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		80.69 1	81.20 1	79.14 10	86.21 20	62.73 12	86.09 9	85.03 16	65.51 15	83.81 1	90.51 14	63.71 2	89.23 8	81.51 1	88.44 13	85.45 89

ESAPD		80.56 2	80.98 2	79.29 7	87.27 6	60.56 38	85.71 16	86.42 6	63.28 36	83.27 4	91.83 3	64.96 1	90.47 1	76.41 13	89.67 6	86.84 42

SMA-MVS		80.28 3	80.39 4	79.95 2	86.60 13	61.95 22	86.33 5	85.75 11	62.49 51	82.20 6	92.28 1	56.53 16	89.70 5	79.85 3	91.48 1	88.19 8

APDe-MVS		80.16 4	80.59 3	78.86 20	86.64 12	60.02 42	88.12 1	86.42 6	62.94 42	82.40 5	92.12 2	59.64 7	89.76 4	78.70 6	88.32 17	86.79 44

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 43	65.37 16	78.78 11	90.64 10	58.63 13	87.24 33	79.00 5	90.37 3	85.26 101

CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 23	85.03 16	66.96 6	77.58 14	90.06 23	59.47 9	89.13 10	78.67 7	89.73 4	87.03 38

SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 59	62.18 19	87.60 2	85.83 9	66.69 11	78.03 13	90.98 7	54.26 36	90.06 2	78.42 8	89.02 9	87.69 19
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 45	60.32 40	83.03 40	85.33 13	62.86 45	80.17 7	90.03 24	61.76 3	88.95 12	74.21 21	88.67 12	88.12 9

ACMMP_Plus		78.77 9	78.78 9	78.74 22	85.44 33	61.04 32	83.84 31	85.16 14	62.88 44	78.10 12	91.26 6	52.51 53	88.39 15	79.34 4	90.52 2	86.78 45

NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 20	84.42 24	66.73 10	74.67 33	89.38 33	55.30 27	89.18 9	74.19 22	87.34 29	86.38 49

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 53	61.62 25	84.17 27	86.85 2	63.23 37	73.84 42	90.25 21	57.68 14	89.96 3	74.62 20	89.03 8	87.89 11
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		78.44 12	78.28 12	78.90 18	84.96 41	61.41 28	84.03 29	83.82 41	59.34 118	79.37 9	89.76 29	59.84 5	87.62 30	76.69 12	86.74 37	87.68 20

MP-MVS-pluss		78.35 13	78.46 10	78.03 32	84.96 41	59.52 47	82.93 42	85.39 12	62.15 56	76.41 18	91.51 4	52.47 55	86.78 45	80.66 2	89.64 7	87.80 16
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 13	78.26 13	78.64 23	86.54 15	63.47 5	86.02 11	83.55 47	63.89 31	73.60 46	90.60 11	54.85 32	86.72 46	77.20 11	88.06 22	85.74 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		78.02 15	78.04 15	77.98 33	86.44 17	60.81 35	85.52 18	84.36 25	60.61 77	79.05 10	90.30 19	55.54 26	88.32 18	73.48 30	87.03 32	84.83 113
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 16	77.65 16	79.10 13	86.71 9	62.81 10	86.29 6	84.32 26	62.82 46	73.96 37	90.50 15	53.20 49	88.35 16	74.02 23	87.05 30	86.13 61

#test#		77.83 17	77.41 19	79.10 13	86.71 9	62.81 10	85.69 17	84.32 26	61.61 65	73.96 37	90.50 15	53.20 49	88.35 16	73.68 26	87.05 30	86.13 61

ACMMPR		77.71 18	77.23 21	79.16 8	86.75 8	62.93 9	86.29 6	84.24 28	62.82 46	73.55 47	90.56 13	49.80 77	88.24 19	74.02 23	87.03 32	86.32 57

SD-MVS		77.70 19	77.62 17	77.93 34	84.47 48	61.88 24	84.55 22	83.87 39	60.37 82	79.89 8	89.38 33	54.97 29	85.58 74	76.12 14	84.94 47	86.33 55

region2R		77.67 20	77.18 22	79.15 9	86.76 7	62.95 8	86.29 6	84.16 30	62.81 48	73.30 50	90.58 12	49.90 75	88.21 20	73.78 25	87.03 32	86.29 59

zzz-MVS		77.61 21	77.36 20	78.35 26	86.08 24	63.57 2	83.37 36	80.97 104	65.13 18	75.77 21	90.88 8	48.63 105	86.66 47	77.23 9	88.17 19	84.81 114

MCST-MVS		77.48 22	77.45 18	77.54 36	86.67 11	58.36 62	83.22 38	86.93 1	56.91 149	74.91 29	88.19 47	59.15 11	87.68 29	73.67 27	87.45 28	86.57 47

HPM-MVS		77.28 23	76.85 24	78.54 24	85.00 40	60.81 35	82.91 43	85.08 15	62.57 49	73.09 53	89.97 26	50.90 71	87.48 31	75.30 15	86.85 35	87.33 33
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 24	76.63 27	79.12 12	86.15 22	60.86 34	84.71 21	84.85 20	61.98 62	73.06 54	88.88 41	53.72 43	89.06 11	68.27 52	88.04 23	87.42 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		77.17 25	76.56 28	79.00 16	86.32 18	62.62 14	85.83 12	83.92 35	64.55 22	72.17 63	90.01 25	47.95 114	88.01 24	71.55 38	86.74 37	86.37 52

CP-MVS		77.12 26	76.68 26	78.43 25	86.05 26	63.18 7	87.55 3	83.45 50	62.44 53	72.68 58	90.50 15	48.18 112	87.34 32	73.59 28	85.71 44	84.76 118

CSCG		76.92 27	76.75 25	77.41 38	83.96 52	59.60 46	82.95 41	86.50 5	60.78 75	75.27 24	84.83 99	60.76 4	86.56 53	67.86 57	87.87 27	86.06 64

MTAPA		76.90 28	76.42 29	78.35 26	86.08 24	63.57 2	74.92 192	80.97 104	65.13 18	75.77 21	90.88 8	48.63 105	86.66 47	77.23 9	88.17 19	84.81 114

test_prior3		76.89 29	76.96 23	76.69 47	84.20 50	57.27 74	81.75 64	84.88 18	60.37 82	75.01 25	89.06 36	56.22 21	86.43 57	72.19 34	88.96 10	86.38 49

PGM-MVS		76.77 30	76.06 31	78.88 19	86.14 23	62.73 12	82.55 51	83.74 42	61.71 63	72.45 62	90.34 18	48.48 109	88.13 21	72.32 33	86.85 35	85.78 70

MVS_0304		76.73 31	76.04 32	78.78 21	81.32 76	58.89 56	82.50 53	84.07 31	67.73 5	72.08 65	87.28 57	49.49 79	89.57 6	73.52 29	86.40 41	87.87 13

mPP-MVS		76.54 32	75.93 34	78.34 28	86.47 16	63.50 4	85.74 15	82.28 71	62.90 43	71.77 67	90.26 20	46.61 132	86.55 54	71.71 37	85.66 45	84.97 110

CANet		76.46 33	75.93 34	78.06 31	81.29 77	57.53 71	82.35 55	83.31 56	67.78 3	70.09 80	86.34 75	54.92 30	88.90 13	72.68 32	84.55 49	87.76 18

CDPH-MVS		76.31 34	75.67 37	78.22 29	85.35 36	59.14 52	81.31 74	84.02 32	56.32 165	74.05 36	88.98 39	53.34 47	87.92 26	69.23 49	88.42 14	87.59 22

train_agg		76.27 35	76.15 30	76.64 51	85.58 31	61.59 26	81.62 68	81.26 94	55.86 172	74.93 27	88.81 42	53.70 44	84.68 101	75.24 17	88.33 15	83.65 158

agg_prior3		76.13 36	75.89 36	76.85 45	85.76 27	62.02 21	81.65 66	81.01 102	55.51 181	73.73 43	88.60 46	53.23 48	84.90 93	75.24 17	88.33 15	83.65 158

ACMMP		76.02 37	75.33 39	78.07 30	85.20 37	61.91 23	85.49 19	84.44 23	63.04 40	69.80 92	89.74 30	45.43 143	87.16 37	72.01 36	82.87 61	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
agg_prior1		75.94 38	76.01 33	75.72 61	85.04 38	59.96 43	81.44 72	81.04 100	56.14 170	74.68 31	88.90 40	53.91 40	84.04 113	75.01 19	87.92 26	83.16 170

PHI-MVS		75.87 39	75.36 38	77.41 38	80.62 89	55.91 99	84.28 24	85.78 10	56.08 171	73.41 49	86.58 69	50.94 70	88.54 14	70.79 42	89.71 5	87.79 17

3Dnovator+	66.72 4	75.84 40	74.57 44	79.66 4	82.40 63	59.92 45	85.83 12	86.32 8	66.92 9	67.80 132	89.24 35	42.03 171	89.38 7	64.07 92	86.50 40	89.69 1

Regformer-2		75.63 41	74.99 40	77.54 36	80.43 91	58.32 63	79.50 98	82.92 63	67.84 1	75.94 20	80.75 193	55.73 24	86.80 43	71.44 40	80.38 82	87.50 24

Regformer-1		75.47 42	74.93 42	77.09 42	80.43 91	57.70 69	79.50 98	82.13 72	67.84 1	75.73 23	80.75 193	56.50 17	86.07 61	71.07 41	80.38 82	87.50 24

casdiffmvs1		75.24 43	74.76 43	76.69 47	80.32 93	55.61 105	82.80 44	83.60 45	52.54 214	76.15 19	86.48 71	59.44 10	85.78 71	69.78 45	81.70 71	88.69 2

APD-MVS_3200maxsize		74.96 44	74.39 46	76.67 50	82.20 64	58.24 64	83.67 32	83.29 57	58.41 131	73.71 44	90.14 22	45.62 138	85.99 65	69.64 47	82.85 62	85.78 70

TSAR-MVS + GP.		74.90 45	74.15 48	77.17 41	82.00 66	58.77 58	81.80 63	78.57 162	58.58 127	74.32 35	84.51 108	55.94 23	87.22 34	67.11 63	84.48 51	85.52 83

DELS-MVS		74.76 46	74.46 45	75.65 64	77.84 147	52.25 144	75.59 176	84.17 29	63.76 32	73.15 52	82.79 133	59.58 8	86.80 43	67.24 62	86.04 43	87.89 11
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
OPM-MVS		74.73 47	74.25 47	76.19 54	80.81 85	59.01 54	82.60 50	83.64 44	63.74 33	72.52 60	87.49 52	47.18 124	85.88 69	69.47 48	80.78 74	83.66 157

canonicalmvs		74.67 48	74.98 41	73.71 100	78.94 118	50.56 170	80.23 84	83.87 39	60.30 87	77.15 15	86.56 70	59.65 6	82.00 166	66.01 70	82.12 65	88.58 5

casdiffmvs		74.55 49	73.78 51	76.87 43	79.00 116	56.18 92	82.36 54	84.45 22	53.88 204	73.46 48	85.76 88	56.38 20	86.59 50	70.70 43	78.04 118	87.83 15

abl_6		74.34 50	73.50 53	76.86 44	82.43 62	60.16 41	83.48 35	81.86 78	58.81 124	73.95 39	89.86 27	41.87 174	86.62 49	67.98 55	81.23 73	83.80 151

HQP_MVS		74.31 51	73.73 52	76.06 55	81.41 74	56.31 87	84.22 25	84.01 33	64.52 24	69.27 104	86.10 79	45.26 147	87.21 35	68.16 53	80.58 78	84.65 119

HPM-MVS_fast		74.30 52	73.46 56	76.80 46	84.45 49	59.04 53	83.65 33	81.05 99	60.15 89	70.43 75	89.84 28	41.09 189	85.59 73	67.61 60	82.90 60	85.77 72

Regformer-4		74.25 53	73.48 54	76.57 52	79.75 104	56.54 86	78.54 109	81.49 86	66.93 8	73.90 40	80.30 203	53.84 42	85.98 66	69.76 46	76.84 132	87.17 35

MVS_111021_HR		74.02 54	73.46 56	75.69 63	83.01 59	60.63 37	77.29 146	78.40 171	61.18 70	70.58 74	85.97 82	54.18 38	84.00 117	67.52 61	82.98 59	82.45 182

MG-MVS		73.96 55	73.89 50	74.16 87	85.65 29	49.69 198	81.59 70	81.29 93	61.45 66	71.05 72	88.11 48	51.77 60	87.73 28	61.05 127	83.09 56	85.05 107

Regformer-3		73.89 56	73.28 58	75.71 62	79.75 104	55.48 108	78.54 109	79.93 126	66.58 12	73.62 45	80.30 203	54.87 31	84.54 104	69.09 50	76.84 132	87.10 37

alignmvs		73.86 57	73.99 49	73.45 112	78.20 136	50.50 172	78.57 107	82.43 69	59.40 116	76.57 16	86.71 63	56.42 19	81.23 179	65.84 72	81.79 67	88.62 3

MSLP-MVS++		73.77 58	73.47 55	74.66 78	83.02 58	59.29 51	82.30 60	81.88 77	59.34 118	71.59 69	86.83 59	45.94 136	83.65 123	65.09 77	85.22 46	81.06 209

HQP-MVS		73.45 59	72.80 61	75.40 68	80.66 86	54.94 111	82.31 57	83.90 37	62.10 57	67.85 127	85.54 92	45.46 141	86.93 41	67.04 64	80.35 84	84.32 126

CLD-MVS		73.33 60	72.68 62	75.29 71	78.82 120	53.33 128	78.23 115	84.79 21	61.30 69	70.41 76	81.04 180	52.41 56	87.12 38	64.61 82	82.49 64	85.41 96
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 61	72.54 63	75.62 65	77.87 146	53.64 122	79.62 96	79.61 131	61.63 64	72.02 66	82.61 138	56.44 18	85.97 67	63.99 95	79.07 106	87.25 34

UA-Net		73.13 62	72.93 60	73.76 96	83.58 54	51.66 150	78.75 102	77.66 179	67.75 4	72.61 59	89.42 31	49.82 76	83.29 130	53.61 169	83.14 55	86.32 57

EPNet		73.09 63	72.16 65	75.90 57	75.95 187	56.28 89	83.05 39	72.39 232	66.53 13	65.27 163	87.00 58	50.40 73	85.47 79	62.48 109	86.32 42	85.94 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
nrg030		72.96 64	73.01 59	72.84 133	75.41 194	50.24 179	80.02 87	82.89 66	58.36 133	74.44 34	86.73 61	58.90 12	80.83 186	65.84 72	74.46 146	87.44 27

CPTT-MVS		72.78 65	72.08 67	74.87 75	84.88 46	61.41 28	84.15 28	77.86 175	55.27 184	67.51 137	88.08 50	41.93 173	81.85 168	69.04 51	80.01 88	81.35 203

LPG-MVS_test		72.74 66	71.74 69	75.76 59	80.22 96	57.51 72	82.55 51	83.40 52	61.32 67	66.67 146	87.33 55	39.15 204	86.59 50	67.70 58	77.30 127	83.19 167

PAPM_NR		72.63 67	71.80 68	75.13 72	81.72 69	53.42 127	79.91 90	83.28 58	59.14 120	66.31 152	85.90 83	51.86 59	86.06 62	57.45 142	80.62 76	85.91 67

VDD-MVS		72.50 68	72.09 66	73.75 98	81.58 70	49.69 198	77.76 132	77.63 180	63.21 38	73.21 51	89.02 38	42.14 170	83.32 129	61.72 124	82.50 63	88.25 7

3Dnovator	64.47 5	72.49 69	71.39 75	75.79 58	77.70 149	58.99 55	80.66 81	83.15 60	62.24 55	65.46 160	86.59 68	42.38 169	85.52 76	59.59 137	84.72 48	82.85 176

MVS_Test		72.45 70	72.46 64	72.42 152	74.88 199	48.50 212	76.28 163	83.14 61	59.40 116	72.46 61	84.68 101	55.66 25	81.12 180	65.98 71	79.66 94	87.63 21

EI-MVSNet-Vis-set		72.42 71	71.59 70	74.91 73	78.47 130	54.02 118	77.05 150	79.33 149	65.03 20	71.68 68	79.35 227	52.75 51	84.89 94	66.46 67	74.23 149	85.83 69

ACMP	63.53 6	72.30 72	71.20 80	75.59 67	80.28 94	57.54 70	82.74 47	82.84 67	60.58 78	65.24 165	86.18 77	39.25 202	86.03 64	66.95 66	76.79 134	83.22 165
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 73	71.21 79	75.31 70	78.50 128	55.93 98	81.63 67	82.12 73	56.24 168	70.02 84	85.68 89	47.05 125	84.34 108	65.27 76	74.41 148	85.67 77

Vis-MVSNet		72.18 74	71.37 76	74.61 81	81.29 77	55.41 109	80.90 77	78.28 173	60.73 76	69.23 107	88.09 49	44.36 156	82.65 155	57.68 141	81.75 69	85.77 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		72.17 75	71.41 73	74.45 84	81.95 67	57.22 76	84.03 29	80.38 121	59.89 97	68.40 116	82.33 145	49.64 78	87.83 27	51.87 179	84.16 53	78.30 242

EPP-MVSNet		72.16 76	71.31 78	74.71 76	78.68 125	49.70 196	82.10 61	81.65 82	60.40 81	65.94 155	85.84 84	51.74 61	86.37 59	55.93 148	79.55 97	88.07 10

DP-MVS Recon		72.15 77	70.73 85	76.40 53	86.57 14	57.99 66	81.15 76	82.96 62	57.03 146	66.78 145	85.56 90	44.50 153	88.11 22	51.77 181	80.23 87	83.10 171

EI-MVSNet-UG-set		71.92 78	71.06 81	74.52 83	77.98 144	53.56 124	76.62 156	79.16 151	64.40 26	71.18 70	78.95 232	52.19 58	84.66 103	65.47 75	73.57 158	85.32 98

VDDNet		71.81 79	71.33 77	73.26 124	82.80 61	47.60 223	78.74 103	75.27 207	59.59 111	72.94 55	89.40 32	41.51 183	83.91 118	58.75 138	82.99 58	88.26 6

LFMVS		71.78 80	71.59 70	72.32 155	83.40 55	46.38 232	79.75 93	71.08 237	64.18 28	72.80 57	88.64 45	42.58 167	83.72 121	57.41 143	84.49 50	86.86 41

PAPR		71.72 81	70.82 84	74.41 85	81.20 81	51.17 154	79.55 97	83.33 55	55.81 175	66.93 144	84.61 104	50.95 69	86.06 62	55.79 151	79.20 104	86.00 65

IS-MVSNet		71.57 82	71.00 82	73.27 123	78.86 119	45.63 237	80.22 85	78.69 160	64.14 29	66.46 148	87.36 54	49.30 83	85.60 72	50.26 190	83.71 54	88.59 4

diffmvs1		71.56 83	71.40 74	72.04 157	71.20 277	48.71 210	74.80 195	77.10 189	60.84 72	71.10 71	85.28 96	52.65 52	80.01 200	70.26 44	79.35 101	87.40 29

MAR-MVS		71.51 84	70.15 92	75.60 66	81.84 68	59.39 49	81.38 73	82.90 65	54.90 191	68.08 124	78.70 233	47.73 116	85.51 77	51.68 183	84.17 52	81.88 190
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
MVSFormer		71.50 85	70.38 89	74.88 74	78.76 122	57.15 81	82.79 45	78.48 166	51.26 236	69.49 99	83.22 129	43.99 159	83.24 131	66.06 68	79.37 98	84.23 133

PVSNet_Blended_VisFu		71.45 86	70.39 88	74.65 79	82.01 65	58.82 57	79.93 89	80.35 123	55.09 187	65.82 159	82.16 151	49.17 97	82.64 156	60.34 131	78.62 114	82.50 181

OMC-MVS		71.40 87	70.60 86	73.78 94	76.60 179	53.15 130	79.74 94	79.78 127	58.37 132	68.75 111	86.45 73	45.43 143	80.60 191	62.58 107	77.73 120	87.58 23

UniMVSNet_NR-MVSNet		71.11 88	71.00 82	71.44 169	79.20 111	44.13 249	76.02 172	82.60 68	66.48 14	68.20 119	84.60 105	56.82 15	82.82 146	54.62 160	70.43 204	87.36 32

PCF-MVS	61.88 8	70.95 89	69.49 106	75.35 69	77.63 152	55.71 101	76.04 171	81.81 80	50.30 244	69.66 93	85.40 95	52.51 53	84.89 94	51.82 180	80.24 86	85.45 89
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		70.83 90	69.56 101	74.64 80	86.21 20	54.63 115	82.34 56	81.81 80	48.22 262	63.01 189	85.83 85	40.92 192	87.10 39	57.91 140	79.79 92	82.18 185

FIs		70.82 91	71.43 72	68.98 207	78.33 133	38.14 294	76.96 152	83.59 46	61.02 71	67.33 139	86.73 61	55.07 28	81.64 171	54.61 162	79.22 103	87.14 36

ACMM	61.98 7	70.80 92	69.73 96	74.02 88	80.59 90	58.59 60	82.68 48	82.02 76	55.46 182	67.18 141	84.39 110	38.51 209	83.17 133	60.65 128	76.10 137	80.30 222
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1neww		70.66 93	69.70 97	73.53 107	73.15 239	50.22 180	78.11 118	80.68 109	59.65 105	69.83 89	81.67 162	49.29 85	84.96 89	64.55 83	70.38 207	85.42 92

v7new		70.66 93	69.70 97	73.53 107	73.15 239	50.22 180	78.11 118	80.68 109	59.65 105	69.83 89	81.67 162	49.29 85	84.96 89	64.55 83	70.38 207	85.42 92

v6		70.66 93	69.70 97	73.53 107	73.14 242	50.21 183	78.11 118	80.67 111	59.65 105	69.82 91	81.65 164	49.29 85	84.96 89	64.55 83	70.39 206	85.42 92

UniMVSNet (Re)		70.63 96	70.20 90	71.89 159	78.55 127	45.29 239	75.94 173	82.92 63	63.68 34	68.16 121	83.59 123	53.89 41	83.49 126	53.97 165	71.12 195	86.89 40

v7		70.57 97	69.48 107	73.85 91	73.50 227	50.92 157	78.27 113	81.43 87	58.93 121	69.61 94	81.49 170	47.56 119	85.43 81	63.94 96	70.62 199	85.21 102

xiu_mvs_v2_base		70.52 98	69.75 95	72.84 133	81.21 80	55.63 104	75.11 187	78.92 155	54.92 190	69.96 86	79.68 216	47.00 129	82.09 165	61.60 126	79.37 98	80.81 217

PS-MVSNAJ		70.51 99	69.70 97	72.93 129	81.52 71	55.79 100	74.92 192	79.00 154	55.04 189	69.88 87	78.66 234	47.05 125	82.19 162	61.61 125	79.58 95	80.83 216

v1141		70.50 100	69.53 102	73.41 116	72.92 249	50.00 190	77.69 133	80.60 113	59.50 113	69.60 95	81.43 171	49.24 94	84.77 98	64.48 87	70.30 213	85.46 88

divwei89l23v2f112		70.50 100	69.53 102	73.41 116	72.91 250	50.00 190	77.69 133	80.59 114	59.50 113	69.60 95	81.43 171	49.26 90	84.77 98	64.48 87	70.31 212	85.47 86

v2v482		70.50 100	69.45 109	73.66 102	72.62 256	50.03 189	77.58 137	80.51 118	59.90 95	69.52 98	82.14 152	47.53 120	84.88 96	65.07 78	70.17 216	86.09 63

v1		70.50 100	69.53 102	73.42 115	72.91 250	50.00 190	77.69 133	80.59 114	59.50 113	69.59 97	81.42 173	49.26 90	84.77 98	64.49 86	70.30 213	85.47 86

mvs-test1		70.44 104	68.19 128	77.18 40	76.10 184	63.22 6	80.59 82	76.06 200	59.83 99	66.32 151	79.87 210	41.56 180	85.53 75	60.60 129	72.77 173	82.80 177

v1144		70.42 105	69.31 110	73.76 96	73.22 235	50.64 165	77.83 130	81.43 87	58.58 127	69.40 102	81.16 177	47.53 120	85.29 84	64.01 94	70.64 198	85.34 97

diffmvs		70.36 106	69.99 94	71.46 168	70.48 283	48.19 215	74.59 200	76.30 195	60.36 86	67.75 135	83.81 120	51.22 65	79.77 201	67.92 56	77.50 123	86.42 48

TranMVSNet+NR-MVSNet		70.36 106	70.10 93	71.17 178	78.64 126	42.97 260	76.53 158	81.16 98	66.95 7	68.53 115	85.42 94	51.61 62	83.07 136	52.32 177	69.70 228	87.46 26

v8		70.33 108	69.28 111	73.49 110	73.15 239	50.22 180	78.62 106	80.78 108	60.79 74	66.45 149	82.11 153	49.35 81	84.98 87	63.58 101	68.71 236	85.28 99

Fast-Effi-MVS+		70.28 109	69.12 113	73.73 99	78.50 128	51.50 153	75.01 189	79.46 144	56.16 169	68.59 112	79.55 223	53.97 39	84.05 112	53.34 171	77.53 122	85.65 79

X-MVStestdata		70.21 110	67.28 149	79.00 16	86.32 18	62.62 14	85.83 12	83.92 35	64.55 22	72.17 63	6.49 363	47.95 114	88.01 24	71.55 38	86.74 37	86.37 52

v10		70.21 110	69.02 114	73.81 93	73.51 226	50.92 157	78.74 103	81.39 89	60.05 91	66.39 150	81.83 160	47.58 118	85.41 82	62.80 106	68.86 235	85.09 106

QAPM		70.05 112	68.81 117	73.78 94	76.54 181	53.43 126	83.23 37	83.48 48	52.89 210	65.90 156	86.29 76	41.55 182	86.49 56	51.01 185	78.40 116	81.42 195

DU-MVS		70.01 113	69.53 102	71.44 169	78.05 142	44.13 249	75.01 189	81.51 85	64.37 27	68.20 119	84.52 106	49.12 100	82.82 146	54.62 160	70.43 204	87.37 30

AdaColmap		69.99 114	68.66 120	73.97 90	84.94 43	57.83 67	82.63 49	78.71 159	56.28 167	64.34 177	84.14 112	41.57 179	87.06 40	46.45 216	78.88 107	77.02 259

v1192		69.97 115	68.68 119	73.85 91	73.19 238	50.94 155	77.68 136	81.36 90	57.51 140	68.95 110	80.85 189	45.28 146	85.33 83	62.97 105	70.37 209	85.27 100

Anonymous20240529		69.91 116	69.02 114	72.56 142	80.19 99	47.65 222	77.56 139	80.99 103	55.45 183	69.88 87	86.76 60	39.24 203	82.18 163	54.04 164	77.10 129	87.85 14

FC-MVSNet-test		69.80 117	70.58 87	67.46 220	77.61 157	34.73 319	76.05 170	83.19 59	60.84 72	65.88 157	86.46 72	54.52 35	80.76 190	52.52 176	78.12 117	86.91 39

v144192		69.71 118	68.51 121	73.33 120	73.10 244	50.13 187	77.54 140	80.64 112	56.65 155	68.57 114	80.55 196	46.87 130	84.96 89	62.98 104	69.66 229	84.89 112

0601test		69.69 119	69.13 112	71.36 173	78.37 132	45.74 236	74.71 197	80.20 124	57.91 136	70.01 85	83.83 118	42.44 168	82.87 143	54.97 157	79.72 93	85.48 85

VNet		69.68 120	70.19 91	68.16 215	79.73 107	41.63 270	70.53 254	77.38 185	60.37 82	70.69 73	86.63 66	51.08 67	77.09 246	53.61 169	81.69 72	85.75 74

jason		69.65 121	68.39 126	73.43 114	78.27 135	56.88 83	77.12 148	73.71 225	46.53 276	69.34 103	83.22 129	43.37 163	79.18 212	64.77 79	79.20 104	84.23 133
jason: jason.
Effi-MVS+-dtu		69.64 122	67.53 140	75.95 56	76.10 184	62.29 18	80.20 86	76.06 200	59.83 99	65.26 164	77.09 262	41.56 180	84.02 116	60.60 129	71.09 196	81.53 193

lupinMVS		69.57 123	68.28 127	73.44 113	78.76 122	57.15 81	76.57 157	73.29 227	46.19 279	69.49 99	82.18 148	43.99 159	79.23 210	64.66 80	79.37 98	83.93 143

NR-MVSNet		69.54 124	68.85 116	71.59 167	78.05 142	43.81 253	74.20 203	80.86 107	65.18 17	62.76 191	84.52 106	52.35 57	83.59 124	50.96 186	70.78 197	87.37 30

MVS_111021_LR		69.50 125	68.78 118	71.65 165	78.38 131	59.33 50	74.82 194	70.11 243	58.08 135	67.83 131	84.68 101	41.96 172	76.34 254	65.62 74	77.54 121	79.30 236

v1921920		69.47 126	68.17 129	73.36 119	73.06 245	50.10 188	77.39 142	80.56 116	56.58 161	68.59 112	80.37 199	44.72 149	84.98 87	62.47 110	69.82 224	85.00 108

test_djsdf		69.45 127	67.74 134	74.58 82	74.57 207	54.92 113	82.79 45	78.48 166	51.26 236	65.41 161	83.49 126	38.37 211	83.24 131	66.06 68	69.25 232	85.56 81

DI_MVS_plusplus_test		69.35 128	68.03 131	73.30 122	71.11 278	50.14 186	75.49 178	79.16 151	54.57 196	62.45 201	80.76 192	44.67 151	84.20 109	64.23 90	79.81 91	85.54 82

Anonymous20231211		69.28 129	68.47 123	71.73 163	80.28 94	47.18 227	79.98 88	82.37 70	54.61 193	67.24 140	84.01 116	39.43 200	82.41 160	55.45 155	72.83 172	85.62 80

EI-MVSNet		69.27 130	68.44 125	71.73 163	74.47 208	49.39 203	75.20 185	78.45 168	59.60 108	69.16 108	76.51 272	51.29 63	82.50 157	59.86 136	71.45 193	83.30 163

test_normal		69.26 131	67.90 133	73.32 121	70.84 281	50.38 175	75.30 181	79.17 150	54.23 201	62.00 209	80.61 195	44.69 150	83.89 119	64.33 89	79.95 90	85.69 76

v1240		69.24 132	67.91 132	73.25 125	73.02 247	49.82 193	77.21 147	80.54 117	56.43 164	68.34 118	80.51 197	43.33 164	84.99 85	62.03 120	69.77 227	84.95 111

IterMVS-LS		69.22 133	68.48 122	71.43 171	74.44 210	49.40 202	76.23 165	77.55 181	59.60 108	65.85 158	81.59 168	51.28 64	81.58 174	59.87 135	69.90 223	83.30 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		69.02 134	69.47 108	67.69 219	77.42 160	41.00 274	74.04 205	79.68 129	60.06 90	69.26 106	84.81 100	51.06 68	77.58 240	54.44 163	74.43 147	84.48 124

v7n		69.01 135	67.36 146	73.98 89	72.51 258	52.65 136	78.54 109	81.30 92	60.26 88	62.67 193	81.62 165	43.61 161	84.49 105	57.01 144	68.70 237	84.79 116

OpenMVS	61.03 9	68.85 136	67.56 138	72.70 138	74.26 213	53.99 119	81.21 75	81.34 91	52.70 211	62.75 192	85.55 91	38.86 207	84.14 111	48.41 206	83.01 57	79.97 226

XVG-OURS-SEG-HR		68.81 137	67.47 142	72.82 135	74.40 211	56.87 84	70.59 253	79.04 153	54.77 192	66.99 143	86.01 81	39.57 199	78.21 233	62.54 108	73.33 162	83.37 162

BH-RMVSNet		68.81 137	67.42 143	72.97 128	80.11 101	52.53 139	74.26 202	76.29 196	58.48 130	68.38 117	84.20 111	42.59 166	83.83 120	46.53 215	75.91 138	82.56 178

UGNet		68.81 137	67.39 144	73.06 127	78.33 133	54.47 116	79.77 92	75.40 206	60.45 80	63.22 186	84.40 109	32.71 281	80.91 185	51.71 182	80.56 80	83.81 148
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
XVG-OURS		68.76 140	67.37 145	72.90 130	74.32 212	57.22 76	70.09 260	78.81 157	55.24 185	67.79 133	85.81 87	36.54 239	78.28 232	62.04 119	75.74 139	83.19 167

V42		68.65 141	67.35 147	72.56 142	68.93 299	50.18 184	72.90 219	79.47 143	56.92 148	69.45 101	80.26 205	46.29 134	82.99 137	64.07 92	67.82 249	84.53 122

PVSNet_Blended		68.59 142	67.72 135	71.19 177	77.03 172	50.57 168	72.51 227	81.52 83	51.91 220	64.22 181	77.77 248	49.13 98	82.87 143	55.82 149	79.58 95	80.14 225

xiu_mvs_v1_base_debu		68.58 143	67.28 149	72.48 146	78.19 137	57.19 78	75.28 182	75.09 211	51.61 228	70.04 81	81.41 174	32.79 277	79.02 220	63.81 97	77.31 124	81.22 205

xiu_mvs_v1_base		68.58 143	67.28 149	72.48 146	78.19 137	57.19 78	75.28 182	75.09 211	51.61 228	70.04 81	81.41 174	32.79 277	79.02 220	63.81 97	77.31 124	81.22 205

xiu_mvs_v1_base_debi		68.58 143	67.28 149	72.48 146	78.19 137	57.19 78	75.28 182	75.09 211	51.61 228	70.04 81	81.41 174	32.79 277	79.02 220	63.81 97	77.31 124	81.22 205

PVSNet_BlendedMVS		68.56 146	67.72 135	71.07 181	77.03 172	50.57 168	74.50 201	81.52 83	53.66 206	64.22 181	79.72 215	49.13 98	82.87 143	55.82 149	73.92 153	79.77 231

1121		68.53 147	67.16 154	72.63 139	85.64 30	61.14 30	73.95 206	66.46 274	44.61 293	70.28 78	86.68 64	41.42 184	80.78 188	53.62 167	81.79 67	75.97 267

WR-MVS		68.47 148	68.47 123	68.44 214	80.20 98	39.84 277	73.75 211	76.07 199	64.68 21	68.11 123	83.63 122	50.39 74	79.14 218	49.78 192	69.66 229	86.34 54

v16		68.38 149	67.01 155	72.47 150	73.22 235	50.29 177	78.10 121	79.59 136	59.71 103	61.72 215	77.60 254	49.28 88	82.89 141	62.36 112	61.54 291	84.23 133

v17		68.37 150	67.00 156	72.48 146	73.22 235	50.31 176	78.10 121	79.58 138	59.71 103	61.67 216	77.60 254	49.31 82	82.89 141	62.37 111	61.48 294	84.23 133

v18		68.33 151	66.96 157	72.42 152	73.13 243	50.16 185	77.97 126	79.57 140	59.57 112	61.80 213	77.50 259	49.30 83	82.90 140	62.31 113	61.50 292	84.20 139

v13		68.29 152	66.84 159	72.63 139	73.50 227	50.83 160	78.25 114	79.58 138	60.05 91	60.76 231	77.68 251	49.11 103	82.77 148	62.17 116	60.45 305	84.30 128

v12		68.28 153	66.83 161	72.60 141	73.43 229	50.74 163	78.18 116	79.59 136	60.01 93	60.89 230	77.66 252	49.12 100	82.77 148	62.18 114	60.46 304	84.29 129

V9		68.27 154	66.84 159	72.56 142	73.39 232	50.63 166	78.10 121	79.60 133	59.94 94	61.05 228	77.62 253	49.18 96	82.77 148	62.17 116	60.48 303	84.27 130

BH-untuned		68.27 154	67.29 148	71.21 176	79.74 106	53.22 129	76.06 169	77.46 184	57.19 142	66.10 153	81.61 166	45.37 145	83.50 125	45.42 231	76.68 136	76.91 263

jajsoiax		68.25 156	66.45 168	73.66 102	75.62 190	55.49 107	80.82 78	78.51 165	52.33 216	64.33 178	84.11 113	28.28 307	81.81 170	63.48 102	70.62 199	83.67 156

V14		68.25 156	66.82 162	72.52 145	73.33 233	50.53 171	78.02 124	79.60 133	59.83 99	61.16 226	77.57 256	49.19 95	82.77 148	62.18 114	60.50 302	84.26 131

v148		68.24 158	67.19 153	71.40 172	70.43 285	47.77 221	75.76 175	77.03 190	58.91 122	67.36 138	80.10 207	48.60 108	81.89 167	60.01 133	66.52 257	84.53 122

v15		68.22 159	66.81 163	72.47 150	73.25 234	50.40 174	77.92 128	79.60 133	59.77 102	61.28 224	77.52 258	49.25 92	82.77 148	62.16 118	60.51 301	84.24 132

CANet_DTU		68.18 160	67.71 137	69.59 199	74.83 201	46.24 233	78.66 105	76.85 192	59.60 108	63.45 185	82.09 154	35.25 250	77.41 242	59.88 134	78.76 111	85.14 103

mvs_tets		68.18 160	66.36 172	73.63 105	75.61 191	55.35 110	80.77 79	78.56 163	52.48 215	64.27 180	84.10 114	27.45 313	81.84 169	63.45 103	70.56 202	83.69 153

v11		68.15 162	66.73 164	72.42 152	73.43 229	50.28 178	77.94 127	79.65 130	59.88 98	61.11 227	77.55 257	48.25 111	82.75 153	61.88 123	60.85 298	84.23 133

mvs_anonymous		68.03 163	67.51 141	69.59 199	72.08 264	44.57 246	71.99 238	75.23 208	51.67 227	67.06 142	82.57 139	54.68 33	77.94 236	56.56 145	75.71 140	86.26 60

PAPM		67.92 164	66.69 165	71.63 166	78.09 140	49.02 206	77.09 149	81.24 96	51.04 239	60.91 229	83.98 117	47.71 117	84.99 85	40.81 262	79.32 102	80.90 215

tttt0517		67.83 165	65.66 181	74.33 86	76.69 177	50.82 161	77.86 129	73.99 222	54.54 197	64.64 175	82.53 141	35.06 251	85.50 78	55.71 152	69.91 222	86.67 46

Test4		67.77 166	65.97 177	73.19 126	68.64 300	50.58 167	74.80 195	80.48 119	54.13 202	59.11 249	79.07 231	33.89 266	83.12 135	63.61 100	79.98 89	85.87 68

VPNet		67.52 167	68.11 130	65.74 247	79.18 112	36.80 306	72.17 232	72.83 230	62.04 60	67.79 133	85.83 85	48.88 104	76.60 251	51.30 184	72.97 171	83.81 148

Fast-Effi-MVS+-dtu		67.37 168	65.33 186	73.48 111	72.94 248	57.78 68	77.47 141	76.88 191	57.60 139	61.97 210	76.85 266	39.31 201	80.49 192	54.72 159	70.28 215	82.17 186

MVS		67.37 168	66.33 173	70.51 188	75.46 193	50.94 155	73.95 206	81.85 79	41.57 317	62.54 197	78.57 238	47.98 113	85.47 79	52.97 174	82.05 66	75.14 276

v748		67.26 170	65.67 180	72.02 158	69.90 293	49.77 195	76.24 164	79.57 140	58.58 127	60.49 234	80.38 198	44.47 155	82.17 164	56.16 147	65.26 265	84.12 142

GBi-Net		67.21 171	66.55 166	69.19 204	77.63 152	43.33 256	77.31 143	77.83 176	56.62 158	65.04 168	82.70 134	41.85 175	80.33 194	47.18 210	72.76 174	83.92 144

test1		67.21 171	66.55 166	69.19 204	77.63 152	43.33 256	77.31 143	77.83 176	56.62 158	65.04 168	82.70 134	41.85 175	80.33 194	47.18 210	72.76 174	83.92 144

MVSTER		67.16 173	65.58 183	71.88 160	70.37 287	49.70 196	70.25 259	78.45 168	51.52 231	69.16 108	80.37 199	38.45 210	82.50 157	60.19 132	71.46 192	83.44 161

v52		67.09 174	65.16 189	72.87 131	66.77 313	51.60 151	73.69 212	79.45 145	57.88 137	62.46 200	78.57 238	40.95 191	83.34 127	61.99 121	64.70 270	83.68 154

V4		67.09 174	65.16 189	72.87 131	66.76 314	51.60 151	73.69 212	79.45 145	57.88 137	62.45 201	78.58 237	40.96 190	83.34 127	61.99 121	64.71 268	83.68 154

Baseline_NR-MVSNet		67.05 176	67.56 138	65.50 253	75.65 189	37.70 298	75.42 179	74.65 216	59.90 95	68.14 122	83.15 132	49.12 100	77.20 244	52.23 178	69.78 225	81.60 192

WR-MVS_H		67.02 177	66.92 158	67.33 223	77.95 145	37.75 297	77.57 138	82.11 74	62.03 61	62.65 194	82.48 142	50.57 72	79.46 206	42.91 249	64.01 274	84.79 116

anonymousdsp		67.00 178	64.82 193	73.57 106	70.09 289	56.13 93	76.35 161	77.35 186	48.43 260	64.99 171	80.84 190	33.01 274	80.34 193	64.66 80	67.64 252	84.23 133

FMVSNet2		66.93 179	66.31 175	68.79 210	77.63 152	42.98 259	76.11 167	77.47 182	56.62 158	65.22 167	82.17 150	41.85 175	80.18 197	47.05 213	72.72 177	83.20 166

BH-w/o		66.85 180	65.83 179	69.90 196	79.29 109	52.46 141	74.66 198	76.65 193	54.51 198	64.85 172	78.12 240	45.59 140	82.95 139	43.26 245	75.54 141	74.27 289

Anonymous202405211		66.84 181	65.99 176	69.40 203	80.19 99	42.21 264	71.11 248	71.31 236	58.80 125	67.90 125	86.39 74	29.83 299	79.65 203	49.60 198	78.78 110	86.33 55

CDS-MVSNet		66.80 182	65.37 184	71.10 180	78.98 117	53.13 132	73.27 215	71.07 238	52.15 218	64.72 173	80.23 206	43.56 162	77.10 245	45.48 229	78.88 107	83.05 172
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 183	65.27 187	71.33 175	79.16 114	53.67 121	73.84 210	69.59 248	52.32 217	65.28 162	81.72 161	44.49 154	77.40 243	42.32 252	78.66 113	82.92 173

FMVSNet1		66.70 184	65.87 178	69.19 204	77.49 159	43.33 256	77.31 143	77.83 176	56.45 163	64.60 176	82.70 134	38.08 216	80.33 194	46.08 220	72.31 185	83.92 144

ab-mvs		66.65 185	66.42 170	67.37 221	76.17 183	41.73 268	70.41 257	76.14 198	53.99 203	65.98 154	83.51 125	49.48 80	76.24 255	48.60 204	73.46 160	84.14 140

PEN-MVS		66.60 186	66.45 168	67.04 224	77.11 170	36.56 308	77.03 151	80.42 120	62.95 41	62.51 199	84.03 115	46.69 131	79.07 219	44.22 235	63.08 282	85.51 84

TAPA-MVS	59.36 10	66.60 186	65.20 188	70.81 183	76.63 178	48.75 209	76.52 159	80.04 125	50.64 242	65.24 165	84.93 98	39.15 204	78.54 225	36.77 280	76.88 131	85.14 103
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 188	65.07 191	71.17 178	79.18 112	49.63 200	73.48 214	75.20 209	52.95 209	67.90 125	80.33 202	39.81 196	83.68 122	43.20 246	73.56 159	80.20 223

CP-MVSNet		66.49 189	66.41 171	66.72 226	77.67 151	36.33 310	76.83 155	79.52 142	62.45 52	62.54 197	83.47 127	46.32 133	78.37 230	45.47 230	63.43 279	85.45 89

PS-CasMVS		66.42 190	66.32 174	66.70 228	77.60 158	36.30 312	76.94 153	79.61 131	62.36 54	62.43 206	83.66 121	45.69 137	78.37 230	45.35 232	63.26 280	85.42 92

FMVSNet3		66.32 191	65.61 182	68.46 213	76.48 182	42.34 263	74.98 191	77.15 188	55.83 174	65.04 168	81.16 177	39.91 194	80.14 198	47.18 210	72.76 174	82.90 175

ACMH+	57.40 11	66.12 192	64.06 195	72.30 156	77.79 148	52.83 134	80.39 83	78.03 174	57.30 141	57.47 271	82.55 140	27.68 311	84.17 110	45.54 227	69.78 225	79.90 227

testing_2		66.02 193	63.77 201	72.76 137	66.03 319	50.48 173	72.93 218	80.36 122	54.41 199	54.25 297	76.76 268	30.89 291	83.16 134	64.19 91	74.08 151	84.65 119

cascas		65.98 194	63.42 206	73.64 104	77.26 168	52.58 138	72.26 231	77.21 187	48.56 257	61.21 225	74.60 289	32.57 285	85.82 70	50.38 189	76.75 135	82.52 180

thisisatest0515		65.83 195	63.50 205	72.82 135	73.75 223	49.50 201	71.32 243	73.12 229	49.39 250	63.82 183	76.50 274	34.95 253	84.84 97	53.20 173	75.49 142	84.13 141

DP-MVS		65.68 196	63.66 203	71.75 162	84.93 44	56.87 84	80.74 80	73.16 228	53.06 208	59.09 250	82.35 144	36.79 237	85.94 68	32.82 298	69.96 221	72.45 307

HyFIR lowres test		65.67 197	63.01 210	73.67 101	79.97 103	55.65 103	69.07 268	75.52 204	42.68 311	63.53 184	77.95 242	40.43 193	81.64 171	46.01 221	71.91 188	83.73 152

DTE-MVSNet		65.58 198	65.34 185	66.31 231	76.06 186	34.79 317	76.43 160	79.38 147	62.55 50	61.66 217	83.83 118	45.60 139	79.15 217	41.64 259	60.88 297	85.00 108

GA-MVS		65.53 199	63.70 202	71.02 182	70.87 280	48.10 216	70.48 255	74.40 218	56.69 154	64.70 174	76.77 267	33.66 268	81.10 181	55.42 156	70.32 211	83.87 147

CNLPA		65.43 200	64.02 196	69.68 197	78.73 124	58.07 65	77.82 131	70.71 240	51.49 232	61.57 219	83.58 124	38.23 214	70.82 281	43.90 239	70.10 218	80.16 224

MVP-Stereo		65.41 201	63.80 200	70.22 190	77.62 156	55.53 106	76.30 162	78.53 164	50.59 243	56.47 278	78.65 235	39.84 195	82.68 154	44.10 238	72.12 187	72.44 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 202	62.73 214	73.40 118	74.89 198	52.78 135	73.09 217	75.13 210	55.69 177	58.48 259	73.73 294	32.86 276	86.32 60	50.63 187	70.11 217	81.10 208
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
pm-mvs1		65.24 203	64.97 192	66.04 241	72.38 259	39.40 282	72.62 225	75.63 203	55.53 180	62.35 208	83.18 131	47.45 122	76.47 252	49.06 201	66.54 256	82.24 184

ACMH	55.70 15	65.20 204	63.57 204	70.07 193	78.07 141	52.01 149	79.48 100	79.69 128	55.75 176	56.59 277	80.98 184	27.12 315	80.94 183	42.90 250	71.58 191	77.25 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 205	63.21 208	70.72 186	81.04 83	54.87 114	78.57 107	77.47 182	48.51 258	55.71 281	81.89 159	33.71 267	79.71 202	41.66 257	70.37 209	77.58 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 206	62.84 212	71.82 161	81.49 73	56.26 90	66.32 285	74.20 220	40.53 323	63.16 188	78.65 235	41.30 185	77.80 238	45.80 223	74.09 150	81.40 196

TransMVSNet (Re)		64.72 207	64.33 194	65.87 246	75.22 196	38.56 291	74.66 198	75.08 214	58.90 123	61.79 214	82.63 137	51.18 66	78.07 235	43.63 242	55.87 318	80.99 214

EG-PatchMatch MVS		64.71 208	62.87 211	70.22 190	77.68 150	53.48 125	77.99 125	78.82 156	53.37 207	56.03 280	77.41 261	24.75 329	84.04 113	46.37 217	73.42 161	73.14 300

LS3D		64.71 208	62.50 216	71.34 174	79.72 108	55.71 101	79.82 91	74.72 215	48.50 259	56.62 276	84.62 103	33.59 269	82.34 161	29.65 323	75.23 143	75.97 267

1314		64.61 210	63.21 208	68.80 209	71.87 269	47.46 224	73.95 206	78.39 172	42.88 310	59.97 237	76.60 271	38.11 215	79.39 208	54.84 158	72.32 184	79.55 232

HY-MVS	56.14 13	64.55 211	63.89 198	66.55 229	74.73 204	41.02 272	69.96 261	74.43 217	49.29 251	61.66 217	80.92 186	47.43 123	76.68 250	44.91 234	71.69 190	81.94 188

Anonymous20240521		64.54 212	64.00 197	66.15 237	74.87 200	34.97 316	74.13 204	79.35 148	56.54 162	58.65 255	83.31 128	37.31 222	80.04 199	41.39 260	70.44 203	83.45 160

XVG-ACMP-BASELINE		64.36 213	62.23 221	70.74 185	72.35 260	52.45 142	70.80 252	78.45 168	53.84 205	59.87 239	81.10 179	16.24 341	79.32 209	55.64 154	71.76 189	80.47 219

CostFormer		64.04 214	62.51 215	68.61 212	71.88 268	45.77 235	71.30 244	70.60 241	47.55 269	64.31 179	76.61 270	41.63 178	79.62 205	49.74 194	69.00 233	80.42 220

1112_ss		64.00 215	63.36 207	65.93 244	79.28 110	42.58 262	71.35 242	72.36 233	46.41 277	60.55 233	77.89 245	46.27 135	73.28 272	46.18 218	69.97 220	81.92 189

pmmvs6		63.69 216	62.82 213	66.27 235	70.63 282	39.27 283	73.13 216	75.47 205	52.69 212	59.75 242	82.30 146	39.71 197	77.03 247	47.40 209	64.35 273	82.53 179

Vis-MVSNet (Re-imp)		63.69 216	63.88 199	63.14 270	74.75 203	31.04 340	71.16 247	63.64 297	56.32 165	59.80 241	84.99 97	44.51 152	75.46 258	39.12 269	80.62 76	82.92 173

conf200view11		63.38 218	62.41 217	66.29 234	77.31 161	38.66 288	72.65 221	69.11 255	57.07 143	62.45 201	81.03 181	37.01 228	79.17 213	31.84 302	73.25 164	81.03 210

tfpn111		63.33 219	62.34 219	66.30 232	77.31 161	38.66 288	72.65 221	69.11 255	57.07 143	62.45 201	81.03 181	37.01 228	79.23 210	31.38 311	73.09 169	81.03 210

thres400		63.31 220	62.18 222	66.72 226	76.85 175	39.62 279	71.96 239	69.44 251	56.63 156	62.61 195	79.83 211	37.18 223	79.17 213	31.84 302	73.25 164	81.36 197

thres600view7		63.30 221	62.27 220	66.41 230	77.18 169	38.87 285	72.35 229	69.11 255	56.98 147	62.37 207	80.96 185	37.01 228	79.00 223	31.43 310	73.05 170	81.36 197

thres100view900		63.28 222	62.41 217	65.89 245	77.31 161	38.66 288	72.65 221	69.11 255	57.07 143	62.45 201	81.03 181	37.01 228	79.17 213	31.84 302	73.25 164	79.83 228

test_0402		63.25 223	61.01 237	69.96 194	80.00 102	54.37 117	76.86 154	72.02 234	54.58 195	58.71 253	80.79 191	35.00 252	84.36 107	26.41 338	64.71 268	71.15 320

tfpn200view9		63.18 224	62.18 222	66.21 236	76.85 175	39.62 279	71.96 239	69.44 251	56.63 156	62.61 195	79.83 211	37.18 223	79.17 213	31.84 302	73.25 164	79.83 228

LTVRE_ROB	55.42 16	63.15 225	61.23 235	68.92 208	76.57 180	47.80 219	59.92 315	76.39 194	54.35 200	58.67 254	82.46 143	29.44 302	81.49 175	42.12 253	71.14 194	77.46 251
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
F-COLMAP		63.05 226	60.87 238	69.58 201	76.99 174	53.63 123	78.12 117	76.16 197	47.97 266	52.41 308	81.61 166	27.87 309	78.11 234	40.07 265	66.66 255	77.00 260

IterMVS		62.79 227	61.27 234	67.35 222	69.37 297	52.04 148	71.17 246	68.24 262	52.63 213	59.82 240	76.91 265	37.32 221	72.36 275	52.80 175	63.19 281	77.66 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		62.77 228	61.84 225	65.55 249	77.28 164	36.87 302	72.15 233	67.78 263	56.79 150	61.46 220	81.92 155	36.88 232	78.42 226	29.86 318	72.46 178	81.36 197

view800		62.77 228	61.84 225	65.55 249	77.28 164	36.87 302	72.15 233	67.78 263	56.79 150	61.46 220	81.92 155	36.88 232	78.42 226	29.86 318	72.46 178	81.36 197

conf0.05thres1000		62.77 228	61.84 225	65.55 249	77.28 164	36.87 302	72.15 233	67.78 263	56.79 150	61.46 220	81.92 155	36.88 232	78.42 226	29.86 318	72.46 178	81.36 197

tfpn		62.77 228	61.84 225	65.55 249	77.28 164	36.87 302	72.15 233	67.78 263	56.79 150	61.46 220	81.92 155	36.88 232	78.42 226	29.86 318	72.46 178	81.36 197

tpmp4_e23		62.71 232	60.13 241	70.45 189	73.40 231	48.39 213	72.82 220	69.49 250	44.88 289	59.91 238	74.99 285	37.79 218	81.47 176	40.22 264	67.71 251	81.48 194

tfpnnormal		62.47 233	61.63 231	64.99 258	74.81 202	39.01 284	71.22 245	73.72 224	55.22 186	60.21 235	80.09 208	41.26 188	76.98 248	30.02 317	68.09 246	78.97 240

MS-PatchMatch		62.42 234	61.46 232	65.31 256	75.21 197	52.10 145	72.05 237	74.05 221	46.41 277	57.42 272	74.36 290	34.35 261	77.57 241	45.62 226	73.67 155	66.26 331

Test_1112_low_res		62.32 235	61.77 229	64.00 264	79.08 115	39.53 281	68.17 277	70.17 242	43.25 306	59.03 251	79.90 209	44.08 157	71.24 280	43.79 241	68.42 238	81.25 204

thres200		62.20 236	61.16 236	65.34 255	75.38 195	39.99 276	69.60 263	69.29 253	55.64 179	61.87 212	76.99 263	37.07 227	78.96 224	31.28 312	73.28 163	77.06 258

PatchFormer-LS_test		62.20 236	60.59 239	67.04 224	72.18 263	46.82 230	70.36 258	68.62 260	51.92 219	59.19 248	70.23 312	36.86 236	75.07 267	50.23 191	65.68 262	79.23 237

tpm2		62.07 238	60.10 242	67.99 216	72.79 253	43.86 252	71.05 249	66.85 272	43.14 308	62.77 190	75.39 283	38.32 212	80.80 187	41.69 256	68.88 234	79.32 235

DWT-MVSNet_test		61.90 239	59.93 243	67.83 217	71.98 267	46.09 234	71.03 250	69.71 244	50.09 245	58.51 258	70.62 309	30.21 296	77.63 239	49.28 199	67.91 247	79.78 230

EPNet_dtu		61.90 239	61.97 224	61.68 278	72.89 252	39.78 278	75.85 174	65.62 278	55.09 187	54.56 293	79.36 226	37.59 219	67.02 297	39.80 268	76.95 130	78.25 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 241	61.35 233	63.46 265	74.58 206	31.48 339	61.42 309	58.14 321	58.71 126	53.02 307	79.55 223	43.07 165	76.80 249	45.69 224	77.96 119	82.11 187

MSDG		61.81 242	59.23 245	69.55 202	72.64 255	52.63 137	70.45 256	75.81 202	51.38 234	53.70 301	76.11 276	29.52 300	81.08 182	37.70 275	65.79 261	74.93 281

SixPastTwentyTwo		61.65 243	58.80 255	70.20 192	75.80 188	47.22 226	75.59 176	69.68 246	54.61 193	54.11 298	79.26 228	27.07 316	82.96 138	43.27 244	49.79 335	80.41 221

pmmvs4		61.48 244	59.39 244	67.76 218	71.57 271	53.86 120	71.42 241	65.34 280	44.20 298	59.46 243	77.92 244	35.90 240	74.71 269	43.87 240	64.87 267	74.71 285

OurMVSNet-221017-0		61.37 245	58.63 258	69.61 198	72.05 265	48.06 217	73.93 209	72.51 231	47.23 272	54.74 290	80.92 186	21.49 336	81.24 178	48.57 205	56.22 317	79.53 233

COLMAP_ROB	52.97 17	61.27 246	58.81 248	68.64 211	74.63 205	52.51 140	78.42 112	73.30 226	49.92 248	50.96 313	81.51 169	23.06 332	79.40 207	31.63 307	65.85 259	74.01 296
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 247	61.67 230	57.70 302	70.43 285	38.45 292	64.19 299	66.47 273	48.05 265	63.22 186	80.86 188	49.28 88	60.47 319	45.25 233	67.28 254	74.19 294

K. test v3		60.47 248	57.11 266	70.56 187	73.74 225	48.22 214	75.10 188	62.55 305	58.27 134	53.62 303	76.31 275	27.81 310	81.59 173	47.42 208	39.18 347	81.88 190

OpenMVS_ROB	52.78 18	60.03 249	58.14 261	65.69 248	70.47 284	44.82 241	75.33 180	70.86 239	45.04 288	56.06 279	76.00 277	26.89 318	79.65 203	35.36 290	67.29 253	72.60 304

conf0.01		59.97 250	58.81 248	63.42 266	74.15 215	33.83 325	68.32 271	64.22 288	51.79 221	58.04 262	79.57 217	35.41 243	75.41 259	29.57 324	68.26 239	81.03 210

conf0.002		59.97 250	58.81 248	63.42 266	74.15 215	33.83 325	68.32 271	64.22 288	51.79 221	58.04 262	79.57 217	35.41 243	75.41 259	29.57 324	68.26 239	81.03 210

CR-MVSNet		59.91 252	57.90 264	65.96 242	69.96 291	52.07 146	65.31 293	63.15 301	42.48 312	59.36 244	74.84 286	35.83 241	70.75 282	45.50 228	64.65 271	75.06 277

PatchmatchNet		59.84 253	58.24 259	64.65 261	73.05 246	46.70 231	69.42 265	62.18 307	47.55 269	58.88 252	71.96 301	34.49 259	69.16 288	42.99 248	63.60 277	78.07 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		59.75 254	58.00 263	64.98 259	74.14 221	48.06 217	63.35 301	63.23 300	49.13 253	59.33 247	71.46 303	37.45 220	69.59 286	41.39 260	62.57 285	77.30 253

WTY-MVS		59.75 254	60.39 240	57.85 300	72.32 261	37.83 296	61.05 313	64.18 294	45.95 284	61.91 211	79.11 230	47.01 128	60.88 318	42.50 251	69.49 231	74.83 282

CVMVSNet		59.63 256	59.14 246	61.08 288	74.47 208	38.84 286	75.20 185	68.74 259	31.15 345	58.24 260	76.51 272	32.39 286	68.58 291	49.77 193	65.84 260	75.81 270

tfpn_ndepth		59.57 257	59.02 247	61.23 283	73.81 222	35.60 314	69.40 266	65.59 279	50.96 240	57.96 268	77.72 249	34.81 254	75.91 257	30.36 315	70.57 201	72.18 313

thresconf0.02		59.40 258	58.81 248	61.17 284	74.15 215	33.83 325	68.32 271	64.22 288	51.79 221	58.04 262	79.57 217	35.41 243	75.41 259	29.57 324	68.26 239	74.25 290

tfpn_n400		59.40 258	58.81 248	61.17 284	74.15 215	33.83 325	68.32 271	64.22 288	51.79 221	58.04 262	79.57 217	35.41 243	75.41 259	29.57 324	68.26 239	74.25 290

tfpnconf		59.40 258	58.81 248	61.17 284	74.15 215	33.83 325	68.32 271	64.22 288	51.79 221	58.04 262	79.57 217	35.41 243	75.41 259	29.57 324	68.26 239	74.25 290

tfpnview11		59.40 258	58.81 248	61.17 284	74.15 215	33.83 325	68.32 271	64.22 288	51.79 221	58.04 262	79.57 217	35.41 243	75.41 259	29.57 324	68.26 239	74.25 290

tpm cat1		59.25 262	56.95 269	66.15 237	72.19 262	46.96 228	68.09 278	65.76 277	40.03 326	57.81 269	70.56 310	38.32 212	74.51 270	38.26 273	61.50 292	77.00 260

tfpn1000		59.24 263	58.70 256	60.86 289	73.75 223	33.99 323	68.86 269	63.98 295	51.25 238	57.29 273	79.51 225	34.58 256	75.26 265	29.08 331	69.99 219	73.32 299

pmmvs-eth3d		58.81 264	56.31 274	66.30 232	67.61 307	52.42 143	72.30 230	64.76 284	43.55 304	54.94 289	74.19 292	28.95 304	72.60 274	43.31 243	57.21 313	73.88 297

RPMNet		58.70 265	56.29 275	65.96 242	69.96 291	52.07 146	65.31 293	62.15 308	43.20 307	59.36 244	70.15 314	35.37 249	70.75 282	36.42 287	64.65 271	75.06 277

tpmvs		58.47 266	56.95 269	63.03 272	70.20 288	41.21 271	67.90 280	67.23 270	49.62 249	54.73 291	70.84 307	34.14 262	76.24 255	36.64 284	61.29 295	71.64 316

PVSNet	50.76 19	58.40 267	57.39 265	61.42 280	75.53 192	44.04 251	61.43 308	63.45 298	47.04 274	56.91 274	73.61 295	27.00 317	64.76 306	39.12 269	72.40 182	75.47 274

tpmrst		58.24 268	58.70 256	56.84 304	66.97 310	34.32 321	69.57 264	61.14 312	47.17 273	58.58 257	71.60 302	41.28 187	60.41 320	49.20 200	62.84 283	75.78 271

Patchmatch-RL test		58.16 269	55.49 279	66.15 237	67.92 306	48.89 208	60.66 314	51.07 346	47.86 267	59.36 244	62.71 338	34.02 264	72.27 277	56.41 146	59.40 308	77.30 253

test-LLR		58.15 270	58.13 262	58.22 297	68.57 301	44.80 242	65.46 290	57.92 322	50.08 246	55.44 284	69.82 315	32.62 282	57.44 329	49.66 196	73.62 156	72.41 309

ppachtmachnet_test		58.06 271	55.38 280	66.10 240	69.51 295	48.99 207	68.01 279	66.13 276	44.50 295	54.05 299	70.74 308	32.09 288	72.34 276	36.68 283	56.71 316	76.99 262

gg-mvs-nofinetune		57.86 272	56.43 273	62.18 276	72.62 256	35.35 315	66.57 282	56.33 330	50.65 241	57.64 270	57.10 345	30.65 292	76.36 253	37.38 277	78.88 107	74.82 283

CMPMVS	42.80 21	57.81 273	55.97 276	63.32 268	60.98 339	47.38 225	64.66 297	69.50 249	32.06 344	46.83 326	77.80 247	29.50 301	71.36 279	48.68 203	73.75 154	71.21 319
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 274	57.07 267	58.22 297	74.21 214	37.18 299	62.46 304	60.88 313	48.88 255	55.29 287	75.99 279	31.68 289	62.04 315	31.87 301	72.35 183	75.43 275

tpm		57.34 275	58.16 260	54.86 312	71.80 270	34.77 318	67.47 281	56.04 333	48.20 263	60.10 236	76.92 264	37.17 225	53.41 346	40.76 263	65.01 266	76.40 266

Patchmtry		57.16 276	56.47 272	59.23 291	69.17 298	34.58 320	62.98 302	63.15 301	44.53 294	56.83 275	74.84 286	35.83 241	68.71 290	40.03 266	60.91 296	74.39 288

AllTest		57.08 277	54.65 284	64.39 262	71.44 272	49.03 204	69.92 262	67.30 268	45.97 282	47.16 324	79.77 213	17.47 338	67.56 294	33.65 295	59.16 309	76.57 264

our_test_3		56.49 278	54.42 286	62.68 274	69.51 295	45.48 238	66.08 286	61.49 311	44.11 301	50.73 316	69.60 317	33.05 273	68.15 292	38.38 272	56.86 314	74.40 287

pmmvs5		56.47 279	55.68 278	58.86 294	61.41 336	36.71 307	66.37 284	62.75 304	40.38 324	53.70 301	76.62 269	34.56 257	67.05 296	40.02 267	65.27 264	72.83 302

test-mter		56.42 280	55.82 277	58.22 297	68.57 301	44.80 242	65.46 290	57.92 322	39.94 327	55.44 284	69.82 315	21.92 335	57.44 329	49.66 196	73.62 156	72.41 309

USDC		56.35 281	54.24 289	62.69 273	64.74 324	40.31 275	65.05 295	73.83 223	43.93 302	47.58 322	77.71 250	15.36 343	75.05 268	38.19 274	61.81 289	72.70 303

PatchMatch-RL		56.25 282	54.55 285	61.32 282	77.06 171	56.07 95	65.57 289	54.10 342	44.13 300	53.49 306	71.27 306	25.20 326	66.78 298	36.52 286	63.66 276	61.12 340

sss		56.17 283	56.57 271	54.96 311	66.93 311	36.32 311	57.94 321	61.69 310	41.67 315	58.64 256	75.32 284	38.72 208	56.25 338	42.04 254	66.19 258	72.31 312

FMVSNet5		55.86 284	54.93 282	58.66 296	71.05 279	36.35 309	64.18 300	62.48 306	46.76 275	50.66 317	74.73 288	25.80 323	64.04 308	33.11 297	65.57 263	75.59 273

RPSCF		55.80 285	54.22 290	60.53 290	65.13 323	42.91 261	64.30 298	57.62 324	36.84 336	58.05 261	82.28 147	28.01 308	56.24 339	37.14 278	58.61 311	82.44 183

EU-MVSNet		55.61 286	54.41 287	59.19 292	65.41 322	33.42 332	72.44 228	71.91 235	28.81 347	51.27 311	73.87 293	24.76 328	69.08 289	43.04 247	58.20 312	75.06 277

TESTMET0.1,1		55.28 287	54.90 283	56.42 305	66.56 315	43.67 254	65.46 290	56.27 331	39.18 329	53.83 300	67.44 323	24.21 330	55.46 343	48.04 207	73.11 168	70.13 323

MIMVSNet1		55.17 288	54.31 288	57.77 301	70.03 290	32.01 337	65.68 288	64.81 283	49.19 252	46.75 327	76.00 277	25.53 325	64.04 308	28.65 332	62.13 288	77.26 256

Anonymous20231206		55.10 289	55.30 281	54.48 314	69.81 294	33.94 324	62.91 303	62.13 309	41.08 318	55.18 288	75.65 281	32.75 280	56.59 335	30.32 316	67.86 248	72.91 301

TinyColmap		54.14 290	51.72 298	61.40 281	66.84 312	41.97 265	66.52 283	68.51 261	44.81 290	42.69 341	75.77 280	11.66 351	72.94 273	31.96 300	56.77 315	69.27 327

EPMVS		53.96 291	53.69 292	54.79 313	66.12 318	31.96 338	62.34 306	49.05 349	44.42 297	55.54 282	71.33 305	30.22 295	56.70 333	41.65 258	62.54 286	75.71 272

PMMVS		53.96 291	53.26 295	56.04 306	62.60 332	50.92 157	61.17 312	56.09 332	32.81 342	53.51 305	66.84 325	34.04 263	59.93 322	44.14 237	68.18 245	57.27 346

test20.03		53.87 293	54.02 291	53.41 318	61.47 335	28.11 346	61.30 310	59.21 317	51.34 235	52.09 309	77.43 260	33.29 272	58.55 326	29.76 322	60.27 306	73.58 298

MDA-MVSNet-bldmvs		53.87 293	50.81 300	63.05 271	66.25 316	48.58 211	56.93 324	63.82 296	48.09 264	41.22 342	70.48 311	30.34 294	68.00 293	34.24 293	45.92 342	72.57 305

TDRefinement		53.44 295	50.72 301	61.60 279	64.31 327	46.96 228	70.89 251	65.27 282	41.78 313	44.61 333	77.98 241	11.52 352	66.36 300	28.57 333	51.59 330	71.49 317

test0.0.03 1		53.32 296	53.59 293	52.50 322	62.81 331	29.45 343	59.51 316	54.11 341	50.08 246	54.40 295	74.31 291	32.62 282	55.92 340	30.50 314	63.95 275	72.15 315

PatchT		53.17 297	53.44 294	52.33 323	68.29 305	25.34 354	58.21 320	54.41 339	44.46 296	54.56 293	69.05 318	33.32 271	60.94 317	36.93 279	61.76 290	70.73 322

UnsupCasMVSNet_eth		53.16 298	52.47 296	55.23 309	59.45 346	33.39 333	59.43 317	69.13 254	45.98 281	50.35 319	72.32 299	29.30 303	58.26 327	42.02 255	44.30 344	74.05 295

PM-MVS		52.33 299	50.19 302	58.75 295	62.10 333	45.14 240	65.75 287	40.38 359	43.60 303	53.52 304	72.65 297	9.16 357	65.87 304	50.41 188	54.18 324	65.24 333

testgi		51.90 300	52.37 297	50.51 327	60.39 342	23.55 357	58.42 319	58.15 320	49.03 254	51.83 310	79.21 229	22.39 333	55.59 341	29.24 330	62.64 284	72.40 311

dp		51.89 301	51.60 299	52.77 321	68.44 304	32.45 335	62.36 305	54.57 338	44.16 299	49.31 320	67.91 320	28.87 306	56.61 334	33.89 294	54.89 321	69.24 328

JIA-IIPM		51.56 302	47.68 310	63.21 269	64.61 325	50.73 164	47.71 344	58.77 319	42.90 309	48.46 321	51.72 349	24.97 327	70.24 285	36.06 289	53.89 325	68.64 329

ADS-MVSNet2		51.33 303	48.76 306	59.07 293	66.02 320	44.60 245	50.90 339	59.76 316	36.90 334	50.74 314	66.18 329	26.38 319	63.11 310	27.17 334	54.76 322	69.50 325

YYNet1		50.73 304	48.96 303	56.03 307	61.10 338	41.78 267	51.94 337	56.44 329	40.94 320	44.84 331	67.80 322	30.08 297	55.08 344	36.77 280	50.71 332	71.22 318

MDA-MVSNet_test_wron		50.71 305	48.95 304	56.00 308	61.17 337	41.84 266	51.90 338	56.45 328	40.96 319	44.79 332	67.84 321	30.04 298	55.07 345	36.71 282	50.69 333	71.11 321

UnsupCasMVSNet_bld		50.07 306	48.87 305	53.66 316	60.97 340	33.67 331	57.62 322	64.56 286	39.47 328	47.38 323	64.02 334	27.47 312	59.32 323	34.69 292	43.68 345	67.98 330

Patchmatch-test		49.08 307	48.28 307	51.50 325	64.40 326	30.85 341	45.68 347	48.46 352	35.60 338	46.10 330	72.10 300	34.47 260	46.37 352	27.08 336	60.65 300	77.27 255

LP		48.51 308	45.51 314	57.52 303	62.86 330	44.53 248	52.38 336	59.84 315	38.11 331	42.81 340	61.02 339	23.23 331	63.02 311	24.10 341	45.24 343	65.02 334

ADS-MVSNet		48.48 309	47.77 308	50.63 326	66.02 320	29.92 342	50.90 339	50.87 348	36.90 334	50.74 314	66.18 329	26.38 319	52.47 348	27.17 334	54.76 322	69.50 325

CHOSEN 280x420		47.83 310	46.36 311	52.24 324	67.37 309	49.78 194	38.91 355	43.11 358	35.00 339	43.27 339	63.30 337	28.95 304	49.19 351	36.53 285	60.80 299	57.76 345

new-patchmatchnet		47.56 311	47.73 309	47.06 332	58.81 347	9.37 365	48.78 343	59.21 317	43.28 305	44.22 334	68.66 319	25.67 324	57.20 332	31.57 309	49.35 338	74.62 286

PVSNet_0	43.31 20	47.46 312	45.64 313	52.92 320	67.60 308	44.65 244	54.06 330	54.64 337	41.59 316	46.15 328	58.75 344	30.99 290	58.66 325	32.18 299	24.81 353	55.46 347

test1235678		45.66 313	44.46 319	49.26 328	59.88 344	28.68 345	56.36 326	55.54 336	39.12 330	40.89 344	63.40 336	14.41 345	57.32 331	21.05 347	49.47 337	61.78 338

test2356		45.61 314	44.66 317	48.47 331	60.15 343	28.08 347	52.44 335	52.83 345	38.01 332	46.13 329	60.98 340	15.08 344	55.54 342	20.43 350	55.85 319	61.78 338

MVS-HIRNet		45.52 315	44.48 318	48.65 330	68.49 303	34.05 322	59.41 318	44.50 357	27.03 349	37.96 348	50.47 352	26.16 322	64.10 307	26.74 337	59.52 307	47.82 350

pmmvs3		44.92 316	41.95 322	53.86 315	52.58 353	43.55 255	62.11 307	46.90 356	26.05 351	40.63 345	60.19 342	11.08 354	57.91 328	31.83 306	46.15 341	60.11 342

testus		44.59 317	43.87 320	46.76 333	59.85 345	24.65 355	53.86 331	55.82 334	36.26 337	43.97 337	63.42 335	8.39 358	53.14 347	20.70 349	52.52 328	62.51 336

1111		44.40 318	45.00 316	42.61 339	57.55 349	17.33 362	53.82 333	57.05 326	40.78 321	44.11 335	66.57 326	13.37 346	45.77 353	22.15 343	49.58 336	64.73 335

testpf		44.11 319	45.40 315	40.26 341	60.52 341	27.34 348	33.26 357	54.33 340	45.87 285	41.08 343	60.26 341	16.46 340	59.14 324	46.09 219	50.68 334	34.31 356

LF4IMVS		42.95 320	42.26 321	45.04 335	48.30 357	32.50 334	54.80 328	48.49 351	28.03 348	40.51 346	70.16 313	9.24 356	43.89 356	31.63 307	49.18 339	58.72 343

testmv		42.25 321	40.11 325	48.66 329	53.23 351	27.02 349	56.62 325	55.74 335	37.25 333	33.10 350	59.52 343	7.78 359	56.58 336	19.61 351	38.13 349	62.40 337

FPMVS		42.18 322	41.11 323	45.39 334	58.03 348	41.01 273	49.50 341	53.81 343	30.07 346	33.71 349	64.03 332	11.69 350	52.08 349	14.01 356	55.11 320	43.09 353

ANet_high		41.38 323	37.47 328	53.11 319	39.73 363	24.45 356	56.94 323	69.69 245	47.65 268	26.04 354	52.32 348	12.44 348	62.38 314	21.80 346	10.61 361	72.49 306

no-one		40.85 324	36.09 329	55.14 310	48.55 356	38.72 287	42.15 353	62.92 303	34.60 341	23.55 355	49.74 353	12.21 349	66.16 302	26.27 339	24.84 352	60.54 341

LCM-MVSNet		40.30 325	35.88 331	53.57 317	42.24 360	29.15 344	45.21 349	60.53 314	22.23 355	28.02 353	50.98 351	3.72 365	61.78 316	31.22 313	38.76 348	69.78 324

N_pmnet		39.35 326	40.28 324	36.54 343	63.76 328	1.62 369	49.37 342	0.76 371	34.62 340	43.61 338	66.38 328	26.25 321	42.57 358	26.02 340	51.77 329	65.44 332

DSMNet-mixed		39.30 327	38.72 327	41.03 340	51.22 354	19.66 359	45.53 348	31.35 364	15.83 359	39.80 347	67.42 324	22.19 334	45.13 355	22.43 342	52.69 327	58.31 344

PMVS	28.69 22	36.22 328	33.29 333	45.02 336	36.82 365	35.98 313	54.68 329	48.74 350	26.31 350	21.02 356	51.61 350	2.88 367	60.10 321	9.99 360	47.58 340	38.99 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		36.16 329	35.94 330	36.83 342	50.82 355	8.52 366	44.84 350	53.49 344	32.72 343	30.11 352	55.08 346	7.11 361	49.47 350	16.60 353	32.68 351	52.50 348

.test1245		34.88 330	39.49 326	21.04 351	57.55 349	17.33 362	53.82 333	57.05 326	40.78 321	44.11 335	66.57 326	13.37 346	45.77 353	22.15 343	0.00 364	0.03 365

Gipuma		34.77 331	31.91 334	43.33 338	62.05 334	37.87 295	20.39 360	67.03 271	23.23 353	18.41 358	25.84 358	4.24 363	62.73 312	14.71 355	51.32 331	29.38 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1.0		34.38 332	45.84 312	0.00 357	87.58 3	0.00 371	0.00 363	86.64 3	63.49 35	83.42 2	91.40 5	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

new_pmnet		34.13 333	34.29 332	33.64 344	52.63 352	18.23 361	44.43 351	33.90 362	22.81 354	30.89 351	53.18 347	10.48 355	35.72 362	20.77 348	39.51 346	46.98 351

pcd1.5k->3k		30.06 334	30.56 335	28.55 348	78.81 121	0.00 371	0.00 363	82.07 75	0.00 367	0.00 369	0.00 369	39.61 198	0.00 367	0.00 366	74.56 145	85.66 78

wuykxyi23d		28.12 335	22.54 340	44.87 337	34.97 366	32.11 336	37.96 356	47.31 354	13.32 360	9.29 365	23.72 360	0.45 370	56.58 336	21.85 345	13.98 357	45.93 352

PNet_i23d		27.88 336	25.99 336	33.55 345	47.54 358	25.89 351	47.24 346	32.91 363	21.44 356	15.90 359	38.09 355	0.85 369	42.76 357	16.90 352	13.03 359	32.00 357

PMMVS2		27.40 337	25.91 337	31.87 347	39.46 364	6.57 367	31.17 358	28.52 365	23.96 352	20.45 357	48.94 354	4.20 364	37.94 361	16.51 354	19.97 354	51.09 349

E-PMN		23.77 338	22.73 339	26.90 349	42.02 361	20.67 358	42.66 352	35.70 360	17.43 357	10.28 363	25.05 359	6.42 362	42.39 359	10.28 359	14.71 356	17.63 359

EMVS		22.97 339	21.84 341	26.36 350	40.20 362	19.53 360	41.95 354	34.64 361	17.09 358	9.73 364	22.83 361	7.29 360	42.22 360	9.18 361	13.66 358	17.32 360

MVE	17.77 23	21.41 340	17.77 342	32.34 346	34.34 367	25.44 353	16.11 361	24.11 366	11.19 361	13.22 361	31.92 356	1.58 368	30.95 363	10.47 358	17.03 355	40.62 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		17.50 341	23.34 338	0.00 357	0.00 372	0.00 371	0.00 363	78.63 161	0.00 367	0.00 369	82.18 148	49.25 92	0.00 367	0.00 366	0.00 364	0.00 367

wuyk23d		13.32 342	12.52 343	15.71 352	47.54 358	26.27 350	31.06 359	1.98 370	4.93 363	5.18 366	1.94 366	0.45 370	18.54 364	6.81 363	12.83 360	2.33 363

tmp_tt		9.43 343	11.14 344	4.30 354	2.38 369	4.40 368	13.62 362	16.08 368	0.39 364	15.89 360	13.06 362	15.80 342	5.54 366	12.63 357	10.46 362	2.95 362

ab-mvs-re		6.49 344	8.65 345	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	77.89 245	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

test123		4.73 345	6.30 346	0.02 355	0.01 370	0.01 370	56.36 326	0.00 372	0.01 365	0.04 367	0.21 368	0.01 372	0.00 367	0.03 365	0.00 364	0.04 364

testmvs		4.52 346	6.03 347	0.01 356	0.01 370	0.00 371	53.86 331	0.00 372	0.01 365	0.04 367	0.27 367	0.00 373	0.00 367	0.04 364	0.00 364	0.03 365

pcd_1.5k_mvsjas		3.92 347	5.23 348	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	0.00 369	47.05 125	0.00 367	0.00 366	0.00 364	0.00 367

sosnet-low-res		0.00 348	0.00 349	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	0.00 369	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

sosnet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	0.00 369	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

uncertanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	0.00 369	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

Regformer		0.00 348	0.00 349	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	0.00 369	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

uanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 371	0.00 363	0.00 372	0.00 367	0.00 369	0.00 369	0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

GSMVS																78.05 246

test_part2					87.58 3	60.47 39				83.42 2

test_part1				0.00 357		0.00 371	0.00 363	86.64 3				0.00 373	0.00 367	0.00 366	0.00 364	0.00 367

sam_mvs1												34.74 255				78.05 246

sam_mvs												33.43 270

semantic-postprocess				65.40 254	71.99 266	50.80 162		69.63 247	45.71 286	60.61 232	77.93 243	36.56 238	65.99 303	55.67 153	63.50 278	79.42 234

ambc				65.13 257	63.72 329	37.07 300	47.66 345	78.78 158		54.37 296	71.42 304	11.24 353	80.94 183	45.64 225	53.85 326	77.38 252

MTGPA								80.97 104

test_post1							68.67 270				3.64 364	32.39 286	69.49 287	44.17 236

test_post											3.55 365	33.90 265	66.52 299

patchmatchnet-post											64.03 332	34.50 258	74.27 271

GG-mvs-BLEND				62.34 275	71.36 276	37.04 301	69.20 267	57.33 325		54.73 291	65.48 331	30.37 293	77.82 237	34.82 291	74.93 144	72.17 314

MTMP							86.03 10	17.08 367

gm-plane-assit					71.40 275	41.72 269			48.85 256		73.31 296		82.48 159	48.90 202

test9_res														75.28 16	88.31 18	83.81 148

TEST9					85.58 31	61.59 26	81.62 68	81.26 94	55.65 178	74.93 27	88.81 42	53.70 44	84.68 101

test_8					85.40 34	60.96 33	81.54 71	81.18 97	55.86 172	74.81 30	88.80 44	53.70 44	84.45 106

agg_prior2														73.09 31	87.93 25	84.33 125

agg_prior					85.04 38	59.96 43		81.04 100		74.68 31			84.04 113

TestCases				64.39 262	71.44 272	49.03 204		67.30 268	45.97 282	47.16 324	79.77 213	17.47 338	67.56 294	33.65 295	59.16 309	76.57 264

test_prior4						62.51 17	82.08 62

test_prior2							81.75 64		60.37 82	75.01 25	89.06 36	56.22 21		72.19 34	88.96 10

test_prior				76.69 47	84.20 50	57.27 74		84.88 18					86.43 57			86.38 49

旧先验2							76.08 168		45.32 287	76.55 17			65.56 305	58.75 138

新几何2							76.12 166

新几何1				70.76 184	85.66 28	61.13 31		66.43 275	44.68 292	70.29 77	86.64 65	41.29 186	75.23 266	49.72 195	81.75 69	75.93 269

旧先验1					83.04 57	53.15 130		67.52 267			87.85 51	44.08 157			80.76 75	78.03 248

无先验							79.66 95	74.30 219	48.40 261				80.78 188	53.62 167		79.03 239

原ACMM2							79.02 101

原ACMM1				74.69 77	85.39 35	59.40 48		83.42 51	51.47 233	70.27 79	86.61 67	48.61 107	86.51 55	53.85 166	87.96 24	78.16 244

test222					83.14 56	58.68 59	72.57 226	63.45 298	41.78 313	67.56 136	86.12 78	37.13 226			78.73 112	74.98 280

testdata2													72.18 278	46.95 214

segment_acmp												54.23 37

testdata				64.66 260	81.52 71	52.93 133		65.29 281	46.09 280	73.88 41	87.46 53	38.08 216	66.26 301	53.31 172	78.48 115	74.78 284

testdata1							72.65 221		60.50 79

test12				77.76 35	84.52 47	58.41 61		83.36 54		72.93 56		54.61 34	88.05 23		88.12 21	86.81 43

plane_prior7					81.41 74	55.96 97

plane_prior6					81.20 81	56.24 91						45.26 147

plane_prior5								84.01 33					87.21 35	68.16 53	80.58 78	84.65 119

plane_prior4											86.10 79

plane_prior3						56.09 94			63.92 30	69.27 104

plane_prior2							84.22 25		64.52 24

plane_prior1					81.27 79

plane_prior						56.31 87	83.58 34		63.19 39						80.48 81

n2								0.00 372

nn								0.00 372

door-mid								47.19 355

lessismore_v0				69.91 195	71.42 274	47.80 219		50.90 347		50.39 318	75.56 282	27.43 314	81.33 177	45.91 222	34.10 350	80.59 218

LGP-MVS_train				75.76 59	80.22 96	57.51 72		83.40 52	61.32 67	66.67 146	87.33 55	39.15 204	86.59 50	67.70 58	77.30 127	83.19 167

test11								83.47 49

door								47.60 353

HQP5-MVS						54.94 111

HQP-NCC					80.66 86		82.31 57		62.10 57	67.85 127

ACMP_Plane					80.66 86		82.31 57		62.10 57	67.85 127

BP-MVS														67.04 64

HQP4-MVS										67.85 127			86.93 41			84.32 126

HQP3-MVS								83.90 37							80.35 84

HQP2-MVS												45.46 141

NP-MVS					80.98 84	56.05 96					85.54 92

MDTV_nov1_ep13_2view						25.89 351	61.22 311		40.10 325	51.10 312		32.97 275		38.49 271		78.61 241

MDTV_nov1_ep13			57.00 268		72.73 254	38.26 293	65.02 296	64.73 285	44.74 291	55.46 283	72.48 298	32.61 284	70.47 284	37.47 276	67.75 250

ACMMP++_ref															74.07 152

ACMMP++															72.16 186

Test By Simon												48.33 110

ITE_SJBPF				62.09 277	66.16 317	44.55 247		64.32 287	47.36 271	55.31 286	80.34 201	19.27 337	62.68 313	36.29 288	62.39 287	79.04 238

DeepMVS_CX				12.03 353	17.97 368	10.91 364		10.60 369	7.46 362	11.07 362	28.36 357	3.28 366	11.29 365	8.01 362	9.74 363	13.89 361

High-resolution multi-view benchmark