120	93.57 72	63.10 72	97.50 43	70.66 137	90.58 69	94.85 58

VDD-MVS		83.06 72	81.81 78	86.81 38	90.86 107	67.70 68	95.40 19	91.50 138	75.46 86	81.78 53	92.34 97	40.09 261	97.13 61	86.85 30	82.04 127	95.60 30

PatchFormer-LS_test		83.14 70	81.81 78	87.12 32	92.34 65	69.92 28	88.64 207	93.32 67	82.07 23	74.87 113	91.62 107	68.91 18	96.08 99	66.07 174	78.45 149	95.37 34

DP-MVS Recon		82.73 75	81.65 80	85.98 64	97.31 3	67.06 84	95.15 25	91.99 119	69.08 202	76.50 102	93.89 68	54.48 161	98.20 22	70.76 136	85.66 102	92.69 122

MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 151	62.88 194	89.77 189	88.93 226	76.78 73	75.55 108	93.10 77	50.31 199	95.38 124	83.82 49	87.02 89	92.26 134

EPP-MVSNet		81.79 90	81.52 81	82.61 143	88.77 143	60.21 233	93.02 76	93.66 53	68.52 211	72.90 128	90.39 120	72.19 12	94.96 132	74.93 105	79.29 141	92.67 123

APD-MVS_3200maxsize		81.64 91	81.32 83	82.59 144	92.36 64	58.74 254	91.39 141	91.01 156	63.35 250	79.72 69	94.62 51	51.82 188	96.14 95	79.71 71	87.93 84	92.89 120

CostFormer		82.33 81	81.15 84	85.86 67	89.01 138	68.46 51	82.39 268	93.01 82	75.59 84	80.25 64	81.57 221	72.03 13	94.96 132	79.06 78	77.48 160	94.16 80

xiu_mvs_v1_base_debu		82.16 84	81.12 85	85.26 89	86.42 177	68.72 47	92.59 93	90.44 167	73.12 131	84.20 39	94.36 55	38.04 271	95.73 110	84.12 45	86.81 90	91.33 143

xiu_mvs_v1_base		82.16 84	81.12 85	85.26 89	86.42 177	68.72 47	92.59 93	90.44 167	73.12 131	84.20 39	94.36 55	38.04 271	95.73 110	84.12 45	86.81 90	91.33 143

xiu_mvs_v1_base_debi		82.16 84	81.12 85	85.26 89	86.42 177	68.72 47	92.59 93	90.44 167	73.12 131	84.20 39	94.36 55	38.04 271	95.73 110	84.12 45	86.81 90	91.33 143

3Dnovator	73.91 6	82.69 78	80.82 88	88.31 14	89.57 125	71.26 13	92.60 91	94.39 39	78.84 48	67.89 198	92.48 93	48.42 216	98.52 15	68.80 152	94.40 20	95.15 47

CDS-MVSNet		81.43 93	80.74 89	83.52 127	86.26 181	64.45 156	92.09 105	90.65 165	75.83 83	73.95 122	89.81 128	63.97 59	92.91 208	71.27 129	82.82 122	93.20 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		81.49 92	80.67 90	83.93 117	91.71 85	62.90 193	92.13 102	92.22 111	71.79 163	71.68 148	93.49 74	50.32 198	96.96 73	78.47 81	84.22 117	91.93 137
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
HQP-MVS		81.14 96	80.64 91	82.64 142	87.54 164	63.66 179	94.06 43	91.70 131	79.80 33	74.18 116	90.30 121	51.63 192	95.61 116	77.63 88	78.90 143	88.63 172

3Dnovator+	73.60 7	82.10 87	80.60 92	86.60 44	90.89 106	66.80 100	95.20 23	93.44 62	74.05 110	67.42 203	92.49 92	49.46 207	97.65 37	70.80 135	91.68 55	95.33 36

API-MVS		82.28 82	80.53 93	87.54 23	96.13 9	70.59 19	93.63 59	91.04 155	65.72 235	75.45 109	92.83 88	56.11 135	98.89 8	64.10 191	89.75 75	93.15 111

IB-MVS	77.80 4	82.18 83	80.46 94	87.35 27	89.14 135	70.28 22	95.59 17	95.17 16	78.85 47	70.19 160	85.82 174	70.66 17	97.67 33	72.19 122	66.52 229	94.09 85
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
1121		81.25 95	80.05 95	84.87 98	92.30 67	64.31 163	87.91 220	91.39 142	59.44 276	79.94 66	92.91 85	57.09 117	97.01 65	66.63 166	92.81 42	93.29 107

Fast-Effi-MVS+		81.14 96	80.01 96	84.51 109	90.24 115	65.86 130	94.12 40	89.15 218	73.81 118	75.37 110	88.26 143	57.26 116	94.53 148	66.97 165	84.92 105	93.15 111

mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 112	68.40 52	86.88 238	90.61 166	74.41 100	70.31 159	84.67 185	63.79 62	92.32 227	73.13 110	85.70 101	95.67 27

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 148	61.80 210	93.44 66	88.26 241	73.96 114	77.73 86	91.76 104	49.94 203	94.76 138	65.84 177	90.37 71	94.65 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
nrg030		80.93 99	79.86 99	84.13 114	83.69 213	68.83 45	93.23 70	91.20 148	75.55 85	75.06 112	88.22 146	63.04 73	94.74 140	81.88 59	66.88 226	88.82 170

1112_ss		80.56 103	79.83 100	82.77 137	88.65 144	60.78 220	92.29 98	88.36 237	72.58 139	72.46 137	94.95 40	65.09 51	93.42 197	66.38 170	77.71 152	94.10 84

HQP_MVS		80.34 107	79.75 101	82.12 166	86.94 172	62.42 199	93.13 72	91.31 145	78.81 49	72.53 134	89.14 133	50.66 196	95.55 120	76.74 91	78.53 147	88.39 177

UA-Net		80.02 114	79.65 102	81.11 189	89.33 129	57.72 261	86.33 243	89.00 225	77.44 65	81.01 57	89.15 132	59.33 100	95.90 103	61.01 215	84.28 115	89.73 162

Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 243	88.46 149	52.29 289	90.41 172	89.12 219	74.24 106	69.13 177	91.91 101	65.77 46	90.09 264	59.00 226	88.09 83	92.33 130

test-LLR		80.10 112	79.56 104	81.72 175	86.93 174	61.17 215	92.70 86	91.54 135	71.51 172	75.62 105	86.94 163	53.83 168	92.38 224	72.21 120	84.76 108	91.60 140

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 162	68.11 61	90.18 177	90.01 190	70.65 186	72.95 127	86.06 172	63.61 65	94.50 149	75.01 104	79.75 137	93.67 97

HPM-MVS_fast		80.25 108	79.55 106	82.33 156	91.55 89	59.95 238	91.32 147	89.16 217	65.23 239	74.71 114	93.07 81	47.81 223	95.74 109	74.87 108	88.23 81	91.31 147

TAMVS		80.37 106	79.45 107	83.13 133	85.14 194	63.37 182	91.23 150	90.76 161	74.81 98	72.65 131	88.49 137	60.63 89	92.95 204	69.41 146	81.95 128	93.08 114

FIs		79.47 125	79.41 108	79.67 215	85.95 186	59.40 245	91.68 133	93.94 43	78.06 56	68.96 181	88.28 141	66.61 37	91.77 238	66.20 173	74.99 176	87.82 184

IS-MVSNet		80.14 111	79.41 108	82.33 156	87.91 160	60.08 237	91.97 113	88.27 240	72.90 135	71.44 150	91.73 106	61.44 81	93.66 192	62.47 208	86.53 96	93.24 108

test-mter		79.96 115	79.38 110	81.72 175	86.93 174	61.17 215	92.70 86	91.54 135	73.85 116	75.62 105	86.94 163	49.84 205	92.38 224	72.21 120	84.76 108	91.60 140

BH-w/o		80.49 105	79.30 111	84.05 116	90.83 108	64.36 162	93.60 60	89.42 208	74.35 105	69.09 178	90.15 123	55.23 143	95.61 116	64.61 186	86.43 98	92.17 135

EPNet_dtu		78.80 136	79.26 112	77.43 253	88.06 157	49.71 302	91.96 114	91.95 122	77.67 61	76.56 101	91.28 109	58.51 107	90.20 259	56.37 234	80.95 132	92.39 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
abl_6		79.82 118	79.20 113	81.70 177	89.85 120	58.34 256	88.47 210	90.07 187	62.56 257	77.71 87	93.08 79	47.65 225	96.78 81	77.94 86	85.45 104	89.99 159

CPTT-MVS		79.59 123	79.16 114	80.89 196	91.54 90	59.80 240	92.10 104	88.54 235	60.42 270	72.96 126	93.28 76	48.27 217	92.80 211	78.89 80	86.50 97	90.06 157

tpmrst		80.57 102	79.14 115	84.84 99	90.10 116	68.28 57	81.70 272	89.72 201	77.63 62	75.96 103	79.54 251	64.94 54	92.71 214	75.43 98	77.28 163	93.55 100

1314		80.70 101	78.95 116	85.94 66	87.77 163	67.56 72	87.91 220	92.55 100	72.17 151	67.44 202	93.09 78	50.27 200	97.04 64	71.68 125	87.64 86	93.23 109

UGNet		79.87 117	78.68 117	83.45 130	89.96 118	61.51 213	92.13 102	90.79 159	76.83 72	78.85 79	86.33 169	38.16 269	96.17 94	67.93 156	87.17 88	92.67 123
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
PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 105	64.97 150	92.47 96	91.05 154	79.18 41	72.43 138	90.51 119	37.05 283	94.06 175	68.06 154	86.00 100	93.90 95

Test_1112_low_res		79.56 124	78.60 119	82.43 149	88.24 154	60.39 229	92.09 105	87.99 244	72.10 153	71.84 144	87.42 156	64.62 55	93.04 201	65.80 178	77.30 162	93.85 96

diffmvs		80.18 109	78.55 120	85.07 93	88.56 145	66.93 90	86.70 241	88.62 232	70.42 188	78.69 81	85.26 178	56.93 123	94.77 137	68.68 153	83.09 119	93.51 101

thres200		79.66 121	78.33 121	83.66 126	92.54 63	65.82 133	93.06 74	96.31 8	74.90 97	73.30 125	88.66 135	59.67 97	95.61 116	47.84 264	78.67 146	89.56 164

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 150	65.49 138	83.00 265	92.67 94	71.82 161	77.36 93	85.01 181	54.50 159	96.59 86	76.35 95	75.63 170	95.32 38

VDDNet		80.50 104	78.26 123	87.21 29	86.19 182	69.79 30	94.48 34	91.31 145	60.42 270	79.34 72	90.91 111	38.48 267	96.56 89	82.16 56	81.05 131	95.27 41

EI-MVSNet		78.97 132	78.22 124	81.25 182	85.33 191	62.73 197	89.53 193	93.21 72	72.39 143	72.14 141	90.13 124	60.99 82	94.72 141	67.73 158	72.49 193	86.29 213

OPM-MVS		79.00 131	78.09 125	81.73 174	83.52 216	63.83 171	91.64 136	90.30 177	76.36 79	71.97 143	89.93 127	46.30 235	95.17 129	75.10 101	77.70 153	86.19 215

FC-MVSNet-test		77.99 152	78.08 126	77.70 248	84.89 197	55.51 276	90.27 175	93.75 50	76.87 70	66.80 212	87.59 153	65.71 47	90.23 258	62.89 203	73.94 182	87.37 195

VPA-MVSNet		79.03 130	78.00 127	82.11 169	85.95 186	64.48 155	93.22 71	94.66 29	75.05 95	74.04 121	84.95 182	52.17 187	93.52 194	74.90 107	67.04 225	88.32 179

mvs-test1		78.74 139	77.95 128	81.14 188	83.22 218	57.13 268	93.96 48	87.78 246	75.42 87	72.68 130	90.80 113	45.08 241	94.54 147	75.08 102	77.49 159	91.74 139

tpm2		79.80 119	77.95 128	85.34 87	88.28 153	68.26 58	81.56 276	91.42 141	70.11 192	77.59 91	80.50 237	67.40 30	94.26 165	67.34 161	77.35 161	93.51 101

OMC-MVS		78.67 142	77.91 130	80.95 195	85.76 190	57.40 266	88.49 209	88.67 230	73.85 116	72.43 138	92.10 99	49.29 209	94.55 146	72.73 115	77.89 151	90.91 150

114514_t		79.17 129	77.67 131	83.68 124	95.32 15	65.53 137	92.85 82	91.60 134	63.49 249	67.92 197	90.63 116	46.65 231	95.72 114	67.01 164	83.54 118	89.79 160

BH-RMVSNet		79.46 126	77.65 132	84.89 97	91.68 86	65.66 134	93.55 62	88.09 242	72.93 134	73.37 124	91.12 110	46.20 236	96.12 96	56.28 235	85.61 103	92.91 119

PCF-MVS	73.15 9	79.29 127	77.63 133	84.29 112	86.06 184	65.96 128	87.03 233	91.10 152	69.86 195	69.79 167	90.64 114	57.54 115	96.59 86	64.37 190	82.29 124	90.32 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		78.15 150	77.55 134	79.98 208	84.46 203	60.26 231	92.25 99	93.20 74	77.50 64	68.88 182	86.61 165	66.10 41	92.13 231	66.38 170	62.55 255	87.54 186

VPNet		78.82 135	77.53 135	82.70 139	84.52 201	66.44 118	93.93 51	92.23 107	80.46 30	72.60 132	88.38 140	49.18 210	93.13 200	72.47 118	63.97 252	88.55 174

DI_MVS_plusplus_test		79.78 120	77.50 136	86.62 43	80.90 236	69.46 35	90.69 165	91.97 121	77.00 68	59.07 253	82.34 206	46.82 228	95.88 104	82.14 57	86.59 95	94.53 69

tfpn200view9		78.79 137	77.43 137	82.88 135	92.21 71	64.49 153	92.05 108	96.28 9	73.48 125	71.75 146	88.26 143	60.07 94	95.32 125	45.16 273	77.58 155	88.83 168

thres400		78.68 140	77.43 137	82.43 149	92.21 71	64.49 153	92.05 108	96.28 9	73.48 125	71.75 146	88.26 143	60.07 94	95.32 125	45.16 273	77.58 155	87.48 188

QAPM		79.95 116	77.39 139	87.64 20	89.63 124	71.41 12	93.30 68	93.70 51	65.34 238	67.39 205	91.75 105	47.83 222	98.96 5	57.71 231	89.81 73	92.54 127

TR-MVS		78.77 138	77.37 140	82.95 134	90.49 109	60.88 218	93.67 58	90.07 187	70.08 193	74.51 115	91.37 108	45.69 237	95.70 115	60.12 220	80.32 134	92.29 132

test_normal		79.66 121	77.36 141	86.54 47	80.72 240	69.21 38	90.68 166	92.16 116	76.99 69	58.63 257	82.03 215	46.70 230	95.86 105	81.74 61	86.63 94	94.56 64

BH-untuned		78.68 140	77.08 142	83.48 129	89.84 121	63.74 174	92.70 86	88.59 233	71.57 169	66.83 211	88.65 136	51.75 190	95.39 123	59.03 225	84.77 107	91.32 146

tpm		78.58 143	77.03 143	83.22 131	85.94 188	64.56 152	83.21 263	91.14 151	78.31 54	73.67 123	79.68 248	64.01 58	92.09 233	66.07 174	71.26 202	93.03 115

conf200view11		78.32 148	77.01 144	82.27 159	91.89 79	63.21 185	91.19 154	96.33 5	72.28 145	70.45 155	87.89 149	60.31 90	95.32 125	45.16 273	77.58 155	88.27 180

thres100view900		78.37 146	77.01 144	82.46 145	91.89 79	63.21 185	91.19 154	96.33 5	72.28 145	70.45 155	87.89 149	60.31 90	95.32 125	45.16 273	77.58 155	88.83 168

AdaColmap		78.94 133	77.00 146	84.76 100	96.34 6	65.86 130	92.66 90	87.97 245	62.18 259	70.56 152	92.37 96	43.53 248	97.35 49	64.50 188	82.86 121	91.05 149

CHOSEN 280x420		77.35 162	76.95 147	78.55 237	87.07 171	62.68 198	69.71 314	82.95 297	68.80 205	71.48 149	87.27 162	66.03 42	84.00 303	76.47 94	82.81 123	88.95 167

UniMVSNet (Re)		77.58 156	76.78 148	79.98 208	84.11 209	60.80 219	91.76 129	93.17 77	76.56 77	69.93 166	84.78 184	63.32 69	92.36 226	64.89 184	62.51 257	86.78 207

thres600view7		78.00 151	76.66 149	82.03 171	91.93 76	63.69 177	91.30 148	96.33 5	72.43 141	70.46 154	87.89 149	60.31 90	94.92 135	42.64 285	76.64 165	87.48 188

tpmp4_e23		78.85 134	76.55 150	85.77 72	89.25 131	68.39 53	81.63 275	91.38 143	70.40 189	75.21 111	79.22 253	67.37 31	94.79 136	58.98 227	75.51 171	94.13 82

MS-PatchMatch		77.90 154	76.50 151	82.12 166	85.99 185	69.95 27	91.75 131	92.70 92	73.97 113	62.58 239	84.44 188	41.11 258	95.78 107	63.76 192	92.17 50	80.62 291

XXY-MVS		77.94 153	76.44 152	82.43 149	82.60 224	64.44 157	92.01 110	91.83 126	73.59 123	70.00 163	85.82 174	54.43 162	94.76 138	69.63 143	68.02 220	88.10 182

PS-MVSNAJss		77.26 166	76.31 153	80.13 205	80.64 247	59.16 249	90.63 169	91.06 153	72.80 136	68.58 187	84.57 187	53.55 172	93.96 182	72.97 111	71.96 196	87.27 200

MVP-Stereo		77.12 168	76.23 154	79.79 214	81.72 230	66.34 121	89.29 195	90.88 157	70.56 187	62.01 242	82.88 199	49.34 208	94.13 171	65.55 180	93.80 27	78.88 304
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GA-MVS		78.33 147	76.23 154	84.65 104	83.65 214	66.30 122	91.44 139	90.14 185	76.01 81	70.32 158	84.02 190	42.50 251	94.72 141	70.98 133	77.00 164	92.94 118

FMVSNet3		77.73 155	76.04 156	82.80 136	91.20 101	68.99 42	91.87 122	91.99 119	73.35 128	67.04 208	83.19 198	56.62 129	92.14 230	59.80 222	69.34 209	87.28 199

EPMVS		78.49 145	75.98 157	86.02 63	91.21 100	69.68 33	80.23 285	91.20 148	75.25 93	72.48 136	78.11 258	54.65 158	93.69 191	57.66 232	83.04 120	94.69 62

OpenMVS	70.45 11	78.54 144	75.92 158	86.41 55	85.93 189	71.68 11	92.74 84	92.51 101	66.49 228	64.56 221	91.96 100	43.88 247	98.10 25	54.61 239	90.65 68	89.44 165

DU-MVS		76.86 173	75.84 159	79.91 210	82.96 222	60.26 231	91.26 149	91.54 135	76.46 78	68.88 182	86.35 167	56.16 133	92.13 231	66.38 170	62.55 255	87.35 197

cascas		78.18 149	75.77 160	85.41 84	87.14 170	69.11 39	92.96 77	91.15 150	66.71 226	70.47 153	86.07 171	37.49 277	96.48 90	70.15 140	79.80 136	90.65 152

WR-MVS		76.76 176	75.74 161	79.82 213	84.60 199	62.27 203	92.60 91	92.51 101	76.06 80	67.87 199	85.34 177	56.76 124	90.24 257	62.20 209	63.69 254	86.94 205

v1neww		77.39 159	75.71 162	82.44 146	80.69 242	66.83 92	91.94 118	90.18 182	74.19 107	69.60 168	82.51 202	54.99 152	94.44 150	71.68 125	65.60 232	86.05 220

v7new		77.39 159	75.71 162	82.44 146	80.69 242	66.83 92	91.94 118	90.18 182	74.19 107	69.60 168	82.51 202	54.99 152	94.44 150	71.68 125	65.60 232	86.05 220

v6		77.39 159	75.71 162	82.44 146	80.67 244	66.82 94	91.94 118	90.18 182	74.19 107	69.60 168	82.50 205	55.00 151	94.44 150	71.68 125	65.60 232	86.05 220

v2v482		77.42 158	75.65 165	82.73 138	80.38 254	67.13 83	91.85 124	90.23 179	75.09 94	69.37 174	83.39 196	53.79 170	94.44 150	71.77 123	65.00 243	86.63 211

v1141		77.28 164	75.57 166	82.42 152	80.63 248	66.73 102	91.96 114	90.42 170	74.41 100	69.46 171	82.12 212	55.09 148	94.40 155	70.99 132	65.05 239	86.12 217

divwei89l23v2f112		77.28 164	75.57 166	82.42 152	80.62 249	66.72 104	91.96 114	90.42 170	74.41 100	69.46 171	82.12 212	55.11 147	94.40 155	71.00 130	65.04 240	86.12 217

v1		77.29 163	75.57 166	82.42 152	80.61 252	66.73 102	91.96 114	90.42 170	74.41 100	69.46 171	82.12 212	55.14 146	94.40 155	71.00 130	65.04 240	86.13 216

view600		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 157	60.79 84	94.19 166	38.90 298	75.31 172	87.48 188

view800		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 157	60.79 84	94.19 166	38.90 298	75.31 172	87.48 188

conf0.05thres1000		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 157	60.79 84	94.19 166	38.90 298	75.31 172	87.48 188

tfpn		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 157	60.79 84	94.19 166	38.90 298	75.31 172	87.48 188

Effi-MVS+-dtu		76.14 183	75.28 173	78.72 236	83.22 218	55.17 278	89.87 187	87.78 246	75.42 87	67.98 196	81.43 222	45.08 241	92.52 220	75.08 102	71.63 197	88.48 175

tfpn_ndepth		76.45 180	75.22 174	80.14 203	90.97 103	58.92 251	90.11 178	93.24 71	65.96 232	67.37 206	90.52 118	66.67 36	92.29 228	37.71 304	74.44 178	89.21 166

IterMVS-LS		76.49 178	75.18 175	80.43 199	84.49 202	62.74 196	90.64 167	88.80 228	72.40 142	65.16 217	81.72 220	60.98 83	92.27 229	67.74 157	64.65 247	86.29 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v7		76.83 175	75.01 176	82.29 158	80.35 255	66.70 106	91.68 133	89.97 191	73.47 127	69.22 176	82.22 209	52.52 183	94.43 154	69.73 142	65.96 231	85.74 231

v1144		76.73 177	74.88 177	82.27 159	80.23 262	66.60 110	91.68 133	90.21 181	73.69 120	69.06 179	81.89 217	52.73 182	94.40 155	69.21 148	65.23 236	85.80 227

PatchmatchNet		77.46 157	74.63 178	85.96 65	89.55 127	70.35 21	79.97 289	89.55 204	72.23 147	70.94 151	76.91 270	57.03 119	92.79 212	54.27 241	81.17 130	94.74 61
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
NR-MVSNet		76.05 185	74.59 179	80.44 198	82.96 222	62.18 204	90.83 161	91.73 128	77.12 67	60.96 243	86.35 167	59.28 101	91.80 237	60.74 216	61.34 268	87.35 197

LPG-MVS_test		75.82 189	74.58 180	79.56 219	84.31 206	59.37 246	90.44 170	89.73 199	69.49 197	64.86 218	88.42 138	38.65 265	94.30 161	72.56 116	72.76 190	85.01 240

V42		76.46 179	74.55 181	82.19 164	79.14 276	67.82 65	90.26 176	89.42 208	73.75 119	68.63 186	81.89 217	51.31 194	94.09 173	71.69 124	64.84 244	84.66 243

TranMVSNet+NR-MVSNet		75.86 188	74.52 182	79.89 211	82.44 225	60.64 226	91.37 144	91.37 144	76.63 75	67.65 201	86.21 170	52.37 186	91.55 242	61.84 211	60.81 271	87.48 188

v148		76.19 182	74.47 183	81.36 180	80.05 266	64.44 157	91.75 131	90.23 179	73.68 121	67.13 207	80.84 233	55.92 139	93.86 188	68.95 150	61.73 264	85.76 230

gg-mvs-nofinetune		77.18 167	74.31 184	85.80 70	91.42 96	68.36 54	71.78 308	94.72 27	49.61 306	77.12 96	45.92 329	77.41 3	93.98 181	67.62 159	93.16 38	95.05 51

CVMVSNet		74.04 208	74.27 185	73.33 279	85.33 191	43.94 316	89.53 193	88.39 236	54.33 296	70.37 157	90.13 124	49.17 211	84.05 300	61.83 212	79.36 139	91.99 136

ACMP	71.68 10	75.58 193	74.23 186	79.62 217	84.97 196	59.64 241	90.80 162	89.07 222	70.39 190	62.95 235	87.30 161	38.28 268	93.87 186	72.89 112	71.45 200	85.36 237
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
X-MVStestdata		76.86 173	74.13 187	85.05 94	93.22 46	63.78 172	92.92 80	92.66 95	73.99 111	78.18 83	10.19 344	55.25 141	97.41 45	79.16 76	91.58 57	93.95 92

v144192		76.05 185	74.03 188	82.12 166	79.50 271	66.55 114	91.39 141	89.71 202	72.30 144	68.17 194	81.33 225	51.75 190	94.03 179	67.94 155	64.19 249	85.77 228

FMVSNet2		76.07 184	74.01 189	82.26 162	88.85 139	67.66 69	91.33 146	91.61 133	70.84 179	65.98 213	82.25 208	48.03 218	92.00 235	58.46 228	68.73 215	87.10 201

tfpn1000		75.25 196	74.00 190	79.03 234	90.30 113	57.56 265	88.55 208	93.36 66	64.14 246	65.17 216	89.76 130	67.06 33	91.46 248	34.54 313	73.09 188	88.06 183

v1192		75.98 187	73.92 191	82.15 165	79.73 267	66.24 124	91.22 151	89.75 196	72.67 137	68.49 192	81.42 223	49.86 204	94.27 163	67.08 163	65.02 242	85.95 224

GBi-Net		75.65 190	73.83 192	81.10 190	88.85 139	65.11 144	90.01 179	90.32 173	70.84 179	67.04 208	80.25 242	48.03 218	91.54 243	59.80 222	69.34 209	86.64 208

test1		75.65 190	73.83 192	81.10 190	88.85 139	65.11 144	90.01 179	90.32 173	70.84 179	67.04 208	80.25 242	48.03 218	91.54 243	59.80 222	69.34 209	86.64 208

PLC	68.80 14	75.23 197	73.68 194	79.86 212	92.93 54	58.68 255	90.64 167	88.30 238	60.90 267	64.43 224	90.53 117	42.38 252	94.57 144	56.52 233	76.54 166	86.33 212
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	70.22 12	74.94 202	73.53 195	79.17 231	90.40 111	52.07 290	89.19 198	89.61 203	62.69 256	70.07 161	92.67 90	48.89 215	94.32 160	38.26 303	79.97 135	91.12 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1921920		75.63 192	73.49 196	82.06 170	79.38 272	66.35 120	91.07 158	89.48 205	71.98 154	67.99 195	81.22 228	49.16 212	93.90 185	66.56 168	64.56 248	85.92 226

Test4		76.45 180	73.45 197	85.45 82	76.07 298	67.61 71	88.38 212	90.83 158	76.71 74	53.06 286	79.65 250	31.61 300	94.35 159	78.47 81	86.22 99	94.40 73

Fast-Effi-MVS+-dtu		75.04 199	73.37 198	80.07 206	80.86 237	59.52 244	91.20 153	85.38 278	71.90 155	65.20 215	84.84 183	41.46 257	92.97 203	66.50 169	72.96 189	87.73 185

v8		75.35 194	73.26 199	81.61 178	80.67 244	66.82 94	89.54 192	89.27 212	71.65 166	63.30 233	80.30 241	54.99 152	94.06 175	67.33 162	62.33 258	83.94 248

XVG-OURS-SEG-HR		74.70 204	73.08 200	79.57 218	78.25 284	57.33 267	80.49 281	87.32 252	63.22 252	68.76 184	90.12 126	44.89 244	91.59 241	70.55 138	74.09 181	89.79 160

v1240		75.21 198	72.98 201	81.88 172	79.20 274	66.00 127	90.75 164	89.11 220	71.63 167	67.41 204	81.22 228	47.36 226	93.87 186	65.46 181	64.72 246	85.77 228

Baseline_NR-MVSNet		73.99 209	72.83 202	77.48 252	80.78 238	59.29 248	91.79 126	84.55 282	68.85 204	68.99 180	80.70 234	56.16 133	92.04 234	62.67 206	60.98 270	81.11 285

ACMM	69.62 13	74.34 205	72.73 203	79.17 231	84.25 208	57.87 259	90.36 173	89.93 192	63.17 253	65.64 214	86.04 173	37.79 275	94.10 172	65.89 176	71.52 199	85.55 234
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test0.0.03 1		72.76 218	72.71 204	72.88 283	80.25 261	47.99 307	91.22 151	89.45 206	71.51 172	62.51 240	87.66 152	53.83 168	85.06 296	50.16 255	67.84 223	85.58 232

MDTV_nov1_ep13			72.61 205		89.06 136	68.48 50	80.33 283	90.11 186	71.84 160	71.81 145	75.92 275	53.01 179	93.92 184	48.04 263	73.38 184

test_djsdf		73.76 212	72.56 206	77.39 254	77.00 292	53.93 283	89.07 201	90.69 162	65.80 233	63.92 226	82.03 215	43.14 250	92.67 216	72.83 113	68.53 216	85.57 233

v10		74.77 203	72.54 207	81.46 179	80.33 259	66.71 105	89.15 199	89.08 221	70.94 177	63.08 234	79.86 246	52.52 183	94.04 178	65.70 179	62.17 259	83.64 250

XVG-OURS		74.25 207	72.46 208	79.63 216	78.45 283	57.59 264	80.33 283	87.39 249	63.86 248	68.76 184	89.62 131	40.50 260	91.72 239	69.00 149	74.25 179	89.58 163

CNLPA		74.31 206	72.30 209	80.32 200	91.49 91	61.66 211	90.85 160	80.72 304	56.67 290	63.85 228	90.64 114	46.75 229	90.84 251	53.79 243	75.99 169	88.47 176

tpm cat1		75.30 195	72.21 210	84.58 106	88.52 146	67.77 66	78.16 300	88.02 243	61.88 263	68.45 193	76.37 271	60.65 88	94.03 179	53.77 244	74.11 180	91.93 137

dp		75.01 200	72.09 211	83.76 119	89.28 130	66.22 125	79.96 290	89.75 196	71.16 175	67.80 200	77.19 266	51.81 189	92.54 219	50.39 254	71.44 201	92.51 128

LCM-MVSNet-Re		72.93 215	71.84 212	76.18 264	88.49 147	48.02 306	80.07 288	70.17 329	73.96 114	52.25 289	80.09 245	49.98 202	88.24 283	67.35 160	84.23 116	92.28 133

pmmvs4		73.92 210	71.81 213	80.25 202	79.17 275	65.24 140	87.43 228	87.26 254	67.64 221	63.46 231	83.91 191	48.96 214	91.53 246	62.94 202	65.49 235	83.96 247

Patchmatch-test1		75.00 201	71.80 214	84.58 106	86.63 176	70.08 24	81.06 278	89.19 215	71.60 168	70.01 162	77.16 268	45.53 238	88.63 277	51.79 250	73.27 185	95.02 55

pmmvs5		73.35 213	71.52 215	78.86 235	78.64 282	60.61 227	91.08 157	86.90 255	67.69 218	63.32 232	83.64 192	44.33 246	90.53 252	62.04 210	66.02 230	85.46 235

jajsoiax		73.05 214	71.51 216	77.67 249	77.46 289	54.83 279	88.81 204	90.04 189	69.13 201	62.85 237	83.51 194	31.16 303	92.75 213	70.83 134	69.80 205	85.43 236

mvs_tets		72.71 219	71.11 217	77.52 250	77.41 290	54.52 281	88.45 211	89.76 195	68.76 206	62.70 238	83.26 197	29.49 307	92.71 214	70.51 139	69.62 207	85.34 238

pm-mvs1		72.89 216	71.09 218	78.26 242	79.10 278	57.62 263	90.80 162	89.30 211	67.66 219	62.91 236	81.78 219	49.11 213	92.95 204	60.29 219	58.89 280	84.22 246

IterMVS		72.65 221	70.83 219	78.09 246	82.17 226	62.96 189	87.64 226	86.28 267	71.56 170	60.44 245	78.85 255	45.42 240	86.66 291	63.30 196	61.83 261	84.65 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		73.79 211	70.82 220	82.70 139	83.15 220	67.96 63	70.25 311	84.00 287	73.67 122	69.97 164	72.41 290	57.82 112	89.48 273	52.99 248	73.13 186	90.64 153

PatchMatch-RL		72.06 222	69.98 221	78.28 240	89.51 128	55.70 275	83.49 257	83.39 293	61.24 266	63.72 229	82.76 200	34.77 290	93.03 202	53.37 247	77.59 154	86.12 217

WR-MVS_H		70.59 238	69.94 222	72.53 285	81.03 235	51.43 293	87.35 230	92.03 118	67.38 223	60.23 246	80.70 234	55.84 140	83.45 307	46.33 269	58.58 281	82.72 266

CP-MVSNet		70.50 240	69.91 223	72.26 288	80.71 241	51.00 296	87.23 231	90.30 177	67.84 217	59.64 248	82.69 201	50.23 201	82.30 314	51.28 251	59.28 275	83.46 255

FMVSNet1		72.71 219	69.91 223	81.10 190	83.60 215	65.11 144	90.01 179	90.32 173	63.92 247	63.56 230	80.25 242	36.35 285	91.54 243	54.46 240	66.75 227	86.64 208

tpmvs		72.88 217	69.76 225	82.22 163	90.98 102	67.05 85	78.22 299	88.30 238	63.10 254	64.35 225	74.98 278	55.09 148	94.27 163	43.25 279	69.57 208	85.34 238

v18		71.94 223	69.43 226	79.50 221	80.74 239	66.82 94	88.16 214	86.66 257	68.95 203	55.55 269	72.66 285	55.03 150	90.15 260	64.78 185	52.30 297	81.54 273

anonymousdsp		71.14 232	69.37 227	76.45 261	72.95 304	54.71 280	84.19 252	88.88 227	61.92 262	62.15 241	79.77 247	38.14 270	91.44 249	68.90 151	67.45 224	83.21 259

v16		71.81 224	69.26 228	79.47 222	80.66 246	66.81 98	87.93 218	86.63 259	68.70 208	55.35 270	72.51 286	54.75 156	90.12 262	64.51 187	52.28 298	81.47 274

v17		71.77 226	69.20 229	79.46 223	80.62 249	66.81 98	87.93 218	86.63 259	68.71 207	55.25 271	72.49 287	54.72 157	90.11 263	64.50 188	51.97 299	81.47 274

PS-CasMVS		69.86 244	69.13 230	72.07 291	80.35 255	50.57 298	87.02 234	89.75 196	67.27 224	59.19 251	82.28 207	46.58 232	82.24 315	50.69 253	59.02 278	83.39 257

v15		71.40 228	68.75 231	79.35 224	80.39 253	66.70 106	87.57 227	86.64 258	68.66 209	54.68 273	72.00 294	54.50 159	89.98 265	63.69 193	50.66 304	81.38 278

v7n		71.31 229	68.65 232	79.28 226	76.40 294	60.77 221	86.71 240	89.45 206	64.17 245	58.77 256	78.24 257	44.59 245	93.54 193	57.76 230	61.75 263	83.52 253

V14		71.29 230	68.61 233	79.31 225	80.34 257	66.65 108	87.39 229	86.61 261	68.41 213	54.49 275	71.91 295	54.25 164	89.96 266	63.50 194	50.62 305	81.33 280

PEN-MVS		69.46 249	68.56 234	72.17 290	79.27 273	49.71 302	86.90 237	89.24 214	67.24 225	59.08 252	82.51 202	47.23 227	83.54 306	48.42 262	57.12 283	83.25 258

V9		71.16 231	68.46 235	79.27 227	80.26 260	66.60 110	87.21 232	86.56 262	68.17 214	54.26 278	71.81 297	54.00 166	89.93 267	63.28 197	50.57 306	81.27 281

MIMVSNet		71.64 227	68.44 236	81.23 183	81.97 229	64.44 157	73.05 307	88.80 228	69.67 196	64.59 220	74.79 279	32.79 294	87.82 286	53.99 242	76.35 167	91.42 142

F-COLMAP		70.66 237	68.44 236	77.32 255	86.37 180	55.91 274	88.00 216	86.32 266	56.94 288	57.28 265	88.07 147	33.58 292	92.49 221	51.02 252	68.37 217	83.55 251

v11		71.05 234	68.32 238	79.23 228	80.34 257	66.57 113	87.01 235	86.55 263	68.11 215	54.40 276	71.66 299	52.94 180	89.91 268	62.71 205	51.12 302	81.21 282

PVSNet_0	68.08 15	71.81 224	68.32 238	82.27 159	84.68 198	62.31 202	88.68 206	90.31 176	75.84 82	57.93 260	80.65 236	37.85 274	94.19 166	69.94 141	29.05 333	90.31 156

v12		71.02 235	68.29 240	79.22 229	80.18 263	66.53 115	87.01 235	86.54 264	67.90 216	54.00 281	71.70 298	53.66 171	89.91 268	63.09 199	50.51 307	81.21 282

v13		70.90 236	68.15 241	79.15 233	80.08 264	66.45 117	86.83 239	86.50 265	67.62 222	53.78 283	71.61 300	53.51 175	89.87 270	62.89 203	50.50 308	81.14 284

v748		70.55 239	67.97 242	78.27 241	75.75 299	58.78 253	86.29 244	89.25 213	65.12 240	56.66 267	77.17 267	45.05 243	92.95 204	58.13 229	58.33 282	83.10 262

TransMVSNet (Re)		70.07 242	67.66 243	77.31 256	80.62 249	59.13 250	91.78 128	84.94 280	65.97 231	60.08 247	80.44 238	50.78 195	91.87 236	48.84 260	45.46 317	80.94 287

tfpnnormal		70.10 241	67.36 244	78.32 239	83.45 217	60.97 217	88.85 203	92.77 90	64.85 241	60.83 244	78.53 256	43.52 249	93.48 195	31.73 321	61.70 265	80.52 292

DTE-MVSNet		68.46 255	67.33 245	71.87 294	77.94 287	49.00 305	86.16 245	88.58 234	66.36 229	58.19 258	82.21 210	46.36 233	83.87 304	44.97 277	55.17 290	82.73 265

testing_2		71.09 233	67.32 246	82.40 155	69.82 315	66.52 116	83.64 255	90.77 160	72.21 148	45.12 312	71.07 304	27.60 312	93.74 189	75.71 97	69.96 204	86.95 204

V4		69.80 245	67.02 247	78.15 244	71.86 307	60.10 235	82.02 269	87.39 249	64.48 242	57.78 262	75.98 273	41.49 255	92.90 209	63.00 200	59.16 276	81.44 277

v52		69.80 245	67.01 248	78.15 244	71.84 308	60.10 235	82.02 269	87.39 249	64.48 242	57.80 261	75.97 274	41.47 256	92.90 209	63.00 200	59.13 277	81.45 276

DP-MVS		69.90 243	66.48 249	80.14 203	95.36 14	62.93 190	89.56 190	76.11 312	50.27 305	57.69 263	85.23 179	39.68 262	95.73 110	33.35 315	71.05 203	81.78 272

LS3D		69.17 250	66.40 250	77.50 251	91.92 77	56.12 273	85.12 247	80.37 305	46.96 312	56.50 268	87.51 155	37.25 278	93.71 190	32.52 320	79.40 138	82.68 267

Anonymous20231206		67.53 261	65.78 251	72.79 284	74.95 300	47.59 309	88.23 213	87.32 252	61.75 265	58.07 259	77.29 264	37.79 275	87.29 289	42.91 281	63.71 253	83.48 254

MSDG		69.54 248	65.73 252	80.96 194	85.11 195	63.71 176	84.19 252	83.28 294	56.95 287	54.50 274	84.03 189	31.50 301	96.03 100	42.87 283	69.13 212	83.14 261

FMVSNet5		68.04 257	65.66 253	75.18 268	84.43 204	57.89 258	83.54 256	86.26 268	61.83 264	53.64 284	73.30 282	37.15 281	85.08 295	48.99 259	61.77 262	82.56 268

XVG-ACMP-BASELINE		68.04 257	65.53 254	75.56 266	74.06 303	52.37 288	78.43 296	85.88 275	62.03 260	58.91 255	81.21 230	20.38 324	91.15 250	60.69 217	68.18 218	83.16 260

EG-PatchMatch MVS		68.55 253	65.41 255	77.96 247	78.69 281	62.93 190	89.86 188	89.17 216	60.55 269	50.27 297	77.73 261	22.60 321	94.06 175	47.18 267	72.65 192	76.88 312

PatchT		69.11 251	65.37 256	80.32 200	82.07 228	63.68 178	67.96 320	87.62 248	50.86 304	69.37 174	65.18 314	57.09 117	88.53 281	41.59 288	66.60 228	88.74 171

RPMNet		69.58 247	65.21 257	82.70 139	83.15 220	67.96 63	70.25 311	86.15 270	46.83 314	69.97 164	65.10 315	56.48 132	89.48 273	35.79 310	73.13 186	90.64 153

ACMH	63.93 17	68.62 252	64.81 258	80.03 207	85.22 193	63.25 183	87.72 224	84.66 281	60.83 268	51.57 292	79.43 252	27.29 313	94.96 132	41.76 286	64.84 244	81.88 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		67.57 260	64.76 259	76.00 265	72.82 306	53.37 285	88.71 205	86.78 256	53.19 297	57.58 264	78.03 259	35.33 288	92.41 223	55.56 237	54.88 292	82.21 269

ACMH+	65.35 16	67.65 259	64.55 260	76.96 258	84.59 200	57.10 269	88.08 215	80.79 303	58.59 281	53.00 287	81.09 232	26.63 315	92.95 204	46.51 268	61.69 266	80.82 288

USDC		67.43 263	64.51 261	76.19 263	77.94 287	55.29 277	78.38 297	85.00 279	73.17 129	48.36 302	80.37 239	21.23 323	92.48 222	52.15 249	64.02 251	80.81 289

Patchmatch-RL test		68.17 256	64.49 262	79.19 230	71.22 310	53.93 283	70.07 313	71.54 328	69.22 200	56.79 266	62.89 318	56.58 130	88.61 278	69.53 145	52.61 296	95.03 54

CMPMVS	48.56 21	66.77 265	64.41 263	73.84 276	70.65 313	50.31 299	77.79 301	85.73 277	45.54 317	44.76 313	82.14 211	35.40 287	90.14 261	63.18 198	74.54 177	81.07 286
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		68.54 254	64.38 264	81.03 193	88.06 157	66.90 91	68.01 318	84.02 286	57.57 282	64.48 222	69.87 305	38.68 263	89.21 276	40.87 290	67.89 221	86.97 202

Patchmtry		67.53 261	63.93 265	78.34 238	82.12 227	64.38 160	68.72 315	84.00 287	48.23 311	59.24 250	72.41 290	57.82 112	89.27 275	46.10 270	56.68 287	81.36 279

LTVRE_ROB	59.60 19	66.27 267	63.54 266	74.45 272	84.00 211	51.55 292	67.08 321	83.53 290	58.78 279	54.94 272	80.31 240	34.54 291	93.23 199	40.64 292	68.03 219	78.58 307
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
ADS-MVSNet2		66.90 264	63.44 267	77.26 257	88.06 157	60.70 224	68.01 318	75.56 317	57.57 282	64.48 222	69.87 305	38.68 263	84.10 299	40.87 290	67.89 221	86.97 202

test2356		64.16 277	63.28 268	66.81 305	69.37 318	39.86 326	87.76 223	86.02 272	59.83 274	53.54 285	73.23 283	34.94 289	80.67 319	39.66 294	65.20 237	79.89 297

UnsupCasMVSNet_eth		65.79 270	63.10 269	73.88 275	70.71 312	50.29 300	81.09 277	89.88 193	72.58 139	49.25 300	74.77 280	32.57 296	87.43 288	55.96 236	41.04 323	83.90 249

EU-MVSNet		64.01 278	63.01 270	67.02 304	74.40 302	38.86 328	83.27 261	86.19 269	45.11 318	54.27 277	81.15 231	36.91 284	80.01 320	48.79 261	57.02 284	82.19 270

OpenMVS_ROB	61.12 18	66.39 266	62.92 271	76.80 260	76.51 293	57.77 260	89.22 196	83.41 292	55.48 294	53.86 282	77.84 260	26.28 316	93.95 183	34.90 312	68.76 214	78.68 306

testgi		64.48 275	62.87 272	69.31 298	71.24 309	40.62 323	85.49 246	79.92 306	65.36 237	54.18 279	83.49 195	23.74 319	84.55 297	41.60 287	60.79 272	82.77 264

test20.03		63.83 279	62.65 273	67.38 303	70.58 314	39.94 324	86.57 242	84.17 284	63.29 251	51.86 290	77.30 263	37.09 282	82.47 312	38.87 302	54.13 294	79.73 299

JIA-IIPM		66.06 268	62.45 274	76.88 259	81.42 234	54.45 282	57.49 332	88.67 230	49.36 307	63.86 227	46.86 328	56.06 136	90.25 255	49.53 258	68.83 213	85.95 224

pmmvs-eth3d		65.53 271	62.32 275	75.19 267	69.39 317	59.59 242	82.80 266	83.43 291	62.52 258	51.30 294	72.49 287	32.86 293	87.16 290	55.32 238	50.73 303	78.83 305

OurMVSNet-221017-0		64.68 273	62.17 276	72.21 289	76.08 297	47.35 310	80.67 280	81.02 302	56.19 291	51.60 291	79.66 249	27.05 314	88.56 280	53.60 245	53.63 295	80.71 290

RPSCF		64.24 276	61.98 277	71.01 295	76.10 296	45.00 313	75.83 304	75.94 314	46.94 313	58.96 254	84.59 186	31.40 302	82.00 316	47.76 265	60.33 274	86.04 223

SixPastTwentyTwo		64.92 272	61.78 278	74.34 274	78.74 280	49.76 301	83.42 260	79.51 308	62.86 255	50.27 297	77.35 262	30.92 305	90.49 253	45.89 271	47.06 314	82.78 263

test_0402		64.54 274	61.09 279	74.92 269	84.10 210	60.75 222	87.95 217	79.71 307	52.03 300	52.41 288	77.20 265	32.21 298	91.64 240	23.14 331	61.03 269	72.36 319

Patchmatch-test		65.86 269	60.94 280	80.62 197	83.75 212	58.83 252	58.91 331	75.26 319	44.50 321	50.95 296	77.09 269	58.81 106	87.90 285	35.13 311	64.03 250	95.12 49

MDA-MVSNet_test_wron		63.78 280	60.16 281	74.64 270	78.15 285	60.41 228	83.49 257	84.03 285	56.17 293	39.17 325	71.59 302	37.22 279	83.24 310	42.87 283	48.73 311	80.26 295

YYNet1		63.76 281	60.14 282	74.62 271	78.06 286	60.19 234	83.46 259	83.99 289	56.18 292	39.25 324	71.56 303	37.18 280	83.34 308	42.90 282	48.70 312	80.32 294

COLMAP_ROB	57.96 20	62.98 283	59.65 283	72.98 282	81.44 233	53.00 287	83.75 254	75.53 318	48.34 310	48.81 301	81.40 224	24.14 317	90.30 254	32.95 317	60.52 273	75.65 315
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
K. test v3		63.09 282	59.61 284	73.53 278	76.26 295	49.38 304	83.27 261	77.15 311	64.35 244	47.77 303	72.32 292	28.73 308	87.79 287	49.93 257	36.69 328	83.41 256

AllTest		61.66 284	58.06 285	72.46 286	79.57 268	51.42 294	80.17 286	68.61 331	51.25 302	45.88 307	81.23 226	19.86 325	86.58 292	38.98 296	57.01 285	79.39 301

UnsupCasMVSNet_bld		61.60 285	57.71 286	73.29 280	68.73 319	51.64 291	78.61 295	89.05 223	57.20 286	46.11 306	61.96 320	28.70 309	88.60 279	50.08 256	38.90 326	79.63 300

MDA-MVSNet-bldmvs		61.54 286	57.70 287	73.05 281	79.53 270	57.00 270	83.08 264	81.23 300	57.57 282	34.91 328	72.45 289	32.79 294	86.26 294	35.81 309	41.95 321	75.89 314

testus		59.36 291	57.51 288	64.90 308	66.72 320	37.56 329	84.98 248	81.09 301	57.46 285	47.72 304	72.76 284	11.43 336	78.78 326	36.56 305	58.91 279	78.36 309

MIMVSNet1		60.16 289	57.33 289	68.67 299	69.71 316	44.13 315	78.92 294	84.21 283	55.05 295	44.63 314	71.85 296	23.91 318	81.54 318	32.63 319	55.03 291	80.35 293

testpf		57.17 293	56.93 290	57.88 315	79.13 277	42.40 317	34.23 338	85.97 274	52.64 298	47.66 305	66.50 309	36.33 286	79.65 322	53.60 245	56.31 288	51.60 332

PM-MVS		59.40 290	56.59 291	67.84 300	63.63 323	41.86 320	76.76 302	63.22 337	59.01 278	51.07 295	72.27 293	11.72 334	83.25 309	61.34 213	50.28 309	78.39 308

new-patchmatchnet		59.30 292	56.48 292	67.79 301	65.86 321	44.19 314	82.47 267	81.77 298	59.94 273	43.65 318	66.20 311	27.67 311	81.68 317	39.34 295	41.40 322	77.50 311

TinyColmap		60.32 287	56.42 293	72.00 292	78.78 279	53.18 286	78.36 298	75.64 315	52.30 299	41.59 323	75.82 276	14.76 332	88.35 282	35.84 308	54.71 293	74.46 316

MVS-HIRNet		60.25 288	55.55 294	74.35 273	84.37 205	56.57 271	71.64 309	74.11 321	34.44 330	45.54 311	42.24 332	31.11 304	89.81 271	40.36 293	76.10 168	76.67 313

1111		56.66 296	54.98 295	61.69 311	61.99 327	31.38 333	79.81 291	83.17 295	45.66 315	41.94 320	65.44 312	41.50 253	79.56 323	27.64 325	47.68 313	74.14 317

DSMNet-mixed		56.78 294	54.44 296	63.79 310	63.21 324	29.44 337	64.43 324	64.10 336	42.12 326	51.32 293	71.60 301	31.76 299	75.04 329	36.23 307	65.20 237	86.87 206

test1235678		55.73 297	52.74 297	64.68 309	60.16 330	35.56 331	81.65 273	81.46 299	51.27 301	38.93 326	62.82 319	17.44 327	78.58 327	30.87 323	50.09 310	79.89 297

LF4IMVS		54.01 300	52.12 298	59.69 313	62.41 326	39.91 325	68.59 316	68.28 333	42.96 324	44.55 315	75.18 277	14.09 333	68.39 333	41.36 289	51.68 300	70.78 322

LP		56.71 295	51.64 299	71.91 293	80.08 264	60.33 230	61.72 326	75.61 316	43.87 323	43.76 317	60.30 322	30.46 306	84.05 300	22.94 332	46.06 316	71.34 321

TDRefinement		55.28 299	51.58 300	66.39 306	59.53 331	46.15 312	76.23 303	72.80 323	44.60 320	42.49 319	76.28 272	15.29 330	82.39 313	33.20 316	43.75 319	70.62 323

pmmvs3		55.51 298	51.50 301	67.53 302	57.90 332	50.93 297	80.37 282	73.66 322	40.63 327	44.15 316	64.75 316	16.30 328	78.97 325	44.77 278	40.98 324	72.69 318

.test1245		46.52 306	49.68 302	37.02 328	61.99 327	31.38 333	79.81 291	83.17 295	45.66 315	41.94 320	65.44 312	41.50 253	79.56 323	27.64 325	0.01 345	0.13 344

Anonymous20231211		53.57 301	49.43 303	66.00 307	65.01 322	42.08 318	80.95 279	72.60 324	38.46 328	41.65 322	64.48 317	15.72 329	84.23 298	25.78 328	40.24 325	71.68 320

N_pmnet		50.55 302	49.11 304	54.88 319	77.17 291	4.02 349	84.36 251	2.00 350	48.59 308	45.86 309	68.82 307	32.22 297	82.80 311	31.58 322	51.38 301	77.81 310

new_pmnet		49.31 303	46.44 305	57.93 314	62.84 325	40.74 322	68.47 317	62.96 338	36.48 329	35.09 327	57.81 324	14.97 331	72.18 330	32.86 318	46.44 315	60.88 330

test12356		47.51 304	44.82 306	55.56 317	52.53 333	21.09 344	71.45 310	76.03 313	44.14 322	30.69 329	58.18 323	9.01 340	76.14 328	26.95 327	34.43 331	69.46 325

testmv		46.98 305	43.53 307	57.35 316	47.75 338	30.41 336	74.99 306	77.69 309	42.84 325	28.03 330	53.36 325	8.18 341	71.18 331	24.36 330	34.55 329	70.46 324

FPMVS		45.64 307	43.10 308	53.23 321	51.42 335	36.46 330	64.97 323	71.91 326	29.13 332	27.53 331	61.55 321	9.83 338	65.01 337	16.00 337	55.58 289	58.22 331

no-one		44.13 308	38.39 309	61.34 312	45.91 340	41.94 319	61.67 327	75.07 320	45.05 319	20.07 334	40.68 335	11.58 335	79.82 321	30.18 324	15.30 336	62.26 329

LCM-MVSNet		40.54 309	35.79 310	54.76 320	36.92 343	30.81 335	51.41 333	69.02 330	22.07 334	24.63 332	45.37 330	4.56 346	65.81 335	33.67 314	34.50 330	67.67 326

ANet_high		40.27 310	35.20 311	55.47 318	34.74 344	34.47 332	63.84 325	71.56 327	48.42 309	18.80 336	41.08 333	9.52 339	64.45 338	20.18 334	8.66 343	67.49 327

PMMVS2		37.93 311	33.61 312	50.92 322	46.31 339	24.76 342	60.55 330	50.05 341	28.94 333	20.93 333	47.59 327	4.41 347	65.13 336	25.14 329	18.55 335	62.87 328

pcd1.5k->3k		31.17 315	31.85 313	29.12 330	81.48 231	0.00 352	0.00 342	91.79 127	0.00 346	0.00 348	0.00 348	41.05 259	0.00 349	0.00 346	72.34 195	87.36 196

Gipuma		34.91 312	31.44 314	45.30 324	70.99 311	39.64 327	19.85 341	72.56 325	20.10 337	16.16 338	21.47 340	5.08 345	71.16 332	13.07 338	43.70 320	25.08 338
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		32.77 313	29.98 315	41.11 326	48.05 336	29.17 338	65.82 322	50.02 342	21.42 335	14.74 339	37.19 336	1.11 350	55.11 340	19.75 335	11.77 338	39.06 334

PMVS	26.43 22	31.84 314	28.16 316	42.89 325	25.87 347	27.58 340	50.92 334	49.78 343	21.37 336	14.17 340	40.81 334	2.01 348	66.62 334	9.61 340	38.88 327	34.49 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
cdsmvs_eth3d_5k		19.86 321	26.47 317	0.00 337	0.00 351	0.00 352	0.00 342	93.45 61	0.00 346	0.00 348	95.27 30	49.56 206	0.00 349	0.00 346	0.00 347	0.00 346

E-PMN		24.61 317	24.00 318	26.45 331	43.74 341	18.44 346	60.86 328	39.66 344	15.11 338	9.53 342	22.10 339	6.52 343	46.94 342	8.31 341	10.14 339	13.98 340

tmp_tt		22.26 320	23.75 319	17.80 333	5.23 348	12.06 348	35.26 337	39.48 345	2.82 343	18.94 335	44.20 331	22.23 322	24.64 345	36.30 306	9.31 342	16.69 339

EMVS		23.76 319	23.20 320	25.46 332	41.52 342	16.90 347	60.56 329	38.79 347	14.62 339	8.99 343	20.24 343	7.35 342	45.82 343	7.25 342	9.46 341	13.64 341

wuykxyi23d		29.03 316	23.09 321	46.84 323	31.67 346	28.82 339	43.46 336	57.72 340	14.39 340	7.52 344	20.84 341	0.64 351	60.29 339	21.57 333	10.04 340	51.40 333

MVE	24.84 23	24.35 318	19.77 322	38.09 327	34.56 345	26.92 341	26.57 339	38.87 346	11.73 341	11.37 341	27.44 337	1.37 349	50.42 341	11.41 339	14.60 337	36.93 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 322	10.95 323	12.33 334	48.05 336	19.89 345	25.89 340	1.92 351	3.58 342	3.12 345	1.37 345	0.64 351	15.77 346	6.23 343	7.77 344	1.35 342

ab-mvs-re		7.91 323	10.55 324	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	94.95 40	0.00 355	0.00 349	0.00 346	0.00 347	0.00 346

testmvs		7.23 324	9.62 325	0.06 336	0.04 349	0.02 351	84.98 248	0.02 352	0.03 344	0.18 346	1.21 346	0.01 354	0.02 347	0.14 344	0.01 345	0.13 344

test123		6.92 325	9.21 326	0.08 335	0.03 350	0.05 350	81.65 273	0.01 353	0.02 345	0.14 347	0.85 347	0.03 353	0.02 347	0.12 345	0.00 347	0.16 343

pcd_1.5k_mvsjas		4.46 326	5.95 327	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	53.55 172	0.00 349	0.00 346	0.00 347	0.00 346

sosnet-low-res		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 347	0.00 346

sosnet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 347	0.00 346

uncertanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 347	0.00 346

Regformer		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 347	0.00 346

uanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 347	0.00 346

test_part2					96.29 7	68.16 60				90.78 3

test_part1								94.26 41				77.03 4			95.18 8	96.11 19

test11111								94.66 29

sam_mvs1												57.85 111

sam_mvs												54.91 155

semantic-postprocess				76.32 262	81.48 231	60.67 225		85.99 273	66.17 230	59.50 249	78.88 254	45.51 239	83.65 305	62.58 207	61.93 260	84.63 245

ambc				69.61 297	61.38 329	41.35 321	49.07 335	85.86 276		50.18 299	66.40 310	10.16 337	88.14 284	45.73 272	44.20 318	79.32 303

MTGPA								92.23 107

test_post1							78.95 293				20.70 342	53.05 178	91.50 247	60.43 218

test_post											23.01 338	56.49 131	92.67 216

patchmatchnet-post											67.62 308	57.62 114	90.25 255

GG-mvs-BLEND				86.53 49	91.91 78	69.67 34	75.02 305	94.75 26		78.67 82	90.85 112	77.91 2	94.56 145	72.25 119	93.74 30	95.36 35

MTMP								32.52 348

gm-plane-assit					88.42 151	67.04 86			78.62 52		91.83 102		97.37 47	76.57 93

test9_res														89.41 10	94.96 9	95.29 39

TEST9					94.18 28	67.28 78	94.16 36	93.51 58	71.75 165	85.52 27	95.33 26	68.01 24	97.27 54

test_8					94.19 27	67.19 80	94.15 38	93.42 63	71.87 157	85.38 29	95.35 25	68.19 22	96.95 74

agg_prior2														86.41 31	94.75 17	95.33 36

agg_prior					94.16 32	66.97 87		93.31 68		84.49 36			96.75 83

TestCases				72.46 286	79.57 268	51.42 294		68.61 331	51.25 302	45.88 307	81.23 226	19.86 325	86.58 292	38.98 296	57.01 285	79.39 301

test_prior4						67.18 82	93.92 52

test_prior2							95.10 27		75.40 89	85.25 31	95.61 21	67.94 25		87.47 23	94.77 14

test_prior				86.42 53	94.71 22	67.35 76		93.10 80					96.84 79			95.05 51

旧先验2							92.00 112		59.37 277	87.54 15			93.47 196	75.39 99

新几何2							91.41 140

新几何1				84.73 101	92.32 66	64.28 165		91.46 140	59.56 275	79.77 68	92.90 86	56.95 122	96.57 88	63.40 195	92.91 40	93.34 104

旧先验1					91.94 75	60.74 223		91.50 138			94.36 55	65.23 49			91.84 52	94.55 65

无先验							92.71 85	92.61 98	62.03 260				97.01 65	66.63 166		93.97 91

原ACMM2							92.01 110

原ACMM1				84.42 110	93.21 48	64.27 166		93.40 64	65.39 236	79.51 71	92.50 91	58.11 110	96.69 85	65.27 182	93.96 24	92.32 131

test222					89.77 122	61.60 212	89.55 191	89.42 208	56.83 289	77.28 94	92.43 94	52.76 181			91.14 64	93.09 113

testdata2													96.09 97	61.26 214

segment_acmp												65.94 43

testdata				81.34 181	89.02 137	57.72 261		89.84 194	58.65 280	85.32 30	94.09 64	57.03 119	93.28 198	69.34 147	90.56 70	93.03 115

testdata1							89.21 197		77.55 63

test12				87.09 34	94.60 24	68.86 44		92.91 86		82.67 49		65.44 48	97.55 41		93.69 32	94.84 59

plane_prior7					86.94 172	61.51 213

plane_prior6					87.23 168	62.32 201						50.66 196

plane_prior5								91.31 145					95.55 120	76.74 91	78.53 147	88.39 177

plane_prior4											89.14 133

plane_prior3						61.95 209			79.09 44	72.53 134

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 169

plane_prior						62.42 199	93.85 55		79.38 37						78.80 145

n2								0.00 354

nn								0.00 354

door-mid								66.01 335

lessismore_v0				73.72 277	72.93 305	47.83 308		61.72 339		45.86 309	73.76 281	28.63 310	89.81 271	47.75 266	31.37 332	83.53 252

LGP-MVS_train				79.56 219	84.31 206	59.37 246		89.73 199	69.49 197	64.86 218	88.42 138	38.65 265	94.30 161	72.56 116	72.76 190	85.01 240

test11								93.01 82

door								66.57 334

HQP5-MVS						63.66 179

HQP-NCC					87.54 164		94.06 43		79.80 33	74.18 116

ACMP_Plane					87.54 164		94.06 43		79.80 33	74.18 116

BP-MVS														77.63 88

HQP4-MVS										74.18 116			95.61 116			88.63 172

HQP3-MVS								91.70 131							78.90 143

HQP2-MVS												51.63 192

NP-MVS					87.41 167	63.04 188					90.30 121

MDTV_nov1_ep13_2view						59.90 239	80.13 287		67.65 220	72.79 129		54.33 163		59.83 221		92.58 126

ACMMP++_ref															71.63 197

ACMMP++															69.72 206

Test By Simon												54.21 165

ITE_SJBPF				70.43 296	74.44 301	47.06 311		77.32 310	60.16 272	54.04 280	83.53 193	23.30 320	84.01 302	43.07 280	61.58 267	80.21 296

DeepMVS_CX				34.71 329	51.45 334	24.73 343		28.48 349	31.46 331	17.49 337	52.75 326	5.80 344	42.60 344	18.18 336	19.42 334	36.81 336

High-resolution multi-view benchmark