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		89.28 1	89.76 1	87.85 18	94.28 16	73.46 14	92.90 8	92.73 39	80.27 13	91.35 2	94.16 20	78.35 3	96.77 9	89.59 1	94.22 43	93.33 53

APDe-MVS		89.15 2	89.63 2	87.73 20	94.49 9	71.69 42	93.83 2	93.96 4	75.70 72	91.06 3	96.03 1	76.84 4	97.03 5	89.09 2	95.65 14	94.47 11

HPM-MVS++		89.02 3	89.15 3	88.63 1	95.01 2	76.03 1	92.38 14	92.85 34	80.26 14	87.78 12	94.27 16	75.89 8	96.81 8	87.45 9	96.44 1	93.05 63

CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 3	73.82 6	90.51 39	93.00 26	80.90 10	88.06 10	94.06 24	76.43 5	96.84 7	88.48 4	95.99 5	94.34 15

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 53	72.96 19	93.73 3	93.67 8	80.19 15	88.10 9	94.80 4	73.76 21	97.11 3	87.51 8	95.82 9	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 51	69.03 80	89.57 62	93.39 15	77.53 39	89.79 5	94.12 22	78.98 2	96.58 20	85.66 12	95.72 10	94.58 7

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 49	72.71 24	91.81 23	93.19 20	77.87 32	90.32 4	94.00 25	74.83 11	93.78 112	87.63 7	94.27 41	93.65 42

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 26	72.13 38	91.41 26	92.35 50	74.62 89	88.90 6	93.85 27	75.75 9	96.00 33	87.80 5	94.63 32	95.04 2

ACMMP_Plus		88.05 9	88.08 9	87.94 11	93.70 24	73.05 18	90.86 33	93.59 9	76.27 66	88.14 8	95.09 3	71.06 37	96.67 13	87.67 6	96.37 3	94.09 22

NCCC		88.06 7	88.01 10	88.24 5	94.41 13	73.62 7	91.22 30	92.83 35	81.50 7	85.79 22	93.47 33	73.02 25	97.00 6	84.90 17	94.94 25	94.10 21

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 27	72.04 39	89.80 55	93.50 11	75.17 83	86.34 17	95.29 2	70.86 38	96.00 33	88.78 3	96.04 4	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		87.71 11	87.64 12	87.93 14	94.36 15	73.88 4	92.71 13	92.65 42	77.57 35	83.84 48	94.40 15	72.24 31	96.28 25	85.65 13	95.30 22	93.62 44

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 17	72.39 33	91.86 22	92.83 35	73.01 125	88.58 7	94.52 7	73.36 22	96.49 21	84.26 27	95.01 24	92.70 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 6	73.54 11	93.04 5	93.24 17	76.78 52	84.91 30	94.44 12	70.78 39	96.61 16	84.53 23	94.89 27	93.66 37

MPTG		87.53 14	87.41 15	87.90 15	94.18 20	74.25 2	90.23 47	92.02 60	79.45 19	85.88 19	94.80 4	68.07 58	96.21 27	86.69 10	95.34 18	93.23 55

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 8	72.46 32	89.82 53	93.82 6	73.07 124	84.86 35	92.89 45	76.22 6	96.33 23	84.89 19	95.13 23	94.40 12

ACMMPR		87.44 15	87.23 17	88.08 7	94.64 4	73.59 8	93.04 5	93.20 19	76.78 52	84.66 36	94.52 7	68.81 56	96.65 14	84.53 23	94.90 26	94.00 28

region2R		87.42 17	87.20 18	88.09 6	94.63 5	73.55 9	93.03 7	93.12 22	76.73 55	84.45 39	94.52 7	69.09 54	96.70 12	84.37 26	94.83 29	94.03 25

#test#		87.33 19	87.13 19	87.94 11	94.58 6	73.54 11	92.34 15	93.24 17	75.23 80	84.91 30	94.44 12	70.78 39	96.61 16	83.75 31	94.89 27	93.66 37

MTAPA		87.23 20	87.00 20	87.90 15	94.18 20	74.25 2	86.58 160	92.02 60	79.45 19	85.88 19	94.80 4	68.07 58	96.21 27	86.69 10	95.34 18	93.23 55

HPM-MVS		87.11 22	86.98 21	87.50 28	93.88 23	72.16 37	92.19 18	93.33 16	76.07 69	83.81 49	93.95 26	69.77 49	96.01 32	85.15 14	94.66 31	94.32 17

CP-MVS		87.11 22	86.92 22	87.68 25	94.20 19	73.86 5	93.98 1	92.82 37	76.62 57	83.68 50	94.46 11	67.93 60	95.95 35	84.20 29	94.39 37	93.23 55

XVS		87.18 21	86.91 23	88.00 9	94.42 11	73.33 16	92.78 9	92.99 28	79.14 21	83.67 51	94.17 19	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

DeepC-MVS	79.81 2	87.08 24	86.88 24	87.69 24	91.16 63	72.32 36	90.31 45	93.94 5	77.12 44	82.82 60	94.23 18	72.13 32	97.09 4	84.83 20	95.37 17	93.65 42
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 26	86.86 25	86.33 46	92.61 47	69.59 72	88.85 79	92.97 31	75.41 76	84.91 30	93.54 29	74.28 18	95.48 43	83.31 32	95.86 7	93.91 30

DeepC-MVS_fast	79.65 3	86.91 25	86.62 26	87.76 19	93.52 29	72.37 35	91.26 27	93.04 23	76.62 57	84.22 44	93.36 35	71.44 35	96.76 10	80.82 51	95.33 20	94.16 19
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 29	86.53 27	86.95 36	89.33 100	71.24 45	88.43 92	92.05 59	82.50 1	86.88 15	90.09 94	74.45 13	95.61 39	84.38 25	90.63 69	94.01 27

Regformer-1		86.41 33	86.33 28	86.64 41	89.33 100	70.93 51	88.43 92	91.39 89	82.14 3	86.65 16	90.09 94	74.39 16	95.01 64	83.97 30	90.63 69	93.97 29

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 13	73.55 9	92.74 11	92.22 53	76.87 50	82.81 61	94.25 17	66.44 72	96.24 26	82.88 40	94.28 40	93.38 50

PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 18	73.38 15	90.22 48	93.04 23	75.53 74	83.86 47	94.42 14	67.87 62	96.64 15	82.70 41	94.57 33	93.66 37

train_agg		86.43 31	86.20 31	87.13 33	93.26 34	72.96 19	88.75 84	91.89 69	68.69 196	85.00 28	93.10 39	74.43 14	95.41 48	84.97 15	95.71 11	93.02 64

CSCG		86.41 33	86.19 32	87.07 35	92.91 41	72.48 31	90.81 34	93.56 10	73.95 97	83.16 56	91.07 76	75.94 7	95.19 55	79.94 58	94.38 38	93.55 46

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 68	70.96 48	92.27 17	94.07 3	72.45 136	85.22 26	91.90 58	69.47 51	96.42 22	83.28 34	95.94 6	94.35 14

CANet		86.45 30	86.10 34	87.51 27	90.09 78	70.94 50	89.70 59	92.59 43	81.78 4	81.32 74	91.43 71	70.34 42	97.23 2	84.26 27	93.36 47	94.37 13

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 42	71.94 40	88.59 89	91.78 75	68.96 193	84.41 40	93.18 38	74.94 10	94.93 65	84.75 22	95.33 20	93.01 66

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 45	69.53 74	91.93 21	92.99 28	73.54 111	85.94 18	94.51 10	65.80 79	95.61 39	83.04 38	92.51 54	93.53 48

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 84	69.44 78	90.45 43	93.00 26	76.70 56	88.01 11	91.23 73	73.28 23	93.91 103	81.50 47	88.80 87	94.77 5

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 31	72.86 23	88.77 82	91.68 79	68.29 202	84.26 43	92.83 47	72.83 26	95.42 47	84.97 15	95.71 11	93.02 64

MVS_0304		86.37 35	85.81 39	88.02 8	90.13 76	72.39 33	89.66 60	92.75 38	81.64 6	82.66 64	92.04 54	64.44 87	97.35 1	84.76 21	94.25 42	94.33 16

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 56	70.24 59	90.71 36	92.86 33	77.46 41	84.22 44	92.81 50	67.16 68	92.94 152	80.36 54	94.35 39	90.16 148

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 115	69.67 71	88.29 101	91.29 91	81.73 5	85.36 24	90.01 96	72.62 28	95.35 53	83.28 34	87.57 102	94.03 25

ACMMP		85.89 40	85.39 42	87.38 29	93.59 28	72.63 27	92.74 11	93.18 21	76.78 52	80.73 83	93.82 28	64.33 88	96.29 24	82.67 42	90.69 68	93.23 55
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 42	85.33 43	86.84 37	91.34 61	72.50 30	89.07 74	87.28 204	76.41 59	85.80 21	90.22 92	74.15 20	95.37 52	81.82 45	91.88 56	92.65 73

alignmvs		85.48 44	85.32 44	85.96 54	89.51 95	69.47 76	89.74 57	92.47 44	76.17 67	87.73 13	91.46 70	70.32 43	93.78 112	81.51 46	88.95 84	94.63 6

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 131	67.93 108	85.52 196	93.44 13	78.70 28	83.63 53	89.03 117	74.57 12	95.71 38	80.26 56	94.04 44	93.66 37
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 41	85.29 46	87.17 32	93.49 30	71.08 46	88.58 90	92.42 48	68.32 201	84.61 37	93.48 31	72.32 30	96.15 30	79.00 60	95.43 16	94.28 18

Regformer-3		85.23 49	85.07 47	85.70 56	88.95 115	69.01 82	88.29 101	89.91 136	80.95 9	85.01 27	90.01 96	72.45 29	94.19 90	82.50 43	87.57 102	93.90 32

UA-Net		85.08 52	84.96 48	85.45 57	92.07 54	68.07 106	89.78 56	90.86 101	82.48 2	84.60 38	93.20 37	69.35 52	95.22 54	71.39 140	90.88 67	93.07 62

abl_6		85.23 49	84.95 49	86.07 52	92.23 52	70.48 58	90.80 35	92.08 58	73.51 112	85.26 25	94.16 20	62.75 114	95.92 36	82.46 44	91.30 63	91.81 97

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 25	70.58 57	92.15 19	91.62 80	73.89 100	82.67 63	94.09 23	62.60 121	95.54 42	80.93 49	92.93 49	93.57 45

MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 59	72.70 25	85.98 175	90.33 118	76.11 68	82.08 67	91.61 65	71.36 36	94.17 92	81.02 48	92.58 53	92.08 90

3Dnovator+	77.84 4	85.48 44	84.47 52	88.51 2	91.08 64	73.49 13	93.18 4	93.78 7	80.79 11	76.66 143	93.37 34	60.40 161	96.75 11	77.20 78	93.73 46	95.29 1

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 140	67.85 109	87.66 116	89.73 140	80.05 17	82.95 57	89.59 103	70.74 41	94.82 72	80.66 53	84.72 133	93.28 54

nrg030		83.88 54	83.53 54	84.96 69	86.77 175	69.28 79	90.46 42	92.67 40	74.79 87	82.95 57	91.33 72	72.70 27	93.09 146	80.79 52	79.28 198	92.50 76

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 44	62.28 217	88.17 105	89.50 145	75.22 81	81.49 73	92.74 51	66.75 69	95.11 58	72.85 121	91.58 59	92.45 77

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 147	67.53 113	87.44 127	89.66 141	79.74 18	82.23 66	89.41 112	70.24 44	94.74 74	79.95 57	83.92 139	92.99 67

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 33	70.86 53	92.09 20	90.38 113	68.75 195	79.57 88	92.83 47	60.60 157	93.04 150	80.92 50	91.56 60	90.86 118

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 79	68.71 92	91.25 28	92.44 45	79.12 23	78.92 95	91.00 80	60.42 159	95.38 50	78.71 63	86.32 120	91.33 106

Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 136	67.45 114	88.89 77	89.15 157	75.50 75	82.27 65	88.28 135	69.61 50	94.45 81	77.81 72	87.84 100	93.84 34

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 172	63.21 203	86.11 173	92.00 63	74.31 92	82.87 59	89.44 111	70.03 45	93.21 137	77.39 77	88.50 96	93.81 35

EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 76	64.47 178	92.32 16	90.73 102	74.45 91	79.35 91	91.10 74	69.05 55	95.12 57	72.78 122	87.22 109	94.13 20

OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 123	70.95 49	89.13 73	91.52 84	77.55 38	80.96 81	91.75 60	60.71 153	94.50 80	79.67 59	86.51 118	89.97 166

EPNet		83.72 57	82.92 63	86.14 51	84.22 207	69.48 75	91.05 32	85.27 224	81.30 8	76.83 140	91.65 62	66.09 75	95.56 41	76.00 89	93.85 45	93.38 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 82	63.30 200	91.59 24	88.46 184	79.04 25	79.49 89	92.16 52	65.10 83	94.28 84	67.71 160	91.86 57	94.95 3

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 75	68.74 90	90.30 46	90.13 127	76.33 65	80.87 82	92.89 45	61.00 150	94.20 89	72.45 128	90.97 65	93.35 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FIs		82.07 78	82.42 66	81.04 190	88.80 122	58.34 243	88.26 103	93.49 12	76.93 49	78.47 103	91.04 77	69.92 47	92.34 170	69.87 148	84.97 129	92.44 78

VNet		82.21 76	82.41 67	81.62 177	90.82 69	60.93 224	84.47 214	89.78 138	76.36 64	84.07 46	91.88 59	64.71 86	90.26 224	70.68 141	88.89 85	93.66 37

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 50	66.37 131	87.93 112	91.80 73	73.82 105	77.32 132	90.66 85	67.90 61	94.90 69	70.37 144	89.48 81	93.19 59

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 65	66.40 130	88.91 76	88.11 187	77.57 35	84.39 42	93.29 36	52.19 217	93.91 103	77.05 81	88.70 89	94.57 9

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 146	72.94 22	90.64 37	92.14 57	77.21 42	75.47 168	92.83 47	58.56 168	94.72 75	73.24 119	92.71 52	92.13 89

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 120	71.58 43	85.15 201	86.16 217	74.69 88	80.47 84	91.04 77	62.29 129	90.55 222	80.33 55	90.08 75	90.20 147

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 10	70.92 52	88.79 81	92.20 54	70.53 164	79.17 92	91.03 79	64.12 90	96.03 31	68.39 159	90.14 74	91.50 103

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 162	70.21 60	90.50 40	90.38 113	68.55 198	81.32 74	89.47 106	61.68 135	93.46 128	78.98 61	90.26 72	92.05 91

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 135	55.97 280	87.95 110	93.42 14	77.10 45	77.38 130	90.98 82	69.96 46	91.79 183	68.46 158	84.50 134	92.33 80

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 100	66.98 123	89.17 68	92.19 55	76.41 59	77.23 135	90.23 91	60.17 162	95.11 58	77.47 75	85.99 124	91.03 112

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 125	67.42 115	87.98 108	90.87 100	74.92 86	79.72 87	91.65 62	62.19 132	93.96 98	75.26 101	86.42 119	93.16 60

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 70	69.76 69	88.74 86	91.70 78	69.39 179	78.96 94	88.46 130	65.47 80	94.87 71	74.42 105	88.57 92	90.24 146

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 132	67.73 112	85.81 183	92.35 50	75.78 70	78.33 109	86.58 189	64.01 91	94.35 82	76.05 88	87.48 107	90.79 119
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 81	81.54 79	82.92 137	88.46 133	63.46 196	87.13 140	92.37 49	80.19 15	78.38 107	89.14 114	71.66 34	93.05 148	70.05 145	76.46 231	92.25 84

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 177	67.27 119	89.27 66	91.51 85	71.75 146	79.37 90	90.22 92	63.15 102	94.27 85	77.69 73	82.36 166	91.49 104

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 108	68.76 88	90.22 48	91.94 67	75.37 78	76.64 144	91.51 67	54.29 200	94.91 67	78.44 65	83.78 140	89.83 170

LFMVS		81.82 83	81.23 82	83.57 109	91.89 57	63.43 198	89.84 52	81.85 266	77.04 47	83.21 54	93.10 39	52.26 216	93.43 132	71.98 134	89.95 77	93.85 33

API-MVS		81.99 80	81.23 82	84.26 88	90.94 66	70.18 65	91.10 31	89.32 150	71.51 152	78.66 99	88.28 135	65.26 81	95.10 61	64.74 187	91.23 64	87.51 234

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 136	64.41 180	87.60 117	93.02 25	78.42 31	78.56 100	88.16 137	69.78 48	93.26 136	69.58 150	76.49 230	91.60 99

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 111	68.03 107	84.46 216	90.02 132	70.67 162	81.30 77	86.53 192	63.17 101	94.19 90	75.60 97	88.54 94	88.57 212

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 95	68.21 104	84.28 223	90.09 128	70.79 159	81.26 78	85.62 214	63.15 102	94.29 83	75.62 96	88.87 86	88.59 210

PAPR		81.66 86	80.89 87	83.99 99	90.27 74	64.00 188	86.76 156	91.77 77	68.84 194	77.13 139	89.50 104	67.63 63	94.88 70	67.55 162	88.52 95	93.09 61

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 62	71.53 44	89.82 53	90.92 99	69.77 174	78.50 101	86.21 201	62.36 128	94.52 79	65.36 181	92.05 55	89.77 174
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 88	80.67 89	84.05 94	90.44 72	64.13 184	89.73 58	85.91 220	71.11 155	83.18 55	93.48 31	50.54 245	93.49 127	73.40 117	88.25 98	94.54 10

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 97	68.69 95	89.97 51	91.50 87	74.46 90	75.04 184	90.41 88	53.82 205	94.54 77	77.56 74	82.91 158	89.86 169
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 142	60.80 227	86.86 150	91.58 82	75.67 73	80.24 85	89.45 110	63.34 96	90.25 225	70.51 143	79.22 199	91.23 109

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 150	63.46 196	87.02 145	91.87 71	79.01 26	78.38 107	89.07 115	65.02 84	93.05 148	70.05 145	76.46 231	92.20 86

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 117	65.40 147	84.43 218	92.00 63	67.62 206	78.11 118	85.05 226	66.02 77	94.27 85	71.52 138	89.50 80	89.01 192

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 148	62.33 215	87.74 115	91.33 90	80.55 12	77.99 121	89.86 98	65.23 82	92.62 160	67.05 169	75.24 249	92.30 82

jason		81.39 91	80.29 95	84.70 76	86.63 176	69.90 67	85.95 176	86.77 208	63.24 245	81.07 80	89.47 106	61.08 149	92.15 174	78.33 68	90.07 76	92.05 91
jason: jason.
lupinMVS		81.39 91	80.27 96	84.76 75	87.35 162	70.21 60	85.55 192	86.41 212	62.85 251	81.32 74	88.61 125	61.68 135	92.24 173	78.41 67	90.26 72	91.83 95

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 117	65.40 147	86.16 171	92.00 63	69.34 182	78.11 118	86.09 204	66.02 77	94.27 85	71.52 138	82.06 167	87.39 236

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 204	63.19 205	86.41 165	88.95 169	74.18 94	78.69 97	87.54 154	66.62 70	92.43 165	72.57 127	80.57 184	90.74 122

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 117	64.51 172	85.53 194	89.39 148	70.79 159	78.49 102	85.06 225	67.54 64	93.58 123	67.03 170	86.58 116	92.32 81

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 187	63.17 206	87.36 128	88.65 180	76.37 63	75.88 160	88.44 131	53.51 207	93.07 147	73.30 118	89.74 79	92.25 84

ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 105	68.37 99	89.99 50	91.60 81	70.28 168	77.25 133	89.66 101	53.37 208	93.53 126	74.24 108	82.85 159	88.85 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1121		80.84 97	79.77 102	84.05 94	93.11 38	70.78 54	84.66 208	85.42 223	57.37 293	81.76 72	92.02 55	63.41 95	94.12 93	67.28 165	92.93 49	87.26 241

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 187	68.78 87	83.54 234	90.50 110	70.66 163	76.71 142	91.66 61	60.69 154	91.26 205	76.94 82	81.58 173	91.83 95

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 162	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 162	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 162	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

UGNet		80.83 99	79.59 107	84.54 78	88.04 143	68.09 105	89.42 63	88.16 186	76.95 48	76.22 154	89.46 108	49.30 254	93.94 100	68.48 157	90.31 71	91.60 99
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		80.40 113	79.54 108	82.98 132	84.10 215	64.51 172	87.57 119	90.22 122	73.25 117	78.47 103	86.65 184	62.83 110	93.86 106	75.72 92	77.02 217	90.58 133

v7new		80.40 113	79.54 108	82.98 132	84.10 215	64.51 172	87.57 119	90.22 122	73.25 117	78.47 103	86.65 184	62.83 110	93.86 106	75.72 92	77.02 217	90.58 133

v6		80.40 113	79.54 108	82.98 132	84.09 217	64.50 176	87.57 119	90.22 122	73.25 117	78.47 103	86.63 186	62.84 109	93.86 106	75.73 91	77.02 217	90.58 133

114514_t		80.68 106	79.51 111	84.20 89	94.09 22	67.27 119	89.64 61	91.11 96	58.75 283	74.08 191	90.72 84	58.10 171	95.04 63	69.70 149	89.42 82	90.30 145

QAPM		80.88 95	79.50 112	85.03 67	88.01 145	68.97 84	91.59 24	92.00 63	66.63 216	75.15 181	92.16 52	57.70 173	95.45 45	63.52 191	88.76 88	90.66 127

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 39	68.91 85	89.36 64	88.97 167	69.27 183	75.70 167	89.69 100	57.20 180	95.77 37	63.06 195	88.41 97	87.50 235

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 194	69.75 70	89.28 65	88.10 189	74.99 84	76.44 149	86.72 176	57.27 177	94.26 88	73.53 115	83.18 156	91.87 94

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 150	63.34 199	86.31 168	91.09 97	79.01 26	72.17 210	89.07 115	67.20 67	92.81 158	66.08 176	75.65 240	92.20 86

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 182	63.20 204	86.79 153	89.34 149	74.19 93	75.45 170	86.72 176	66.62 70	92.39 167	72.58 126	76.86 222	90.75 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 118	79.32 117	83.27 118	83.98 224	65.37 150	90.50 40	90.38 113	68.55 198	76.19 155	88.70 121	56.44 184	93.46 128	78.98 61	80.14 191	90.97 115

v1141		80.19 122	79.31 118	82.85 141	83.84 227	64.12 185	87.14 137	90.08 129	73.13 120	78.27 111	86.39 195	62.67 119	93.75 116	75.40 99	76.83 225	90.68 124

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 227	64.11 187	87.13 140	90.08 129	73.13 120	78.27 111	86.39 195	62.69 117	93.75 116	75.40 99	76.82 226	90.68 124

v1		80.19 122	79.31 118	82.85 141	83.83 229	64.12 185	87.14 137	90.07 131	73.13 120	78.27 111	86.38 199	62.72 116	93.75 116	75.41 98	76.82 226	90.68 124

v2v482		80.23 120	79.29 121	83.05 128	83.62 232	64.14 183	87.04 144	89.97 133	73.61 108	78.18 117	87.22 163	61.10 148	93.82 109	76.11 87	76.78 228	91.18 110

v7		80.24 119	79.26 122	83.15 122	84.07 221	64.94 161	87.56 122	90.67 103	72.26 141	78.28 110	86.51 193	61.45 140	94.03 97	75.14 102	77.41 211	90.49 138

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 186	69.02 81	83.03 238	90.39 112	71.09 156	77.63 127	91.49 69	54.62 199	91.35 203	75.71 94	83.47 148	91.54 101

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 123	58.35 242	85.06 202	88.61 182	78.56 29	77.65 126	88.34 133	63.81 94	90.66 221	64.98 185	77.22 214	91.80 98

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 203	57.97 249	82.59 239	87.62 198	67.40 211	76.17 158	88.56 128	68.47 57	89.59 234	70.65 142	86.05 123	93.47 49

v1144		80.03 126	79.03 126	83.01 130	83.78 230	64.51 172	87.11 142	90.57 108	71.96 145	78.08 120	86.20 202	61.41 141	93.94 100	74.93 103	77.23 213	90.60 130

v8		79.97 128	79.02 127	82.80 146	84.09 217	64.50 176	87.96 109	90.29 121	74.13 96	75.24 179	86.81 173	62.88 107	93.89 105	74.39 106	75.40 245	90.00 159

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 133	60.91 225	83.84 229	89.24 155	70.36 166	79.03 93	88.87 119	63.23 100	90.21 226	65.12 182	82.57 164	92.28 83

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 155	68.99 83	83.65 231	91.46 88	63.00 248	77.77 125	90.28 89	66.10 74	95.09 62	61.40 211	88.22 99	90.94 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 132	78.67 130	82.97 136	84.06 222	64.95 160	87.88 114	90.62 106	73.11 123	75.11 182	86.56 190	61.46 139	94.05 96	73.68 111	75.55 242	89.90 167

VPNet		78.69 152	78.66 131	78.76 231	88.31 138	55.72 286	84.45 217	86.63 210	76.79 51	78.26 114	90.55 87	59.30 164	89.70 233	66.63 171	77.05 216	90.88 117

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 110	65.91 138	86.07 174	88.52 183	72.18 142	75.42 171	87.69 149	61.15 147	93.54 125	60.38 218	86.83 113	86.70 254

diffmvs		79.51 135	78.59 133	82.25 159	83.31 239	62.66 212	84.17 224	88.11 187	67.64 204	76.09 159	87.47 156	64.01 91	91.15 208	71.71 137	84.82 132	92.94 68

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 252	65.32 151	86.12 172	89.55 143	69.64 178	70.55 226	85.82 209	57.24 179	93.81 110	76.85 83	88.55 93	92.41 79

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 194	68.74 90	88.77 82	88.10 189	74.99 84	74.97 185	83.49 244	57.27 177	93.36 133	73.53 115	80.88 178	91.18 110

WR-MVS_H		78.51 154	78.49 136	78.56 234	88.02 144	56.38 275	88.43 92	92.67 40	77.14 43	73.89 192	87.55 153	66.25 73	89.24 241	58.92 230	73.55 264	90.06 157

test_normal		79.81 130	78.45 137	83.89 103	82.70 256	65.40 147	85.82 182	89.48 146	69.39 179	70.12 235	85.66 212	57.15 181	93.71 121	77.08 80	88.62 91	92.56 75

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 242	88.64 127	51.78 302	86.70 157	79.63 287	74.14 95	75.11 182	90.83 83	61.29 144	89.75 231	58.10 239	91.60 58	92.69 72

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 99	65.93 137	84.95 204	87.15 205	73.56 110	78.19 116	89.79 99	56.67 183	93.36 133	59.53 226	86.74 114	90.13 150

v1192		79.59 134	78.43 140	83.07 127	83.55 234	64.52 170	86.93 148	90.58 107	70.83 158	77.78 124	85.90 205	59.15 165	93.94 100	73.96 110	77.19 215	90.76 120

v144192		79.47 138	78.37 141	82.78 149	83.35 237	63.96 189	86.96 146	90.36 116	69.99 171	77.50 128	85.67 211	60.66 155	93.77 114	74.27 107	76.58 229	90.62 128

CP-MVSNet		78.22 158	78.34 142	77.84 244	87.83 149	54.54 291	87.94 111	91.17 95	77.65 33	73.48 194	88.49 129	62.24 131	88.43 254	62.19 202	74.07 257	90.55 136

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 248	85.79 183	56.21 278	86.78 154	85.76 221	73.60 109	77.93 122	87.57 152	65.02 84	88.99 245	67.14 168	75.33 246	87.63 231

OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 191	69.91 66	90.57 38	90.97 98	66.70 212	72.17 210	91.91 57	54.70 197	93.96 98	61.81 208	90.95 66	88.41 218

V42		79.38 141	78.24 145	82.83 144	81.10 278	65.50 146	85.55 192	89.82 137	71.57 151	78.21 115	86.12 203	60.66 155	93.18 141	75.64 95	75.46 244	89.81 172

PS-CasMVS		78.01 166	78.09 146	77.77 246	87.71 155	54.39 293	88.02 107	91.22 92	77.50 40	73.26 196	88.64 124	60.73 152	88.41 255	61.88 206	73.88 261	90.53 137

v1921920		79.22 143	78.03 147	82.80 146	83.30 240	63.94 190	86.80 152	90.33 118	69.91 172	77.48 129	85.53 216	58.44 169	93.75 116	73.60 114	76.85 223	90.71 123

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 200	66.57 129	89.25 67	90.16 126	69.20 185	75.46 169	89.49 105	45.75 274	93.13 144	76.84 84	80.80 180	90.11 151

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 91	62.99 210	88.16 106	91.51 85	65.77 224	77.14 138	91.09 75	60.91 151	93.21 137	50.26 275	87.05 111	92.17 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVSTER		79.01 147	77.88 150	82.38 157	83.07 246	64.80 164	84.08 228	88.95 169	69.01 192	78.69 97	87.17 166	54.70 197	92.43 165	74.69 104	80.57 184	89.89 168

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 11	73.33 16	92.78 9	92.99 28	79.14 21	83.67 51	12.47 341	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

v148		78.72 151	77.80 152	81.47 181	82.73 255	61.96 220	86.30 169	88.08 191	73.26 116	76.18 156	85.47 218	62.46 127	92.36 169	71.92 136	73.82 262	90.09 153

v1240		78.99 148	77.78 153	82.64 153	83.21 241	63.54 193	86.62 159	90.30 120	69.74 177	77.33 131	85.68 210	57.04 182	93.76 115	73.13 120	76.92 220	90.62 128

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 199	66.37 131	89.17 68	90.19 125	69.38 181	75.40 172	89.46 108	44.17 280	93.15 142	76.78 85	80.70 182	90.14 149

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 157	68.23 103	84.40 220	86.20 216	67.49 209	76.36 150	86.54 191	61.54 138	90.79 219	61.86 207	87.33 108	90.49 138
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PEN-MVS		77.73 173	77.69 156	77.84 244	87.07 170	53.91 295	87.91 113	91.18 94	77.56 37	73.14 198	88.82 120	61.23 145	89.17 242	59.95 221	72.37 270	90.43 141

v7n		78.97 149	77.58 157	83.14 123	83.45 236	65.51 145	88.32 99	91.21 93	73.69 107	72.41 207	86.32 200	57.93 172	93.81 110	69.18 153	75.65 240	90.11 151

TAMVS		78.89 150	77.51 158	83.03 129	87.80 150	67.79 111	84.72 207	85.05 227	67.63 205	76.75 141	87.70 148	62.25 130	90.82 218	58.53 235	87.13 110	90.49 138

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 157	63.01 207	88.39 96	89.28 151	71.63 148	75.34 174	87.28 159	54.80 193	91.11 209	62.72 196	79.57 193	90.09 153

test1		78.40 155	77.40 159	81.40 183	87.60 157	63.01 207	88.39 96	89.28 151	71.63 148	75.34 174	87.28 159	54.80 193	91.11 209	62.72 196	79.57 193	90.09 153

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 121	65.13 157	84.87 205	87.85 195	69.75 175	74.52 189	84.74 232	61.34 142	93.11 145	58.24 238	85.84 126	84.27 281

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 157	62.89 211	87.47 126	89.02 160	71.63 148	75.29 178	87.28 159	54.80 193	91.10 212	62.38 200	79.38 196	89.61 177

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 284	67.08 121	87.24 135	89.53 144	65.66 226	75.16 180	87.19 165	52.52 210	92.25 172	77.17 79	79.34 197	89.61 177

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 161	60.21 231	83.37 236	87.78 196	66.11 220	75.37 173	87.06 171	63.27 98	90.48 223	61.38 212	82.43 165	90.40 143

MVS		78.19 161	76.99 165	81.78 167	85.66 185	66.99 122	84.66 208	90.47 111	55.08 303	72.02 214	85.27 221	63.83 93	94.11 95	66.10 175	89.80 78	84.24 282

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 251	87.20 168	51.60 303	80.06 259	80.46 278	75.20 82	67.69 264	86.72 176	62.48 126	88.98 246	63.44 192	89.25 83	91.51 102

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 173	62.36 214	86.52 162	88.77 176	71.13 154	75.34 174	86.66 183	54.07 203	91.10 212	62.72 196	79.57 193	89.45 179

DTE-MVSNet		76.99 191	76.80 168	77.54 250	86.24 179	53.06 299	87.52 124	90.66 105	77.08 46	72.50 204	88.67 123	60.48 158	89.52 235	57.33 246	70.74 281	90.05 158

CNLPA		78.08 163	76.79 169	81.97 164	90.40 73	71.07 47	87.59 118	84.55 230	66.03 223	72.38 208	89.64 102	57.56 175	86.04 272	59.61 224	83.35 153	88.79 199

pm-mvs1		77.25 189	76.68 170	78.93 228	84.22 207	58.62 240	86.41 165	88.36 185	71.37 153	73.31 195	88.01 141	61.22 146	89.15 243	64.24 189	73.01 266	89.03 191

v748		77.97 167	76.65 171	81.92 166	82.29 262	63.28 201	87.53 123	90.35 117	73.50 113	70.76 225	85.55 215	58.28 170	92.81 158	68.81 156	72.76 269	89.67 176

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 245	66.96 125	86.94 147	87.45 203	72.45 136	71.49 220	84.17 235	54.79 196	91.58 200	67.61 161	80.31 188	89.30 181

1112_ss		77.40 188	76.43 173	80.32 200	89.11 114	60.41 230	83.65 231	87.72 197	62.13 259	73.05 199	86.72 176	62.58 123	89.97 228	62.11 205	80.80 180	90.59 132

PAPM		77.68 175	76.40 174	81.51 180	87.29 167	61.85 221	83.78 230	89.59 142	64.74 233	71.23 221	88.70 121	62.59 122	93.66 122	52.66 266	87.03 112	89.01 192

v52		77.94 170	76.37 175	82.67 151	79.39 296	65.52 143	86.43 163	89.94 134	72.28 139	72.15 212	84.94 228	55.70 188	93.44 130	73.64 112	72.84 268	89.06 185

V4		77.95 168	76.37 175	82.67 151	79.40 295	65.52 143	86.43 163	89.94 134	72.28 139	72.14 213	84.95 227	55.72 187	93.44 130	73.64 112	72.86 267	89.05 189

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 58	67.80 110	88.19 104	89.46 147	64.33 238	69.87 241	88.38 132	53.66 206	93.58 123	58.86 231	82.73 161	87.86 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v16		77.69 174	76.36 178	81.68 174	84.15 212	64.63 169	87.33 130	88.99 164	72.69 134	69.31 249	82.08 257	62.80 113	91.79 183	72.70 124	67.23 292	88.63 204

v18		77.67 177	76.35 179	81.64 176	84.09 217	64.47 178	87.27 133	89.01 162	72.59 135	69.39 246	82.04 259	62.85 108	91.80 182	72.72 123	67.20 293	88.63 204

v17		77.68 175	76.35 179	81.69 173	84.15 212	64.65 167	87.33 130	88.99 164	72.70 133	69.25 250	82.07 258	62.82 112	91.79 183	72.69 125	67.15 294	88.63 204

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 139	63.24 202	84.61 212	86.40 213	67.55 208	77.81 123	86.48 194	54.10 202	93.15 142	57.75 242	82.72 162	87.20 242

v15		77.51 182	76.12 182	81.66 175	84.09 217	64.65 167	87.14 137	88.96 168	72.76 131	68.90 251	81.91 266	62.74 115	91.73 187	72.32 129	66.29 299	88.61 207

V14		77.52 180	76.12 182	81.70 172	84.15 212	64.77 165	87.21 136	88.95 169	72.80 130	68.79 252	81.94 265	62.69 117	91.72 189	72.31 130	66.27 300	88.60 208

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 174	63.01 207	88.39 96	89.28 151	70.49 165	74.39 190	87.28 159	49.06 257	91.11 209	60.91 215	78.52 201	90.09 153

V9		77.52 180	76.11 185	81.73 171	84.19 211	64.89 162	87.26 134	88.94 172	72.87 129	68.65 255	81.96 264	62.65 120	91.72 189	72.27 131	66.24 301	88.60 208

v12		77.51 182	76.09 186	81.76 170	84.22 207	64.99 159	87.30 132	88.93 173	72.92 126	68.48 259	81.97 262	62.54 124	91.70 192	72.24 132	66.21 303	88.58 211

v13		77.50 184	76.07 187	81.77 168	84.23 206	65.07 158	87.34 129	88.91 174	72.92 126	68.35 260	81.97 262	62.53 125	91.69 193	72.20 133	66.22 302	88.56 213

v11		77.45 185	76.06 188	81.59 179	84.22 207	64.52 170	87.11 142	89.02 160	72.76 131	68.76 253	81.90 267	62.09 133	91.71 191	71.98 134	66.73 295	88.56 213

Test4		77.83 172	75.90 189	83.62 106	80.24 286	65.25 153	85.27 198	90.67 103	69.03 191	66.48 276	83.75 240	43.07 285	93.00 151	75.93 90	88.66 90	92.62 74

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 81	68.58 97	78.70 273	87.50 201	56.38 298	75.80 162	86.84 172	58.67 167	91.40 202	61.58 210	85.75 127	90.34 144

WTY-MVS		75.65 215	75.68 191	75.57 266	86.40 178	56.82 266	77.92 279	82.40 255	65.10 230	76.18 156	87.72 147	63.13 105	80.90 294	60.31 219	81.96 168	89.00 194

XXY-MVS		75.41 218	75.56 192	74.96 270	83.59 233	57.82 253	80.59 256	83.87 236	66.54 217	74.93 186	88.31 134	63.24 99	80.09 298	62.16 203	76.85 223	86.97 248

conf200view11		76.55 196	75.55 193	79.57 216	89.52 93	56.99 263	85.83 180	83.23 245	73.94 98	76.32 151	87.12 167	51.89 224	91.95 177	48.33 282	83.75 142	89.78 173

thres100view900		76.50 198	75.55 193	79.33 218	89.52 93	56.99 263	85.83 180	83.23 245	73.94 98	76.32 151	87.12 167	51.89 224	91.95 177	48.33 282	83.75 142	89.07 183

thres600view7		76.50 198	75.44 195	79.68 211	89.40 98	57.16 260	85.53 194	83.23 245	73.79 106	76.26 153	87.09 169	51.89 224	91.89 181	48.05 288	83.72 146	90.00 159

Test_1112_low_res		76.40 202	75.44 195	79.27 219	89.28 106	58.09 245	81.69 247	87.07 206	59.53 277	72.48 206	86.67 182	61.30 143	89.33 239	60.81 217	80.15 190	90.41 142

HyFIR lowres test		77.53 179	75.40 197	83.94 102	89.59 91	66.62 127	80.36 257	88.64 181	56.29 299	76.45 146	85.17 222	57.64 174	93.28 135	61.34 213	83.10 157	91.91 93

tfpn200view9		76.42 201	75.37 198	79.55 217	89.13 112	57.65 255	85.17 199	83.60 238	73.41 114	76.45 146	86.39 195	52.12 218	91.95 177	48.33 282	83.75 142	89.07 183

thres400		76.50 198	75.37 198	79.86 207	89.13 112	57.65 255	85.17 199	83.60 238	73.41 114	76.45 146	86.39 195	52.12 218	91.95 177	48.33 282	83.75 142	90.00 159

1314		76.53 197	75.30 200	80.21 203	83.93 225	62.32 216	84.66 208	88.81 175	60.23 271	70.16 234	84.07 237	55.30 191	90.73 220	67.37 164	83.21 155	87.59 233

view600		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

view800		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

conf0.05thres1000		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

tfpn		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

GA-MVS		76.87 193	75.17 205	81.97 164	82.75 254	62.58 213	81.44 251	86.35 215	72.16 144	74.74 187	82.89 247	46.20 269	92.02 176	68.85 155	81.09 176	91.30 108

EPNet_dtu		75.46 217	74.86 206	77.23 254	82.57 259	54.60 290	86.89 149	83.09 249	71.64 147	66.25 278	85.86 207	55.99 186	88.04 259	54.92 256	86.55 117	89.05 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 192	74.82 207	83.37 115	90.45 71	67.36 118	89.15 72	86.94 207	61.87 261	69.52 244	90.61 86	51.71 232	94.53 78	46.38 299	86.71 115	88.21 220

cascas		76.72 195	74.64 208	82.99 131	85.78 184	65.88 139	82.33 241	89.21 156	60.85 267	72.74 201	81.02 274	47.28 263	93.75 116	67.48 163	85.02 128	89.34 180

DP-MVS		76.78 194	74.57 209	83.42 112	93.29 32	69.46 77	88.55 91	83.70 237	63.98 242	70.20 231	88.89 118	54.01 204	94.80 73	46.66 296	81.88 170	86.01 265

TransMVSNet (Re)		75.39 219	74.56 210	77.86 243	85.50 189	57.10 262	86.78 154	86.09 219	72.17 143	71.53 219	87.34 158	63.01 106	89.31 240	56.84 249	61.83 311	87.17 243

LTVRE_ROB	69.57 13	76.25 204	74.54 211	81.41 182	88.60 128	64.38 181	79.24 267	89.12 158	70.76 161	69.79 243	87.86 142	49.09 256	93.20 139	56.21 252	80.16 189	86.65 255
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		75.55 216	74.47 212	78.82 230	87.78 153	57.85 252	83.07 237	83.51 241	72.44 138	75.84 161	84.42 234	52.08 220	91.75 186	47.41 290	83.64 147	86.86 250

MVP-Stereo		76.12 209	74.46 213	81.13 189	85.37 190	69.79 68	84.42 219	87.95 193	65.03 231	67.46 266	85.33 220	53.28 209	91.73 187	58.01 240	83.27 154	81.85 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		76.38 203	74.33 214	82.50 155	89.28 106	66.95 126	88.41 95	89.03 159	64.05 240	66.83 272	88.61 125	46.78 266	92.89 153	57.48 243	78.55 200	87.67 230

XVG-ACMP-BASELINE		76.11 210	74.27 215	81.62 177	83.20 242	64.67 166	83.60 233	89.75 139	69.75 175	71.85 215	87.09 169	32.78 317	92.11 175	69.99 147	80.43 187	88.09 222

ACMH+	68.96 14	76.01 211	74.01 216	82.03 163	88.60 128	65.31 152	88.86 78	87.55 199	70.25 169	67.75 263	87.47 156	41.27 295	93.19 140	58.37 236	75.94 236	87.60 232

ACMH	67.68 16	75.89 212	73.93 217	81.77 168	88.71 126	66.61 128	88.62 88	89.01 162	69.81 173	66.78 273	86.70 181	41.95 294	91.51 201	55.64 253	78.14 206	87.17 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 220	73.90 218	79.27 219	82.65 258	58.27 244	80.80 252	82.73 253	61.57 262	75.33 177	83.13 246	55.52 189	91.07 215	64.98 185	78.34 205	88.45 216

sss		73.60 231	73.64 219	73.51 281	82.80 253	55.01 288	76.12 285	81.69 267	62.47 256	74.68 188	85.85 208	57.32 176	78.11 306	60.86 216	80.93 177	87.39 236

pmmvs6		74.69 221	73.39 220	78.61 233	81.38 273	57.48 258	86.64 158	87.95 193	64.99 232	70.18 232	86.61 187	50.43 246	89.52 235	62.12 204	70.18 283	88.83 197

IB-MVS	68.01 15	75.85 213	73.36 221	83.31 116	84.76 198	66.03 134	83.38 235	85.06 226	70.21 170	69.40 245	81.05 273	45.76 273	94.66 76	65.10 183	75.49 243	89.25 182
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		75.73 214	73.34 222	82.89 140	77.37 304	65.22 154	84.10 227	90.54 109	69.09 187	60.46 300	81.15 272	40.48 298	92.84 157	76.36 86	80.54 186	90.60 130

tfpnnormal		74.39 223	73.16 223	78.08 241	86.10 181	58.05 246	84.65 211	87.53 200	70.32 167	71.22 222	85.63 213	54.97 192	89.86 229	43.03 309	75.02 250	86.32 258

PatchFormer-LS_test		74.50 222	73.05 224	78.86 229	82.95 250	59.55 236	81.65 248	82.30 257	67.44 210	71.62 218	78.15 293	52.34 214	88.92 250	65.05 184	75.90 237	88.12 221

IterMVS		74.29 224	72.94 225	78.35 239	81.53 270	63.49 195	81.58 249	82.49 254	68.06 203	69.99 238	83.69 242	51.66 233	85.54 275	65.85 178	71.64 276	86.01 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn_ndepth		73.70 229	72.75 226	76.52 258	87.78 153	54.92 289	84.32 222	80.28 282	67.57 207	72.50 204	84.82 229	50.12 248	89.44 238	45.73 302	81.66 172	85.20 271

MS-PatchMatch		73.83 228	72.67 227	77.30 253	83.87 226	66.02 135	81.82 244	84.66 229	61.37 265	68.61 257	82.82 249	47.29 262	88.21 256	59.27 227	84.32 137	77.68 314

CVMVSNet		72.99 241	72.58 228	74.25 277	84.28 204	50.85 309	86.41 165	83.45 243	44.56 324	73.23 197	87.54 154	49.38 252	85.70 274	65.90 177	78.44 203	86.19 260

test-LLR		72.94 242	72.43 229	74.48 274	81.35 274	58.04 247	78.38 274	77.46 296	66.66 213	69.95 239	79.00 289	48.06 260	79.24 300	66.13 173	84.83 130	86.15 261

OurMVSNet-221017-0		74.26 225	72.42 230	79.80 209	83.76 231	59.59 233	85.92 178	86.64 209	66.39 218	66.96 271	87.58 151	39.46 301	91.60 199	65.76 179	69.27 285	88.22 219

tpmrst		72.39 244	72.13 231	73.18 283	80.54 283	49.91 313	79.91 262	79.08 290	63.11 246	71.69 217	79.95 282	55.32 190	82.77 289	65.66 180	73.89 260	86.87 249

pmmvs4		74.03 227	71.91 232	80.39 197	81.96 265	68.32 100	81.45 250	82.14 259	59.32 278	69.87 241	85.13 223	52.40 213	88.13 258	60.21 220	74.74 253	84.73 279

DWT-MVSNet_test		73.70 229	71.86 233	79.21 221	82.91 251	58.94 238	82.34 240	82.17 258	65.21 228	71.05 224	78.31 291	44.21 279	90.17 227	63.29 194	77.28 212	88.53 215

Patchmatch-test1		73.49 232	71.85 234	78.41 238	84.05 223	62.17 218	79.96 261	79.29 289	66.30 219	72.38 208	79.58 286	51.95 223	85.08 279	55.46 254	77.67 209	87.99 223

EG-PatchMatch MVS		74.04 226	71.82 235	80.71 195	84.92 197	67.42 115	85.86 179	88.08 191	66.04 222	64.22 289	83.85 238	35.10 316	92.56 163	57.44 244	80.83 179	82.16 300

tpm		72.37 246	71.71 236	74.35 276	82.19 263	52.00 300	79.22 268	77.29 298	64.56 235	72.95 200	83.68 243	51.35 234	83.26 288	58.33 237	75.80 238	87.81 228

tpm2		73.26 237	71.46 237	78.63 232	83.34 238	56.71 269	80.65 255	80.40 279	56.63 297	73.55 193	82.02 260	51.80 231	91.24 206	56.35 251	78.42 204	87.95 224

RPSCF		73.23 238	71.46 237	78.54 235	82.50 260	59.85 232	82.18 242	82.84 252	58.96 280	71.15 223	89.41 112	45.48 276	84.77 281	58.82 232	71.83 275	91.02 114

PatchmatchNet		73.12 239	71.33 239	78.49 237	83.18 243	60.85 226	79.63 263	78.57 291	64.13 239	71.73 216	79.81 285	51.20 236	85.97 273	57.40 245	76.36 233	88.66 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 234	71.27 240	79.67 212	81.32 276	65.19 155	75.92 287	80.30 280	59.92 274	72.73 202	81.19 270	52.50 211	86.69 266	59.84 222	77.71 207	87.11 246

SixPastTwentyTwo		73.37 234	71.26 241	79.70 210	85.08 196	57.89 251	85.57 188	83.56 240	71.03 157	65.66 280	85.88 206	42.10 292	92.57 162	59.11 229	63.34 308	88.65 203

tpmp4_e23		73.45 233	71.17 242	80.31 201	83.55 234	59.56 235	81.88 243	82.33 256	57.94 288	70.51 228	81.62 268	51.19 237	91.63 198	53.96 260	77.51 210	89.75 175

MSDG		73.36 236	70.99 243	80.49 196	84.51 202	65.80 140	80.71 254	86.13 218	65.70 225	65.46 281	83.74 241	44.60 277	90.91 217	51.13 270	76.89 221	84.74 278

PatchMatch-RL		72.38 245	70.90 244	76.80 257	88.60 128	67.38 117	79.53 264	76.17 302	62.75 253	69.36 247	82.00 261	45.51 275	84.89 280	53.62 262	80.58 183	78.12 312

PVSNet	64.34 18	72.08 247	70.87 245	75.69 264	86.21 180	56.44 273	74.37 297	80.73 275	62.06 260	70.17 233	82.23 255	42.86 287	83.31 287	54.77 257	84.45 136	87.32 239

test_0402		72.79 243	70.44 246	79.84 208	88.13 141	65.99 136	85.93 177	84.29 232	65.57 227	67.40 268	85.49 217	46.92 265	92.61 161	35.88 320	74.38 256	80.94 304

COLMAP_ROB	66.92 17	73.01 240	70.41 247	80.81 193	87.13 169	65.63 142	88.30 100	84.19 234	62.96 249	63.80 292	87.69 149	38.04 307	92.56 163	46.66 296	74.91 251	84.24 282
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 250	70.39 248	74.48 274	81.35 274	58.04 247	78.38 274	77.46 296	60.32 270	69.95 239	79.00 289	36.08 314	79.24 300	66.13 173	84.83 130	86.15 261

pmmvs5		71.55 249	70.20 249	75.61 265	77.83 301	56.39 274	81.74 246	80.89 272	57.76 289	67.46 266	84.49 233	49.26 255	85.32 278	57.08 248	75.29 247	85.11 275

MDTV_nov1_ep13			69.97 250		83.18 243	53.48 297	77.10 283	80.18 284	60.45 268	69.33 248	80.44 278	48.89 258	86.90 265	51.60 268	78.51 202

MIMVSNet		70.69 255	69.30 251	74.88 271	84.52 201	56.35 276	75.87 289	79.42 288	64.59 234	67.76 262	82.41 252	41.10 296	81.54 293	46.64 298	81.34 174	86.75 253

tpmvs		71.09 252	69.29 252	76.49 259	82.04 264	56.04 279	78.92 271	81.37 271	64.05 240	67.18 270	78.28 292	49.74 251	89.77 230	49.67 278	72.37 270	83.67 286

Patchmtry		70.74 254	69.16 253	75.49 267	80.72 280	54.07 294	74.94 296	80.30 280	58.34 284	70.01 236	81.19 270	52.50 211	86.54 268	53.37 263	71.09 279	85.87 267

TESTMET0.1,1		69.89 263	69.00 254	72.55 284	79.27 298	56.85 265	78.38 274	74.71 312	57.64 290	68.09 261	77.19 300	37.75 308	76.70 311	63.92 190	84.09 138	84.10 285

RPMNet		71.62 248	68.94 255	79.67 212	81.32 276	65.19 155	75.92 287	78.30 293	57.60 291	72.73 202	76.45 303	52.30 215	86.69 266	48.14 287	77.71 207	87.11 246

PMMVS		69.34 265	68.67 256	71.35 291	75.67 311	62.03 219	75.17 291	73.46 317	50.00 320	68.68 254	79.05 287	52.07 221	78.13 305	61.16 214	82.77 160	73.90 322

K. test v3		71.19 251	68.51 257	79.21 221	83.04 248	57.78 254	84.35 221	76.91 300	72.90 128	62.99 295	82.86 248	39.27 302	91.09 214	61.65 209	52.66 326	88.75 200

USDC		70.33 259	68.37 258	76.21 261	80.60 282	56.23 277	79.19 269	86.49 211	60.89 266	61.29 297	85.47 218	31.78 320	89.47 237	53.37 263	76.21 234	82.94 297

tpm cat1		70.57 256	68.31 259	77.35 252	82.41 261	57.95 250	78.08 278	80.22 283	52.04 315	68.54 258	77.66 298	52.00 222	87.84 261	51.77 267	72.07 274	86.25 259

OpenMVS_ROB	64.09 19	70.56 257	68.19 260	77.65 247	80.26 285	59.41 237	85.01 203	82.96 251	58.76 282	65.43 282	82.33 253	37.63 310	91.23 207	45.34 305	76.03 235	82.32 298

EPMVS		69.02 266	68.16 261	71.59 287	79.61 292	49.80 315	77.40 281	66.93 333	62.82 252	70.01 236	79.05 287	45.79 272	77.86 308	56.58 250	75.26 248	87.13 245

CMPMVS	51.72 21	70.19 261	68.16 261	76.28 260	73.15 320	57.55 257	79.47 265	83.92 235	48.02 322	56.48 315	84.81 230	43.13 284	86.42 270	62.67 199	81.81 171	84.89 276
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 253	68.09 263	79.58 214	85.15 192	63.62 191	84.58 213	79.83 285	62.31 257	60.32 301	86.73 174	32.02 318	88.96 248	50.28 273	71.57 277	86.15 261

gg-mvs-nofinetune		69.95 262	67.96 264	75.94 262	83.07 246	54.51 292	77.23 282	70.29 324	63.11 246	70.32 230	62.33 325	43.62 282	88.69 252	53.88 261	87.76 101	84.62 280

FMVSNet5		69.50 264	67.96 264	74.15 278	82.97 249	55.35 287	80.01 260	82.12 260	62.56 255	63.02 293	81.53 269	36.92 311	81.92 291	48.42 281	74.06 258	85.17 274

PatchT		68.46 270	67.85 266	70.29 295	80.70 281	43.93 323	72.47 300	74.88 308	60.15 272	70.55 226	76.57 302	49.94 250	81.59 292	50.58 271	74.83 252	85.34 270

pmmvs-eth3d		70.50 258	67.83 267	78.52 236	77.37 304	66.18 133	81.82 244	81.51 269	58.90 281	63.90 291	80.42 279	42.69 288	86.28 271	58.56 234	65.30 305	83.11 292

Anonymous20231206		68.60 267	67.80 268	71.02 293	80.23 287	50.75 310	78.30 277	80.47 277	56.79 296	66.11 279	82.63 251	46.35 267	78.95 302	43.62 308	75.70 239	83.36 289

Patchmatch-RL test		70.24 260	67.78 269	77.61 248	77.43 303	59.57 234	71.16 302	70.33 323	62.94 250	68.65 255	72.77 313	50.62 244	85.49 276	69.58 150	66.58 297	87.77 229

test0.0.03 1		68.00 271	67.69 270	68.90 300	77.55 302	47.43 317	75.70 290	72.95 319	66.66 213	66.56 274	82.29 254	48.06 260	75.87 315	44.97 306	74.51 255	83.41 288

EU-MVSNet		68.53 269	67.61 271	71.31 292	78.51 300	47.01 319	84.47 214	84.27 233	42.27 325	66.44 277	84.79 231	40.44 299	83.76 283	58.76 233	68.54 291	83.17 290

test20.03		67.45 273	66.95 272	68.94 299	75.48 314	44.84 321	77.50 280	77.67 295	66.66 213	63.01 294	83.80 239	47.02 264	78.40 304	42.53 311	68.86 289	83.58 287

MIMVSNet1		68.58 268	66.78 273	73.98 279	80.07 288	51.82 301	80.77 253	84.37 231	64.40 237	59.75 304	82.16 256	36.47 312	83.63 285	42.73 310	70.33 282	86.48 257

testgi		66.67 278	66.53 274	67.08 305	75.62 312	41.69 328	75.93 286	76.50 301	66.11 220	65.20 285	86.59 188	35.72 315	74.71 319	43.71 307	73.38 265	84.84 277

UnsupCasMVSNet_eth		67.33 274	65.99 275	71.37 289	73.48 317	51.47 305	75.16 292	85.19 225	65.20 229	60.78 299	80.93 277	42.35 289	77.20 310	57.12 247	53.69 325	85.44 269

dp		66.80 276	65.43 276	70.90 294	79.74 291	48.82 316	75.12 294	74.77 310	59.61 276	64.08 290	77.23 299	42.89 286	80.72 295	48.86 280	66.58 297	83.16 291

TinyColmap		67.30 275	64.81 277	74.76 273	81.92 266	56.68 270	80.29 258	81.49 270	60.33 269	56.27 316	83.22 245	24.77 327	87.66 263	45.52 303	69.47 284	79.95 308

CHOSEN 280x420		66.51 279	64.71 278	71.90 286	81.45 271	63.52 194	57.98 331	68.95 331	53.57 310	62.59 296	76.70 301	46.22 268	75.29 318	55.25 255	79.68 192	76.88 320

TDRefinement		67.49 272	64.34 279	76.92 255	73.47 318	61.07 223	84.86 206	82.98 250	59.77 275	58.30 307	85.13 223	26.06 325	87.89 260	47.92 289	60.59 316	81.81 302

PM-MVS		66.41 280	64.14 280	73.20 282	73.92 315	56.45 272	78.97 270	64.96 337	63.88 244	64.72 286	80.24 280	19.84 333	83.44 286	66.24 172	64.52 307	79.71 309

MDA-MVSNet-bldmvs		66.68 277	63.66 281	75.75 263	79.28 297	60.56 229	73.92 298	78.35 292	64.43 236	50.13 326	79.87 284	44.02 281	83.67 284	46.10 300	56.86 320	83.03 294

ADS-MVSNet2		66.20 282	63.33 282	74.82 272	79.92 289	58.75 239	67.55 319	75.19 306	53.37 311	65.25 283	75.86 304	42.32 290	80.53 296	41.57 312	68.91 287	85.18 272

Patchmatch-test		64.82 285	63.24 283	69.57 297	79.42 294	49.82 314	63.49 326	69.05 330	51.98 316	59.95 303	80.13 281	50.91 239	70.98 329	40.66 314	73.57 263	87.90 226

MDA-MVSNet_test_wron		65.03 283	62.92 284	71.37 289	75.93 309	56.73 267	69.09 314	74.73 311	57.28 294	54.03 319	77.89 295	45.88 270	74.39 321	49.89 277	61.55 312	82.99 295

YYNet1		65.03 283	62.91 285	71.38 288	75.85 310	56.60 271	69.12 313	74.66 314	57.28 294	54.12 318	77.87 296	45.85 271	74.48 320	49.95 276	61.52 313	83.05 293

ADS-MVSNet		64.36 287	62.88 286	68.78 302	79.92 289	47.17 318	67.55 319	71.18 322	53.37 311	65.25 283	75.86 304	42.32 290	73.99 323	41.57 312	68.91 287	85.18 272

JIA-IIPM		66.32 281	62.82 287	76.82 256	77.09 307	61.72 222	65.34 323	75.38 304	58.04 287	64.51 287	62.32 326	42.05 293	86.51 269	51.45 269	69.22 286	82.21 299

LF4IMVS		64.02 288	62.19 288	69.50 298	70.90 325	53.29 298	76.13 284	77.18 299	52.65 314	58.59 305	80.98 275	23.55 328	76.52 312	53.06 265	66.66 296	78.68 311

Anonymous20231211		64.82 285	61.79 289	73.91 280	77.11 306	50.92 308	85.29 197	81.53 268	54.19 305	57.98 308	78.03 294	26.90 323	87.83 262	37.92 317	57.12 319	82.99 295

new-patchmatchnet		61.73 290	61.73 290	61.70 313	72.74 321	24.50 344	69.16 312	78.03 294	61.40 263	56.72 314	75.53 306	38.42 305	76.48 313	45.95 301	57.67 318	84.13 284

UnsupCasMVSNet_bld		63.70 289	61.53 291	70.21 296	73.69 316	51.39 306	72.82 299	81.89 265	55.63 301	57.81 309	71.80 315	38.67 304	78.61 303	49.26 279	52.21 327	80.63 305

PVSNet_0	57.27 20	61.67 291	59.27 292	68.85 301	79.61 292	57.44 259	68.01 317	73.44 318	55.93 300	58.54 306	70.41 318	44.58 278	77.55 309	47.01 291	35.91 332	71.55 324

test2356		59.50 293	58.08 293	63.74 309	71.23 324	41.88 326	67.59 318	72.42 321	53.72 309	57.65 310	70.74 317	26.31 324	72.40 326	32.03 327	71.06 280	76.93 318

testus		59.00 295	57.91 294	62.25 312	72.25 322	39.09 331	69.74 307	75.02 307	53.04 313	57.21 312	73.72 311	18.76 335	70.33 330	32.86 323	68.57 290	77.35 315

LP		61.36 292	57.78 295	72.09 285	75.54 313	58.53 241	67.16 321	75.22 305	51.90 317	54.13 317	69.97 319	37.73 309	80.45 297	32.74 324	55.63 322	77.29 316

MVS-HIRNet		59.14 294	57.67 296	63.57 310	81.65 268	43.50 324	71.73 301	65.06 336	39.59 329	51.43 324	57.73 329	38.34 306	82.58 290	39.53 315	73.95 259	64.62 329

testpf		56.51 300	57.58 297	53.30 320	71.99 323	41.19 329	46.89 336	69.32 329	58.06 286	52.87 323	69.45 321	27.99 322	72.73 325	59.59 225	62.07 310	45.98 334

DSMNet-mixed		57.77 298	56.90 298	60.38 314	67.70 330	35.61 334	69.18 311	53.97 340	32.30 335	57.49 311	79.88 283	40.39 300	68.57 333	38.78 316	72.37 270	76.97 317

test1235678		58.74 296	56.89 299	64.30 307	69.70 326	41.87 327	71.05 303	74.87 309	54.06 306	50.63 325	71.53 316	25.30 326	74.10 322	31.80 328	63.10 309	76.93 318

1111		57.11 299	56.82 300	57.97 317	69.10 327	28.28 339	68.90 315	74.54 315	54.01 307	53.71 320	74.51 308	23.09 329	67.90 334	32.28 325	61.26 314	77.73 313

pmmvs3		57.79 297	54.26 301	68.37 303	64.02 332	56.72 268	75.12 294	65.17 335	40.20 327	52.93 322	69.86 320	20.36 332	75.48 317	45.45 304	55.25 324	72.90 323

N_pmnet		52.79 304	53.26 302	51.40 323	78.99 299	7.68 348	69.52 309	3.89 349	51.63 318	57.01 313	74.98 307	40.83 297	65.96 336	37.78 318	64.67 306	80.56 307

FPMVS		53.68 303	51.64 303	59.81 315	65.08 331	51.03 307	69.48 310	69.58 327	41.46 326	40.67 329	72.32 314	16.46 338	70.00 331	24.24 335	65.42 304	58.40 331

testmv		53.85 302	51.03 304	62.31 311	61.46 334	38.88 332	70.95 306	74.69 313	51.11 319	41.26 328	66.85 322	14.28 339	72.13 327	29.19 330	49.51 329	75.93 321

new_pmnet		50.91 306	50.29 305	52.78 321	68.58 329	34.94 337	63.71 325	56.63 339	39.73 328	44.95 327	65.47 324	21.93 331	58.48 338	34.98 321	56.62 321	64.92 328

.test1245		45.55 309	50.02 306	32.14 329	69.10 327	28.28 339	68.90 315	74.54 315	54.01 307	53.71 320	74.51 308	23.09 329	67.90 334	32.28 325	0.02 344	0.25 343

LCM-MVSNet		54.25 301	49.68 307	67.97 304	53.73 339	45.28 320	66.85 322	80.78 274	35.96 331	39.45 331	62.23 327	8.70 345	78.06 307	48.24 286	51.20 328	80.57 306

test12356		49.28 308	48.51 308	51.59 322	62.06 333	19.11 345	60.40 328	72.45 320	47.60 323	40.64 330	65.68 323	13.84 340	68.72 332	27.29 332	46.67 331	66.94 327

ANet_high		50.57 307	46.10 309	63.99 308	48.67 342	39.13 330	70.99 305	80.85 273	61.39 264	31.18 334	57.70 330	17.02 337	73.65 324	31.22 329	15.89 341	79.18 310

no-one		51.08 305	45.79 310	66.95 306	57.92 337	50.49 312	59.63 330	76.04 303	48.04 321	31.85 332	56.10 332	19.12 334	80.08 299	36.89 319	26.52 334	70.29 325

Gipuma		45.18 310	41.86 311	55.16 319	77.03 308	51.52 304	32.50 339	80.52 276	32.46 333	27.12 335	35.02 336	9.52 344	75.50 316	22.31 336	60.21 317	38.45 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 311	40.28 312	55.82 318	40.82 345	42.54 325	65.12 324	63.99 338	34.43 332	24.48 336	57.12 331	3.92 347	76.17 314	17.10 338	55.52 323	48.75 332
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 312	38.86 313	46.69 325	53.84 338	16.45 346	48.61 335	49.92 342	37.49 330	31.67 333	60.97 328	8.14 346	56.42 339	28.42 331	30.72 333	67.19 326

PNet_i23d		38.26 314	35.42 314	46.79 324	58.74 335	35.48 335	59.65 329	51.25 341	32.45 334	23.44 339	47.53 334	2.04 349	58.96 337	25.60 334	18.09 339	45.92 335

pcd1.5k->3k		34.07 315	35.26 315	30.50 330	86.92 171	0.00 351	0.00 341	91.58 82	0.00 345	0.00 347	0.00 347	56.23 185	0.00 348	0.00 345	82.60 163	91.49 104

wuykxyi23d		39.76 313	33.18 316	59.51 316	46.98 343	44.01 322	57.70 332	67.74 332	24.13 337	13.98 343	34.33 337	1.27 350	71.33 328	34.23 322	18.23 337	63.18 330

E-PMN		31.77 316	30.64 317	35.15 327	52.87 340	27.67 341	57.09 333	47.86 343	24.64 336	16.40 341	33.05 338	11.23 342	54.90 340	14.46 340	18.15 338	22.87 338

EMVS		30.81 317	29.65 318	34.27 328	50.96 341	25.95 343	56.58 334	46.80 344	24.01 338	15.53 342	30.68 339	12.47 341	54.43 341	12.81 341	17.05 340	22.43 339

cdsmvs_eth3d_5k		19.96 319	26.61 319	0.00 336	0.00 350	0.00 351	0.00 341	89.26 154	0.00 345	0.00 347	88.61 125	61.62 137	0.00 348	0.00 345	0.00 347	0.00 345

MVE	26.22 23	30.37 318	25.89 320	43.81 326	44.55 344	35.46 336	28.87 340	39.07 345	18.20 339	18.58 340	40.18 335	2.68 348	47.37 342	17.07 339	23.78 336	48.60 333
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 320	21.40 321	10.23 333	4.82 347	10.11 347	34.70 338	30.74 347	1.48 342	23.91 338	26.07 340	28.42 321	13.41 345	27.12 333	15.35 342	7.17 340

wuyk23d		16.82 321	15.94 322	19.46 332	58.74 335	31.45 338	39.22 337	3.74 350	6.84 341	6.04 344	2.70 344	1.27 350	24.29 344	10.54 342	14.40 343	2.63 341

ab-mvs-re		7.23 322	9.64 323	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	86.72 176	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

test123		6.12 323	8.11 324	0.14 334	0.06 349	0.09 349	71.05 303	0.03 352	0.04 344	0.25 346	1.30 346	0.05 352	0.03 347	0.21 344	0.01 346	0.29 342

testmvs		6.04 324	8.02 325	0.10 335	0.08 348	0.03 350	69.74 307	0.04 351	0.05 343	0.31 345	1.68 345	0.02 353	0.04 346	0.24 343	0.02 344	0.25 343

pcd_1.5k_mvsjas		5.26 325	7.02 326	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	63.15 102	0.00 348	0.00 345	0.00 347	0.00 345

sosnet-low-res		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

sosnet		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

uncertanet		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

Regformer		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

uanet		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

test_part2					95.06 1	72.65 26				91.80 1

test_part1								94.09 2				81.79 1			96.38 2	93.74 36

test11111								94.22 1

sam_mvs1												51.32 235

sam_mvs												50.01 249

semantic-postprocess				80.11 204	82.69 257	64.85 163		83.47 242	69.16 186	70.49 229	84.15 236	50.83 243	88.15 257	69.23 152	72.14 273	87.34 238

ambc				75.24 269	73.16 319	50.51 311	63.05 327	87.47 202		64.28 288	77.81 297	17.80 336	89.73 232	57.88 241	60.64 315	85.49 268

MTGPA								92.02 60

test_post1							78.90 272				5.43 343	48.81 259	85.44 277	59.25 228

test_post											5.46 342	50.36 247	84.24 282

patchmatchnet-post											74.00 310	51.12 238	88.60 253

GG-mvs-BLEND				75.38 268	81.59 269	55.80 285	79.32 266	69.63 326		67.19 269	73.67 312	43.24 283	88.90 251	50.41 272	84.50 134	81.45 303

MTMP								32.83 346

gm-plane-assit					81.40 272	53.83 296			62.72 254		80.94 276		92.39 167	63.40 193

test9_res														84.90 17	95.70 13	92.87 69

TEST9					93.26 34	72.96 19	88.75 84	91.89 69	68.44 200	85.00 28	93.10 39	74.36 17	95.41 48

test_8					93.13 36	72.57 29	88.68 87	91.84 72	68.69 196	84.87 34	93.10 39	74.43 14	95.16 56

agg_prior2														82.91 39	95.45 15	92.70 70

agg_prior					92.85 42	71.94 40		91.78 75		84.41 40			94.93 65

TestCases				79.58 214	85.15 192	63.62 191		79.83 285	62.31 257	60.32 301	86.73 174	32.02 318	88.96 248	50.28 273	71.57 277	86.15 261

test_prior4						72.60 28	89.01 75

test_prior2							88.85 79		75.41 76	84.91 30	93.54 29	74.28 18		83.31 32	95.86 7

test_prior				86.33 46	92.61 47	69.59 72		92.97 31					95.48 43			93.91 30

旧先验2							86.56 161		58.10 285	87.04 14			88.98 246	74.07 109

新几何2							86.29 170

新几何1				83.42 112	93.13 36	70.71 55		85.48 222	57.43 292	81.80 71	91.98 56	63.28 97	92.27 171	64.60 188	92.99 48	87.27 240

旧先验1					91.96 55	65.79 141		86.37 214			93.08 43	69.31 53			92.74 51	88.74 201

无先验							87.48 125	88.98 166	60.00 273				94.12 93	67.28 165		88.97 195

原ACMM2							86.86 150

原ACMM1				84.35 85	93.01 40	68.79 86		92.44 45	63.96 243	81.09 79	91.57 66	66.06 76	95.45 45	67.19 167	94.82 30	88.81 198

test222					91.50 60	68.26 102	84.16 225	83.20 248	54.63 304	79.74 86	91.63 64	58.97 166			91.42 61	86.77 252

testdata2													91.01 216	62.37 201

segment_acmp												73.08 24

testdata				79.97 206	90.90 67	64.21 182		84.71 228	59.27 279	85.40 23	92.91 44	62.02 134	89.08 244	68.95 154	91.37 62	86.63 256

testdata1							84.14 226		75.71 71

test12				86.80 38	92.63 46	70.70 56		91.79 74		82.71 62		71.67 33	96.16 29		94.50 34	93.54 47

plane_prior7					90.08 79	68.51 98

plane_prior6					89.84 84	68.70 94						60.42 159

plane_prior5								92.44 45					95.38 50	78.71 63	86.32 120	91.33 106

plane_prior4											91.00 80

plane_prior3						68.60 96			78.44 30	78.92 95

plane_prior2							91.25 28		79.12 23

plane_prior1					89.90 83

plane_prior						68.71 92	90.38 44		77.62 34						86.16 122

n2								0.00 353

nn								0.00 353

door-mid								69.98 325

lessismore_v0				78.97 227	81.01 279	57.15 261		65.99 334		61.16 298	82.82 249	39.12 303	91.34 204	59.67 223	46.92 330	88.43 217

LGP-MVS_train				84.50 79	89.23 108	68.76 88		91.94 67	75.37 78	76.64 144	91.51 67	54.29 200	94.91 67	78.44 65	83.78 140	89.83 170

test11								92.23 52

door								69.44 328

HQP5-MVS						66.98 123

HQP-NCC					89.33 100		89.17 68		76.41 59	77.23 135

ACMP_Plane					89.33 100		89.17 68		76.41 59	77.23 135

BP-MVS														77.47 75

HQP4-MVS										77.24 134			95.11 58			91.03 112

HQP3-MVS								92.19 55							85.99 124

HQP2-MVS												60.17 162

NP-MVS					89.62 90	68.32 100					90.24 90

MDTV_nov1_ep13_2view						37.79 333	75.16 292		55.10 302	66.53 275		49.34 253		53.98 259		87.94 225

ACMMP++_ref															81.95 169

ACMMP++															81.25 175

Test By Simon												64.33 88

ITE_SJBPF				78.22 240	81.77 267	60.57 228		83.30 244	69.25 184	67.54 265	87.20 164	36.33 313	87.28 264	54.34 258	74.62 254	86.80 251

DeepMVS_CX				27.40 331	40.17 346	26.90 342		24.59 348	17.44 340	23.95 337	48.61 333	9.77 343	26.48 343	18.06 337	24.47 335	28.83 337

High-resolution multi-view benchmark