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.

ESAPD		89.48 1	89.98 1	88.01 10	94.80 4	72.69 28	91.59 27	94.10 1	75.90 71	92.29 1	95.66 3	81.67 1	97.38 1	87.44 11	96.34 4	93.95 32

HSP-MVS		89.28 2	89.76 2	87.85 21	94.28 18	73.46 15	92.90 8	92.73 42	80.27 13	91.35 5	94.16 23	78.35 3	96.77 12	89.59 1	94.22 45	93.33 60

APDe-MVS		89.15 3	89.63 3	87.73 23	94.49 11	71.69 45	93.83 2	93.96 5	75.70 74	91.06 6	96.03 1	76.84 4	97.03 7	89.09 2	95.65 15	94.47 13

SMA-MVS		89.08 4	89.23 4	88.61 2	94.25 19	73.73 7	92.40 14	93.63 10	74.77 93	92.29 1	95.97 2	74.28 18	97.24 3	88.58 4	96.91 1	94.87 5

HPM-MVS++		89.02 5	89.15 5	88.63 1	95.01 3	76.03 1	92.38 16	92.85 37	80.26 14	87.78 15	94.27 19	75.89 8	96.81 11	87.45 10	96.44 2	93.05 70

CNVR-MVS		88.93 6	89.13 6	88.33 4	94.77 5	73.82 6	90.51 43	93.00 29	80.90 10	88.06 13	94.06 27	76.43 5	96.84 9	88.48 5	95.99 6	94.34 17

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 57	72.96 21	93.73 3	93.67 9	80.19 15	88.10 12	94.80 7	73.76 22	97.11 5	87.51 9	95.82 10	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 42	92.24 55	69.03 84	89.57 68	93.39 18	77.53 39	89.79 8	94.12 25	78.98 2	96.58 23	85.66 14	95.72 11	94.58 8

SD-MVS		88.06 9	88.50 9	86.71 43	92.60 53	72.71 26	91.81 26	93.19 23	77.87 32	90.32 7	94.00 28	74.83 11	93.78 120	87.63 8	94.27 43	93.65 49

TSAR-MVS + MP.		88.02 12	88.11 10	87.72 25	93.68 30	72.13 41	91.41 30	92.35 55	74.62 95	88.90 9	93.85 31	75.75 9	96.00 36	87.80 6	94.63 34	95.04 2

ACMMP_Plus		88.05 11	88.08 11	87.94 13	93.70 28	73.05 19	90.86 37	93.59 11	76.27 67	88.14 11	95.09 6	71.06 39	96.67 16	87.67 7	96.37 3	94.09 24

NCCC		88.06 9	88.01 12	88.24 6	94.41 15	73.62 8	91.22 34	92.83 38	81.50 7	85.79 25	93.47 37	73.02 26	97.00 8	84.90 19	94.94 27	94.10 23

MP-MVS-pluss		87.67 14	87.72 13	87.54 29	93.64 31	72.04 42	89.80 60	93.50 13	75.17 87	86.34 20	95.29 4	70.86 40	96.00 36	88.78 3	96.04 5	94.58 8
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		87.71 13	87.64 14	87.93 16	94.36 17	73.88 4	92.71 13	92.65 45	77.57 35	83.84 53	94.40 18	72.24 33	96.28 28	85.65 15	95.30 23	93.62 51
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		87.44 17	87.52 15	87.19 34	94.24 20	72.39 36	91.86 25	92.83 38	73.01 132	88.58 10	94.52 10	73.36 23	96.49 24	84.26 29	95.01 25	92.70 79
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		87.58 15	87.47 16	87.94 13	94.58 8	73.54 12	93.04 5	93.24 20	76.78 53	84.91 34	94.44 15	70.78 41	96.61 19	84.53 25	94.89 29	93.66 44

zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 23	74.25 2	90.23 51	92.02 65	79.45 19	85.88 22	94.80 7	68.07 62	96.21 30	86.69 12	95.34 19	93.23 62

GST-MVS		87.42 19	87.26 18	87.89 20	94.12 25	72.97 20	92.39 15	93.43 16	76.89 50	84.68 40	93.99 29	70.67 44	96.82 10	84.18 32	95.01 25	93.90 35

MCST-MVS		87.37 21	87.25 19	87.73 23	94.53 10	72.46 35	89.82 58	93.82 7	73.07 131	84.86 39	92.89 49	76.22 6	96.33 26	84.89 21	95.13 24	94.40 14

ACMMPR		87.44 17	87.23 20	88.08 8	94.64 6	73.59 9	93.04 5	93.20 22	76.78 53	84.66 41	94.52 10	68.81 60	96.65 17	84.53 25	94.90 28	94.00 30

region2R		87.42 19	87.20 21	88.09 7	94.63 7	73.55 10	93.03 7	93.12 25	76.73 56	84.45 44	94.52 10	69.09 58	96.70 15	84.37 28	94.83 31	94.03 27

#test#		87.33 22	87.13 22	87.94 13	94.58 8	73.54 12	92.34 17	93.24 20	75.23 84	84.91 34	94.44 15	70.78 41	96.61 19	83.75 34	94.89 29	93.66 44

MTAPA		87.23 23	87.00 23	87.90 17	94.18 23	74.25 2	86.58 170	92.02 65	79.45 19	85.88 22	94.80 7	68.07 62	96.21 30	86.69 12	95.34 19	93.23 62

HPM-MVS		87.11 25	86.98 24	87.50 31	93.88 27	72.16 40	92.19 20	93.33 19	76.07 70	83.81 54	93.95 30	69.77 52	96.01 35	85.15 16	94.66 33	94.32 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 25	86.92 25	87.68 28	94.20 22	73.86 5	93.98 1	92.82 40	76.62 58	83.68 55	94.46 14	67.93 64	95.95 38	84.20 31	94.39 39	93.23 62

XVS		87.18 24	86.91 26	88.00 11	94.42 13	73.33 17	92.78 9	92.99 31	79.14 21	83.67 56	94.17 22	67.45 69	96.60 21	83.06 39	94.50 36	94.07 25

DeepC-MVS	79.81 2	87.08 27	86.88 27	87.69 27	91.16 67	72.32 39	90.31 49	93.94 6	77.12 44	82.82 65	94.23 21	72.13 34	97.09 6	84.83 22	95.37 18	93.65 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		86.73 29	86.86 28	86.33 49	92.61 51	69.59 76	88.85 87	92.97 34	75.41 80	84.91 34	93.54 33	74.28 18	95.48 46	83.31 35	95.86 8	93.91 33

DeepC-MVS_fast	79.65 3	86.91 28	86.62 29	87.76 22	93.52 33	72.37 38	91.26 31	93.04 26	76.62 58	84.22 49	93.36 39	71.44 37	96.76 13	80.82 57	95.33 21	94.16 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		86.63 32	86.53 30	86.95 39	89.33 109	71.24 48	88.43 100	92.05 64	82.50 1	86.88 18	90.09 101	74.45 13	95.61 42	84.38 27	90.63 71	94.01 29

Regformer-1		86.41 36	86.33 31	86.64 44	89.33 109	70.93 54	88.43 100	91.39 95	82.14 3	86.65 19	90.09 101	74.39 16	95.01 67	83.97 33	90.63 71	93.97 31

mPP-MVS		86.67 31	86.32 32	87.72 25	94.41 15	73.55 10	92.74 11	92.22 58	76.87 51	82.81 67	94.25 20	66.44 77	96.24 29	82.88 43	94.28 42	93.38 57

PGM-MVS		86.68 30	86.27 33	87.90 17	94.22 21	73.38 16	90.22 52	93.04 26	75.53 78	83.86 52	94.42 17	67.87 66	96.64 18	82.70 44	94.57 35	93.66 44

train_agg		86.43 34	86.20 34	87.13 36	93.26 38	72.96 21	88.75 92	91.89 74	68.69 208	85.00 32	93.10 43	74.43 14	95.41 51	84.97 17	95.71 12	93.02 72

CSCG		86.41 36	86.19 35	87.07 38	92.91 45	72.48 34	90.81 38	93.56 12	73.95 103	83.16 61	91.07 82	75.94 7	95.19 58	79.94 64	94.38 40	93.55 53

PHI-MVS		86.43 34	86.17 36	87.24 33	90.88 73	70.96 51	92.27 19	94.07 4	72.45 143	85.22 30	91.90 62	69.47 55	96.42 25	83.28 37	95.94 7	94.35 16

CANet		86.45 33	86.10 37	87.51 30	90.09 85	70.94 53	89.70 64	92.59 46	81.78 4	81.32 83	91.43 75	70.34 45	97.23 4	84.26 29	93.36 49	94.37 15

agg_prior1		86.22 39	86.09 38	86.62 45	92.85 46	71.94 43	88.59 97	91.78 80	68.96 205	84.41 45	93.18 42	74.94 10	94.93 68	84.75 24	95.33 21	93.01 74

APD-MVS_3200maxsize		85.97 41	85.88 39	86.22 52	92.69 49	69.53 78	91.93 24	92.99 31	73.54 118	85.94 21	94.51 13	65.80 84	95.61 42	83.04 41	92.51 56	93.53 55

canonicalmvs		85.91 42	85.87 40	86.04 57	89.84 91	69.44 82	90.45 47	93.00 29	76.70 57	88.01 14	91.23 77	73.28 24	93.91 111	81.50 52	88.80 91	94.77 6

agg_prior3		86.16 40	85.85 41	87.10 37	93.31 35	72.86 25	88.77 90	91.68 84	68.29 220	84.26 48	92.83 51	72.83 28	95.42 50	84.97 17	95.71 12	93.02 72

MVS_0304		86.37 38	85.81 42	88.02 9	90.13 83	72.39 36	89.66 66	92.75 41	81.64 6	82.66 70	92.04 58	64.44 93	97.35 2	84.76 23	94.25 44	94.33 18

MSLP-MVS++		85.43 49	85.76 43	84.45 86	91.93 60	70.24 62	90.71 40	92.86 36	77.46 41	84.22 49	92.81 54	67.16 73	92.94 164	80.36 60	94.35 41	90.16 161

Regformer-4		85.68 46	85.45 44	86.35 48	88.95 126	69.67 74	88.29 110	91.29 97	81.73 5	85.36 28	90.01 104	72.62 30	95.35 56	83.28 37	87.57 106	94.03 27

ACMMP		85.89 43	85.39 45	87.38 32	93.59 32	72.63 30	92.74 11	93.18 24	76.78 53	80.73 92	93.82 32	64.33 94	96.29 27	82.67 45	90.69 70	93.23 62
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
TSAR-MVS + GP.		85.71 45	85.33 46	86.84 40	91.34 65	72.50 33	89.07 81	87.28 214	76.41 60	85.80 24	90.22 99	74.15 21	95.37 55	81.82 48	91.88 58	92.65 82

alignmvs		85.48 47	85.32 47	85.96 59	89.51 103	69.47 80	89.74 62	92.47 48	76.17 68	87.73 16	91.46 74	70.32 46	93.78 120	81.51 51	88.95 87	94.63 7

DELS-MVS		85.41 50	85.30 48	85.77 60	88.49 142	67.93 113	85.52 209	93.44 15	78.70 28	83.63 58	89.03 129	74.57 12	95.71 41	80.26 62	94.04 46	93.66 44
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
CDPH-MVS		85.76 44	85.29 49	87.17 35	93.49 34	71.08 49	88.58 98	92.42 52	68.32 219	84.61 42	93.48 35	72.32 32	96.15 33	79.00 67	95.43 17	94.28 20

Regformer-3		85.23 52	85.07 50	85.70 61	88.95 126	69.01 86	88.29 110	89.91 145	80.95 9	85.01 31	90.01 104	72.45 31	94.19 98	82.50 46	87.57 106	93.90 35

UA-Net		85.08 55	84.96 51	85.45 62	92.07 58	68.07 111	89.78 61	90.86 108	82.48 2	84.60 43	93.20 41	69.35 56	95.22 57	71.39 146	90.88 69	93.07 69

abl_6		85.23 52	84.95 52	86.07 55	92.23 56	70.48 61	90.80 39	92.08 63	73.51 119	85.26 29	94.16 23	62.75 119	95.92 39	82.46 47	91.30 65	91.81 108

HPM-MVS_fast		85.35 51	84.95 52	86.57 47	93.69 29	70.58 60	92.15 22	91.62 85	73.89 107	82.67 69	94.09 26	62.60 126	95.54 45	80.93 55	92.93 51	93.57 52

MVS_111021_HR		85.14 54	84.75 54	86.32 51	91.65 63	72.70 27	85.98 187	90.33 125	76.11 69	82.08 73	91.61 69	71.36 38	94.17 100	81.02 53	92.58 55	92.08 101

3Dnovator+	77.84 4	85.48 47	84.47 55	88.51 3	91.08 68	73.49 14	93.18 4	93.78 8	80.79 11	76.66 157	93.37 38	60.40 166	96.75 14	77.20 85	93.73 48	95.29 1

casdiffmvs1		84.76 56	84.33 56	86.04 57	89.40 106	68.78 91	89.67 65	92.54 47	66.43 237	85.41 26	90.75 90	72.88 27	94.76 80	81.64 50	90.24 76	94.57 10

EI-MVSNet-Vis-set		84.19 57	83.81 57	85.31 64	88.18 152	67.85 114	87.66 125	89.73 149	80.05 17	82.95 62	89.59 114	70.74 43	94.82 77	80.66 59	84.72 142	93.28 61

nrg030		83.88 59	83.53 58	84.96 74	86.77 196	69.28 83	90.46 46	92.67 43	74.79 92	82.95 62	91.33 76	72.70 29	93.09 157	80.79 58	79.28 218	92.50 87

MG-MVS		83.41 67	83.45 59	83.28 125	92.74 48	62.28 230	88.17 114	89.50 154	75.22 85	81.49 81	92.74 55	66.75 74	95.11 61	72.85 128	91.58 61	92.45 88

EI-MVSNet-UG-set		83.81 60	83.38 60	85.09 71	87.87 160	67.53 118	87.44 136	89.66 150	79.74 18	82.23 72	89.41 123	70.24 47	94.74 81	79.95 63	83.92 149	92.99 75

CPTT-MVS		83.73 61	83.33 61	84.92 77	93.28 37	70.86 56	92.09 23	90.38 120	68.75 207	79.57 99	92.83 51	60.60 162	93.04 161	80.92 56	91.56 62	90.86 131

casdiffmvs		83.96 58	83.25 62	86.07 55	88.48 143	69.60 75	89.26 73	92.40 53	68.07 221	82.82 65	90.03 103	69.77 52	94.86 76	81.79 49	86.64 123	93.75 42

HQP_MVS		83.64 63	83.14 63	85.14 69	90.08 86	68.71 97	91.25 32	92.44 49	79.12 23	78.92 106	91.00 86	60.42 164	95.38 53	78.71 70	86.32 128	91.33 118

Effi-MVS+		83.62 64	83.08 64	85.24 67	88.38 148	67.45 119	88.89 85	89.15 167	75.50 79	82.27 71	88.28 148	69.61 54	94.45 88	77.81 79	87.84 104	93.84 39

MVS_Test		83.15 70	83.06 65	83.41 122	86.86 193	63.21 216	86.11 185	92.00 68	74.31 98	82.87 64	89.44 122	70.03 48	93.21 148	77.39 84	88.50 100	93.81 40

EPP-MVSNet		83.40 68	83.02 66	84.57 82	90.13 83	64.47 185	92.32 18	90.73 109	74.45 97	79.35 102	91.10 80	69.05 59	95.12 60	72.78 129	87.22 113	94.13 22

OPM-MVS		83.50 65	82.95 67	85.14 69	88.79 134	70.95 52	89.13 80	91.52 89	77.55 38	80.96 90	91.75 64	60.71 158	94.50 87	79.67 65	86.51 126	89.97 179

EPNet		83.72 62	82.92 68	86.14 54	84.22 229	69.48 79	91.05 36	85.27 234	81.30 8	76.83 153	91.65 66	66.09 80	95.56 44	76.00 96	93.85 47	93.38 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 70	82.81 69	84.18 96	89.94 89	63.30 213	91.59 27	88.46 195	79.04 25	79.49 100	92.16 56	65.10 89	94.28 91	67.71 172	91.86 59	94.95 3

Vis-MVSNet		83.46 66	82.80 70	85.43 63	90.25 82	68.74 95	90.30 50	90.13 134	76.33 66	80.87 91	92.89 49	61.00 155	94.20 97	72.45 135	90.97 67	93.35 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
diffmvs1		82.63 77	82.51 71	82.96 148	83.87 248	63.47 208	85.19 211	89.42 157	75.58 77	81.38 82	89.89 106	67.42 71	91.69 206	81.01 54	88.88 89	93.71 43

FIs		82.07 84	82.42 72	81.04 204	88.80 133	58.34 259	88.26 112	93.49 14	76.93 49	78.47 114	91.04 83	69.92 50	92.34 182	69.87 158	84.97 139	92.44 89

VNet		82.21 82	82.41 73	81.62 191	90.82 74	60.93 238	84.47 229	89.78 147	76.36 65	84.07 51	91.88 63	64.71 92	90.26 239	70.68 149	88.89 88	93.66 44

PAPM_NR		83.02 73	82.41 73	84.82 79	92.47 54	66.37 136	87.93 121	91.80 78	73.82 112	77.32 145	90.66 92	67.90 65	94.90 72	70.37 152	89.48 84	93.19 66

VDD-MVS		83.01 74	82.36 75	84.96 74	91.02 70	66.40 135	88.91 84	88.11 198	77.57 35	84.39 47	93.29 40	52.19 224	93.91 111	77.05 88	88.70 93	94.57 10

3Dnovator	76.31 5	83.38 69	82.31 76	86.59 46	87.94 159	72.94 24	90.64 41	92.14 62	77.21 42	75.47 184	92.83 51	58.56 173	94.72 82	73.24 126	92.71 54	92.13 100

MVS_111021_LR		82.61 79	82.11 77	84.11 97	88.82 131	71.58 46	85.15 214	86.16 227	74.69 94	80.47 94	91.04 83	62.29 134	90.55 237	80.33 61	90.08 78	90.20 160

DP-MVS Recon		83.11 72	82.09 78	86.15 53	94.44 12	70.92 55	88.79 89	92.20 59	70.53 175	79.17 103	91.03 85	64.12 96	96.03 34	68.39 171	90.14 77	91.50 114

MVSFormer		82.85 75	82.05 79	85.24 67	87.35 183	70.21 63	90.50 44	90.38 120	68.55 210	81.32 83	89.47 117	61.68 140	93.46 139	78.98 68	90.26 74	92.05 102

FC-MVSNet-test		81.52 94	82.02 80	80.03 219	88.42 147	55.97 297	87.95 119	93.42 17	77.10 45	77.38 143	90.98 88	69.96 49	91.79 196	68.46 170	84.50 144	92.33 91

HQP-MVS		82.61 79	82.02 80	84.37 88	89.33 109	66.98 128	89.17 75	92.19 60	76.41 60	77.23 148	90.23 98	60.17 167	95.11 61	77.47 82	85.99 133	91.03 125

OMC-MVS		82.69 76	81.97 82	84.85 78	88.75 136	67.42 120	87.98 117	90.87 107	74.92 91	79.72 98	91.65 66	62.19 137	93.96 106	75.26 108	86.42 127	93.16 67

PVSNet_Blended_VisFu		82.62 78	81.83 83	84.96 74	90.80 75	69.76 72	88.74 94	91.70 83	69.39 191	78.96 105	88.46 143	65.47 86	94.87 75	74.42 112	88.57 96	90.24 159

CLD-MVS		82.31 81	81.65 84	84.29 93	88.47 144	67.73 117	85.81 196	92.35 55	75.78 72	78.33 120	86.58 204	64.01 97	94.35 89	76.05 95	87.48 111	90.79 132
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 87	81.54 85	82.92 149	88.46 145	63.46 209	87.13 150	92.37 54	80.19 15	78.38 118	89.14 125	71.66 36	93.05 159	70.05 155	76.46 252	92.25 95

PS-MVSNAJss		82.07 84	81.31 86	84.34 91	86.51 198	67.27 124	89.27 72	91.51 90	71.75 155	79.37 101	90.22 99	63.15 107	94.27 92	77.69 80	82.36 179	91.49 115

LPG-MVS_test		82.08 83	81.27 87	84.50 84	89.23 117	68.76 93	90.22 52	91.94 72	75.37 82	76.64 158	91.51 71	54.29 207	94.91 70	78.44 72	83.78 150	89.83 183

LFMVS		81.82 89	81.23 88	83.57 117	91.89 61	63.43 211	89.84 57	81.85 281	77.04 47	83.21 59	93.10 43	52.26 223	93.43 143	71.98 141	89.95 80	93.85 37

API-MVS		81.99 86	81.23 88	84.26 94	90.94 71	70.18 68	91.10 35	89.32 160	71.51 161	78.66 110	88.28 148	65.26 87	95.10 64	64.74 201	91.23 66	87.51 252

diffmvs		81.48 97	81.21 90	82.31 172	83.28 263	62.72 225	85.09 215	88.63 192	74.99 88	78.31 121	88.81 133	65.80 84	91.36 218	79.03 66	86.95 117	92.84 78

UniMVSNet (Re)		81.60 93	81.11 91	83.09 136	88.38 148	64.41 187	87.60 126	93.02 28	78.42 31	78.56 111	88.16 151	69.78 51	93.26 147	69.58 161	76.49 251	91.60 110

xiu_mvs_v2_base		81.69 90	81.05 92	83.60 115	89.15 120	68.03 112	84.46 231	90.02 139	70.67 172	81.30 86	86.53 207	63.17 106	94.19 98	75.60 104	88.54 98	88.57 230

PS-MVSNAJ		81.69 90	81.02 93	83.70 113	89.51 103	68.21 109	84.28 238	90.09 135	70.79 169	81.26 87	85.62 236	63.15 107	94.29 90	75.62 103	88.87 90	88.59 228

PAPR		81.66 92	80.89 94	83.99 106	90.27 81	64.00 196	86.76 166	91.77 82	68.84 206	77.13 152	89.50 115	67.63 67	94.88 74	67.55 174	88.52 99	93.09 68

MAR-MVS		81.84 88	80.70 95	85.27 66	91.32 66	71.53 47	89.82 58	90.92 105	69.77 186	78.50 112	86.21 217	62.36 133	94.52 86	65.36 194	92.05 57	89.77 190
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
VDDNet		81.52 94	80.67 96	84.05 101	90.44 79	64.13 192	89.73 63	85.91 230	71.11 165	83.18 60	93.48 35	50.54 260	93.49 138	73.40 124	88.25 102	94.54 12

ACMP	74.13 6	81.51 96	80.57 97	84.36 89	89.42 105	68.69 100	89.97 56	91.50 93	74.46 96	75.04 200	90.41 95	53.82 212	94.54 84	77.56 81	82.91 170	89.86 182
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 117	80.55 98	80.76 208	88.07 155	60.80 241	86.86 160	91.58 87	75.67 75	80.24 95	89.45 121	63.34 101	90.25 240	70.51 151	79.22 219	91.23 121

DU-MVS		81.12 102	80.52 99	82.90 150	87.80 170	63.46 209	87.02 155	91.87 76	79.01 26	78.38 118	89.07 127	65.02 90	93.05 159	70.05 155	76.46 252	92.20 97

0601test		81.17 100	80.47 100	83.24 128	89.13 121	63.62 201	86.21 181	89.95 141	72.43 146	81.78 78	89.61 112	57.50 181	93.58 132	70.75 147	86.90 118	92.52 85

Anonymous20240521		81.17 100	80.47 100	83.24 128	89.13 121	63.62 201	86.21 181	89.95 141	72.43 146	81.78 78	89.61 112	57.50 181	93.58 132	70.75 147	86.90 118	92.52 85

PVSNet_Blended		80.98 103	80.34 102	82.90 150	88.85 128	65.40 152	84.43 233	92.00 68	67.62 225	78.11 130	85.05 248	66.02 82	94.27 92	71.52 144	89.50 83	89.01 208

TranMVSNet+NR-MVSNet		80.84 106	80.31 103	82.42 169	87.85 161	62.33 228	87.74 124	91.33 96	80.55 12	77.99 133	89.86 107	65.23 88	92.62 172	67.05 182	75.24 270	92.30 93

jason		81.39 98	80.29 104	84.70 81	86.63 197	69.90 70	85.95 188	86.77 218	63.24 268	81.07 89	89.47 117	61.08 154	92.15 186	78.33 75	90.07 79	92.05 102
jason: jason.
lupinMVS		81.39 98	80.27 105	84.76 80	87.35 183	70.21 63	85.55 205	86.41 222	62.85 274	81.32 83	88.61 138	61.68 140	92.24 185	78.41 74	90.26 74	91.83 106

PVSNet_BlendedMVS		80.60 117	80.02 106	82.36 171	88.85 128	65.40 152	86.16 183	92.00 68	69.34 194	78.11 130	86.09 220	66.02 82	94.27 92	71.52 144	82.06 180	87.39 254

EI-MVSNet		80.52 120	79.98 107	82.12 173	84.28 226	63.19 218	86.41 175	88.95 179	74.18 100	78.69 108	87.54 169	66.62 75	92.43 177	72.57 134	80.57 198	90.74 135

Fast-Effi-MVS+		80.81 109	79.92 108	83.47 118	88.85 128	64.51 179	85.53 207	89.39 158	70.79 169	78.49 113	85.06 247	67.54 68	93.58 132	67.03 183	86.58 124	92.32 92

CANet_DTU		80.61 116	79.87 109	82.83 156	85.60 208	63.17 219	87.36 137	88.65 190	76.37 64	75.88 176	88.44 144	53.51 214	93.07 158	73.30 125	89.74 82	92.25 95

ACMM	73.20 8	80.78 114	79.84 110	83.58 116	89.31 114	68.37 104	89.99 55	91.60 86	70.28 179	77.25 146	89.66 110	53.37 215	93.53 137	74.24 115	82.85 171	88.85 214
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1121		80.84 106	79.77 111	84.05 101	93.11 42	70.78 57	84.66 223	85.42 233	57.37 318	81.76 80	92.02 59	63.41 100	94.12 101	67.28 177	92.93 51	87.26 259

XVG-OURS-SEG-HR		80.81 109	79.76 112	83.96 108	85.60 208	68.78 91	83.54 249	90.50 117	70.66 173	76.71 156	91.66 65	60.69 159	91.26 221	76.94 89	81.58 186	91.83 106

xiu_mvs_v1_base_debu		80.80 111	79.72 113	84.03 103	87.35 183	70.19 65	85.56 202	88.77 186	69.06 200	81.83 74	88.16 151	50.91 247	92.85 166	78.29 76	87.56 108	89.06 201

xiu_mvs_v1_base		80.80 111	79.72 113	84.03 103	87.35 183	70.19 65	85.56 202	88.77 186	69.06 200	81.83 74	88.16 151	50.91 247	92.85 166	78.29 76	87.56 108	89.06 201

xiu_mvs_v1_base_debi		80.80 111	79.72 113	84.03 103	87.35 183	70.19 65	85.56 202	88.77 186	69.06 200	81.83 74	88.16 151	50.91 247	92.85 166	78.29 76	87.56 108	89.06 201

UGNet		80.83 108	79.59 116	84.54 83	88.04 156	68.09 110	89.42 69	88.16 197	76.95 48	76.22 169	89.46 119	49.30 274	93.94 108	68.48 169	90.31 73	91.60 110
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
v1neww		80.40 122	79.54 117	82.98 143	84.10 237	64.51 179	87.57 128	90.22 129	73.25 124	78.47 114	86.65 199	62.83 115	93.86 114	75.72 99	77.02 238	90.58 146

v7new		80.40 122	79.54 117	82.98 143	84.10 237	64.51 179	87.57 128	90.22 129	73.25 124	78.47 114	86.65 199	62.83 115	93.86 114	75.72 99	77.02 238	90.58 146

v6		80.40 122	79.54 117	82.98 143	84.09 239	64.50 183	87.57 128	90.22 129	73.25 124	78.47 114	86.63 201	62.84 114	93.86 114	75.73 98	77.02 238	90.58 146

114514_t		80.68 115	79.51 120	84.20 95	94.09 26	67.27 124	89.64 67	91.11 102	58.75 308	74.08 207	90.72 91	58.10 176	95.04 66	69.70 159	89.42 85	90.30 158

QAPM		80.88 104	79.50 121	85.03 72	88.01 158	68.97 88	91.59 27	92.00 68	66.63 235	75.15 197	92.16 56	57.70 178	95.45 48	63.52 205	88.76 92	90.66 140

AdaColmap		80.58 119	79.42 122	84.06 100	93.09 43	68.91 89	89.36 70	88.97 177	69.27 195	75.70 183	89.69 109	57.20 187	95.77 40	63.06 209	88.41 101	87.50 253

mvs-test1		80.88 104	79.40 123	85.29 65	85.13 216	69.75 73	89.28 71	88.10 199	74.99 88	76.44 163	86.72 191	57.27 184	94.26 96	73.53 122	83.18 167	91.87 105

NR-MVSNet		80.23 129	79.38 124	82.78 162	87.80 170	63.34 212	86.31 178	91.09 103	79.01 26	72.17 232	89.07 127	67.20 72	92.81 170	66.08 189	75.65 261	92.20 97

IterMVS-LS		80.06 135	79.38 124	82.11 174	85.89 203	63.20 217	86.79 163	89.34 159	74.19 99	75.45 186	86.72 191	66.62 75	92.39 179	72.58 133	76.86 243	90.75 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 127	79.32 126	83.27 126	83.98 246	65.37 155	90.50 44	90.38 120	68.55 210	76.19 170	88.70 134	56.44 191	93.46 139	78.98 68	80.14 205	90.97 128

v1141		80.19 131	79.31 127	82.85 153	83.84 250	64.12 193	87.14 147	90.08 136	73.13 127	78.27 123	86.39 210	62.67 124	93.75 124	75.40 106	76.83 246	90.68 137

divwei89l23v2f112		80.19 131	79.31 127	82.85 153	83.84 250	64.11 195	87.13 150	90.08 136	73.13 127	78.27 123	86.39 210	62.69 122	93.75 124	75.40 106	76.82 247	90.68 137

v1		80.19 131	79.31 127	82.85 153	83.83 252	64.12 193	87.14 147	90.07 138	73.13 127	78.27 123	86.38 214	62.72 121	93.75 124	75.41 105	76.82 247	90.68 137

v2v482		80.23 129	79.29 130	83.05 139	83.62 255	64.14 191	87.04 154	89.97 140	73.61 115	78.18 129	87.22 177	61.10 153	93.82 117	76.11 94	76.78 249	91.18 122

v7		80.24 128	79.26 131	83.15 133	84.07 243	64.94 167	87.56 131	90.67 110	72.26 150	78.28 122	86.51 208	61.45 145	94.03 105	75.14 109	77.41 232	90.49 151

XVG-OURS		80.41 121	79.23 132	83.97 107	85.64 207	69.02 85	83.03 259	90.39 119	71.09 166	77.63 139	91.49 73	54.62 206	91.35 219	75.71 101	83.47 159	91.54 112

WR-MVS		79.49 146	79.22 133	80.27 216	88.79 134	58.35 258	85.06 216	88.61 193	78.56 29	77.65 138	88.34 146	63.81 99	90.66 236	64.98 199	77.22 235	91.80 109

mvs_anonymous		79.42 149	79.11 134	80.34 213	84.45 225	57.97 265	82.59 260	87.62 208	67.40 230	76.17 173	88.56 141	68.47 61	89.59 249	70.65 150	86.05 132	93.47 56

v1144		80.03 136	79.03 135	83.01 141	83.78 253	64.51 179	87.11 152	90.57 115	71.96 154	78.08 132	86.20 218	61.41 146	93.94 108	74.93 110	77.23 234	90.60 143

v8		79.97 138	79.02 136	82.80 159	84.09 239	64.50 183	87.96 118	90.29 128	74.13 102	75.24 195	86.81 188	62.88 112	93.89 113	74.39 113	75.40 266	90.00 172

ab-mvs		79.51 145	78.97 137	81.14 202	88.46 145	60.91 239	83.84 244	89.24 165	70.36 177	79.03 104	88.87 131	63.23 105	90.21 241	65.12 196	82.57 177	92.28 94

Anonymous20240529		80.19 131	78.89 138	84.10 98	90.60 76	64.75 172	88.95 83	90.90 106	65.97 244	80.59 93	91.17 79	49.97 265	93.73 129	69.16 165	82.70 175	93.81 40

PCF-MVS	73.52 7	80.38 125	78.84 139	85.01 73	87.71 175	68.99 87	83.65 246	91.46 94	63.00 271	77.77 137	90.28 96	66.10 79	95.09 65	61.40 225	88.22 103	90.94 129
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 142	78.67 140	82.97 147	84.06 244	64.95 166	87.88 123	90.62 113	73.11 130	75.11 198	86.56 205	61.46 144	94.05 104	73.68 118	75.55 263	89.90 180

VPNet		78.69 164	78.66 141	78.76 246	88.31 150	55.72 303	84.45 232	86.63 220	76.79 52	78.26 126	90.55 94	59.30 169	89.70 248	66.63 184	77.05 237	90.88 130

BH-untuned		79.47 147	78.60 142	82.05 175	89.19 119	65.91 143	86.07 186	88.52 194	72.18 151	75.42 187	87.69 164	61.15 152	93.54 136	60.38 232	86.83 120	86.70 272

DI_MVS_plusplus_test		79.89 139	78.58 143	83.85 112	82.89 275	65.32 156	86.12 184	89.55 152	69.64 190	70.55 249	85.82 231	57.24 186	93.81 118	76.85 90	88.55 97	92.41 90

Effi-MVS+-dtu		80.03 136	78.57 144	84.42 87	85.13 216	68.74 95	88.77 90	88.10 199	74.99 88	74.97 201	83.49 267	57.27 184	93.36 144	73.53 122	80.88 192	91.18 122

WR-MVS_H		78.51 166	78.49 145	78.56 249	88.02 157	56.38 292	88.43 100	92.67 43	77.14 43	73.89 208	87.55 168	66.25 78	89.24 256	58.92 244	73.55 285	90.06 170

test_normal		79.81 140	78.45 146	83.89 111	82.70 279	65.40 152	85.82 195	89.48 155	69.39 191	70.12 258	85.66 234	57.15 188	93.71 130	77.08 87	88.62 95	92.56 84

Vis-MVSNet (Re-imp)		78.36 169	78.45 146	78.07 258	88.64 138	51.78 326	86.70 167	79.63 302	74.14 101	75.11 198	90.83 89	61.29 149	89.75 246	58.10 253	91.60 60	92.69 81

BH-RMVSNet		79.61 143	78.44 148	83.14 134	89.38 108	65.93 142	84.95 218	87.15 215	73.56 117	78.19 128	89.79 108	56.67 190	93.36 144	59.53 240	86.74 121	90.13 163

v1192		79.59 144	78.43 149	83.07 138	83.55 257	64.52 177	86.93 158	90.58 114	70.83 168	77.78 136	85.90 227	59.15 170	93.94 108	73.96 117	77.19 236	90.76 133

v144192		79.47 147	78.37 150	82.78 162	83.35 260	63.96 197	86.96 156	90.36 123	69.99 183	77.50 140	85.67 233	60.66 160	93.77 122	74.27 114	76.58 250	90.62 141

CP-MVSNet		78.22 171	78.34 151	77.84 260	87.83 168	54.54 308	87.94 120	91.17 101	77.65 33	73.48 210	88.49 142	62.24 136	88.43 276	62.19 216	74.07 278	90.55 149

Baseline_NR-MVSNet		78.15 175	78.33 152	77.61 264	85.79 204	56.21 295	86.78 164	85.76 231	73.60 116	77.93 134	87.57 167	65.02 90	88.99 267	67.14 181	75.33 267	87.63 249

OpenMVS	72.83 10	79.77 141	78.33 152	84.09 99	85.17 213	69.91 69	90.57 42	90.97 104	66.70 231	72.17 232	91.91 61	54.70 204	93.96 106	61.81 222	90.95 68	88.41 236

V42		79.38 152	78.24 154	82.83 156	81.10 301	65.50 151	85.55 205	89.82 146	71.57 160	78.21 127	86.12 219	60.66 160	93.18 152	75.64 102	75.46 265	89.81 185

PS-CasMVS		78.01 179	78.09 155	77.77 262	87.71 175	54.39 310	88.02 116	91.22 98	77.50 40	73.26 212	88.64 137	60.73 157	88.41 277	61.88 220	73.88 282	90.53 150

v1921920		79.22 154	78.03 156	82.80 159	83.30 262	63.94 198	86.80 162	90.33 125	69.91 184	77.48 141	85.53 238	58.44 174	93.75 124	73.60 121	76.85 244	90.71 136

jajsoiax		79.29 153	77.96 157	83.27 126	84.68 222	66.57 134	89.25 74	90.16 133	69.20 197	75.46 185	89.49 116	45.75 296	93.13 155	76.84 91	80.80 194	90.11 164

TAPA-MVS	73.13 9	79.15 155	77.94 158	82.79 161	89.59 98	62.99 223	88.16 115	91.51 90	65.77 245	77.14 151	91.09 81	60.91 156	93.21 148	50.26 290	87.05 115	92.17 99
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		79.40 150	77.91 159	83.90 110	88.10 154	63.84 199	88.37 107	84.05 246	71.45 162	76.78 154	89.12 126	49.93 269	94.89 73	70.18 154	83.18 167	92.96 76

MVSTER		79.01 158	77.88 160	82.38 170	83.07 269	64.80 170	84.08 242	88.95 179	69.01 204	78.69 108	87.17 180	54.70 204	92.43 177	74.69 111	80.57 198	89.89 181

X-MVStestdata		80.37 126	77.83 161	88.00 11	94.42 13	73.33 17	92.78 9	92.99 31	79.14 21	83.67 56	12.47 365	67.45 69	96.60 21	83.06 39	94.50 36	94.07 25

v148		78.72 163	77.80 162	81.47 195	82.73 278	61.96 233	86.30 179	88.08 201	73.26 123	76.18 171	85.47 240	62.46 132	92.36 181	71.92 143	73.82 283	90.09 166

v1240		78.99 159	77.78 163	82.64 166	83.21 264	63.54 205	86.62 169	90.30 127	69.74 189	77.33 144	85.68 232	57.04 189	93.76 123	73.13 127	76.92 241	90.62 141

mvs_tets		79.13 156	77.77 164	83.22 130	84.70 221	66.37 136	89.17 75	90.19 132	69.38 193	75.40 188	89.46 119	44.17 302	93.15 153	76.78 92	80.70 196	90.14 162

thisisatest0530		79.40 150	77.76 165	84.31 92	87.69 177	65.10 163	87.36 137	84.26 244	70.04 182	77.42 142	88.26 150	49.94 267	94.79 79	70.20 153	84.70 143	93.03 71

CDS-MVSNet		79.07 157	77.70 166	83.17 132	87.60 178	68.23 108	84.40 235	86.20 226	67.49 228	76.36 164	86.54 206	61.54 143	90.79 234	61.86 221	87.33 112	90.49 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20231211		78.97 160	77.69 167	82.81 158	90.54 77	64.29 189	90.11 54	91.51 90	65.01 254	76.16 174	88.13 155	50.56 259	93.03 162	69.68 160	77.56 230	91.11 124

PEN-MVS		77.73 186	77.69 167	77.84 260	87.07 191	53.91 312	87.91 122	91.18 100	77.56 37	73.14 214	88.82 132	61.23 150	89.17 263	59.95 235	72.37 291	90.43 154

v7n		78.97 160	77.58 169	83.14 134	83.45 259	65.51 150	88.32 108	91.21 99	73.69 114	72.41 229	86.32 215	57.93 177	93.81 118	69.18 164	75.65 261	90.11 164

TAMVS		78.89 162	77.51 170	83.03 140	87.80 170	67.79 116	84.72 222	85.05 237	67.63 224	76.75 155	87.70 163	62.25 135	90.82 233	58.53 249	87.13 114	90.49 151

GBi-Net		78.40 167	77.40 171	81.40 197	87.60 178	63.01 220	88.39 104	89.28 161	71.63 157	75.34 190	87.28 173	54.80 200	91.11 224	62.72 210	79.57 213	90.09 166

test1		78.40 167	77.40 171	81.40 197	87.60 178	63.01 220	88.39 104	89.28 161	71.63 157	75.34 190	87.28 173	54.80 200	91.11 224	62.72 210	79.57 213	90.09 166

BH-w/o		78.21 172	77.33 173	80.84 206	88.81 132	65.13 162	84.87 219	87.85 205	69.75 187	74.52 205	84.74 254	61.34 147	93.11 156	58.24 252	85.84 135	84.27 305

FMVSNet2		78.20 173	77.21 174	81.20 200	87.60 178	62.89 224	87.47 135	89.02 170	71.63 157	75.29 194	87.28 173	54.80 200	91.10 227	62.38 214	79.38 216	89.61 193

anonymousdsp		78.60 165	77.15 175	82.98 143	80.51 307	67.08 126	87.24 145	89.53 153	65.66 247	75.16 196	87.19 179	52.52 217	92.25 184	77.17 86	79.34 217	89.61 193

HY-MVS	69.67 12	77.95 181	77.15 175	80.36 212	87.57 182	60.21 246	83.37 257	87.78 206	66.11 240	75.37 189	87.06 186	63.27 103	90.48 238	61.38 226	82.43 178	90.40 156

Anonymous202405211		78.25 170	77.01 177	81.99 177	91.03 69	60.67 242	84.77 221	83.90 248	70.65 174	80.00 96	91.20 78	41.08 319	91.43 216	65.21 195	85.26 137	93.85 37

MVS		78.19 174	76.99 178	81.78 181	85.66 206	66.99 127	84.66 223	90.47 118	55.08 328	72.02 237	85.27 243	63.83 98	94.11 103	66.10 188	89.80 81	84.24 306

LCM-MVSNet-Re		77.05 204	76.94 179	77.36 269	87.20 189	51.60 327	80.06 281	80.46 293	75.20 86	67.69 287	86.72 191	62.48 131	88.98 268	63.44 206	89.25 86	91.51 113

FMVSNet3		77.88 184	76.85 180	80.97 205	86.84 194	62.36 227	86.52 172	88.77 186	71.13 164	75.34 190	86.66 198	54.07 210	91.10 227	62.72 210	79.57 213	89.45 195

DTE-MVSNet		76.99 205	76.80 181	77.54 266	86.24 200	53.06 323	87.52 133	90.66 112	77.08 46	72.50 220	88.67 136	60.48 163	89.52 250	57.33 260	70.74 302	90.05 171

CNLPA		78.08 176	76.79 182	81.97 178	90.40 80	71.07 50	87.59 127	84.55 240	66.03 243	72.38 230	89.64 111	57.56 180	86.04 294	59.61 238	83.35 164	88.79 217

pm-mvs1		77.25 203	76.68 183	78.93 243	84.22 229	58.62 256	86.41 175	88.36 196	71.37 163	73.31 211	88.01 156	61.22 151	89.15 264	64.24 203	73.01 287	89.03 207

v748		77.97 180	76.65 184	81.92 180	82.29 285	63.28 214	87.53 132	90.35 124	73.50 120	70.76 248	85.55 237	58.28 175	92.81 170	68.81 168	72.76 290	89.67 192

Fast-Effi-MVS+-dtu		78.02 178	76.49 185	82.62 167	83.16 268	66.96 130	86.94 157	87.45 213	72.45 143	71.49 243	84.17 257	54.79 203	91.58 214	67.61 173	80.31 202	89.30 197

1112_ss		77.40 201	76.43 186	80.32 214	89.11 125	60.41 245	83.65 246	87.72 207	62.13 282	73.05 215	86.72 191	62.58 128	89.97 243	62.11 219	80.80 194	90.59 145

PAPM		77.68 188	76.40 187	81.51 194	87.29 188	61.85 234	83.78 245	89.59 151	64.74 256	71.23 244	88.70 134	62.59 127	93.66 131	52.66 280	87.03 116	89.01 208

v52		77.94 183	76.37 188	82.67 164	79.39 321	65.52 148	86.43 173	89.94 143	72.28 148	72.15 234	84.94 250	55.70 195	93.44 141	73.64 119	72.84 289	89.06 201

V4		77.95 181	76.37 188	82.67 164	79.40 320	65.52 148	86.43 173	89.94 143	72.28 148	72.14 235	84.95 249	55.72 194	93.44 141	73.64 119	72.86 288	89.05 205

PLC	70.83 11	78.05 177	76.37 188	83.08 137	91.88 62	67.80 115	88.19 113	89.46 156	64.33 261	69.87 264	88.38 145	53.66 213	93.58 132	58.86 245	82.73 173	87.86 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v16		77.69 187	76.36 191	81.68 188	84.15 234	64.63 176	87.33 140	88.99 174	72.69 141	69.31 272	82.08 280	62.80 118	91.79 196	72.70 131	67.23 314	88.63 222

v18		77.67 190	76.35 192	81.64 190	84.09 239	64.47 185	87.27 143	89.01 172	72.59 142	69.39 269	82.04 282	62.85 113	91.80 195	72.72 130	67.20 315	88.63 222

v17		77.68 188	76.35 192	81.69 187	84.15 234	64.65 174	87.33 140	88.99 174	72.70 140	69.25 273	82.07 281	62.82 117	91.79 196	72.69 132	67.15 316	88.63 222

TR-MVS		77.44 199	76.18 194	81.20 200	88.24 151	63.24 215	84.61 227	86.40 223	67.55 227	77.81 135	86.48 209	54.10 209	93.15 153	57.75 256	82.72 174	87.20 260

v15		77.51 195	76.12 195	81.66 189	84.09 239	64.65 174	87.14 147	88.96 178	72.76 138	68.90 274	81.91 289	62.74 120	91.73 200	72.32 136	66.29 322	88.61 225

V14		77.52 193	76.12 195	81.70 186	84.15 234	64.77 171	87.21 146	88.95 179	72.80 137	68.79 275	81.94 288	62.69 122	91.72 202	72.31 137	66.27 323	88.60 226

FMVSNet1		77.44 199	76.12 195	81.40 197	86.81 195	63.01 220	88.39 104	89.28 161	70.49 176	74.39 206	87.28 173	49.06 277	91.11 224	60.91 229	78.52 221	90.09 166

V9		77.52 193	76.11 198	81.73 185	84.19 233	64.89 168	87.26 144	88.94 182	72.87 136	68.65 278	81.96 287	62.65 125	91.72 202	72.27 138	66.24 324	88.60 226

v12		77.51 195	76.09 199	81.76 184	84.22 229	64.99 165	87.30 142	88.93 183	72.92 133	68.48 282	81.97 285	62.54 129	91.70 205	72.24 139	66.21 326	88.58 229

v13		77.50 197	76.07 200	81.77 182	84.23 228	65.07 164	87.34 139	88.91 184	72.92 133	68.35 283	81.97 285	62.53 130	91.69 206	72.20 140	66.22 325	88.56 231

v11		77.45 198	76.06 201	81.59 193	84.22 229	64.52 177	87.11 152	89.02 170	72.76 138	68.76 276	81.90 290	62.09 138	91.71 204	71.98 141	66.73 317	88.56 231

Test4		77.83 185	75.90 202	83.62 114	80.24 309	65.25 158	85.27 210	90.67 110	69.03 203	66.48 299	83.75 263	43.07 307	93.00 163	75.93 97	88.66 94	92.62 83

CHOSEN 1792x2688		77.63 191	75.69 203	83.44 119	89.98 88	68.58 102	78.70 296	87.50 211	56.38 323	75.80 178	86.84 187	58.67 172	91.40 217	61.58 224	85.75 136	90.34 157

WTY-MVS		75.65 230	75.68 204	75.57 289	86.40 199	56.82 283	77.92 303	82.40 270	65.10 251	76.18 171	87.72 162	63.13 110	80.90 317	60.31 233	81.96 181	89.00 210

XXY-MVS		75.41 233	75.56 205	74.96 294	83.59 256	57.82 269	80.59 278	83.87 249	66.54 236	74.93 202	88.31 147	63.24 104	80.09 321	62.16 217	76.85 244	86.97 266

conf200view11		76.55 210	75.55 206	79.57 231	89.52 100	56.99 279	85.83 192	83.23 259	73.94 104	76.32 165	87.12 181	51.89 231	91.95 190	48.33 298	83.75 152	89.78 186

thres100view900		76.50 213	75.55 206	79.33 233	89.52 100	56.99 279	85.83 192	83.23 259	73.94 104	76.32 165	87.12 181	51.89 231	91.95 190	48.33 298	83.75 152	89.07 199

tfpn111		76.54 211	75.51 208	79.61 228	89.52 100	56.99 279	85.83 192	83.23 259	73.94 104	76.32 165	87.12 181	51.89 231	92.06 188	48.04 305	83.73 156	89.78 186

thres600view7		76.50 213	75.44 209	79.68 225	89.40 106	57.16 276	85.53 207	83.23 259	73.79 113	76.26 168	87.09 184	51.89 231	91.89 194	48.05 304	83.72 157	90.00 172

Test_1112_low_res		76.40 217	75.44 209	79.27 234	89.28 115	58.09 261	81.69 269	87.07 216	59.53 301	72.48 222	86.67 197	61.30 148	89.33 254	60.81 231	80.15 204	90.41 155

HyFIR lowres test		77.53 192	75.40 211	83.94 109	89.59 98	66.62 132	80.36 279	88.64 191	56.29 324	76.45 160	85.17 244	57.64 179	93.28 146	61.34 227	83.10 169	91.91 104

thisisatest0515		77.33 202	75.38 212	83.18 131	85.27 212	63.80 200	82.11 264	83.27 258	65.06 252	75.91 175	83.84 261	49.54 271	94.27 92	67.24 179	86.19 130	91.48 117

tfpn200view9		76.42 216	75.37 213	79.55 232	89.13 121	57.65 271	85.17 212	83.60 251	73.41 121	76.45 160	86.39 210	52.12 225	91.95 190	48.33 298	83.75 152	89.07 199

thres400		76.50 213	75.37 213	79.86 221	89.13 121	57.65 271	85.17 212	83.60 251	73.41 121	76.45 160	86.39 210	52.12 225	91.95 190	48.33 298	83.75 152	90.00 172

1314		76.53 212	75.30 215	80.21 217	83.93 247	62.32 229	84.66 223	88.81 185	60.23 294	70.16 257	84.07 259	55.30 198	90.73 235	67.37 176	83.21 166	87.59 251

view600		76.20 220	75.21 216	79.16 238	89.64 93	55.82 298	85.74 197	82.06 276	73.88 108	75.74 179	87.85 158	51.84 235	91.66 208	46.75 309	83.42 160	90.00 172

view800		76.20 220	75.21 216	79.16 238	89.64 93	55.82 298	85.74 197	82.06 276	73.88 108	75.74 179	87.85 158	51.84 235	91.66 208	46.75 309	83.42 160	90.00 172

conf0.05thres1000		76.20 220	75.21 216	79.16 238	89.64 93	55.82 298	85.74 197	82.06 276	73.88 108	75.74 179	87.85 158	51.84 235	91.66 208	46.75 309	83.42 160	90.00 172

tfpn		76.20 220	75.21 216	79.16 238	89.64 93	55.82 298	85.74 197	82.06 276	73.88 108	75.74 179	87.85 158	51.84 235	91.66 208	46.75 309	83.42 160	90.00 172

GA-MVS		76.87 207	75.17 220	81.97 178	82.75 277	62.58 226	81.44 273	86.35 225	72.16 153	74.74 203	82.89 270	46.20 291	92.02 189	68.85 167	81.09 190	91.30 120

EPNet_dtu		75.46 232	74.86 221	77.23 272	82.57 282	54.60 307	86.89 159	83.09 264	71.64 156	66.25 301	85.86 229	55.99 193	88.04 281	54.92 270	86.55 125	89.05 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 206	74.82 222	83.37 123	90.45 78	67.36 123	89.15 79	86.94 217	61.87 284	69.52 267	90.61 93	51.71 240	94.53 85	46.38 316	86.71 122	88.21 238

cascas		76.72 209	74.64 223	82.99 142	85.78 205	65.88 144	82.33 262	89.21 166	60.85 290	72.74 217	81.02 297	47.28 284	93.75 124	67.48 175	85.02 138	89.34 196

DP-MVS		76.78 208	74.57 224	83.42 120	93.29 36	69.46 81	88.55 99	83.70 250	63.98 265	70.20 254	88.89 130	54.01 211	94.80 78	46.66 313	81.88 183	86.01 288

TransMVSNet (Re)		75.39 234	74.56 225	77.86 259	85.50 210	57.10 278	86.78 164	86.09 229	72.17 152	71.53 242	87.34 172	63.01 111	89.31 255	56.84 263	61.83 334	87.17 261

LTVRE_ROB	69.57 13	76.25 219	74.54 226	81.41 196	88.60 139	64.38 188	79.24 289	89.12 168	70.76 171	69.79 266	87.86 157	49.09 276	93.20 150	56.21 266	80.16 203	86.65 273
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
thres200		75.55 231	74.47 227	78.82 245	87.78 173	57.85 268	83.07 258	83.51 254	72.44 145	75.84 177	84.42 256	52.08 227	91.75 199	47.41 307	83.64 158	86.86 268

MVP-Stereo		76.12 224	74.46 228	81.13 203	85.37 211	69.79 71	84.42 234	87.95 203	65.03 253	67.46 289	85.33 242	53.28 216	91.73 200	58.01 254	83.27 165	81.85 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		76.38 218	74.33 229	82.50 168	89.28 115	66.95 131	88.41 103	89.03 169	64.05 263	66.83 295	88.61 138	46.78 287	92.89 165	57.48 257	78.55 220	87.67 248

XVG-ACMP-BASELINE		76.11 225	74.27 230	81.62 191	83.20 265	64.67 173	83.60 248	89.75 148	69.75 187	71.85 238	87.09 184	32.78 340	92.11 187	69.99 157	80.43 201	88.09 240

ACMH+	68.96 14	76.01 226	74.01 231	82.03 176	88.60 139	65.31 157	88.86 86	87.55 209	70.25 180	67.75 286	87.47 171	41.27 317	93.19 151	58.37 250	75.94 257	87.60 250

ACMH	67.68 16	75.89 227	73.93 232	81.77 182	88.71 137	66.61 133	88.62 96	89.01 172	69.81 185	66.78 296	86.70 196	41.95 316	91.51 215	55.64 267	78.14 226	87.17 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 235	73.90 233	79.27 234	82.65 281	58.27 260	80.80 274	82.73 268	61.57 285	75.33 193	83.13 269	55.52 196	91.07 230	64.98 199	78.34 225	88.45 234

sss		73.60 248	73.64 234	73.51 304	82.80 276	55.01 305	76.12 309	81.69 282	62.47 279	74.68 204	85.85 230	57.32 183	78.11 329	60.86 230	80.93 191	87.39 254

pmmvs6		74.69 236	73.39 235	78.61 248	81.38 296	57.48 274	86.64 168	87.95 203	64.99 255	70.18 255	86.61 202	50.43 261	89.52 250	62.12 218	70.18 304	88.83 215

IB-MVS	68.01 15	75.85 228	73.36 236	83.31 124	84.76 220	66.03 139	83.38 250	85.06 236	70.21 181	69.40 268	81.05 296	45.76 295	94.66 83	65.10 197	75.49 264	89.25 198
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
testing_2		75.73 229	73.34 237	82.89 152	77.37 329	65.22 159	84.10 241	90.54 116	69.09 199	60.46 325	81.15 295	40.48 321	92.84 169	76.36 93	80.54 200	90.60 143

tfpnnormal		74.39 238	73.16 238	78.08 257	86.10 202	58.05 262	84.65 226	87.53 210	70.32 178	71.22 245	85.63 235	54.97 199	89.86 244	43.03 333	75.02 271	86.32 280

PatchFormer-LS_test		74.50 237	73.05 239	78.86 244	82.95 273	59.55 251	81.65 270	82.30 272	67.44 229	71.62 241	78.15 318	52.34 221	88.92 272	65.05 198	75.90 258	88.12 239

IterMVS		74.29 239	72.94 240	78.35 254	81.53 293	63.49 207	81.58 271	82.49 269	68.06 222	69.99 261	83.69 265	51.66 241	85.54 297	65.85 191	71.64 297	86.01 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn_ndepth		73.70 244	72.75 241	76.52 276	87.78 173	54.92 306	84.32 237	80.28 297	67.57 226	72.50 220	84.82 251	50.12 263	89.44 253	45.73 319	81.66 185	85.20 295

MS-PatchMatch		73.83 243	72.67 242	77.30 271	83.87 248	66.02 140	81.82 266	84.66 239	61.37 288	68.61 280	82.82 272	47.29 283	88.21 278	59.27 241	84.32 147	77.68 338

CVMVSNet		72.99 263	72.58 243	74.25 301	84.28 226	50.85 332	86.41 175	83.45 256	44.56 348	73.23 213	87.54 169	49.38 272	85.70 296	65.90 190	78.44 223	86.19 282

tfpn1000		73.44 251	72.49 244	76.29 282	87.81 169	53.69 314	84.05 243	78.81 312	67.99 223	72.09 236	86.27 216	49.95 266	89.04 266	44.09 330	81.38 187	86.15 283

test-LLR		72.94 264	72.43 245	74.48 298	81.35 297	58.04 263	78.38 297	77.46 319	66.66 232	69.95 262	79.00 313	48.06 280	79.24 323	66.13 186	84.83 140	86.15 283

conf0.01		73.67 246	72.42 246	77.42 267	87.85 161	53.28 317	83.38 250	79.08 305	68.40 213	72.45 223	86.08 221	50.60 253	89.19 257	44.25 324	79.66 207	89.78 186

conf0.002		73.67 246	72.42 246	77.42 267	87.85 161	53.28 317	83.38 250	79.08 305	68.40 213	72.45 223	86.08 221	50.60 253	89.19 257	44.25 324	79.66 207	89.78 186

thresconf0.02		73.39 252	72.42 246	76.31 278	87.85 161	53.28 317	83.38 250	79.08 305	68.40 213	72.45 223	86.08 221	50.60 253	89.19 257	44.25 324	79.66 207	86.48 275

tfpn_n400		73.39 252	72.42 246	76.31 278	87.85 161	53.28 317	83.38 250	79.08 305	68.40 213	72.45 223	86.08 221	50.60 253	89.19 257	44.25 324	79.66 207	86.48 275

tfpnconf		73.39 252	72.42 246	76.31 278	87.85 161	53.28 317	83.38 250	79.08 305	68.40 213	72.45 223	86.08 221	50.60 253	89.19 257	44.25 324	79.66 207	86.48 275

tfpnview11		73.39 252	72.42 246	76.31 278	87.85 161	53.28 317	83.38 250	79.08 305	68.40 213	72.45 223	86.08 221	50.60 253	89.19 257	44.25 324	79.66 207	86.48 275

OurMVSNet-221017-0		74.26 240	72.42 246	79.80 223	83.76 254	59.59 248	85.92 190	86.64 219	66.39 238	66.96 294	87.58 166	39.46 324	91.60 213	65.76 192	69.27 306	88.22 237

tpmrst		72.39 266	72.13 253	73.18 306	80.54 306	49.91 336	79.91 284	79.08 305	63.11 269	71.69 240	79.95 305	55.32 197	82.77 312	65.66 193	73.89 281	86.87 267

pmmvs4		74.03 242	71.91 254	80.39 211	81.96 288	68.32 105	81.45 272	82.14 274	59.32 302	69.87 264	85.13 245	52.40 220	88.13 280	60.21 234	74.74 274	84.73 303

DWT-MVSNet_test		73.70 244	71.86 255	79.21 236	82.91 274	58.94 253	82.34 261	82.17 273	65.21 249	71.05 247	78.31 315	44.21 301	90.17 242	63.29 208	77.28 233	88.53 233

Patchmatch-test1		73.49 249	71.85 256	78.41 253	84.05 245	62.17 231	79.96 283	79.29 304	66.30 239	72.38 230	79.58 309	51.95 230	85.08 301	55.46 268	77.67 229	87.99 241

EG-PatchMatch MVS		74.04 241	71.82 257	80.71 209	84.92 219	67.42 120	85.86 191	88.08 201	66.04 242	64.22 313	83.85 260	35.10 339	92.56 175	57.44 258	80.83 193	82.16 324

tpm		72.37 268	71.71 258	74.35 300	82.19 286	52.00 324	79.22 290	77.29 321	64.56 258	72.95 216	83.68 266	51.35 242	83.26 311	58.33 251	75.80 259	87.81 246

tpm2		73.26 259	71.46 259	78.63 247	83.34 261	56.71 286	80.65 277	80.40 294	56.63 322	73.55 209	82.02 283	51.80 239	91.24 222	56.35 265	78.42 224	87.95 242

RPSCF		73.23 260	71.46 259	78.54 250	82.50 283	59.85 247	82.18 263	82.84 267	58.96 305	71.15 246	89.41 123	45.48 298	84.77 303	58.82 246	71.83 296	91.02 127

PatchmatchNet		73.12 261	71.33 261	78.49 252	83.18 266	60.85 240	79.63 285	78.57 313	64.13 262	71.73 239	79.81 308	51.20 244	85.97 295	57.40 259	76.36 254	88.66 220
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 256	71.27 262	79.67 226	81.32 299	65.19 160	75.92 311	80.30 295	59.92 297	72.73 218	81.19 293	52.50 218	86.69 288	59.84 236	77.71 227	87.11 264

SixPastTwentyTwo		73.37 256	71.26 263	79.70 224	85.08 218	57.89 267	85.57 201	83.56 253	71.03 167	65.66 303	85.88 228	42.10 314	92.57 174	59.11 243	63.34 331	88.65 221

tpmp4_e23		73.45 250	71.17 264	80.31 215	83.55 257	59.56 250	81.88 265	82.33 271	57.94 313	70.51 251	81.62 291	51.19 245	91.63 212	53.96 274	77.51 231	89.75 191

MSDG		73.36 258	70.99 265	80.49 210	84.51 224	65.80 145	80.71 276	86.13 228	65.70 246	65.46 304	83.74 264	44.60 299	90.91 232	51.13 285	76.89 242	84.74 302

PatchMatch-RL		72.38 267	70.90 266	76.80 275	88.60 139	67.38 122	79.53 286	76.17 325	62.75 276	69.36 270	82.00 284	45.51 297	84.89 302	53.62 276	80.58 197	78.12 336

PVSNet	64.34 18	72.08 269	70.87 267	75.69 287	86.21 201	56.44 290	74.37 321	80.73 289	62.06 283	70.17 256	82.23 278	42.86 309	83.31 310	54.77 271	84.45 146	87.32 257

test_0402		72.79 265	70.44 268	79.84 222	88.13 153	65.99 141	85.93 189	84.29 242	65.57 248	67.40 291	85.49 239	46.92 286	92.61 173	35.88 343	74.38 277	80.94 328

COLMAP_ROB	66.92 17	73.01 262	70.41 269	80.81 207	87.13 190	65.63 147	88.30 109	84.19 245	62.96 272	63.80 316	87.69 164	38.04 330	92.56 175	46.66 313	74.91 272	84.24 306
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 272	70.39 270	74.48 298	81.35 297	58.04 263	78.38 297	77.46 319	60.32 293	69.95 262	79.00 313	36.08 337	79.24 323	66.13 186	84.83 140	86.15 283

pmmvs5		71.55 271	70.20 271	75.61 288	77.83 326	56.39 291	81.74 268	80.89 286	57.76 314	67.46 289	84.49 255	49.26 275	85.32 300	57.08 262	75.29 268	85.11 299

MDTV_nov1_ep13			69.97 272		83.18 266	53.48 315	77.10 307	80.18 299	60.45 291	69.33 271	80.44 301	48.89 278	86.90 287	51.60 283	78.51 222

MIMVSNet		70.69 277	69.30 273	74.88 295	84.52 223	56.35 293	75.87 313	79.42 303	64.59 257	67.76 285	82.41 275	41.10 318	81.54 316	46.64 315	81.34 188	86.75 271

tpmvs		71.09 274	69.29 274	76.49 277	82.04 287	56.04 296	78.92 294	81.37 285	64.05 263	67.18 293	78.28 316	49.74 270	89.77 245	49.67 293	72.37 291	83.67 311

Patchmtry		70.74 276	69.16 275	75.49 291	80.72 303	54.07 311	74.94 320	80.30 295	58.34 309	70.01 259	81.19 293	52.50 218	86.54 290	53.37 277	71.09 300	85.87 291

TESTMET0.1,1		69.89 286	69.00 276	72.55 307	79.27 323	56.85 282	78.38 297	74.71 335	57.64 315	68.09 284	77.19 324	37.75 331	76.70 334	63.92 204	84.09 148	84.10 309

RPMNet		71.62 270	68.94 277	79.67 226	81.32 299	65.19 160	75.92 311	78.30 315	57.60 316	72.73 218	76.45 327	52.30 222	86.69 288	48.14 303	77.71 227	87.11 264

PMMVS		69.34 288	68.67 278	71.35 314	75.67 335	62.03 232	75.17 315	73.46 340	50.00 344	68.68 277	79.05 311	52.07 228	78.13 328	61.16 228	82.77 172	73.90 346

K. test v3		71.19 273	68.51 279	79.21 236	83.04 271	57.78 270	84.35 236	76.91 323	72.90 135	62.99 319	82.86 271	39.27 325	91.09 229	61.65 223	52.66 348	88.75 218

USDC		70.33 281	68.37 280	76.21 284	80.60 305	56.23 294	79.19 291	86.49 221	60.89 289	61.29 322	85.47 240	31.78 343	89.47 252	53.37 277	76.21 255	82.94 321

tpm cat1		70.57 278	68.31 281	77.35 270	82.41 284	57.95 266	78.08 301	80.22 298	52.04 339	68.54 281	77.66 322	52.00 229	87.84 283	51.77 281	72.07 295	86.25 281

OpenMVS_ROB	64.09 19	70.56 279	68.19 282	77.65 263	80.26 308	59.41 252	85.01 217	82.96 266	58.76 307	65.43 305	82.33 276	37.63 333	91.23 223	45.34 322	76.03 256	82.32 322

EPMVS		69.02 290	68.16 283	71.59 310	79.61 317	49.80 338	77.40 305	66.93 356	62.82 275	70.01 259	79.05 311	45.79 294	77.86 331	56.58 264	75.26 269	87.13 263

CMPMVS	51.72 21	70.19 283	68.16 283	76.28 283	73.15 344	57.55 273	79.47 287	83.92 247	48.02 346	56.48 339	84.81 252	43.13 306	86.42 292	62.67 213	81.81 184	84.89 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 275	68.09 285	79.58 229	85.15 214	63.62 201	84.58 228	79.83 300	62.31 280	60.32 326	86.73 189	32.02 341	88.96 270	50.28 288	71.57 298	86.15 283

gg-mvs-nofinetune		69.95 285	67.96 286	75.94 285	83.07 269	54.51 309	77.23 306	70.29 347	63.11 269	70.32 253	62.33 349	43.62 304	88.69 274	53.88 275	87.76 105	84.62 304

FMVSNet5		69.50 287	67.96 286	74.15 302	82.97 272	55.35 304	80.01 282	82.12 275	62.56 278	63.02 317	81.53 292	36.92 334	81.92 314	48.42 297	74.06 279	85.17 298

PatchT		68.46 294	67.85 288	70.29 318	80.70 304	43.93 346	72.47 324	74.88 331	60.15 295	70.55 249	76.57 326	49.94 267	81.59 315	50.58 286	74.83 273	85.34 294

pmmvs-eth3d		70.50 280	67.83 289	78.52 251	77.37 329	66.18 138	81.82 266	81.51 283	58.90 306	63.90 315	80.42 302	42.69 310	86.28 293	58.56 248	65.30 328	83.11 317

Anonymous20231206		68.60 291	67.80 290	71.02 316	80.23 310	50.75 333	78.30 300	80.47 292	56.79 321	66.11 302	82.63 274	46.35 289	78.95 325	43.62 332	75.70 260	83.36 314

Patchmatch-RL test		70.24 282	67.78 291	77.61 264	77.43 328	59.57 249	71.16 326	70.33 346	62.94 273	68.65 278	72.77 337	50.62 252	85.49 298	69.58 161	66.58 320	87.77 247

test0.0.03 1		68.00 295	67.69 292	68.90 323	77.55 327	47.43 340	75.70 314	72.95 342	66.66 232	66.56 297	82.29 277	48.06 280	75.87 338	44.97 323	74.51 276	83.41 313

EU-MVSNet		68.53 293	67.61 293	71.31 315	78.51 325	47.01 342	84.47 229	84.27 243	42.27 349	66.44 300	84.79 253	40.44 322	83.76 306	58.76 247	68.54 312	83.17 315

ppachtmachnet_test		70.04 284	67.34 294	78.14 256	79.80 314	61.13 236	79.19 291	80.59 290	59.16 304	65.27 306	79.29 310	46.75 288	87.29 285	49.33 294	66.72 318	86.00 290

our_test_3		69.14 289	67.00 295	75.57 289	79.80 314	58.80 254	77.96 302	77.81 317	59.55 300	62.90 320	78.25 317	47.43 282	83.97 305	51.71 282	67.58 313	83.93 310

test20.03		67.45 297	66.95 296	68.94 322	75.48 338	44.84 344	77.50 304	77.67 318	66.66 232	63.01 318	83.80 262	47.02 285	78.40 327	42.53 335	68.86 310	83.58 312

MIMVSNet1		68.58 292	66.78 297	73.98 303	80.07 311	51.82 325	80.77 275	84.37 241	64.40 260	59.75 329	82.16 279	36.47 335	83.63 308	42.73 334	70.33 303	86.48 275

testgi		66.67 302	66.53 298	67.08 328	75.62 336	41.69 351	75.93 310	76.50 324	66.11 240	65.20 309	86.59 203	35.72 338	74.71 342	43.71 331	73.38 286	84.84 301

UnsupCasMVSNet_eth		67.33 298	65.99 299	71.37 312	73.48 341	51.47 329	75.16 316	85.19 235	65.20 250	60.78 324	80.93 300	42.35 311	77.20 333	57.12 261	53.69 347	85.44 293

dp		66.80 300	65.43 300	70.90 317	79.74 316	48.82 339	75.12 318	74.77 333	59.61 299	64.08 314	77.23 323	42.89 308	80.72 318	48.86 296	66.58 320	83.16 316

TinyColmap		67.30 299	64.81 301	74.76 297	81.92 289	56.68 287	80.29 280	81.49 284	60.33 292	56.27 340	83.22 268	24.77 349	87.66 284	45.52 320	69.47 305	79.95 332

CHOSEN 280x420		66.51 303	64.71 302	71.90 309	81.45 294	63.52 206	57.98 355	68.95 354	53.57 334	62.59 321	76.70 325	46.22 290	75.29 341	55.25 269	79.68 206	76.88 344

TDRefinement		67.49 296	64.34 303	76.92 273	73.47 342	61.07 237	84.86 220	82.98 265	59.77 298	58.30 332	85.13 245	26.06 347	87.89 282	47.92 306	60.59 339	81.81 326

PM-MVS		66.41 304	64.14 304	73.20 305	73.92 339	56.45 289	78.97 293	64.96 360	63.88 267	64.72 310	80.24 303	19.84 355	83.44 309	66.24 185	64.52 330	79.71 333

MDA-MVSNet-bldmvs		66.68 301	63.66 305	75.75 286	79.28 322	60.56 244	73.92 322	78.35 314	64.43 259	50.13 350	79.87 307	44.02 303	83.67 307	46.10 317	56.86 342	83.03 319

ADS-MVSNet2		66.20 306	63.33 306	74.82 296	79.92 312	58.75 255	67.55 343	75.19 329	53.37 335	65.25 307	75.86 328	42.32 312	80.53 319	41.57 336	68.91 308	85.18 296

Patchmatch-test		64.82 309	63.24 307	69.57 320	79.42 319	49.82 337	63.49 350	69.05 353	51.98 340	59.95 328	80.13 304	50.91 247	70.98 352	40.66 338	73.57 284	87.90 244

MDA-MVSNet_test_wron		65.03 307	62.92 308	71.37 312	75.93 333	56.73 284	69.09 338	74.73 334	57.28 319	54.03 343	77.89 319	45.88 292	74.39 344	49.89 292	61.55 335	82.99 320

YYNet1		65.03 307	62.91 309	71.38 311	75.85 334	56.60 288	69.12 337	74.66 337	57.28 319	54.12 342	77.87 320	45.85 293	74.48 343	49.95 291	61.52 336	83.05 318

ADS-MVSNet		64.36 310	62.88 310	68.78 325	79.92 312	47.17 341	67.55 343	71.18 345	53.37 335	65.25 307	75.86 328	42.32 312	73.99 346	41.57 336	68.91 308	85.18 296

JIA-IIPM		66.32 305	62.82 311	76.82 274	77.09 331	61.72 235	65.34 347	75.38 327	58.04 312	64.51 311	62.32 350	42.05 315	86.51 291	51.45 284	69.22 307	82.21 323

LF4IMVS		64.02 311	62.19 312	69.50 321	70.90 349	53.29 316	76.13 308	77.18 322	52.65 338	58.59 330	80.98 298	23.55 350	76.52 335	53.06 279	66.66 319	78.68 335

new-patchmatchnet		61.73 313	61.73 313	61.70 336	72.74 345	24.50 367	69.16 336	78.03 316	61.40 286	56.72 338	75.53 330	38.42 328	76.48 336	45.95 318	57.67 341	84.13 308

UnsupCasMVSNet_bld		63.70 312	61.53 314	70.21 319	73.69 340	51.39 330	72.82 323	81.89 280	55.63 326	57.81 333	71.80 339	38.67 327	78.61 326	49.26 295	52.21 349	80.63 329

PVSNet_0	57.27 20	61.67 314	59.27 315	68.85 324	79.61 317	57.44 275	68.01 341	73.44 341	55.93 325	58.54 331	70.41 342	44.58 300	77.55 332	47.01 308	35.91 354	71.55 348

test2356		59.50 316	58.08 316	63.74 332	71.23 348	41.88 349	67.59 342	72.42 344	53.72 333	57.65 334	70.74 341	26.31 346	72.40 349	32.03 350	71.06 301	76.93 342

testus		59.00 318	57.91 317	62.25 335	72.25 346	39.09 354	69.74 331	75.02 330	53.04 337	57.21 336	73.72 335	18.76 357	70.33 353	32.86 346	68.57 311	77.35 339

LP		61.36 315	57.78 318	72.09 308	75.54 337	58.53 257	67.16 345	75.22 328	51.90 341	54.13 341	69.97 343	37.73 332	80.45 320	32.74 347	55.63 344	77.29 340

MVS-HIRNet		59.14 317	57.67 319	63.57 333	81.65 291	43.50 347	71.73 325	65.06 359	39.59 353	51.43 348	57.73 353	38.34 329	82.58 313	39.53 339	73.95 280	64.62 353

testpf		56.51 323	57.58 320	53.30 343	71.99 347	41.19 352	46.89 360	69.32 352	58.06 311	52.87 347	69.45 345	27.99 345	72.73 348	59.59 239	62.07 333	45.98 358

DSMNet-mixed		57.77 321	56.90 321	60.38 337	67.70 354	35.61 357	69.18 335	53.97 363	32.30 359	57.49 335	79.88 306	40.39 323	68.57 356	38.78 340	72.37 291	76.97 341

test1235678		58.74 319	56.89 322	64.30 330	69.70 350	41.87 350	71.05 327	74.87 332	54.06 330	50.63 349	71.53 340	25.30 348	74.10 345	31.80 351	63.10 332	76.93 342

1111		57.11 322	56.82 323	57.97 340	69.10 351	28.28 362	68.90 339	74.54 338	54.01 331	53.71 344	74.51 332	23.09 351	67.90 357	32.28 348	61.26 337	77.73 337

pmmvs3		57.79 320	54.26 324	68.37 326	64.02 356	56.72 285	75.12 318	65.17 358	40.20 351	52.93 346	69.86 344	20.36 354	75.48 340	45.45 321	55.25 346	72.90 347

N_pmnet		52.79 327	53.26 325	51.40 346	78.99 324	7.68 371	69.52 333	3.89 372	51.63 342	57.01 337	74.98 331	40.83 320	65.96 359	37.78 341	64.67 329	80.56 331

FPMVS		53.68 326	51.64 326	59.81 338	65.08 355	51.03 331	69.48 334	69.58 350	41.46 350	40.67 353	72.32 338	16.46 360	70.00 354	24.24 358	65.42 327	58.40 355

testmv		53.85 325	51.03 327	62.31 334	61.46 358	38.88 355	70.95 330	74.69 336	51.11 343	41.26 352	66.85 346	14.28 361	72.13 350	29.19 353	49.51 351	75.93 345

new_pmnet		50.91 329	50.29 328	52.78 344	68.58 353	34.94 360	63.71 349	56.63 362	39.73 352	44.95 351	65.47 348	21.93 353	58.48 361	34.98 344	56.62 343	64.92 352

v1.0		37.66 338	50.21 329	0.00 359	95.06 1	0.00 374	0.00 365	94.09 2	75.63 76	91.80 3	95.29 4	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

.test1245		45.55 332	50.02 330	32.14 352	69.10 351	28.28 362	68.90 339	74.54 338	54.01 331	53.71 344	74.51 332	23.09 351	67.90 357	32.28 348	0.02 366	0.25 367

LCM-MVSNet		54.25 324	49.68 331	67.97 327	53.73 363	45.28 343	66.85 346	80.78 288	35.96 355	39.45 355	62.23 351	8.70 367	78.06 330	48.24 302	51.20 350	80.57 330

test12356		49.28 331	48.51 332	51.59 345	62.06 357	19.11 368	60.40 352	72.45 343	47.60 347	40.64 354	65.68 347	13.84 362	68.72 355	27.29 355	46.67 353	66.94 351

ANet_high		50.57 330	46.10 333	63.99 331	48.67 366	39.13 353	70.99 329	80.85 287	61.39 287	31.18 358	57.70 354	17.02 359	73.65 347	31.22 352	15.89 363	79.18 334

no-one		51.08 328	45.79 334	66.95 329	57.92 361	50.49 335	59.63 354	76.04 326	48.04 345	31.85 356	56.10 356	19.12 356	80.08 322	36.89 342	26.52 356	70.29 349

Gipuma		45.18 333	41.86 335	55.16 342	77.03 332	51.52 328	32.50 363	80.52 291	32.46 357	27.12 359	35.02 360	9.52 366	75.50 339	22.31 359	60.21 340	38.45 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 334	40.28 336	55.82 341	40.82 369	42.54 348	65.12 348	63.99 361	34.43 356	24.48 360	57.12 355	3.92 369	76.17 337	17.10 361	55.52 345	48.75 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 335	38.86 337	46.69 348	53.84 362	16.45 369	48.61 359	49.92 365	37.49 354	31.67 357	60.97 352	8.14 368	56.42 362	28.42 354	30.72 355	67.19 350

PNet_i23d		38.26 337	35.42 338	46.79 347	58.74 359	35.48 358	59.65 353	51.25 364	32.45 358	23.44 363	47.53 358	2.04 371	58.96 360	25.60 357	18.09 361	45.92 359

pcd1.5k->3k		34.07 339	35.26 339	30.50 353	86.92 192	0.00 374	0.00 365	91.58 87	0.00 369	0.00 371	0.00 371	56.23 192	0.00 371	0.00 368	82.60 176	91.49 115

wuykxyi23d		39.76 336	33.18 340	59.51 339	46.98 367	44.01 345	57.70 356	67.74 355	24.13 361	13.98 367	34.33 361	1.27 372	71.33 351	34.23 345	18.23 359	63.18 354

E-PMN		31.77 340	30.64 341	35.15 350	52.87 364	27.67 364	57.09 357	47.86 366	24.64 360	16.40 365	33.05 362	11.23 364	54.90 363	14.46 363	18.15 360	22.87 362

EMVS		30.81 341	29.65 342	34.27 351	50.96 365	25.95 366	56.58 358	46.80 367	24.01 362	15.53 366	30.68 363	12.47 363	54.43 364	12.81 364	17.05 362	22.43 363

cdsmvs_eth3d_5k		19.96 343	26.61 343	0.00 359	0.00 374	0.00 374	0.00 365	89.26 164	0.00 369	0.00 371	88.61 138	61.62 142	0.00 371	0.00 368	0.00 369	0.00 369

MVE	26.22 23	30.37 342	25.89 344	43.81 349	44.55 368	35.46 359	28.87 364	39.07 368	18.20 363	18.58 364	40.18 359	2.68 370	47.37 365	17.07 362	23.78 358	48.60 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 344	21.40 345	10.23 356	4.82 371	10.11 370	34.70 362	30.74 370	1.48 366	23.91 362	26.07 364	28.42 344	13.41 368	27.12 356	15.35 364	7.17 364

wuyk23d		16.82 345	15.94 346	19.46 355	58.74 359	31.45 361	39.22 361	3.74 373	6.84 365	6.04 368	2.70 368	1.27 372	24.29 367	10.54 365	14.40 365	2.63 365

ab-mvs-re		7.23 346	9.64 347	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	86.72 191	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

test123		6.12 347	8.11 348	0.14 357	0.06 373	0.09 372	71.05 327	0.03 375	0.04 368	0.25 370	1.30 370	0.05 374	0.03 370	0.21 367	0.01 368	0.29 366

testmvs		6.04 348	8.02 349	0.10 358	0.08 372	0.03 373	69.74 331	0.04 374	0.05 367	0.31 369	1.68 369	0.02 375	0.04 369	0.24 366	0.02 366	0.25 367

pcd_1.5k_mvsjas		5.26 349	7.02 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	63.15 107	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																88.96 212

test_part2					95.06 1	72.65 29				91.80 3

test_part1				0.00 359		0.00 374	0.00 365	94.09 2				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												51.32 243				88.96 212

sam_mvs												50.01 264

semantic-postprocess				80.11 218	82.69 280	64.85 169		83.47 255	69.16 198	70.49 252	84.15 258	50.83 251	88.15 279	69.23 163	72.14 294	87.34 256

ambc				75.24 293	73.16 343	50.51 334	63.05 351	87.47 212		64.28 312	77.81 321	17.80 358	89.73 247	57.88 255	60.64 338	85.49 292

MTGPA								92.02 65

test_post1							78.90 295				5.43 367	48.81 279	85.44 299	59.25 242

test_post											5.46 366	50.36 262	84.24 304

patchmatchnet-post											74.00 334	51.12 246	88.60 275

GG-mvs-BLEND				75.38 292	81.59 292	55.80 302	79.32 288	69.63 349		67.19 292	73.67 336	43.24 305	88.90 273	50.41 287	84.50 144	81.45 327

MTMP							92.18 21	32.83 369

gm-plane-assit					81.40 295	53.83 313			62.72 277		80.94 299		92.39 179	63.40 207

test9_res														84.90 19	95.70 14	92.87 77

TEST9					93.26 38	72.96 21	88.75 92	91.89 74	68.44 212	85.00 32	93.10 43	74.36 17	95.41 51

test_8					93.13 40	72.57 32	88.68 95	91.84 77	68.69 208	84.87 38	93.10 43	74.43 14	95.16 59

agg_prior2														82.91 42	95.45 16	92.70 79

agg_prior					92.85 46	71.94 43		91.78 80		84.41 45			94.93 68

TestCases				79.58 229	85.15 214	63.62 201		79.83 300	62.31 280	60.32 326	86.73 189	32.02 341	88.96 270	50.28 288	71.57 298	86.15 283

test_prior4						72.60 31	89.01 82

test_prior2							88.85 87		75.41 80	84.91 34	93.54 33	74.28 18		83.31 35	95.86 8

test_prior				86.33 49	92.61 51	69.59 76		92.97 34					95.48 46			93.91 33

旧先验2							86.56 171		58.10 310	87.04 17			88.98 268	74.07 116

新几何2							86.29 180

新几何1				83.42 120	93.13 40	70.71 58		85.48 232	57.43 317	81.80 77	91.98 60	63.28 102	92.27 183	64.60 202	92.99 50	87.27 258

旧先验1					91.96 59	65.79 146		86.37 224			93.08 47	69.31 57			92.74 53	88.74 219

无先验							87.48 134	88.98 176	60.00 296				94.12 101	67.28 177		88.97 211

原ACMM2							86.86 160

原ACMM1				84.35 90	93.01 44	68.79 90		92.44 49	63.96 266	81.09 88	91.57 70	66.06 81	95.45 48	67.19 180	94.82 32	88.81 216

test222					91.50 64	68.26 107	84.16 239	83.20 263	54.63 329	79.74 97	91.63 68	58.97 171			91.42 63	86.77 270

testdata2													91.01 231	62.37 215

segment_acmp												73.08 25

testdata				79.97 220	90.90 72	64.21 190		84.71 238	59.27 303	85.40 27	92.91 48	62.02 139	89.08 265	68.95 166	91.37 64	86.63 274

testdata1							84.14 240		75.71 73

test12				86.80 41	92.63 50	70.70 59		91.79 79		82.71 68		71.67 35	96.16 32		94.50 36	93.54 54

plane_prior7					90.08 86	68.51 103

plane_prior6					89.84 91	68.70 99						60.42 164

plane_prior5								92.44 49					95.38 53	78.71 70	86.32 128	91.33 118

plane_prior4											91.00 86

plane_prior3						68.60 101			78.44 30	78.92 106

plane_prior2							91.25 32		79.12 23

plane_prior1					89.90 90

plane_prior						68.71 97	90.38 48		77.62 34						86.16 131

n2								0.00 376

nn								0.00 376

door-mid								69.98 348

lessismore_v0				78.97 242	81.01 302	57.15 277		65.99 357		61.16 323	82.82 272	39.12 326	91.34 220	59.67 237	46.92 352	88.43 235

LGP-MVS_train				84.50 84	89.23 117	68.76 93		91.94 72	75.37 82	76.64 158	91.51 71	54.29 207	94.91 70	78.44 72	83.78 150	89.83 183

test11								92.23 57

door								69.44 351

HQP5-MVS						66.98 128

HQP-NCC					89.33 109		89.17 75		76.41 60	77.23 148

ACMP_Plane					89.33 109		89.17 75		76.41 60	77.23 148

BP-MVS														77.47 82

HQP4-MVS										77.24 147			95.11 61			91.03 125

HQP3-MVS								92.19 60							85.99 133

HQP2-MVS												60.17 167

NP-MVS					89.62 97	68.32 105					90.24 97

MDTV_nov1_ep13_2view						37.79 356	75.16 316		55.10 327	66.53 298		49.34 273		53.98 273		87.94 243

ACMMP++_ref															81.95 182

ACMMP++															81.25 189

Test By Simon												64.33 94

ITE_SJBPF				78.22 255	81.77 290	60.57 243		83.30 257	69.25 196	67.54 288	87.20 178	36.33 336	87.28 286	54.34 272	74.62 275	86.80 269

DeepMVS_CX				27.40 354	40.17 370	26.90 365		24.59 371	17.44 364	23.95 361	48.61 357	9.77 365	26.48 366	18.06 360	24.47 357	28.83 361

High-resolution multi-view benchmark