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.

SMA-MVS		80.28 4	80.39 5	79.95 3	86.60 16	61.95 21	86.33 8	85.75 15	62.49 56	82.20 8	92.28 1	56.53 22	89.70 8	79.85 3	91.48 1	88.19 9

test_0728_THIRD									65.04 19	83.82 2	92.00 3	64.69 4	90.75 2	79.48 4	90.63 2	88.09 11

DVP-MVS		80.84 2	81.64 1	78.42 28	87.75 4	59.07 58	87.85 3	85.03 24	64.26 29	83.82 2	92.00 3	64.82 2	90.75 2	78.66 9	90.61 3	85.45 93

test_0728_SECOND				79.19 9	87.82 3	59.11 57	87.85 3	87.15 2					90.84 1	78.66 9	90.61 3	87.62 23

ACMMP_NAP		78.77 10	78.78 10	78.74 23	85.44 38	61.04 34	83.84 39	85.16 21	62.88 47	78.10 16	91.26 8	52.51 61	88.39 20	79.34 5	90.52 5	86.78 47

HPM-MVS++		79.88 6	80.14 6	79.10 14	88.17 1	64.80 1	86.59 7	83.70 51	65.37 15	78.78 13	90.64 12	58.63 15	87.24 40	79.00 6	90.37 6	85.26 102

CNVR-MVS		79.84 7	79.97 7	79.45 6	87.90 2	62.17 20	84.37 29	85.03 24	66.96 5	77.58 19	90.06 29	59.47 12	89.13 13	78.67 8	89.73 7	87.03 40

PHI-MVS		75.87 41	75.36 41	77.41 41	80.62 95	55.91 104	84.28 30	85.78 14	56.08 159	73.41 55	86.58 77	50.94 79	88.54 18	70.79 48	89.71 8	87.79 18

DPE-MVS		80.56 3	80.98 3	79.29 8	87.27 8	60.56 41	85.71 21	86.42 9	63.28 39	83.27 6	91.83 6	64.96 1	90.47 4	76.41 18	89.67 9	86.84 44

MP-MVS-pluss		78.35 15	78.46 12	78.03 35	84.96 46	59.52 50	82.93 50	85.39 17	62.15 61	76.41 23	91.51 7	52.47 63	86.78 53	80.66 2	89.64 10	87.80 17
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
9.14			78.75 11		83.10 62		84.15 34	88.26 1	59.90 99	78.57 15	90.36 21	57.51 19	86.86 50	77.39 12	89.52 11

DeepC-MVS	69.38 2	78.56 12	78.14 17	79.83 4	83.60 58	61.62 25	84.17 33	86.85 4	63.23 40	73.84 50	90.25 27	57.68 17	89.96 6	74.62 25	89.03 12	87.89 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		79.48 8	79.31 8	79.98 2	83.01 65	62.18 19	87.60 5	85.83 13	66.69 10	78.03 17	90.98 9	54.26 43	90.06 5	78.42 11	89.02 13	87.69 20
Skip Steuart: Steuart Systems R&D Blog.
test_prior3		76.89 32	76.96 27	76.69 50	84.20 55	57.27 80	81.75 69	84.88 27	60.37 88	75.01 30	89.06 43	56.22 26	86.43 66	72.19 39	88.96 14	86.38 52

test_prior2							81.75 69		60.37 88	75.01 30	89.06 43	56.22 26		72.19 39	88.96 14

DPM-MVS		75.47 44	75.00 43	76.88 47	81.38 82	59.16 55	79.94 94	85.71 16	56.59 149	72.46 68	86.76 67	56.89 20	87.86 33	66.36 74	88.91 16	83.64 149

DeepPCF-MVS	69.58 1	79.03 9	79.00 9	79.13 12	84.92 50	60.32 43	83.03 48	85.33 19	62.86 48	80.17 9	90.03 30	61.76 6	88.95 15	74.21 26	88.67 17	88.12 10

MSP-MVS		81.06 1	81.40 2	80.02 1	86.21 23	62.73 12	86.09 12	86.83 5	65.51 14	83.81 4	90.51 17	63.71 5	89.23 11	81.51 1	88.44 18	88.09 11

CDPH-MVS		76.31 36	75.67 40	78.22 32	85.35 41	59.14 56	81.31 78	84.02 40	56.32 153	74.05 43	88.98 46	53.34 55	87.92 31	69.23 55	88.42 19	87.59 24

GST-MVS		78.14 17	77.85 19	78.99 19	86.05 30	61.82 24	85.84 16	85.21 20	63.56 38	74.29 42	90.03 30	52.56 60	88.53 19	74.79 24	88.34 20	86.63 49

train_agg		76.27 37	76.15 34	76.64 53	85.58 36	61.59 26	81.62 72	81.26 101	55.86 161	74.93 32	88.81 49	53.70 51	84.68 102	75.24 22	88.33 21	83.65 148

APDe-MVS		80.16 5	80.59 4	78.86 22	86.64 15	60.02 45	88.12 1	86.42 9	62.94 45	82.40 7	92.12 2	59.64 10	89.76 7	78.70 7	88.32 22	86.79 46

test9_res														75.28 21	88.31 23	83.81 140

zzz-MVS		77.61 24	77.36 24	78.35 29	86.08 28	63.57 2	83.37 44	80.97 110	65.13 17	75.77 25	90.88 10	48.63 99	86.66 56	77.23 13	88.17 24	84.81 114

MTAPA		76.90 31	76.42 33	78.35 29	86.08 28	63.57 2	74.92 186	80.97 110	65.13 17	75.77 25	90.88 10	48.63 99	86.66 56	77.23 13	88.17 24	84.81 114

test12				77.76 38	84.52 52	58.41 67		83.36 61		72.93 62		54.61 40	88.05 28		88.12 26	86.81 45

MP-MVS		78.35 15	78.26 16	78.64 24	86.54 18	63.47 5	86.02 15	83.55 54	63.89 34	73.60 53	90.60 14	54.85 38	86.72 54	77.20 15	88.06 27	85.74 82
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	68.24 3	77.25 27	76.63 31	79.12 13	86.15 26	60.86 36	84.71 27	84.85 29	61.98 68	73.06 60	88.88 48	53.72 50	89.06 14	68.27 58	88.04 28	87.42 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
原ACMM1				74.69 82	85.39 40	59.40 51		83.42 58	51.47 213	70.27 87	86.61 75	48.61 101	86.51 64	53.85 160	87.96 29	78.16 229

agg_prior2														73.09 36	87.93 30	84.33 126

agg_prior1		75.94 40	76.01 36	75.72 65	85.04 43	59.96 46	81.44 76	81.04 107	56.14 158	74.68 36	88.90 47	53.91 47	84.04 114	75.01 23	87.92 31	83.16 160

CSCG		76.92 30	76.75 29	77.41 41	83.96 57	59.60 49	82.95 49	86.50 8	60.78 81	75.27 28	84.83 105	60.76 7	86.56 61	67.86 62	87.87 32	86.06 69

save filter2										77.75 18	90.63 13	52.59 59	83.03 136	76.83 16	87.61 33	83.44 150

MCST-MVS		77.48 25	77.45 22	77.54 39	86.67 14	58.36 68	83.22 46	86.93 3	56.91 141	74.91 34	88.19 53	59.15 13	87.68 36	73.67 32	87.45 34	86.57 50

NCCC		78.58 11	78.31 14	79.39 7	87.51 7	62.61 16	85.20 26	84.42 33	66.73 9	74.67 38	89.38 40	55.30 33	89.18 12	74.19 27	87.34 35	86.38 52

testtj		78.47 13	78.43 13	78.61 25	86.82 9	60.67 39	86.07 13	85.38 18	62.12 62	78.65 14	90.29 25	55.76 29	89.31 10	73.55 34	87.22 36	85.84 74

HFP-MVS		78.01 19	77.65 20	79.10 14	86.71 12	62.81 10	86.29 9	84.32 35	62.82 49	73.96 45	90.50 18	53.20 56	88.35 21	74.02 28	87.05 37	86.13 66

#test#		77.83 20	77.41 23	79.10 14	86.71 12	62.81 10	85.69 22	84.32 35	61.61 71	73.96 45	90.50 18	53.20 56	88.35 21	73.68 31	87.05 37	86.13 66

region2R		77.67 23	77.18 26	79.15 11	86.76 10	62.95 8	86.29 9	84.16 39	62.81 51	73.30 56	90.58 15	49.90 84	88.21 25	73.78 30	87.03 39	86.29 64

ACMMPR		77.71 21	77.23 25	79.16 10	86.75 11	62.93 9	86.29 9	84.24 37	62.82 49	73.55 54	90.56 16	49.80 86	88.24 24	74.02 28	87.03 39	86.32 61

APD-MVS		78.02 18	78.04 18	77.98 36	86.44 20	60.81 37	85.52 23	84.36 34	60.61 83	79.05 12	90.30 24	55.54 32	88.32 23	73.48 35	87.03 39	84.83 113
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		76.77 33	76.06 35	78.88 21	86.14 27	62.73 12	82.55 58	83.74 50	61.71 69	72.45 70	90.34 23	48.48 103	88.13 26	72.32 38	86.85 42	85.78 76

HPM-MVS		77.28 26	76.85 28	78.54 26	85.00 45	60.81 37	82.91 51	85.08 22	62.57 54	73.09 59	89.97 33	50.90 80	87.48 38	75.30 20	86.85 42	87.33 35
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		78.44 14	78.28 15	78.90 20	84.96 46	61.41 28	84.03 36	83.82 49	59.34 111	79.37 11	89.76 36	59.84 8	87.62 37	76.69 17	86.74 44	87.68 21
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVS		77.17 28	76.56 32	79.00 17	86.32 21	62.62 14	85.83 17	83.92 43	64.55 23	72.17 71	90.01 32	47.95 107	88.01 29	71.55 44	86.74 44	86.37 56

X-MVStestdata		70.21 108	67.28 148	79.00 17	86.32 21	62.62 14	85.83 17	83.92 43	64.55 23	72.17 71	6.49 332	47.95 107	88.01 29	71.55 44	86.74 44	86.37 56

3Dnovator+	66.72 4	75.84 42	74.57 49	79.66 5	82.40 69	59.92 48	85.83 17	86.32 11	66.92 8	67.80 134	89.24 42	42.03 165	89.38 9	64.07 93	86.50 47	89.69 1

EPNet		73.09 68	72.16 71	75.90 61	75.95 192	56.28 95	83.05 47	72.39 224	66.53 12	65.27 167	87.00 65	50.40 82	85.47 88	62.48 108	86.32 48	85.94 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		74.76 49	74.46 50	75.65 68	77.84 154	52.25 152	75.59 170	84.17 38	63.76 35	73.15 58	82.79 140	59.58 11	86.80 51	67.24 68	86.04 49	87.89 14
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
CP-MVS		77.12 29	76.68 30	78.43 27	86.05 30	63.18 7	87.55 6	83.45 57	62.44 58	72.68 64	90.50 18	48.18 105	87.34 39	73.59 33	85.71 50	84.76 118

mPP-MVS		76.54 34	75.93 37	78.34 31	86.47 19	63.50 4	85.74 20	82.28 79	62.90 46	71.77 74	90.26 26	46.61 126	86.55 62	71.71 42	85.66 51	84.97 110

MSLP-MVS++		73.77 63	73.47 61	74.66 84	83.02 64	59.29 54	82.30 65	81.88 85	59.34 111	71.59 77	86.83 66	45.94 130	83.65 124	65.09 85	85.22 52	81.06 196

SD-MVS		77.70 22	77.62 21	77.93 37	84.47 53	61.88 23	84.55 28	83.87 47	60.37 88	79.89 10	89.38 40	54.97 35	85.58 82	76.12 19	84.94 53	86.33 59
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
3Dnovator	64.47 5	72.49 74	71.39 80	75.79 62	77.70 156	58.99 62	80.66 85	83.15 68	62.24 60	65.46 164	86.59 76	42.38 163	85.52 84	59.59 128	84.72 54	82.85 166

CS-MVS		74.18 58	73.60 58	75.92 60	78.99 123	52.53 146	80.61 86	85.93 12	61.17 77	71.15 79	79.34 217	54.52 41	88.86 17	66.07 75	84.67 55	86.38 52

CANet		76.46 35	75.93 37	78.06 34	81.29 83	57.53 77	82.35 60	83.31 63	67.78 3	70.09 88	86.34 82	54.92 36	88.90 16	72.68 37	84.55 56	87.76 19

LFMVS		71.78 85	71.59 76	72.32 144	83.40 60	46.38 222	79.75 99	71.08 229	64.18 31	72.80 63	88.64 52	42.58 160	83.72 122	57.41 134	84.49 57	86.86 43

TSAR-MVS + GP.		74.90 47	74.15 53	77.17 45	82.00 72	58.77 64	81.80 68	78.57 150	58.58 120	74.32 41	84.51 114	55.94 28	87.22 41	67.11 69	84.48 58	85.52 89

MAR-MVS		71.51 89	70.15 99	75.60 70	81.84 74	59.39 52	81.38 77	82.90 73	54.90 179	68.08 126	78.70 222	47.73 109	85.51 85	51.68 178	84.17 59	81.88 181
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
API-MVS		72.17 80	71.41 79	74.45 91	81.95 73	57.22 82	84.03 36	80.38 121	59.89 102	68.40 118	82.33 152	49.64 87	87.83 34	51.87 174	84.16 60	78.30 227

IS-MVSNet		71.57 88	71.00 87	73.27 124	78.86 126	45.63 229	80.22 90	78.69 148	64.14 32	66.46 151	87.36 62	49.30 90	85.60 80	50.26 185	83.71 61	88.59 5

UA-Net		73.13 67	72.93 66	73.76 104	83.58 59	51.66 158	78.75 109	77.66 168	67.75 4	72.61 66	89.42 38	49.82 85	83.29 130	53.61 163	83.14 62	86.32 61

MG-MVS		73.96 60	73.89 55	74.16 96	85.65 34	49.69 188	81.59 74	81.29 100	61.45 72	71.05 80	88.11 54	51.77 69	87.73 35	61.05 116	83.09 63	85.05 107

OpenMVS	61.03 9	68.85 134	67.56 137	72.70 136	74.26 219	53.99 125	81.21 79	81.34 98	52.70 198	62.75 197	85.55 98	38.86 199	84.14 112	48.41 201	83.01 64	79.97 210

SR-MVS		76.13 38	75.70 39	77.40 43	85.87 32	61.20 31	85.52 23	82.19 80	59.99 98	75.10 29	90.35 22	47.66 111	86.52 63	71.64 43	82.99 65	84.47 125

VDDNet		71.81 84	71.33 82	73.26 125	82.80 67	47.60 213	78.74 110	75.27 196	59.59 108	72.94 61	89.40 39	41.51 177	83.91 119	58.75 129	82.99 65	88.26 7

MVS_111021_HR		74.02 59	73.46 62	75.69 67	83.01 65	60.63 40	77.29 139	78.40 160	61.18 76	70.58 82	85.97 90	54.18 45	84.00 118	67.52 66	82.98 67	82.45 172

EIA-MVS		74.46 53	73.84 56	76.33 56	79.27 117	55.24 116	79.22 107	85.00 26	64.97 21	72.65 65	79.46 213	53.65 54	87.87 32	67.45 67	82.91 68	85.89 73

HPM-MVS_fast		74.30 56	73.46 62	76.80 49	84.45 54	59.04 60	83.65 41	81.05 106	60.15 95	70.43 83	89.84 35	41.09 183	85.59 81	67.61 65	82.90 69	85.77 79

ACMMP		76.02 39	75.33 42	78.07 33	85.20 42	61.91 22	85.49 25	84.44 32	63.04 43	69.80 98	89.74 37	45.43 137	87.16 44	72.01 41	82.87 70	85.14 103
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
APD-MVS_3200maxsize		74.96 46	74.39 51	76.67 52	82.20 70	58.24 70	83.67 40	83.29 64	58.41 123	73.71 51	90.14 28	45.62 132	85.99 74	69.64 51	82.85 71	85.78 76

casdiffmvs		74.80 48	74.89 47	74.53 89	75.59 198	50.37 175	78.17 121	85.06 23	62.80 52	74.40 40	87.86 57	57.88 16	83.61 125	69.46 54	82.79 72	89.59 2

baseline		74.61 52	74.70 48	74.34 93	75.70 194	49.99 183	77.54 132	84.63 31	62.73 53	73.98 44	87.79 59	57.67 18	83.82 121	69.49 52	82.74 73	89.20 3

VDD-MVS		72.50 73	72.09 72	73.75 106	81.58 76	49.69 188	77.76 127	77.63 169	63.21 41	73.21 57	89.02 45	42.14 164	83.32 129	61.72 113	82.50 74	88.25 8

CLD-MVS		73.33 65	72.68 68	75.29 76	78.82 127	53.33 135	78.23 120	84.79 30	61.30 75	70.41 84	81.04 177	52.41 64	87.12 45	64.61 90	82.49 75	85.41 97
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		74.67 51	74.98 45	73.71 108	78.94 125	50.56 172	80.23 89	83.87 47	60.30 93	77.15 20	86.56 78	59.65 9	82.00 159	66.01 78	82.12 76	88.58 6

MVS		67.37 159	66.33 164	70.51 176	75.46 200	50.94 162	73.95 198	81.85 87	41.57 297	62.54 202	78.57 226	47.98 106	85.47 88	52.97 168	82.05 77	75.14 261

alignmvs		73.86 62	73.99 54	73.45 117	78.20 143	50.50 173	78.57 114	82.43 77	59.40 109	76.57 21	86.71 71	56.42 25	81.23 171	65.84 80	81.79 78	88.62 4

1121		68.53 145	67.16 153	72.63 137	85.64 35	61.14 32	73.95 198	66.46 261	44.61 273	70.28 86	86.68 72	41.42 178	80.78 181	53.62 161	81.79 78	75.97 252

新几何1				70.76 172	85.66 33	61.13 33		66.43 262	44.68 272	70.29 85	86.64 73	41.29 180	75.23 245	49.72 190	81.75 80	75.93 254

Vis-MVSNet		72.18 79	71.37 81	74.61 87	81.29 83	55.41 114	80.90 81	78.28 162	60.73 82	69.23 109	88.09 55	44.36 148	82.65 148	57.68 132	81.75 80	85.77 79
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		69.68 119	70.19 98	68.16 206	79.73 113	41.63 264	70.53 241	77.38 174	60.37 88	70.69 81	86.63 74	51.08 76	77.09 233	53.61 163	81.69 82	85.75 81

abl_6		74.34 54	73.50 59	76.86 48	82.43 68	60.16 44	83.48 43	81.86 86	58.81 117	73.95 47	89.86 34	41.87 168	86.62 58	67.98 61	81.23 83	83.80 143

OPM-MVS		74.73 50	74.25 52	76.19 57	80.81 91	59.01 61	82.60 57	83.64 52	63.74 36	72.52 67	87.49 60	47.18 117	85.88 78	69.47 53	80.78 84	83.66 147

旧先验1					83.04 63	53.15 137		67.52 253			87.85 58	44.08 149			80.76 85	78.03 234

PAPM_NR		72.63 72	71.80 74	75.13 77	81.72 75	53.42 134	79.91 96	83.28 65	59.14 113	66.31 155	85.90 91	51.86 68	86.06 71	57.45 133	80.62 86	85.91 72

Vis-MVSNet (Re-imp)		63.69 204	63.88 187	63.14 254	74.75 209	31.04 318	71.16 233	63.64 277	56.32 153	59.80 228	84.99 103	44.51 145	75.46 244	39.12 264	80.62 86	82.92 163

HQP_MVS		74.31 55	73.73 57	76.06 58	81.41 80	56.31 93	84.22 31	84.01 41	64.52 25	69.27 106	86.10 86	45.26 141	87.21 42	68.16 59	80.58 88	84.65 119

plane_prior5								84.01 41					87.21 42	68.16 59	80.58 88	84.65 119

UGNet		68.81 135	67.39 143	73.06 127	78.33 140	54.47 122	79.77 98	75.40 195	60.45 86	63.22 191	84.40 115	32.71 259	80.91 178	51.71 177	80.56 90	83.81 140
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
plane_prior						56.31 93	83.58 42		63.19 42						80.48 91

Regformer-1		75.47 44	74.93 46	77.09 46	80.43 97	57.70 75	79.50 104	82.13 81	67.84 1	75.73 27	80.75 188	56.50 23	86.07 70	71.07 47	80.38 92	87.50 27

Regformer-2		75.63 43	74.99 44	77.54 39	80.43 97	58.32 69	79.50 104	82.92 71	67.84 1	75.94 24	80.75 188	55.73 30	86.80 51	71.44 46	80.38 92	87.50 27

HQP3-MVS								83.90 45							80.35 94

HQP-MVS		73.45 64	72.80 67	75.40 72	80.66 92	54.94 117	82.31 62	83.90 45	62.10 63	67.85 129	85.54 99	45.46 135	86.93 48	67.04 70	80.35 94	84.32 127

PCF-MVS	61.88 8	70.95 94	69.49 106	75.35 73	77.63 159	55.71 106	76.04 165	81.81 88	50.30 224	69.66 99	85.40 102	52.51 61	84.89 98	51.82 175	80.24 96	85.45 93
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 82	70.73 90	76.40 55	86.57 17	57.99 72	81.15 80	82.96 70	57.03 138	66.78 146	85.56 97	44.50 146	88.11 27	51.77 176	80.23 97	83.10 161

CPTT-MVS		72.78 70	72.08 73	74.87 80	84.88 51	61.41 28	84.15 34	77.86 164	55.27 172	67.51 138	88.08 56	41.93 167	81.85 161	69.04 57	80.01 98	81.35 189

DI_MVS_plusplus_test		69.35 127	68.03 131	73.30 123	71.11 259	50.14 179	75.49 172	79.16 139	54.57 184	62.45 205	80.76 187	44.67 144	84.20 110	64.23 91	79.81 99	85.54 88

114514_t		70.83 95	69.56 104	74.64 86	86.21 23	54.63 121	82.34 61	81.81 88	48.22 243	63.01 194	85.83 93	40.92 184	87.10 46	57.91 131	79.79 100	82.18 175

test_yl		69.69 117	69.13 111	71.36 160	78.37 138	45.74 227	74.71 189	80.20 124	57.91 129	70.01 93	83.83 125	42.44 161	82.87 140	54.97 149	79.72 101	85.48 91

DCV-MVSNet		69.69 117	69.13 111	71.36 160	78.37 138	45.74 227	74.71 189	80.20 124	57.91 129	70.01 93	83.83 125	42.44 161	82.87 140	54.97 149	79.72 101	85.48 91

MVS_Test		72.45 75	72.46 70	72.42 143	74.88 206	48.50 202	76.28 158	83.14 69	59.40 109	72.46 68	84.68 107	55.66 31	81.12 172	65.98 79	79.66 103	87.63 22

PS-MVSNAJ		70.51 101	69.70 103	72.93 129	81.52 77	55.79 105	74.92 186	79.00 142	55.04 177	69.88 96	78.66 223	47.05 119	82.19 156	61.61 114	79.58 104	80.83 199

PVSNet_Blended		68.59 140	67.72 134	71.19 165	77.03 174	50.57 170	72.51 215	81.52 91	51.91 206	64.22 186	77.77 235	49.13 94	82.87 140	55.82 141	79.58 104	80.14 208

EPP-MVSNet		72.16 81	71.31 83	74.71 81	78.68 131	49.70 186	82.10 66	81.65 90	60.40 87	65.94 158	85.84 92	51.74 70	86.37 68	55.93 140	79.55 106	88.07 13

xiu_mvs_v2_base		70.52 100	69.75 101	72.84 131	81.21 86	55.63 109	75.11 180	78.92 143	54.92 178	69.96 95	79.68 210	47.00 123	82.09 158	61.60 115	79.37 107	80.81 200

MVSFormer		71.50 90	70.38 96	74.88 79	78.76 128	57.15 87	82.79 52	78.48 154	51.26 216	69.49 101	83.22 135	43.99 151	83.24 131	66.06 76	79.37 107	84.23 130

lupinMVS		69.57 122	68.28 127	73.44 118	78.76 128	57.15 87	76.57 152	73.29 219	46.19 261	69.49 101	82.18 155	43.99 151	79.23 200	64.66 88	79.37 107	83.93 135

PAPM		67.92 154	66.69 156	71.63 153	78.09 147	49.02 196	77.09 143	81.24 103	51.04 218	60.91 219	83.98 123	47.71 110	84.99 93	40.81 258	79.32 110	80.90 198

FIs		70.82 96	71.43 78	68.98 197	78.33 140	38.14 285	76.96 146	83.59 53	61.02 78	67.33 140	86.73 69	55.07 34	81.64 164	54.61 155	79.22 111	87.14 38

jason		69.65 120	68.39 126	73.43 119	78.27 142	56.88 89	77.12 142	73.71 216	46.53 258	69.34 105	83.22 135	43.37 155	79.18 201	64.77 87	79.20 112	84.23 130
jason: jason.
PAPR		71.72 87	70.82 89	74.41 92	81.20 87	51.17 160	79.55 103	83.33 62	55.81 164	66.93 145	84.61 110	50.95 78	86.06 71	55.79 143	79.20 112	86.00 70

ETV-MVS		71.78 85	70.60 91	75.30 75	79.85 109	53.54 131	77.27 140	83.26 66	57.92 128	66.49 150	79.39 214	52.07 67	86.69 55	60.05 123	79.14 114	85.66 84

Effi-MVS+		73.31 66	72.54 69	75.62 69	77.87 153	53.64 128	79.62 102	79.61 132	61.63 70	72.02 73	82.61 145	56.44 24	85.97 76	63.99 96	79.07 115	87.25 36

gg-mvs-nofinetune		57.86 251	56.43 252	62.18 260	72.62 237	35.35 302	66.57 262	56.33 306	50.65 221	57.64 249	57.10 318	30.65 270	76.36 240	37.38 272	78.88 116	74.82 268

CDS-MVSNet		66.80 170	65.37 175	71.10 168	78.98 124	53.13 139	73.27 205	71.07 230	52.15 204	64.72 177	80.23 199	43.56 154	77.10 232	45.48 225	78.88 116	83.05 162
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
AdaColmap		69.99 112	68.66 120	73.97 99	84.94 48	57.83 73	82.63 56	78.71 147	56.28 155	64.34 181	84.14 118	41.57 173	87.06 47	46.45 213	78.88 116	77.02 244

Anonymous202405211		66.84 169	65.99 167	69.40 192	80.19 104	42.21 257	71.11 235	71.31 228	58.80 118	67.90 127	86.39 81	29.83 278	79.65 193	49.60 193	78.78 119	86.33 59

CANet_DTU		68.18 150	67.71 136	69.59 188	74.83 207	46.24 224	78.66 112	76.85 180	59.60 105	63.45 190	82.09 161	35.25 229	77.41 229	59.88 125	78.76 120	85.14 103

test222					83.14 61	58.68 65	72.57 214	63.45 278	41.78 293	67.56 137	86.12 85	37.13 218			78.73 121	74.98 265

TAMVS		66.78 171	65.27 178	71.33 163	79.16 121	53.67 127	73.84 202	69.59 240	52.32 203	65.28 166	81.72 165	44.49 147	77.40 230	42.32 249	78.66 122	82.92 163

PVSNet_Blended_VisFu		71.45 91	70.39 95	74.65 85	82.01 71	58.82 63	79.93 95	80.35 123	55.09 175	65.82 162	82.16 158	49.17 93	82.64 149	60.34 121	78.62 123	82.50 171

testdata				64.66 245	81.52 77	52.93 140		65.29 268	46.09 262	73.88 49	87.46 61	38.08 208	66.26 283	53.31 166	78.48 124	74.78 269

QAPM		70.05 110	68.81 117	73.78 102	76.54 184	53.43 133	83.23 45	83.48 55	52.89 197	65.90 159	86.29 83	41.55 176	86.49 65	51.01 180	78.40 125	81.42 185

FC-MVSNet-test		69.80 115	70.58 93	67.46 211	77.61 164	34.73 305	76.05 164	83.19 67	60.84 79	65.88 160	86.46 79	54.52 41	80.76 183	52.52 170	78.12 126	86.91 41

LCM-MVSNet-Re		61.88 226	61.35 217	63.46 251	74.58 212	31.48 317	61.42 290	58.14 299	58.71 119	53.02 287	79.55 211	43.07 157	76.80 236	45.69 220	77.96 127	82.11 178

diffmvs		70.69 98	70.43 94	71.46 155	69.45 276	48.95 198	72.93 208	78.46 156	57.27 135	71.69 75	83.97 124	51.48 72	77.92 222	70.70 49	77.95 128	87.53 26

OMC-MVS		71.40 92	70.60 91	73.78 102	76.60 182	53.15 137	79.74 100	79.78 128	58.37 124	68.75 113	86.45 80	45.43 137	80.60 184	62.58 106	77.73 129	87.58 25

MVS_111021_LR		69.50 124	68.78 118	71.65 152	78.38 137	59.33 53	74.82 188	70.11 235	58.08 127	67.83 133	84.68 107	41.96 166	76.34 241	65.62 82	77.54 130	79.30 220

Fast-Effi-MVS+		70.28 107	69.12 113	73.73 107	78.50 134	51.50 159	75.01 183	79.46 136	56.16 157	68.59 114	79.55 211	53.97 46	84.05 113	53.34 165	77.53 131	85.65 85

xiu_mvs_v1_base_debu		68.58 141	67.28 148	72.48 140	78.19 144	57.19 84	75.28 175	75.09 200	51.61 208	70.04 89	81.41 171	32.79 255	79.02 208	63.81 97	77.31 132	81.22 191

xiu_mvs_v1_base		68.58 141	67.28 148	72.48 140	78.19 144	57.19 84	75.28 175	75.09 200	51.61 208	70.04 89	81.41 171	32.79 255	79.02 208	63.81 97	77.31 132	81.22 191

xiu_mvs_v1_base_debi		68.58 141	67.28 148	72.48 140	78.19 144	57.19 84	75.28 175	75.09 200	51.61 208	70.04 89	81.41 171	32.79 255	79.02 208	63.81 97	77.31 132	81.22 191

LPG-MVS_test		72.74 71	71.74 75	75.76 63	80.22 101	57.51 78	82.55 58	83.40 59	61.32 73	66.67 148	87.33 63	39.15 196	86.59 59	67.70 63	77.30 135	83.19 157

LGP-MVS_train				75.76 63	80.22 101	57.51 78		83.40 59	61.32 73	66.67 148	87.33 63	39.15 196	86.59 59	67.70 63	77.30 135	83.19 157

Anonymous20240529		69.91 114	69.02 114	72.56 138	80.19 104	47.65 211	77.56 131	80.99 109	55.45 171	69.88 96	86.76 67	39.24 195	82.18 157	54.04 157	77.10 137	87.85 16

EPNet_dtu		61.90 224	61.97 211	61.68 263	72.89 233	39.78 272	75.85 168	65.62 265	55.09 175	54.56 272	79.36 215	37.59 211	67.02 279	39.80 263	76.95 138	78.25 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	59.36 10	66.60 174	65.20 179	70.81 171	76.63 181	48.75 200	76.52 154	80.04 126	50.64 222	65.24 169	84.93 104	39.15 196	78.54 214	36.77 275	76.88 139	85.14 103
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-3		73.89 61	73.28 64	75.71 66	79.75 110	55.48 113	78.54 116	79.93 127	66.58 11	73.62 52	80.30 196	54.87 37	84.54 105	69.09 56	76.84 140	87.10 39

Regformer-4		74.25 57	73.48 60	76.57 54	79.75 110	56.54 92	78.54 116	81.49 94	66.93 7	73.90 48	80.30 196	53.84 49	85.98 75	69.76 50	76.84 140	87.17 37

ACMP	63.53 6	72.30 77	71.20 85	75.59 71	80.28 99	57.54 76	82.74 54	82.84 75	60.58 84	65.24 169	86.18 84	39.25 194	86.03 73	66.95 72	76.79 142	83.22 155
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		65.98 182	63.42 195	73.64 112	77.26 170	52.58 145	72.26 219	77.21 176	48.56 238	61.21 218	74.60 268	32.57 263	85.82 79	50.38 184	76.75 143	82.52 170

BH-untuned		68.27 147	67.29 147	71.21 164	79.74 112	53.22 136	76.06 163	77.46 173	57.19 136	66.10 156	81.61 167	45.37 139	83.50 127	45.42 227	76.68 144	76.91 248

ET-MVSNet_ETH3D		67.96 153	65.72 171	74.68 83	76.67 180	55.62 110	75.11 180	74.74 204	52.91 196	60.03 224	80.12 200	33.68 245	82.64 149	61.86 112	76.34 145	85.78 76

ACMM	61.98 7	70.80 97	69.73 102	74.02 97	80.59 96	58.59 66	82.68 55	82.02 84	55.46 170	67.18 142	84.39 116	38.51 201	83.17 133	60.65 117	76.10 146	80.30 205
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-RMVSNet		68.81 135	67.42 142	72.97 128	80.11 106	52.53 146	74.26 194	76.29 183	58.48 122	68.38 119	84.20 117	42.59 159	83.83 120	46.53 212	75.91 147	82.56 168

XVG-OURS		68.76 138	67.37 144	72.90 130	74.32 218	57.22 82	70.09 247	78.81 145	55.24 173	67.79 135	85.81 95	36.54 224	78.28 217	62.04 110	75.74 148	83.19 157

mvs_anonymous		68.03 152	67.51 140	69.59 188	72.08 246	44.57 238	71.99 222	75.23 197	51.67 207	67.06 143	82.57 146	54.68 39	77.94 221	56.56 136	75.71 149	86.26 65

BH-w/o		66.85 168	65.83 169	69.90 183	79.29 115	52.46 149	74.66 191	76.65 181	54.51 186	64.85 176	78.12 227	45.59 134	82.95 139	43.26 242	75.54 150	74.27 274

thisisatest0515		65.83 183	63.50 194	72.82 133	73.75 222	49.50 191	71.32 229	73.12 221	49.39 231	63.82 188	76.50 251	34.95 233	84.84 101	53.20 167	75.49 151	84.13 134

LS3D		64.71 196	62.50 205	71.34 162	79.72 114	55.71 106	79.82 97	74.72 205	48.50 240	56.62 254	84.62 109	33.59 247	82.34 155	29.65 311	75.23 152	75.97 252

GG-mvs-BLEND				62.34 259	71.36 258	37.04 292	69.20 254	57.33 303		54.73 270	65.48 309	30.37 272	77.82 223	34.82 286	74.93 153	72.17 294

nrg030		72.96 69	73.01 65	72.84 131	75.41 201	50.24 176	80.02 92	82.89 74	58.36 125	74.44 39	86.73 69	58.90 14	80.83 179	65.84 80	74.46 154	87.44 30

VPA-MVSNet		69.02 132	69.47 107	67.69 210	77.42 167	41.00 268	74.04 197	79.68 130	60.06 96	69.26 108	84.81 106	51.06 77	77.58 227	54.44 156	74.43 155	84.48 124

PS-MVSNAJss		72.24 78	71.21 84	75.31 74	78.50 134	55.93 103	81.63 71	82.12 82	56.24 156	70.02 92	85.68 96	47.05 119	84.34 109	65.27 84	74.41 156	85.67 83

EI-MVSNet-Vis-set		72.42 76	71.59 76	74.91 78	78.47 136	54.02 124	77.05 144	79.33 138	65.03 20	71.68 76	79.35 216	52.75 58	84.89 98	66.46 73	74.23 157	85.83 75

CHOSEN 1792x2688		65.08 194	62.84 201	71.82 148	81.49 79	56.26 96	66.32 265	74.20 211	40.53 301	63.16 193	78.65 224	41.30 179	77.80 224	45.80 219	74.09 158	81.40 186

testing_2		66.02 181	63.77 190	72.76 135	66.03 298	50.48 174	72.93 208	80.36 122	54.41 187	54.25 276	76.76 245	30.89 269	83.16 134	64.19 92	74.08 159	84.65 119

ACMMP++_ref															74.07 160

PVSNet_BlendedMVS		68.56 144	67.72 134	71.07 169	77.03 174	50.57 170	74.50 193	81.52 91	53.66 192	64.22 186	79.72 209	49.13 94	82.87 140	55.82 141	73.92 161	79.77 215

CMPMVS	42.80 21	57.81 252	55.97 255	63.32 252	60.98 318	47.38 215	64.66 278	69.50 241	32.06 315	46.83 306	77.80 234	29.50 280	71.36 260	48.68 198	73.75 162	71.21 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		62.42 217	61.46 216	65.31 242	75.21 204	52.10 153	72.05 221	74.05 212	46.41 259	57.42 251	74.36 269	34.35 239	77.57 228	45.62 222	73.67 163	66.26 311

test-LLR		58.15 249	58.13 241	58.22 276	68.57 280	44.80 234	65.46 271	57.92 300	50.08 227	55.44 262	69.82 295	32.62 260	57.44 308	49.66 191	73.62 164	72.41 290

test-mter		56.42 259	55.82 256	58.22 276	68.57 280	44.80 234	65.46 271	57.92 300	39.94 305	55.44 262	69.82 295	21.92 315	57.44 308	49.66 191	73.62 164	72.41 290

EI-MVSNet-UG-set		71.92 83	71.06 86	74.52 90	77.98 151	53.56 130	76.62 151	79.16 139	64.40 27	71.18 78	78.95 221	52.19 66	84.66 104	65.47 83	73.57 166	85.32 99

TR-MVS		66.59 176	65.07 180	71.17 166	79.18 119	49.63 190	73.48 204	75.20 198	52.95 195	67.90 127	80.33 195	39.81 189	83.68 123	43.20 243	73.56 167	80.20 206

UniMVSNet_ETH3D		67.60 157	67.07 154	69.18 196	77.39 168	42.29 256	74.18 196	75.59 192	60.37 88	66.77 147	86.06 88	37.64 210	78.93 213	52.16 173	73.49 168	86.32 61

ab-mvs		66.65 173	66.42 161	67.37 212	76.17 188	41.73 262	70.41 244	76.14 185	53.99 189	65.98 157	83.51 132	49.48 88	76.24 242	48.60 199	73.46 169	84.14 133

EG-PatchMatch MVS		64.71 196	62.87 200	70.22 177	77.68 157	53.48 132	77.99 123	78.82 144	53.37 193	56.03 258	77.41 238	24.75 308	84.04 114	46.37 214	73.42 170	73.14 281

XVG-OURS-SEG-HR		68.81 135	67.47 141	72.82 133	74.40 217	56.87 90	70.59 240	79.04 141	54.77 180	66.99 144	86.01 89	39.57 191	78.21 218	62.54 107	73.33 171	83.37 152

thres200		62.20 220	61.16 221	65.34 241	75.38 202	39.99 270	69.60 250	69.29 244	55.64 168	61.87 212	76.99 240	37.07 219	78.96 212	31.28 305	73.28 172	77.06 243

thres100view900		63.28 209	62.41 206	65.89 234	77.31 169	38.66 281	72.65 211	69.11 246	57.07 137	62.45 205	81.03 178	37.01 220	79.17 202	31.84 297	73.25 173	79.83 212

tfpn200view9		63.18 211	62.18 209	66.21 226	76.85 177	39.62 273	71.96 223	69.44 242	56.63 144	62.61 200	79.83 205	37.18 215	79.17 202	31.84 297	73.25 173	79.83 212

thres400		63.31 207	62.18 209	66.72 218	76.85 177	39.62 273	71.96 223	69.44 242	56.63 144	62.61 200	79.83 205	37.18 215	79.17 202	31.84 297	73.25 173	81.36 187

TESTMET0.1,1		55.28 266	54.90 262	56.42 283	66.56 293	43.67 245	65.46 271	56.27 307	39.18 307	53.83 279	67.44 303	24.21 309	55.46 318	48.04 203	73.11 176	70.13 303

thres600view7		63.30 208	62.27 207	66.41 222	77.18 171	38.87 279	72.35 217	69.11 246	56.98 139	62.37 208	80.96 180	37.01 220	79.00 211	31.43 304	73.05 177	81.36 187

VPNet		67.52 158	68.11 130	65.74 236	79.18 119	36.80 293	72.17 220	72.83 222	62.04 66	67.79 135	85.83 93	48.88 98	76.60 238	51.30 179	72.97 178	83.81 140

Anonymous20231211		69.28 128	68.47 123	71.73 150	80.28 99	47.18 217	79.98 93	82.37 78	54.61 181	67.24 141	84.01 122	39.43 192	82.41 154	55.45 147	72.83 179	85.62 86

mvs-test1		70.44 103	68.19 128	77.18 44	76.10 189	63.22 6	80.59 87	76.06 187	59.83 103	66.32 154	79.87 204	41.56 174	85.53 83	60.60 118	72.77 180	82.80 167

GBi-Net		67.21 161	66.55 157	69.19 193	77.63 159	43.33 247	77.31 136	77.83 165	56.62 146	65.04 172	82.70 141	41.85 169	80.33 187	47.18 207	72.76 181	83.92 136

test1		67.21 161	66.55 157	69.19 193	77.63 159	43.33 247	77.31 136	77.83 165	56.62 146	65.04 172	82.70 141	41.85 169	80.33 187	47.18 207	72.76 181	83.92 136

FMVSNet3		66.32 179	65.61 173	68.46 203	76.48 185	42.34 255	74.98 185	77.15 177	55.83 163	65.04 172	81.16 174	39.91 187	80.14 191	47.18 207	72.76 181	82.90 165

FMVSNet2		66.93 167	66.31 166	68.79 200	77.63 159	42.98 250	76.11 161	77.47 171	56.62 146	65.22 171	82.17 157	41.85 169	80.18 190	47.05 210	72.72 184	83.20 156

thisisatest0530		67.92 154	65.78 170	74.33 94	76.29 186	51.03 161	76.89 148	74.25 210	53.67 191	65.59 163	81.76 164	35.15 230	85.50 86	55.94 139	72.47 185	86.47 51

PVSNet	50.76 19	58.40 245	57.39 243	61.42 265	75.53 199	44.04 242	61.43 289	63.45 278	47.04 256	56.91 252	73.61 275	27.00 296	64.76 287	39.12 264	72.40 186	75.47 259

MIMVSNet		57.35 253	57.07 246	58.22 276	74.21 220	37.18 290	62.46 285	60.88 292	48.88 236	55.29 265	75.99 256	31.68 267	62.04 295	31.87 296	72.35 187	75.43 260

1314		64.61 198	63.21 197	68.80 199	71.87 251	47.46 214	73.95 198	78.39 161	42.88 290	59.97 225	76.60 248	38.11 207	79.39 198	54.84 151	72.32 188	79.55 216

FMVSNet1		66.70 172	65.87 168	69.19 193	77.49 166	43.33 247	77.31 136	77.83 165	56.45 151	64.60 180	82.70 141	38.08 208	80.33 187	46.08 216	72.31 189	83.92 136

ACMMP++															72.16 190

MVP-Stereo		65.41 189	63.80 189	70.22 177	77.62 163	55.53 111	76.30 157	78.53 152	50.59 223	56.47 256	78.65 224	39.84 188	82.68 147	44.10 235	72.12 191	72.44 289
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HyFIR lowres test		65.67 185	63.01 199	73.67 109	79.97 108	55.65 108	69.07 255	75.52 193	42.68 291	63.53 189	77.95 229	40.43 185	81.64 164	46.01 217	71.91 192	83.73 144

XVG-ACMP-BASELINE		64.36 200	62.23 208	70.74 173	72.35 242	52.45 150	70.80 239	78.45 157	53.84 190	59.87 226	81.10 176	16.24 320	79.32 199	55.64 146	71.76 193	80.47 202

HY-MVS	56.14 13	64.55 199	63.89 186	66.55 221	74.73 210	41.02 266	69.96 248	74.43 207	49.29 232	61.66 214	80.92 181	47.43 116	76.68 237	44.91 230	71.69 194	81.94 179

D2MVS		62.30 219	60.29 227	68.34 205	66.46 294	48.42 203	65.70 268	73.42 217	47.71 249	58.16 246	75.02 264	30.51 271	77.71 225	53.96 159	71.68 195	78.90 225

ACMH	55.70 15	65.20 192	63.57 193	70.07 180	78.07 148	52.01 157	79.48 106	79.69 129	55.75 165	56.59 255	80.98 179	27.12 294	80.94 176	42.90 247	71.58 196	77.25 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 163	65.58 174	71.88 147	70.37 266	49.70 186	70.25 246	78.45 157	51.52 211	69.16 110	80.37 192	38.45 202	82.50 151	60.19 122	71.46 197	83.44 150

EI-MVSNet		69.27 129	68.44 125	71.73 150	74.47 214	49.39 193	75.20 178	78.45 157	59.60 105	69.16 110	76.51 249	51.29 73	82.50 151	59.86 127	71.45 198	83.30 153

LTVRE_ROB	55.42 16	63.15 212	61.23 220	68.92 198	76.57 183	47.80 208	59.92 296	76.39 182	54.35 188	58.67 240	82.46 150	29.44 281	81.49 168	42.12 251	71.14 199	77.46 237
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
UniMVSNet (Re)		70.63 99	70.20 97	71.89 146	78.55 133	45.29 231	75.94 167	82.92 71	63.68 37	68.16 123	83.59 130	53.89 48	83.49 128	53.97 158	71.12 200	86.89 42

Effi-MVS+-dtu		69.64 121	67.53 139	75.95 59	76.10 189	62.29 18	80.20 91	76.06 187	59.83 103	65.26 168	77.09 239	41.56 174	84.02 117	60.60 118	71.09 201	81.53 184

NR-MVSNet		69.54 123	68.85 116	71.59 154	78.05 149	43.81 244	74.20 195	80.86 114	65.18 16	62.76 196	84.52 112	52.35 65	83.59 126	50.96 181	70.78 202	87.37 32

v1144		70.42 104	69.31 109	73.76 104	73.22 225	50.64 169	77.83 125	81.43 95	58.58 120	69.40 104	81.16 174	47.53 113	85.29 92	64.01 95	70.64 203	85.34 98

jajsoiax		68.25 148	66.45 159	73.66 110	75.62 196	55.49 112	80.82 82	78.51 153	52.33 202	64.33 182	84.11 119	28.28 286	81.81 163	63.48 101	70.62 204	83.67 146

mvs_tets		68.18 150	66.36 163	73.63 113	75.61 197	55.35 115	80.77 83	78.56 151	52.48 201	64.27 184	84.10 120	27.45 292	81.84 162	63.45 102	70.56 205	83.69 145

test_normal		58.37 246	54.53 265	69.89 184	61.13 316	42.67 253	33.28 328	80.90 113	57.45 133	42.61 317	61.36 315	23.16 310	82.75 146	60.43 120	70.47 206	84.24 129

UniMVSNet_NR-MVSNet		71.11 93	71.00 87	71.44 156	79.20 118	44.13 240	76.02 166	82.60 76	66.48 13	68.20 121	84.60 111	56.82 21	82.82 144	54.62 153	70.43 207	87.36 34

DU-MVS		70.01 111	69.53 105	71.44 156	78.05 149	44.13 240	75.01 183	81.51 93	64.37 28	68.20 121	84.52 112	49.12 96	82.82 144	54.62 153	70.43 207	87.37 32

v1192		69.97 113	68.68 119	73.85 100	73.19 226	50.94 162	77.68 128	81.36 97	57.51 132	68.95 112	80.85 184	45.28 140	85.33 91	62.97 104	70.37 209	85.27 101

PLC	56.13 14	65.09 193	63.21 197	70.72 174	81.04 89	54.87 120	78.57 114	77.47 171	48.51 239	55.71 259	81.89 162	33.71 244	79.71 192	41.66 255	70.37 209	77.58 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 187	63.70 191	71.02 170	70.87 261	48.10 205	70.48 242	74.40 208	56.69 142	64.70 178	76.77 244	33.66 246	81.10 173	55.42 148	70.32 211	83.87 139

Fast-Effi-MVS+-dtu		67.37 159	65.33 177	73.48 116	72.94 232	57.78 74	77.47 134	76.88 179	57.60 131	61.97 210	76.85 243	39.31 193	80.49 185	54.72 152	70.28 212	82.17 177

v2v482		70.50 102	69.45 108	73.66 110	72.62 237	50.03 182	77.58 129	80.51 119	59.90 99	69.52 100	82.14 159	47.53 113	84.88 100	65.07 86	70.17 213	86.09 68

IB-MVS	56.42 12	65.40 190	62.73 203	73.40 120	74.89 205	52.78 142	73.09 207	75.13 199	55.69 166	58.48 244	73.73 274	32.86 254	86.32 69	50.63 182	70.11 214	81.10 195
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
CNLPA		65.43 188	64.02 185	69.68 186	78.73 130	58.07 71	77.82 126	70.71 232	51.49 212	61.57 216	83.58 131	38.23 206	70.82 262	43.90 236	70.10 215	80.16 207

1112_ss		64.00 202	63.36 196	65.93 233	79.28 116	42.58 254	71.35 228	72.36 225	46.41 259	60.55 221	77.89 232	46.27 129	73.28 253	46.18 215	69.97 216	81.92 180

DP-MVS		65.68 184	63.66 192	71.75 149	84.93 49	56.87 90	80.74 84	73.16 220	53.06 194	59.09 236	82.35 151	36.79 223	85.94 77	32.82 293	69.96 217	72.45 288

tttt0517		67.83 156	65.66 172	74.33 94	76.69 179	50.82 166	77.86 124	73.99 213	54.54 185	64.64 179	82.53 148	35.06 231	85.50 86	55.71 144	69.91 218	86.67 48

IterMVS-LS		69.22 131	68.48 122	71.43 158	74.44 216	49.40 192	76.23 159	77.55 170	59.60 105	65.85 161	81.59 169	51.28 74	81.58 167	59.87 126	69.90 219	83.30 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		69.47 125	68.17 129	73.36 121	73.06 229	50.10 181	77.39 135	80.56 117	56.58 150	68.59 114	80.37 192	44.72 143	84.98 95	62.47 109	69.82 220	85.00 108

Baseline_NR-MVSNet		67.05 164	67.56 137	65.50 238	75.65 195	37.70 289	75.42 173	74.65 206	59.90 99	68.14 124	83.15 138	49.12 96	77.20 231	52.23 172	69.78 221	81.60 183

ACMH+	57.40 11	66.12 180	64.06 184	72.30 145	77.79 155	52.83 141	80.39 88	78.03 163	57.30 134	57.47 250	82.55 147	27.68 290	84.17 111	45.54 223	69.78 221	79.90 211

v1240		69.24 130	67.91 132	73.25 126	73.02 231	49.82 184	77.21 141	80.54 118	56.43 152	68.34 120	80.51 191	43.33 156	84.99 93	62.03 111	69.77 223	84.95 111

TranMVSNet+NR-MVSNet		70.36 105	70.10 100	71.17 166	78.64 132	42.97 251	76.53 153	81.16 105	66.95 6	68.53 117	85.42 101	51.61 71	83.07 135	52.32 171	69.70 224	87.46 29

v144192		69.71 116	68.51 121	73.33 122	73.10 228	50.13 180	77.54 132	80.64 116	56.65 143	68.57 116	80.55 190	46.87 124	84.96 97	62.98 103	69.66 225	84.89 112

WR-MVS		68.47 146	68.47 123	68.44 204	80.20 103	39.84 271	73.75 203	76.07 186	64.68 22	68.11 125	83.63 129	50.39 83	79.14 206	49.78 187	69.66 225	86.34 58

WTY-MVS		59.75 238	60.39 226	57.85 279	72.32 243	37.83 287	61.05 294	64.18 275	45.95 266	61.91 211	79.11 220	47.01 122	60.88 298	42.50 248	69.49 227	74.83 267

test_djsdf		69.45 126	67.74 133	74.58 88	74.57 213	54.92 119	82.79 52	78.48 154	51.26 216	65.41 165	83.49 133	38.37 203	83.24 131	66.06 76	69.25 228	85.56 87

CostFormer		64.04 201	62.51 204	68.61 202	71.88 250	45.77 226	71.30 230	70.60 233	47.55 251	64.31 183	76.61 247	41.63 172	79.62 195	49.74 189	69.00 229	80.42 203

tpm2		62.07 222	60.10 228	67.99 207	72.79 234	43.86 243	71.05 236	66.85 259	43.14 288	62.77 195	75.39 262	38.32 204	80.80 180	41.69 254	68.88 230	79.32 219

v10		70.21 108	69.02 114	73.81 101	73.51 224	50.92 164	78.74 110	81.39 96	60.05 97	66.39 153	81.83 163	47.58 112	85.41 90	62.80 105	68.86 231	85.09 106

v8		70.33 106	69.28 110	73.49 115	73.15 227	50.22 177	78.62 113	80.78 115	60.79 80	66.45 152	82.11 160	49.35 89	84.98 95	63.58 100	68.71 232	85.28 100

v7n		69.01 133	67.36 145	73.98 98	72.51 240	52.65 143	78.54 116	81.30 99	60.26 94	62.67 198	81.62 166	43.61 153	84.49 106	57.01 135	68.70 233	84.79 116

MVS_0304		58.51 243	57.36 244	61.96 262	70.04 269	41.83 260	69.40 253	65.46 266	50.73 219	53.30 286	74.06 272	22.65 312	70.18 267	42.16 250	68.44 234	73.86 279

Test_1112_low_res		62.32 218	61.77 212	64.00 249	79.08 122	39.53 275	68.17 257	70.17 234	43.25 286	59.03 237	79.90 203	44.08 149	71.24 261	43.79 238	68.42 235	81.25 190

PMMVS		53.96 270	53.26 275	56.04 284	62.60 310	50.92 164	61.17 293	56.09 308	32.81 314	53.51 284	66.84 305	34.04 241	59.93 302	44.14 234	68.18 236	57.27 319

tfpnnormal		62.47 216	61.63 214	64.99 244	74.81 208	39.01 278	71.22 231	73.72 215	55.22 174	60.21 222	80.09 202	41.26 182	76.98 235	30.02 309	68.09 237	78.97 224

DWT-MVSNet_test		61.90 224	59.93 229	67.83 208	71.98 249	46.09 225	71.03 237	69.71 236	50.09 226	58.51 243	70.62 289	30.21 275	77.63 226	49.28 194	67.91 238	79.78 214

Anonymous20231206		55.10 268	55.30 260	54.48 291	69.81 273	33.94 309	62.91 284	62.13 288	41.08 298	55.18 266	75.65 258	32.75 258	56.59 313	30.32 308	67.86 239	72.91 282

V42		68.65 139	67.35 146	72.56 138	68.93 279	50.18 178	72.90 210	79.47 135	56.92 140	69.45 103	80.26 198	46.29 128	82.99 137	64.07 93	67.82 240	84.53 122

MDTV_nov1_ep13			57.00 247		72.73 235	38.26 284	65.02 277	64.73 272	44.74 271	55.46 261	72.48 278	32.61 262	70.47 265	37.47 271	67.75 241

anonymousdsp		67.00 166	64.82 182	73.57 114	70.09 268	56.13 98	76.35 156	77.35 175	48.43 241	64.99 175	80.84 185	33.01 252	80.34 186	64.66 88	67.64 242	84.23 130

OpenMVS_ROB	52.78 18	60.03 235	58.14 240	65.69 237	70.47 263	44.82 233	75.33 174	70.86 231	45.04 269	56.06 257	76.00 254	26.89 297	79.65 193	35.36 285	67.29 243	72.60 285

XXY-MVS		60.68 232	61.67 213	57.70 281	70.43 264	38.45 283	64.19 280	66.47 260	48.05 246	63.22 191	80.86 183	49.28 91	60.47 299	45.25 229	67.28 244	74.19 275

baseline2		63.42 206	61.26 219	69.89 184	72.55 239	47.62 212	71.54 226	68.38 251	50.11 225	54.82 268	75.55 260	43.06 158	80.96 175	48.13 202	67.16 245	81.11 194

F-COLMAP		63.05 213	60.87 224	69.58 190	76.99 176	53.63 129	78.12 122	76.16 184	47.97 247	52.41 288	81.61 167	27.87 288	78.11 219	40.07 260	66.66 246	77.00 245

pm-mvs1		65.24 191	64.97 181	66.04 230	72.38 241	39.40 276	72.62 213	75.63 191	55.53 169	62.35 209	83.18 137	47.45 115	76.47 239	49.06 196	66.54 247	82.24 174

v148		68.24 149	67.19 152	71.40 159	70.43 264	47.77 210	75.76 169	77.03 178	58.91 115	67.36 139	80.10 201	48.60 102	81.89 160	60.01 124	66.52 248	84.53 122

sss		56.17 262	56.57 250	54.96 288	66.93 290	36.32 299	57.94 302	61.69 289	41.67 295	58.64 241	75.32 263	38.72 200	56.25 314	42.04 252	66.19 249	72.31 293

COLMAP_ROB	52.97 17	61.27 231	58.81 233	68.64 201	74.63 211	52.51 148	78.42 119	73.30 218	49.92 229	50.96 293	81.51 170	23.06 311	79.40 197	31.63 301	65.85 250	74.01 277
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		59.63 239	59.14 232	61.08 268	74.47 214	38.84 280	75.20 178	68.74 248	31.15 316	58.24 245	76.51 249	32.39 264	68.58 272	49.77 188	65.84 251	75.81 255

MSDG		61.81 227	59.23 231	69.55 191	72.64 236	52.63 144	70.45 243	75.81 189	51.38 214	53.70 280	76.11 253	29.52 279	81.08 174	37.70 270	65.79 252	74.93 266

PatchFormer-LS_test		62.20 220	60.59 225	67.04 215	72.18 245	46.82 220	70.36 245	68.62 249	51.92 205	59.19 235	70.23 292	36.86 222	75.07 246	50.23 186	65.68 253	79.23 221

FMVSNet5		55.86 263	54.93 261	58.66 275	71.05 260	36.35 297	64.18 281	62.48 285	46.76 257	50.66 297	74.73 267	25.80 302	64.04 289	33.11 292	65.57 254	75.59 258

pmmvs5		56.47 258	55.68 257	58.86 273	61.41 314	36.71 294	66.37 264	62.75 283	40.38 302	53.70 280	76.62 246	34.56 235	67.05 278	40.02 262	65.27 255	72.83 283

miper_lstm_enhance		62.03 223	60.88 223	65.49 239	66.71 292	46.25 223	56.29 307	75.70 190	50.68 220	61.27 217	75.48 261	40.21 186	68.03 274	56.31 138	65.25 256	82.18 175

tpm		57.34 254	58.16 239	54.86 289	71.80 252	34.77 304	67.47 261	56.04 309	48.20 244	60.10 223	76.92 241	37.17 217	53.41 321	40.76 259	65.01 257	76.40 251

pmmvs4		61.48 229	59.39 230	67.76 209	71.57 253	53.86 126	71.42 227	65.34 267	44.20 278	59.46 230	77.92 231	35.90 225	74.71 248	43.87 237	64.87 258	74.71 270

test_0402		63.25 210	61.01 222	69.96 181	80.00 107	54.37 123	76.86 149	72.02 226	54.58 183	58.71 239	80.79 186	35.00 232	84.36 108	26.41 319	64.71 259	71.15 300

CR-MVSNet		59.91 236	57.90 242	65.96 231	69.96 271	52.07 154	65.31 274	63.15 281	42.48 292	59.36 231	74.84 265	35.83 226	70.75 263	45.50 224	64.65 260	75.06 262

RPMNet		58.70 242	56.29 254	65.96 231	69.96 271	52.07 154	65.31 274	62.15 287	43.20 287	59.36 231	70.15 294	35.37 228	70.75 263	36.42 282	64.65 260	75.06 262

pmmvs6		63.69 204	62.82 202	66.27 225	70.63 262	39.27 277	73.13 206	75.47 194	52.69 199	59.75 229	82.30 153	39.71 190	77.03 234	47.40 205	64.35 262	82.53 169

WR-MVS_H		67.02 165	66.92 155	67.33 214	77.95 152	37.75 288	77.57 130	82.11 83	62.03 67	62.65 199	82.48 149	50.57 81	79.46 196	42.91 246	64.01 263	84.79 116

test0.0.03 1		53.32 275	53.59 273	52.50 299	62.81 309	29.45 321	59.51 297	54.11 313	50.08 227	54.40 274	74.31 270	32.62 260	55.92 316	30.50 307	63.95 264	72.15 295

PatchMatch-RL		56.25 261	54.55 264	61.32 267	77.06 173	56.07 100	65.57 270	54.10 314	44.13 280	53.49 285	71.27 286	25.20 305	66.78 280	36.52 281	63.66 265	61.12 314

PatchmatchNet		59.84 237	58.24 238	64.65 246	73.05 230	46.70 221	69.42 252	62.18 286	47.55 251	58.88 238	71.96 282	34.49 237	69.16 269	42.99 245	63.60 266	78.07 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-SCA-FT		62.49 215	61.52 215	65.40 240	71.99 248	50.80 167	71.15 234	69.63 239	45.71 267	60.61 220	77.93 230	37.45 212	65.99 284	55.67 145	63.50 267	79.42 218

CP-MVSNet		66.49 177	66.41 162	66.72 218	77.67 158	36.33 298	76.83 150	79.52 134	62.45 57	62.54 202	83.47 134	46.32 127	78.37 215	45.47 226	63.43 268	85.45 93

PS-CasMVS		66.42 178	66.32 165	66.70 220	77.60 165	36.30 300	76.94 147	79.61 132	62.36 59	62.43 207	83.66 128	45.69 131	78.37 215	45.35 228	63.26 269	85.42 96

IterMVS		62.79 214	61.27 218	67.35 213	69.37 277	52.04 156	71.17 232	68.24 252	52.63 200	59.82 227	76.91 242	37.32 214	72.36 256	52.80 169	63.19 270	77.66 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		66.60 174	66.45 159	67.04 215	77.11 172	36.56 295	77.03 145	80.42 120	62.95 44	62.51 204	84.03 121	46.69 125	79.07 207	44.22 231	63.08 271	85.51 90

tpmrst		58.24 247	58.70 235	56.84 282	66.97 289	34.32 307	69.57 251	61.14 291	47.17 255	58.58 242	71.60 283	41.28 181	60.41 300	49.20 195	62.84 272	75.78 256

testgi		51.90 279	52.37 277	50.51 304	60.39 320	23.55 329	58.42 300	58.15 298	49.03 235	51.83 290	79.21 219	22.39 313	55.59 317	29.24 312	62.64 273	72.40 292

SCA		60.49 233	58.38 237	66.80 217	74.14 221	48.06 206	63.35 282	63.23 280	49.13 234	59.33 234	72.10 280	37.45 212	74.27 251	44.17 232	62.57 274	78.05 231

EPMVS		53.96 270	53.69 272	54.79 290	66.12 297	31.96 316	62.34 287	49.05 319	44.42 277	55.54 260	71.33 285	30.22 274	56.70 311	41.65 256	62.54 275	75.71 257

ITE_SJBPF				62.09 261	66.16 296	44.55 239		64.32 274	47.36 253	55.31 264	80.34 194	19.27 317	62.68 293	36.29 283	62.39 276	79.04 222

MIMVSNet1		55.17 267	54.31 268	57.77 280	70.03 270	32.01 315	65.68 269	64.81 270	49.19 233	46.75 307	76.00 254	25.53 304	64.04 289	28.65 313	62.13 277	77.26 241

baseline1		63.81 203	63.87 188	63.62 250	76.29 186	36.36 296	71.78 225	67.29 256	56.05 160	64.23 185	82.95 139	47.11 118	74.41 250	47.30 206	61.85 278	80.10 209

USDC		56.35 260	54.24 269	62.69 257	64.74 303	40.31 269	65.05 276	73.83 214	43.93 282	47.58 302	77.71 236	15.36 322	75.05 247	38.19 269	61.81 279	72.70 284

PatchT		53.17 276	53.44 274	52.33 300	68.29 284	25.34 327	58.21 301	54.41 312	44.46 276	54.56 272	69.05 298	33.32 249	60.94 297	36.93 274	61.76 280	70.73 302

tpm cat1		59.25 240	56.95 248	66.15 227	72.19 244	46.96 218	68.09 258	65.76 264	40.03 304	57.81 248	70.56 290	38.32 204	74.51 249	38.26 268	61.50 281	77.00 245

tpmvs		58.47 244	56.95 248	63.03 256	70.20 267	41.21 265	67.90 260	67.23 257	49.62 230	54.73 270	70.84 287	34.14 240	76.24 242	36.64 279	61.29 282	71.64 296

Patchmtry		57.16 255	56.47 251	59.23 270	69.17 278	34.58 306	62.98 283	63.15 281	44.53 274	56.83 253	74.84 265	35.83 226	68.71 271	40.03 261	60.91 283	74.39 273

DTE-MVSNet		65.58 186	65.34 176	66.31 223	76.06 191	34.79 303	76.43 155	79.38 137	62.55 55	61.66 214	83.83 125	45.60 133	79.15 205	41.64 257	60.88 284	85.00 108

CHOSEN 280x420		47.83 288	46.36 291	52.24 301	67.37 288	49.78 185	38.91 327	43.11 327	35.00 312	43.27 315	63.30 313	28.95 283	49.19 324	36.53 280	60.80 285	57.76 318

Patchmatch-test		49.08 286	48.28 287	51.50 302	64.40 305	30.85 319	45.68 321	48.46 322	35.60 311	46.10 309	72.10 280	34.47 238	46.37 325	27.08 317	60.65 286	77.27 240

test20.03		53.87 272	54.02 271	53.41 295	61.47 313	28.11 323	61.30 291	59.21 295	51.34 215	52.09 289	77.43 237	33.29 250	58.55 305	29.76 310	60.27 287	73.58 280

MVS-HIRNet		45.52 291	44.48 293	48.65 305	68.49 282	34.05 308	59.41 299	44.50 326	27.03 320	37.96 322	50.47 324	26.16 301	64.10 288	26.74 318	59.52 288	47.82 322

Patchmatch-RL test		58.16 248	55.49 258	66.15 227	67.92 285	48.89 199	60.66 295	51.07 316	47.86 248	59.36 231	62.71 314	34.02 242	72.27 258	56.41 137	59.40 289	77.30 239

AllTest		57.08 256	54.65 263	64.39 247	71.44 254	49.03 194	69.92 249	67.30 254	45.97 264	47.16 304	79.77 207	17.47 318	67.56 276	33.65 290	59.16 290	76.57 249

TestCases				64.39 247	71.44 254	49.03 194		67.30 254	45.97 264	47.16 304	79.77 207	17.47 318	67.56 276	33.65 290	59.16 290	76.57 249

RPSCF		55.80 264	54.22 270	60.53 269	65.13 302	42.91 252	64.30 279	57.62 302	36.84 310	58.05 247	82.28 154	28.01 287	56.24 315	37.14 273	58.61 292	82.44 173

EU-MVSNet		55.61 265	54.41 267	59.19 271	65.41 301	33.42 311	72.44 216	71.91 227	28.81 318	51.27 291	73.87 273	24.76 307	69.08 270	43.04 244	58.20 293	75.06 262

pmmvs-eth3d		58.81 241	56.31 253	66.30 224	67.61 286	52.42 151	72.30 218	64.76 271	43.55 284	54.94 267	74.19 271	28.95 283	72.60 255	43.31 240	57.21 294	73.88 278

our_test_3		56.49 257	54.42 266	62.68 258	69.51 274	45.48 230	66.08 266	61.49 290	44.11 281	50.73 296	69.60 297	33.05 251	68.15 273	38.38 267	56.86 295	74.40 272

TinyColmap		54.14 269	51.72 278	61.40 266	66.84 291	41.97 258	66.52 263	68.51 250	44.81 270	42.69 316	75.77 257	11.66 325	72.94 254	31.96 295	56.77 296	69.27 307

ppachtmachnet_test		58.06 250	55.38 259	66.10 229	69.51 274	48.99 197	68.01 259	66.13 263	44.50 275	54.05 278	70.74 288	32.09 266	72.34 257	36.68 278	56.71 297	76.99 247

OurMVSNet-221017-0		61.37 230	58.63 236	69.61 187	72.05 247	48.06 206	73.93 201	72.51 223	47.23 254	54.74 269	80.92 181	21.49 316	81.24 170	48.57 200	56.22 298	79.53 217

TransMVSNet (Re)		64.72 195	64.33 183	65.87 235	75.22 203	38.56 282	74.66 191	75.08 203	58.90 116	61.79 213	82.63 144	51.18 75	78.07 220	43.63 239	55.87 299	80.99 197

FPMVS		42.18 294	41.11 296	45.39 307	58.03 323	41.01 267	49.50 316	53.81 315	30.07 317	33.71 323	64.03 310	11.69 324	52.08 323	14.01 326	55.11 300	43.09 324

dp		51.89 280	51.60 279	52.77 298	68.44 283	32.45 314	62.36 286	54.57 311	44.16 279	49.31 300	67.91 300	28.87 285	56.61 312	33.89 289	54.89 301	69.24 308

ADS-MVSNet2		51.33 282	48.76 286	59.07 272	66.02 299	44.60 237	50.90 314	59.76 294	36.90 308	50.74 294	66.18 307	26.38 298	63.11 291	27.17 315	54.76 302	69.50 305

ADS-MVSNet		48.48 287	47.77 288	50.63 303	66.02 299	29.92 320	50.90 314	50.87 318	36.90 308	50.74 294	66.18 307	26.38 298	52.47 322	27.17 315	54.76 302	69.50 305

PM-MVS		52.33 278	50.19 282	58.75 274	62.10 311	45.14 232	65.75 267	40.38 328	43.60 283	53.52 283	72.65 277	9.16 331	65.87 285	50.41 183	54.18 304	65.24 313

JIA-IIPM		51.56 281	47.68 290	63.21 253	64.61 304	50.73 168	47.71 319	58.77 297	42.90 289	48.46 301	51.72 321	24.97 306	70.24 266	36.06 284	53.89 305	68.64 309

ambc				65.13 243	63.72 308	37.07 291	47.66 320	78.78 146		54.37 275	71.42 284	11.24 327	80.94 176	45.64 221	53.85 306	77.38 238

DSMNet-mixed		39.30 298	38.72 298	41.03 311	51.22 326	19.66 331	45.53 322	31.35 332	15.83 329	39.80 321	67.42 304	22.19 314	45.13 326	22.43 321	52.69 307	58.31 317

N_pmnet		39.35 297	40.28 297	36.54 312	63.76 307	1.62 338	49.37 317	0.76 339	34.62 313	43.61 314	66.38 306	26.25 300	42.57 328	26.02 320	51.77 308	65.44 312

TDRefinement		53.44 274	50.72 281	61.60 264	64.31 306	46.96 218	70.89 238	65.27 269	41.78 293	44.61 312	77.98 228	11.52 326	66.36 282	28.57 314	51.59 309	71.49 297

Gipuma		34.77 300	31.91 303	43.33 310	62.05 312	37.87 286	20.39 331	67.03 258	23.23 324	18.41 329	25.84 328	4.24 334	62.73 292	14.71 325	51.32 310	29.38 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		50.73 283	48.96 283	56.03 285	61.10 317	41.78 261	51.94 312	56.44 305	40.94 300	44.84 310	67.80 302	30.08 276	55.08 319	36.77 275	50.71 311	71.22 298

MDA-MVSNet_test_wron		50.71 284	48.95 284	56.00 286	61.17 315	41.84 259	51.90 313	56.45 304	40.96 299	44.79 311	67.84 301	30.04 277	55.07 320	36.71 277	50.69 312	71.11 301

SixPastTwentyTwo		61.65 228	58.80 234	70.20 179	75.80 193	47.22 216	75.59 170	69.68 238	54.61 181	54.11 277	79.26 218	27.07 295	82.96 138	43.27 241	49.79 313	80.41 204

new-patchmatchnet		47.56 289	47.73 289	47.06 306	58.81 322	9.37 335	48.78 318	59.21 295	43.28 285	44.22 313	68.66 299	25.67 303	57.20 310	31.57 303	49.35 314	74.62 271

LF4IMVS		42.95 293	42.26 294	45.04 308	48.30 327	32.50 313	54.80 308	48.49 321	28.03 319	40.51 320	70.16 293	9.24 330	43.89 327	31.63 301	49.18 315	58.72 316

PMVS	28.69 22	36.22 299	33.29 302	45.02 309	36.82 334	35.98 301	54.68 309	48.74 320	26.31 321	21.02 327	51.61 322	2.88 338	60.10 301	9.99 330	47.58 316	38.99 326
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 292	41.95 295	53.86 292	52.58 325	43.55 246	62.11 288	46.90 325	26.05 322	40.63 319	60.19 316	11.08 328	57.91 307	31.83 300	46.15 317	60.11 315

MDA-MVSNet-bldmvs		53.87 272	50.81 280	63.05 255	66.25 295	48.58 201	56.93 305	63.82 276	48.09 245	41.22 318	70.48 291	30.34 273	68.00 275	34.24 288	45.92 318	72.57 286

UnsupCasMVSNet_eth		53.16 277	52.47 276	55.23 287	59.45 321	33.39 312	59.43 298	69.13 245	45.98 263	50.35 299	72.32 279	29.30 282	58.26 306	42.02 253	44.30 319	74.05 276

UnsupCasMVSNet_bld		50.07 285	48.87 285	53.66 293	60.97 319	33.67 310	57.62 303	64.56 273	39.47 306	47.38 303	64.02 312	27.47 291	59.32 303	34.69 287	43.68 320	67.98 310

new_pmnet		34.13 301	34.29 301	33.64 313	52.63 324	18.23 333	44.43 324	33.90 331	22.81 325	30.89 324	53.18 319	10.48 329	35.72 332	20.77 323	39.51 321	46.98 323

K. test v3		60.47 234	57.11 245	70.56 175	73.74 223	48.22 204	75.10 182	62.55 284	58.27 126	53.62 282	76.31 252	27.81 289	81.59 166	47.42 204	39.18 322	81.88 181

LCM-MVSNet		40.30 296	35.88 300	53.57 294	42.24 329	29.15 322	45.21 323	60.53 293	22.23 326	28.02 325	50.98 323	3.72 336	61.78 296	31.22 306	38.76 323	69.78 304

lessismore_v0				69.91 182	71.42 256	47.80 208		50.90 317		50.39 298	75.56 259	27.43 293	81.33 169	45.91 218	34.10 324	80.59 201

PVSNet_0	43.31 20	47.46 290	45.64 292	52.92 297	67.60 287	44.65 236	54.06 310	54.64 310	41.59 296	46.15 308	58.75 317	30.99 268	58.66 304	32.18 294	24.81 325	55.46 320

PMMVS2		27.40 302	25.91 304	31.87 315	39.46 333	6.57 336	31.17 329	28.52 333	23.96 323	20.45 328	48.94 325	4.20 335	37.94 331	16.51 324	19.97 326	51.09 321

MVE	17.77 23	21.41 305	17.77 308	32.34 314	34.34 335	25.44 326	16.11 332	24.11 334	11.19 330	13.22 331	31.92 326	1.58 339	30.95 333	10.47 328	17.03 327	40.62 325
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 303	22.73 306	26.90 316	42.02 330	20.67 330	42.66 325	35.70 329	17.43 327	10.28 333	25.05 329	6.42 333	42.39 329	10.28 329	14.71 328	17.63 328

EMVS		22.97 304	21.84 307	26.36 317	40.20 331	19.53 332	41.95 326	34.64 330	17.09 328	9.73 334	22.83 330	7.29 332	42.22 330	9.18 331	13.66 329	17.32 329

wuyk23d		13.32 307	12.52 309	15.71 318	47.54 328	26.27 324	31.06 330	1.98 338	4.93 332	5.18 335	1.94 335	0.45 340	18.54 334	6.81 333	12.83 330	2.33 332

ANet_high		41.38 295	37.47 299	53.11 296	39.73 332	24.45 328	56.94 304	69.69 237	47.65 250	26.04 326	52.32 320	12.44 323	62.38 294	21.80 322	10.61 331	72.49 287

tmp_tt		9.43 308	11.14 310	4.30 320	2.38 337	4.40 337	13.62 333	16.08 336	0.39 333	15.89 330	13.06 331	15.80 321	5.54 336	12.63 327	10.46 332	2.95 331

DeepMVS_CX				12.03 319	17.97 336	10.91 334		10.60 337	7.46 331	11.07 332	28.36 327	3.28 337	11.29 335	8.01 332	9.74 333	13.89 330

test_part1				0.00 323		0.00 340	0.00 334	86.64 7				0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

cdsmvs_eth3d_5k		17.50 306	23.34 305	0.00 323	0.00 340	0.00 340	0.00 334	78.63 149	0.00 336	0.00 338	82.18 155	49.25 92	0.00 337	0.00 336	0.00 334	0.00 335

pcd_1.5k_mvsjas		3.92 312	5.23 314	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	0.00 338	47.05 119	0.00 337	0.00 336	0.00 334	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	0.00 338	0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	0.00 338	0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	0.00 338	0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	0.00 338	0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

testmvs		4.52 311	6.03 313	0.01 322	0.01 338	0.00 340	53.86 311	0.00 340	0.01 334	0.04 336	0.27 336	0.00 342	0.00 337	0.04 334	0.00 334	0.03 334

test123		4.73 310	6.30 312	0.02 321	0.01 338	0.01 339	56.36 306	0.00 340	0.01 334	0.04 336	0.21 337	0.01 341	0.00 337	0.03 335	0.00 334	0.04 333

ab-mvs-re		6.49 309	8.65 311	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	77.89 232	0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 340	0.00 334	0.00 340	0.00 336	0.00 338	0.00 338	0.00 342	0.00 337	0.00 336	0.00 334	0.00 335

save fliter					86.17 25	61.30 30	83.98 38	79.66 131	59.00 114

test0726					87.75 4	59.07 58	87.86 2	86.83 5	64.26 29	84.19 1	91.92 5	64.82 2

GSMVS																78.05 231

test_part2					87.58 6	60.47 42				83.42 5

sam_mvs1												34.74 234				78.05 231

sam_mvs												33.43 248

MTGPA								80.97 110

test_post1							68.67 256				3.64 333	32.39 264	69.49 268	44.17 232

test_post											3.55 334	33.90 243	66.52 281

patchmatchnet-post											64.03 310	34.50 236	74.27 251

MTMP							86.03 14	17.08 335

gm-plane-assit					71.40 257	41.72 263			48.85 237		73.31 276		82.48 153	48.90 197

TEST9					85.58 36	61.59 26	81.62 72	81.26 101	55.65 167	74.93 32	88.81 49	53.70 51	84.68 102

test_8					85.40 39	60.96 35	81.54 75	81.18 104	55.86 161	74.81 35	88.80 51	53.70 51	84.45 107

agg_prior					85.04 43	59.96 46		81.04 107		74.68 36			84.04 114

test_prior4						62.51 17	82.08 67

test_prior				76.69 50	84.20 55	57.27 80		84.88 27					86.43 66			86.38 52

旧先验2							76.08 162		45.32 268	76.55 22			65.56 286	58.75 129

新几何2							76.12 160

无先验							79.66 101	74.30 209	48.40 242				80.78 181	53.62 161		79.03 223

原ACMM2							79.02 108

testdata2													72.18 259	46.95 211

segment_acmp												54.23 44

testdata1							72.65 211		60.50 85

plane_prior7					81.41 80	55.96 102

plane_prior6					81.20 87	56.24 97						45.26 141

plane_prior4											86.10 86

plane_prior3						56.09 99			63.92 33	69.27 106

plane_prior2							84.22 31		64.52 25

plane_prior1					81.27 85

n2								0.00 340

nn								0.00 340

door-mid								47.19 324

test11								83.47 56

door								47.60 323

HQP5-MVS						54.94 117

HQP-NCC					80.66 92		82.31 62		62.10 63	67.85 129

ACMP_Plane					80.66 92		82.31 62		62.10 63	67.85 129

BP-MVS														67.04 70

HQP4-MVS										67.85 129			86.93 48			84.32 127

HQP2-MVS												45.46 135

NP-MVS					80.98 90	56.05 101					85.54 99

MDTV_nov1_ep13_2view						25.89 325	61.22 292		40.10 303	51.10 292		32.97 253		38.49 266		78.61 226

Test By Simon												48.33 104

High-resolution multi-view benchmark