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.

test_part1								94.09 2				81.79 1			96.38 2	93.74 36

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

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

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

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

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

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

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

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

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

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
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

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

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

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

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_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

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

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

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.
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
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

segment_acmp												73.08 24

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

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 31	72.86 23	88.77 82	91.68 79	68.29 207	84.26 43	92.83 47	72.83 26	95.42 47	84.97 15	95.71 11	93.02 64

nrg030		83.88 54	83.53 54	84.96 69	86.77 181	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	79.28 204	92.50 76

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

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

CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 30	71.08 46	88.58 90	92.42 48	68.32 206	84.61 37	93.48 31	72.32 30	96.15 30	79.00 60	95.43 16	94.28 18

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

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
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

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	76.46 237	92.25 84

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
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

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

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
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

#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

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

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

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

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

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 178	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

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

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

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	76.49 236	91.60 99

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

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

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

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

旧先验1					91.96 55	65.79 141		86.37 214			93.08 43	69.31 53			92.74 51	88.74 203

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

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

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

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 209	57.97 249	82.59 245	87.62 198	67.40 217	76.17 158	88.56 128	68.47 57	89.59 234	70.65 142	86.05 123	93.47 49

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

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

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

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

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

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

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	85.06 231	67.54 64	93.58 123	67.03 170	86.58 116	92.32 81

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

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	12.47 347	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 156	63.34 199	86.31 168	91.09 97	79.01 26	72.17 215	89.07 115	67.20 67	92.81 158	66.08 176	75.65 246	92.20 86

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

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		80.52 111	79.98 98	82.12 160	84.28 210	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	80.57 185	90.74 122

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 188	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	76.86 228	90.75 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
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

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	73.55 270	90.06 157

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 161	68.99 83	83.65 232	91.46 88	63.00 254	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
EPNet		83.72 57	82.92 63	86.14 51	84.22 213	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
原ACMM1				84.35 85	93.01 40	68.79 86		92.44 45	63.96 249	81.09 79	91.57 66	66.06 76	95.45 45	67.19 167	94.82 30	88.81 200

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	86.09 205	66.02 77	94.27 85	71.52 138	82.06 167	87.39 238

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 117	65.40 147	84.43 218	92.00 63	67.62 212	78.11 118	85.05 232	66.02 77	94.27 85	71.52 138	89.50 80	89.01 193

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

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

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	87.51 236

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	75.24 255	92.30 82

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

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 156	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	76.46 237	92.20 86

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 248	85.79 189	56.21 278	86.78 154	85.76 221	73.60 109	77.93 122	87.57 152	65.02 84	88.99 251	67.14 168	75.33 252	87.63 233

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

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

Test By Simon												64.33 88

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
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

diffmvs		79.51 135	78.59 133	82.25 159	83.31 245	62.66 212	84.17 224	88.11 187	67.64 210	76.09 159	87.47 156	64.01 91	91.15 208	71.71 137	84.82 132	92.94 68

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
MVS		78.19 161	76.99 165	81.78 167	85.66 191	66.99 122	84.66 208	90.47 111	55.08 309	72.02 220	85.27 227	63.83 93	94.11 95	66.10 175	89.80 78	84.24 289

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	77.22 220	91.80 98

1121		80.84 97	79.77 102	84.05 94	93.11 38	70.78 54	84.66 208	85.42 223	57.37 299	81.76 72	92.02 55	63.41 95	94.12 93	67.28 165	92.93 49	87.26 243

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	79.22 205	91.23 109

新几何1				83.42 112	93.13 36	70.71 55		85.48 222	57.43 298	81.80 71	91.98 56	63.28 97	92.27 171	64.60 188	92.99 48	87.27 242

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 167	60.21 231	83.37 242	87.78 196	66.11 226	75.37 173	87.06 171	63.27 98	90.48 223	61.38 212	82.43 165	90.40 143

XXY-MVS		75.41 218	75.56 192	74.96 276	83.59 239	57.82 253	80.59 262	83.87 236	66.54 223	74.93 186	88.31 134	63.24 99	80.09 304	62.16 203	76.85 229	86.97 250

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 133	60.91 225	83.84 230	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

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	88.57 214

pcd_1.5k_mvsjas		5.26 331	7.02 332	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	0.00 353	63.15 102	0.00 354	0.00 351	0.00 353	0.00 352

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 183	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

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	85.62 220	63.15 102	94.29 83	75.62 96	88.87 86	88.59 212

WTY-MVS		75.65 215	75.68 191	75.57 272	86.40 184	56.82 266	77.92 285	82.40 255	65.10 236	76.18 156	87.72 147	63.13 105	80.90 300	60.31 219	81.96 168	89.00 195

TransMVSNet (Re)		75.39 219	74.56 210	77.86 243	85.50 195	57.10 262	86.78 154	86.09 219	72.17 143	71.53 225	87.34 158	63.01 106	89.31 240	56.84 249	61.83 317	87.17 245

v8		79.97 128	79.02 127	82.80 146	84.09 223	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	75.40 251	90.00 159

v18		77.67 177	76.35 179	81.64 176	84.09 223	64.47 178	87.27 133	89.01 162	72.59 135	69.39 252	82.04 265	62.85 108	91.80 182	72.72 123	67.20 299	88.63 206

v6		80.40 113	79.54 108	82.98 132	84.09 223	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	77.02 223	90.58 133

v1neww		80.40 113	79.54 108	82.98 132	84.10 221	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	77.02 223	90.58 133

v7new		80.40 113	79.54 108	82.98 132	84.10 221	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	77.02 223	90.58 133

v17		77.68 175	76.35 179	81.69 173	84.15 218	64.65 167	87.33 130	88.99 164	72.70 133	69.25 256	82.07 264	62.82 112	91.79 183	72.69 125	67.15 300	88.63 206

v16		77.69 174	76.36 178	81.68 174	84.15 218	64.63 169	87.33 130	88.99 164	72.69 134	69.31 255	82.08 263	62.80 113	91.79 183	72.70 124	67.23 298	88.63 206

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

v15		77.51 182	76.12 182	81.66 175	84.09 223	64.65 167	87.14 137	88.96 168	72.76 131	68.90 257	81.91 272	62.74 115	91.73 187	72.32 129	66.29 305	88.61 209

v1		80.19 122	79.31 118	82.85 141	83.83 235	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	76.82 232	90.68 124

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 233	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	76.82 232	90.68 124

V14		77.52 180	76.12 182	81.70 172	84.15 218	64.77 165	87.21 136	88.95 169	72.80 130	68.79 258	81.94 271	62.69 117	91.72 189	72.31 130	66.27 306	88.60 210

v1141		80.19 122	79.31 118	82.85 141	83.84 233	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	76.83 231	90.68 124

V9		77.52 180	76.11 185	81.73 171	84.19 217	64.89 162	87.26 134	88.94 172	72.87 129	68.65 261	81.96 270	62.65 120	91.72 189	72.27 131	66.24 307	88.60 210

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

PAPM		77.68 175	76.40 174	81.51 180	87.29 173	61.85 221	83.78 231	89.59 142	64.74 239	71.23 227	88.70 121	62.59 122	93.66 122	52.66 266	87.03 112	89.01 193

1112_ss		77.40 188	76.43 173	80.32 200	89.11 114	60.41 230	83.65 232	87.72 197	62.13 265	73.05 199	86.72 176	62.58 123	89.97 228	62.11 205	80.80 181	90.59 132

v12		77.51 182	76.09 186	81.76 170	84.22 213	64.99 159	87.30 132	88.93 173	72.92 126	68.48 265	81.97 268	62.54 124	91.70 192	72.24 132	66.21 309	88.58 213

v13		77.50 184	76.07 187	81.77 168	84.23 212	65.07 158	87.34 129	88.91 174	72.92 126	68.35 266	81.97 268	62.53 125	91.69 193	72.20 133	66.22 308	88.56 215

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 252	87.20 174	51.60 309	80.06 265	80.46 278	75.20 82	67.69 270	86.72 176	62.48 126	88.98 252	63.44 192	89.25 83	91.51 102

v148		78.72 151	77.80 152	81.47 181	82.73 261	61.96 220	86.30 169	88.08 191	73.26 116	76.18 156	85.47 224	62.46 127	92.36 169	71.92 136	73.82 268	90.09 153

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	86.21 202	62.36 128	94.52 79	65.36 181	92.05 55	89.77 175
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
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

TAMVS		78.89 150	77.51 158	83.03 129	87.80 156	67.79 111	84.72 207	85.05 227	67.63 211	76.75 141	87.70 148	62.25 130	90.82 218	58.53 235	87.13 110	90.49 138

CP-MVSNet		78.22 158	78.34 142	77.84 244	87.83 154	54.54 291	87.94 111	91.17 95	77.65 33	73.48 194	88.49 129	62.24 131	88.43 260	62.19 202	74.07 263	90.55 136

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

v11		77.45 185	76.06 188	81.59 179	84.22 213	64.52 170	87.11 142	89.02 160	72.76 131	68.76 259	81.90 273	62.09 133	91.71 191	71.98 134	66.73 301	88.56 215

testdata				79.97 206	90.90 67	64.21 182		84.71 228	59.27 285	85.40 23	92.91 44	62.02 134	89.08 249	68.95 154	91.37 62	86.63 258

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 168	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

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 168	70.21 60	85.55 192	86.41 212	62.85 257	81.32 74	88.61 125	61.68 135	92.24 173	78.41 67	90.26 72	91.83 95

cdsmvs_eth3d_5k		19.96 325	26.61 325	0.00 342	0.00 356	0.00 357	0.00 347	89.26 154	0.00 351	0.00 353	88.61 125	61.62 137	0.00 354	0.00 351	0.00 353	0.00 352

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 163	68.23 103	84.40 220	86.20 216	67.49 215	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
v10		79.74 132	78.67 130	82.97 136	84.06 228	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	75.55 248	89.90 167

v7		80.24 119	79.26 122	83.15 122	84.07 227	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	77.41 217	90.49 138

v1144		80.03 126	79.03 126	83.01 130	83.78 236	64.51 172	87.11 142	90.57 108	71.96 145	78.08 120	86.20 203	61.41 141	93.94 100	74.93 103	77.23 219	90.60 130

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	84.74 238	61.34 142	93.11 145	58.24 238	85.84 126	84.27 288

Test_1112_low_res		76.40 202	75.44 195	79.27 219	89.28 106	58.09 245	81.69 253	87.07 206	59.53 283	72.48 206	86.67 182	61.30 143	89.33 239	60.81 217	80.15 191	90.41 142

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 242	88.64 127	51.78 308	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

PEN-MVS		77.73 173	77.69 156	77.84 244	87.07 176	53.91 295	87.91 113	91.18 94	77.56 37	73.14 198	88.82 120	61.23 145	89.17 247	59.95 221	72.37 276	90.43 141

pm-mvs1		77.25 189	76.68 170	78.93 228	84.22 213	58.62 240	86.41 165	88.36 185	71.37 153	73.31 195	88.01 141	61.22 146	89.15 248	64.24 189	73.01 272	89.03 192

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	86.70 256

v2v482		80.23 120	79.29 121	83.05 128	83.62 238	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	76.78 234	91.18 110

jason		81.39 91	80.29 95	84.70 76	86.63 182	69.90 67	85.95 176	86.77 208	63.24 251	81.07 80	89.47 106	61.08 149	92.15 174	78.33 68	90.07 76	92.05 91
jason: jason.
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
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	65.77 230	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
PS-CasMVS		78.01 166	78.09 146	77.77 246	87.71 161	54.39 293	88.02 107	91.22 92	77.50 40	73.26 196	88.64 124	60.73 152	88.41 261	61.88 206	73.88 267	90.53 137

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

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 193	68.78 87	83.54 235	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

v144192		79.47 138	78.37 141	82.78 149	83.35 243	63.96 189	86.96 146	90.36 116	69.99 171	77.50 128	85.67 217	60.66 155	93.77 114	74.27 107	76.58 235	90.62 128

V42		79.38 141	78.24 145	82.83 144	81.10 284	65.50 146	85.55 192	89.82 137	71.57 151	78.21 115	86.12 204	60.66 155	93.18 141	75.64 95	75.46 250	89.81 172

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

DTE-MVSNet		76.99 191	76.80 168	77.54 250	86.24 185	53.06 305	87.52 124	90.66 105	77.08 46	72.50 204	88.67 123	60.48 158	89.52 235	57.33 246	70.74 287	90.05 158

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

plane_prior6					89.84 84	68.70 94						60.42 159

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

HQP2-MVS												60.17 162

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

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	77.05 222	90.88 117

v1192		79.59 134	78.43 140	83.07 127	83.55 240	64.52 170	86.93 148	90.58 107	70.83 158	77.78 124	85.90 211	59.15 165	93.94 100	73.96 110	77.19 221	90.76 120

test222					91.50 60	68.26 102	84.16 225	83.20 248	54.63 310	79.74 86	91.63 64	58.97 166			91.42 61	86.77 254

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 81	68.58 97	78.70 279	87.50 201	56.38 304	75.80 162	86.84 172	58.67 167	91.40 202	61.58 210	85.75 127	90.34 144

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

v1921920		79.22 143	78.03 147	82.80 146	83.30 246	63.94 190	86.80 152	90.33 118	69.91 172	77.48 129	85.53 222	58.44 169	93.75 116	73.60 114	76.85 229	90.71 123

v748		77.97 167	76.65 171	81.92 166	82.29 268	63.28 201	87.53 123	90.35 117	73.50 113	70.76 231	85.55 221	58.28 170	92.81 158	68.81 156	72.76 275	89.67 177

114514_t		80.68 106	79.51 111	84.20 89	94.09 22	67.27 119	89.64 61	91.11 96	58.75 289	74.08 191	90.72 84	58.10 171	95.04 63	69.70 149	89.42 82	90.30 145

v7n		78.97 149	77.58 157	83.14 123	83.45 242	65.51 145	88.32 99	91.21 93	73.69 107	72.41 212	86.32 200	57.93 172	93.81 110	69.18 153	75.65 246	90.11 151

QAPM		80.88 95	79.50 112	85.03 67	88.01 145	68.97 84	91.59 24	92.00 63	66.63 222	75.15 181	92.16 52	57.70 173	95.45 45	63.52 191	88.76 88	90.66 127

HyFIR lowres test		77.53 179	75.40 197	83.94 102	89.59 91	66.62 127	80.36 263	88.64 181	56.29 305	76.45 146	85.17 228	57.64 174	93.28 135	61.34 213	83.10 157	91.91 93

CNLPA		78.08 163	76.79 169	81.97 164	90.40 73	71.07 47	87.59 118	84.55 230	66.03 229	72.38 213	89.64 102	57.56 175	86.04 278	59.61 224	83.35 153	88.79 201

sss		73.60 232	73.64 219	73.51 287	82.80 259	55.01 288	76.12 291	81.69 267	62.47 262	74.68 188	85.85 214	57.32 176	78.11 312	60.86 216	80.93 178	87.39 238

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 200	68.74 90	88.77 82	88.10 189	74.99 84	74.97 185	83.49 250	57.27 177	93.36 133	73.53 115	80.88 179	91.18 110

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 200	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

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 258	65.32 151	86.12 172	89.55 143	69.64 178	70.55 232	85.82 215	57.24 179	93.81 110	76.85 83	88.55 93	92.41 79

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	87.50 237

test_normal		79.81 130	78.45 137	83.89 103	82.70 262	65.40 147	85.82 182	89.48 146	69.39 179	70.12 241	85.66 218	57.15 181	93.71 121	77.08 80	88.62 91	92.56 75

v1240		78.99 148	77.78 153	82.64 153	83.21 247	63.54 193	86.62 159	90.30 120	69.74 177	77.33 131	85.68 216	57.04 182	93.76 115	73.13 120	76.92 226	90.62 128

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

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 230	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	80.14 192	90.97 115

pcd1.5k->3k		34.07 321	35.26 321	30.50 336	86.92 177	0.00 357	0.00 347	91.58 82	0.00 351	0.00 353	0.00 353	56.23 185	0.00 354	0.00 351	82.60 163	91.49 104

EPNet_dtu		75.46 217	74.86 206	77.23 255	82.57 265	54.60 290	86.89 149	83.09 249	71.64 147	66.25 284	85.86 213	55.99 186	88.04 265	54.92 256	86.55 117	89.05 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
V4		77.95 168	76.37 175	82.67 151	79.40 301	65.52 143	86.43 163	89.94 134	72.28 139	72.14 218	84.95 233	55.72 187	93.44 130	73.64 112	72.86 273	89.05 190

v52		77.94 170	76.37 175	82.67 151	79.39 302	65.52 143	86.43 163	89.94 134	72.28 139	72.15 217	84.94 234	55.70 188	93.44 130	73.64 112	72.84 274	89.06 186

CostFormer		75.24 220	73.90 218	79.27 219	82.65 264	58.27 244	80.80 258	82.73 253	61.57 268	75.33 177	83.13 252	55.52 189	91.07 215	64.98 185	78.34 211	88.45 218

tpmrst		72.39 250	72.13 237	73.18 289	80.54 289	49.91 319	79.91 268	79.08 290	63.11 252	71.69 223	79.95 288	55.32 190	82.77 295	65.66 180	73.89 266	86.87 251

1314		76.53 197	75.30 200	80.21 203	83.93 231	62.32 216	84.66 208	88.81 175	60.23 277	70.16 240	84.07 243	55.30 191	90.73 220	67.37 164	83.21 155	87.59 235

tfpnnormal		74.39 223	73.16 223	78.08 241	86.10 187	58.05 246	84.65 211	87.53 200	70.32 167	71.22 228	85.63 219	54.97 192	89.86 229	43.03 315	75.02 256	86.32 264

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 163	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	79.57 199	90.09 153

test1		78.40 155	77.40 159	81.40 183	87.60 163	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	79.57 199	90.09 153

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 163	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	79.38 202	89.61 178

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 251	66.96 125	86.94 147	87.45 203	72.45 136	71.49 226	84.17 241	54.79 196	91.58 200	67.61 161	80.31 189	89.30 182

MVSTER		79.01 147	77.88 150	82.38 157	83.07 252	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	80.57 185	89.89 168

OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 197	69.91 66	90.57 38	90.97 98	66.70 218	72.17 215	91.91 57	54.70 197	93.96 98	61.81 208	90.95 66	88.41 220

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 192	69.02 81	83.03 244	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

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

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

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 139	63.24 202	84.61 212	86.40 213	67.55 214	77.81 123	86.48 194	54.10 202	93.15 142	57.75 242	82.72 162	87.20 244

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 179	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	79.57 199	89.45 180

DP-MVS		76.78 194	74.57 209	83.42 112	93.29 32	69.46 77	88.55 91	83.70 237	63.98 248	70.20 237	88.89 118	54.01 204	94.80 73	46.66 296	81.88 170	86.01 272

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
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	64.33 244	69.87 247	88.38 132	53.66 206	93.58 123	58.86 231	82.73 161	87.86 229
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 193	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	88.85 198
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		76.12 209	74.46 213	81.13 189	85.37 196	69.79 68	84.42 219	87.95 193	65.03 237	67.46 272	85.33 226	53.28 209	91.73 187	58.01 240	83.27 154	81.85 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 290	67.08 121	87.24 135	89.53 144	65.66 232	75.16 180	87.19 165	52.52 210	92.25 172	77.17 79	79.34 203	89.61 178

CR-MVSNet		73.37 240	71.27 246	79.67 212	81.32 282	65.19 155	75.92 293	80.30 280	59.92 280	72.73 202	81.19 276	52.50 211	86.69 272	59.84 222	77.71 213	87.11 248

Patchmtry		70.74 260	69.16 259	75.49 273	80.72 286	54.07 294	74.94 302	80.30 280	58.34 290	70.01 242	81.19 276	52.50 211	86.54 274	53.37 263	71.09 285	85.87 274

pmmvs4		74.03 227	71.91 238	80.39 197	81.96 271	68.32 100	81.45 256	82.14 259	59.32 284	69.87 247	85.13 229	52.40 213	88.13 264	60.21 220	74.74 259	84.73 286

PatchFormer-LS_test		74.50 222	73.05 224	78.86 229	82.95 256	59.55 236	81.65 254	82.30 257	67.44 216	71.62 224	78.15 299	52.34 214	88.92 256	65.05 184	75.90 243	88.12 223

RPMNet		71.62 254	68.94 261	79.67 212	81.32 282	65.19 155	75.92 293	78.30 299	57.60 297	72.73 202	76.45 309	52.30 215	86.69 272	48.14 287	77.71 213	87.11 248

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

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

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	89.07 184

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

thres200		75.55 216	74.47 212	78.82 230	87.78 159	57.85 252	83.07 243	83.51 241	72.44 138	75.84 161	84.42 240	52.08 220	91.75 186	47.41 290	83.64 147	86.86 252

PMMVS		69.34 271	68.67 262	71.35 297	75.67 317	62.03 219	75.17 297	73.46 323	50.00 326	68.68 260	79.05 293	52.07 221	78.13 311	61.16 214	82.77 160	73.90 329

tpm cat1		70.57 262	68.31 265	77.35 253	82.41 267	57.95 250	78.08 284	80.22 283	52.04 321	68.54 264	77.66 304	52.00 222	87.84 267	51.77 267	72.07 280	86.25 265

Patchmatch-test1		73.49 233	71.85 240	78.41 238	84.05 229	62.17 218	79.96 267	79.29 289	66.30 225	72.38 213	79.58 292	51.95 223	85.08 285	55.46 254	77.67 215	87.99 225

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	89.07 184

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

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

tpm2		73.26 243	71.46 243	78.63 232	83.34 244	56.71 269	80.65 261	80.40 279	56.63 303	73.55 193	82.02 266	51.80 231	91.24 206	56.35 251	78.42 210	87.95 226

LS3D		76.95 192	74.82 207	83.37 115	90.45 71	67.36 118	89.15 72	86.94 207	61.87 267	69.52 250	90.61 86	51.71 232	94.53 78	46.38 299	86.71 115	88.21 222

IterMVS		74.29 224	72.94 225	78.35 239	81.53 276	63.49 195	81.58 255	82.49 254	68.06 208	69.99 244	83.69 248	51.66 233	85.54 281	65.85 178	71.64 282	86.01 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 252	71.71 242	74.35 282	82.19 269	52.00 306	79.22 274	77.29 304	64.56 241	72.95 200	83.68 249	51.35 234	83.26 294	58.33 237	75.80 244	87.81 230

sam_mvs1												51.32 235				88.96 197

PatchmatchNet		73.12 245	71.33 245	78.49 237	83.18 249	60.85 226	79.63 269	78.57 297	64.13 245	71.73 222	79.81 291	51.20 236	85.97 279	57.40 245	76.36 239	88.66 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		73.45 234	71.17 248	80.31 201	83.55 240	59.56 235	81.88 249	82.33 256	57.94 294	70.51 234	81.62 274	51.19 237	91.63 198	53.96 260	77.51 216	89.75 176

patchmatchnet-post											74.00 316	51.12 238	88.60 259

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 168	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	89.06 186

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 168	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	89.06 186

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 168	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	89.06 186

Patchmatch-test		64.82 291	63.24 289	69.57 303	79.42 300	49.82 320	63.49 332	69.05 336	51.98 322	59.95 309	80.13 287	50.91 239	70.98 335	40.66 320	73.57 269	87.90 228

semantic-postprocess				80.11 204	82.69 263	64.85 163		83.47 242	69.16 186	70.49 235	84.15 242	50.83 243	88.15 263	69.23 152	72.14 279	87.34 240

Patchmatch-RL test		70.24 266	67.78 275	77.61 248	77.43 309	59.57 234	71.16 308	70.33 329	62.94 256	68.65 261	72.77 319	50.62 244	85.49 282	69.58 150	66.58 303	87.77 231

conf0.002		73.67 231	72.42 231	77.42 251	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	89.78 173

thresconf0.02		73.39 236	72.42 231	76.31 261	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 259

tfpn_n400		73.39 236	72.42 231	76.31 261	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 259

tfpnconf		73.39 236	72.42 231	76.31 261	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 259

tfpnview11		73.39 236	72.42 231	76.31 261	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 259

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	50.54 250	93.49 127	73.40 117	88.25 98	94.54 10

pmmvs6		74.69 221	73.39 220	78.61 233	81.38 279	57.48 258	86.64 158	87.95 193	64.99 238	70.18 238	86.61 187	50.43 251	89.52 235	62.12 204	70.18 289	88.83 199

test_post											5.46 348	50.36 252	84.24 288

tfpn_ndepth		73.70 229	72.75 226	76.52 259	87.78 159	54.92 289	84.32 222	80.28 282	67.57 213	72.50 204	84.82 235	50.12 253	89.44 238	45.73 302	81.66 172	85.20 278

sam_mvs												50.01 254

tfpn1000		73.44 235	72.49 229	76.29 265	87.81 155	53.69 297	84.05 229	78.81 296	67.99 209	72.09 219	86.27 201	49.95 255	89.04 250	44.09 312	81.38 174	86.15 267

PatchT		68.46 276	67.85 272	70.29 301	80.70 287	43.93 329	72.47 306	74.88 314	60.15 278	70.55 232	76.57 308	49.94 256	81.59 298	50.58 271	74.83 258	85.34 277

tpmvs		71.09 258	69.29 258	76.49 260	82.04 270	56.04 279	78.92 277	81.37 271	64.05 246	67.18 276	78.28 298	49.74 257	89.77 230	49.67 278	72.37 276	83.67 293

CVMVSNet		72.99 247	72.58 228	74.25 283	84.28 210	50.85 315	86.41 165	83.45 243	44.56 330	73.23 197	87.54 154	49.38 258	85.70 280	65.90 177	78.44 209	86.19 266

MDTV_nov1_ep13_2view						37.79 339	75.16 298		55.10 308	66.53 281		49.34 259		53.98 259		87.94 227

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	49.30 260	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
pmmvs5		71.55 255	70.20 255	75.61 271	77.83 307	56.39 274	81.74 252	80.89 272	57.76 295	67.46 272	84.49 239	49.26 261	85.32 284	57.08 248	75.29 253	85.11 282

LTVRE_ROB	69.57 13	76.25 204	74.54 211	81.41 182	88.60 128	64.38 181	79.24 273	89.12 158	70.76 161	69.79 249	87.86 142	49.09 262	93.20 139	56.21 252	80.16 190	86.65 257
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
FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 180	63.01 207	88.39 96	89.28 151	70.49 165	74.39 190	87.28 159	49.06 263	91.11 209	60.91 215	78.52 207	90.09 153

MDTV_nov1_ep13			69.97 256		83.18 249	53.48 298	77.10 289	80.18 284	60.45 274	69.33 254	80.44 284	48.89 264	86.90 271	51.60 268	78.51 208

test_post1							78.90 278				5.43 349	48.81 265	85.44 283	59.25 228

test-LLR		72.94 248	72.43 230	74.48 280	81.35 280	58.04 247	78.38 280	77.46 302	66.66 219	69.95 245	79.00 295	48.06 266	79.24 306	66.13 173	84.83 130	86.15 267

test0.0.03 1		68.00 277	67.69 276	68.90 306	77.55 308	47.43 323	75.70 296	72.95 325	66.66 219	66.56 280	82.29 260	48.06 266	75.87 321	44.97 306	74.51 261	83.41 295

MS-PatchMatch		73.83 228	72.67 227	77.30 254	83.87 232	66.02 135	81.82 250	84.66 229	61.37 271	68.61 263	82.82 255	47.29 268	88.21 262	59.27 227	84.32 137	77.68 321

cascas		76.72 195	74.64 208	82.99 131	85.78 190	65.88 139	82.33 247	89.21 156	60.85 273	72.74 201	81.02 280	47.28 269	93.75 116	67.48 163	85.02 128	89.34 181

test20.03		67.45 279	66.95 278	68.94 305	75.48 320	44.84 327	77.50 286	77.67 301	66.66 219	63.01 300	83.80 245	47.02 270	78.40 310	42.53 317	68.86 295	83.58 294

test_0402		72.79 249	70.44 252	79.84 208	88.13 141	65.99 136	85.93 177	84.29 232	65.57 233	67.40 274	85.49 223	46.92 271	92.61 161	35.88 326	74.38 262	80.94 311

F-COLMAP		76.38 203	74.33 214	82.50 155	89.28 106	66.95 126	88.41 95	89.03 159	64.05 246	66.83 278	88.61 125	46.78 272	92.89 153	57.48 243	78.55 206	87.67 232

Anonymous20231206		68.60 273	67.80 274	71.02 299	80.23 293	50.75 316	78.30 283	80.47 277	56.79 302	66.11 285	82.63 257	46.35 273	78.95 308	43.62 314	75.70 245	83.36 296

CHOSEN 280x420		66.51 285	64.71 284	71.90 292	81.45 277	63.52 194	57.98 337	68.95 337	53.57 316	62.59 302	76.70 307	46.22 274	75.29 324	55.25 255	79.68 193	76.88 327

GA-MVS		76.87 193	75.17 205	81.97 164	82.75 260	62.58 213	81.44 257	86.35 215	72.16 144	74.74 187	82.89 253	46.20 275	92.02 176	68.85 155	81.09 177	91.30 108

MDA-MVSNet_test_wron		65.03 289	62.92 290	71.37 295	75.93 315	56.73 267	69.09 320	74.73 317	57.28 300	54.03 325	77.89 301	45.88 276	74.39 327	49.89 277	61.55 318	82.99 302

YYNet1		65.03 289	62.91 291	71.38 294	75.85 316	56.60 271	69.12 319	74.66 320	57.28 300	54.12 324	77.87 302	45.85 277	74.48 326	49.95 276	61.52 319	83.05 300

EPMVS		69.02 272	68.16 267	71.59 293	79.61 298	49.80 321	77.40 287	66.93 339	62.82 258	70.01 242	79.05 293	45.79 278	77.86 314	56.58 250	75.26 254	87.13 247

IB-MVS	68.01 15	75.85 213	73.36 221	83.31 116	84.76 204	66.03 134	83.38 236	85.06 226	70.21 170	69.40 251	81.05 279	45.76 279	94.66 76	65.10 183	75.49 249	89.25 183
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
jajsoiax		79.29 142	77.96 148	83.27 118	84.68 206	66.57 129	89.25 67	90.16 126	69.20 185	75.46 169	89.49 105	45.75 280	93.13 144	76.84 84	80.80 181	90.11 151

PatchMatch-RL		72.38 251	70.90 250	76.80 258	88.60 128	67.38 117	79.53 270	76.17 308	62.75 259	69.36 253	82.00 267	45.51 281	84.89 286	53.62 262	80.58 184	78.12 319

RPSCF		73.23 244	71.46 243	78.54 235	82.50 266	59.85 232	82.18 248	82.84 252	58.96 286	71.15 229	89.41 112	45.48 282	84.77 287	58.82 232	71.83 281	91.02 114

MSDG		73.36 242	70.99 249	80.49 196	84.51 208	65.80 140	80.71 260	86.13 218	65.70 231	65.46 287	83.74 247	44.60 283	90.91 217	51.13 270	76.89 227	84.74 285

PVSNet_0	57.27 20	61.67 297	59.27 298	68.85 307	79.61 298	57.44 259	68.01 323	73.44 324	55.93 306	58.54 312	70.41 324	44.58 284	77.55 315	47.01 291	35.91 338	71.55 331

DWT-MVSNet_test		73.70 229	71.86 239	79.21 221	82.91 257	58.94 238	82.34 246	82.17 258	65.21 234	71.05 230	78.31 297	44.21 285	90.17 227	63.29 194	77.28 218	88.53 217

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 205	66.37 131	89.17 68	90.19 125	69.38 181	75.40 172	89.46 108	44.17 286	93.15 142	76.78 85	80.70 183	90.14 149

MDA-MVSNet-bldmvs		66.68 283	63.66 287	75.75 269	79.28 303	60.56 229	73.92 304	78.35 298	64.43 242	50.13 332	79.87 290	44.02 287	83.67 290	46.10 300	56.86 326	83.03 301

gg-mvs-nofinetune		69.95 268	67.96 270	75.94 268	83.07 252	54.51 292	77.23 288	70.29 330	63.11 252	70.32 236	62.33 331	43.62 288	88.69 258	53.88 261	87.76 101	84.62 287

GG-mvs-BLEND				75.38 274	81.59 275	55.80 285	79.32 272	69.63 332		67.19 275	73.67 318	43.24 289	88.90 257	50.41 272	84.50 134	81.45 310

CMPMVS	51.72 21	70.19 267	68.16 267	76.28 266	73.15 326	57.55 257	79.47 271	83.92 235	48.02 328	56.48 321	84.81 236	43.13 290	86.42 276	62.67 199	81.81 171	84.89 283
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Test4		77.83 172	75.90 189	83.62 106	80.24 292	65.25 153	85.27 198	90.67 103	69.03 191	66.48 282	83.75 246	43.07 291	93.00 151	75.93 90	88.66 90	92.62 74

dp		66.80 282	65.43 282	70.90 300	79.74 297	48.82 322	75.12 300	74.77 316	59.61 282	64.08 296	77.23 305	42.89 292	80.72 301	48.86 280	66.58 303	83.16 298

PVSNet	64.34 18	72.08 253	70.87 251	75.69 270	86.21 186	56.44 273	74.37 303	80.73 275	62.06 266	70.17 239	82.23 261	42.86 293	83.31 293	54.77 257	84.45 136	87.32 241

pmmvs-eth3d		70.50 264	67.83 273	78.52 236	77.37 310	66.18 133	81.82 250	81.51 269	58.90 287	63.90 297	80.42 285	42.69 294	86.28 277	58.56 234	65.30 311	83.11 299

UnsupCasMVSNet_eth		67.33 280	65.99 281	71.37 295	73.48 323	51.47 311	75.16 298	85.19 225	65.20 235	60.78 305	80.93 283	42.35 295	77.20 316	57.12 247	53.69 331	85.44 276

ADS-MVSNet2		66.20 288	63.33 288	74.82 278	79.92 295	58.75 239	67.55 325	75.19 312	53.37 317	65.25 289	75.86 310	42.32 296	80.53 302	41.57 318	68.91 293	85.18 279

ADS-MVSNet		64.36 293	62.88 292	68.78 308	79.92 295	47.17 324	67.55 325	71.18 328	53.37 317	65.25 289	75.86 310	42.32 296	73.99 329	41.57 318	68.91 293	85.18 279

SixPastTwentyTwo		73.37 240	71.26 247	79.70 210	85.08 202	57.89 251	85.57 188	83.56 240	71.03 157	65.66 286	85.88 212	42.10 298	92.57 162	59.11 229	63.34 314	88.65 205

JIA-IIPM		66.32 287	62.82 293	76.82 257	77.09 313	61.72 222	65.34 329	75.38 310	58.04 293	64.51 293	62.32 332	42.05 299	86.51 275	51.45 269	69.22 292	82.21 306

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	66.78 279	86.70 181	41.95 300	91.51 201	55.64 253	78.14 212	87.17 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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	67.75 269	87.47 156	41.27 301	93.19 140	58.37 236	75.94 242	87.60 234

MIMVSNet		70.69 261	69.30 257	74.88 277	84.52 207	56.35 276	75.87 295	79.42 288	64.59 240	67.76 268	82.41 258	41.10 302	81.54 299	46.64 298	81.34 175	86.75 255

N_pmnet		52.79 310	53.26 308	51.40 329	78.99 305	7.68 354	69.52 315	3.89 355	51.63 324	57.01 319	74.98 313	40.83 303	65.96 342	37.78 324	64.67 312	80.56 314

testing_2		75.73 214	73.34 222	82.89 140	77.37 310	65.22 154	84.10 227	90.54 109	69.09 187	60.46 306	81.15 278	40.48 304	92.84 157	76.36 86	80.54 187	90.60 130

EU-MVSNet		68.53 275	67.61 277	71.31 298	78.51 306	47.01 325	84.47 214	84.27 233	42.27 331	66.44 283	84.79 237	40.44 305	83.76 289	58.76 233	68.54 297	83.17 297

DSMNet-mixed		57.77 304	56.90 304	60.38 320	67.70 336	35.61 340	69.18 317	53.97 346	32.30 341	57.49 317	79.88 289	40.39 306	68.57 339	38.78 322	72.37 276	76.97 324

OurMVSNet-221017-0		74.26 225	72.42 231	79.80 209	83.76 237	59.59 233	85.92 178	86.64 209	66.39 224	66.96 277	87.58 151	39.46 307	91.60 199	65.76 179	69.27 291	88.22 221

K. test v3		71.19 257	68.51 263	79.21 221	83.04 254	57.78 254	84.35 221	76.91 306	72.90 128	62.99 301	82.86 254	39.27 308	91.09 214	61.65 209	52.66 332	88.75 202

lessismore_v0				78.97 227	81.01 285	57.15 261		65.99 340		61.16 304	82.82 255	39.12 309	91.34 204	59.67 223	46.92 336	88.43 219

UnsupCasMVSNet_bld		63.70 295	61.53 297	70.21 302	73.69 322	51.39 312	72.82 305	81.89 265	55.63 307	57.81 315	71.80 321	38.67 310	78.61 309	49.26 279	52.21 333	80.63 312

new-patchmatchnet		61.73 296	61.73 296	61.70 319	72.74 327	24.50 350	69.16 318	78.03 300	61.40 269	56.72 320	75.53 312	38.42 311	76.48 319	45.95 301	57.67 324	84.13 291

MVS-HIRNet		59.14 300	57.67 302	63.57 316	81.65 274	43.50 330	71.73 307	65.06 342	39.59 335	51.43 330	57.73 335	38.34 312	82.58 296	39.53 321	73.95 265	64.62 336

COLMAP_ROB	66.92 17	73.01 246	70.41 253	80.81 193	87.13 175	65.63 142	88.30 100	84.19 234	62.96 255	63.80 298	87.69 149	38.04 313	92.56 163	46.66 296	74.91 257	84.24 289
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 269	69.00 260	72.55 290	79.27 304	56.85 265	78.38 280	74.71 318	57.64 296	68.09 267	77.19 306	37.75 314	76.70 317	63.92 190	84.09 138	84.10 292

LP		61.36 298	57.78 301	72.09 291	75.54 319	58.53 241	67.16 327	75.22 311	51.90 323	54.13 323	69.97 325	37.73 315	80.45 303	32.74 330	55.63 328	77.29 323

OpenMVS_ROB	64.09 19	70.56 263	68.19 266	77.65 247	80.26 291	59.41 237	85.01 203	82.96 251	58.76 288	65.43 288	82.33 259	37.63 316	91.23 207	45.34 305	76.03 241	82.32 305

FMVSNet5		69.50 270	67.96 270	74.15 284	82.97 255	55.35 287	80.01 266	82.12 260	62.56 261	63.02 299	81.53 275	36.92 317	81.92 297	48.42 281	74.06 264	85.17 281

MIMVSNet1		68.58 274	66.78 279	73.98 285	80.07 294	51.82 307	80.77 259	84.37 231	64.40 243	59.75 310	82.16 262	36.47 318	83.63 291	42.73 316	70.33 288	86.48 259

ITE_SJBPF				78.22 240	81.77 273	60.57 228		83.30 244	69.25 184	67.54 271	87.20 164	36.33 319	87.28 270	54.34 258	74.62 260	86.80 253

test-mter		71.41 256	70.39 254	74.48 280	81.35 280	58.04 247	78.38 280	77.46 302	60.32 276	69.95 245	79.00 295	36.08 320	79.24 306	66.13 173	84.83 130	86.15 267

testgi		66.67 284	66.53 280	67.08 311	75.62 318	41.69 334	75.93 292	76.50 307	66.11 226	65.20 291	86.59 188	35.72 321	74.71 325	43.71 313	73.38 271	84.84 284

EG-PatchMatch MVS		74.04 226	71.82 241	80.71 195	84.92 203	67.42 115	85.86 179	88.08 191	66.04 228	64.22 295	83.85 244	35.10 322	92.56 163	57.44 244	80.83 180	82.16 307

XVG-ACMP-BASELINE		76.11 210	74.27 215	81.62 177	83.20 248	64.67 166	83.60 234	89.75 139	69.75 175	71.85 221	87.09 169	32.78 323	92.11 175	69.99 147	80.43 188	88.09 224

AllTest		70.96 259	68.09 269	79.58 214	85.15 198	63.62 191	84.58 213	79.83 285	62.31 263	60.32 307	86.73 174	32.02 324	88.96 254	50.28 273	71.57 283	86.15 267

TestCases				79.58 214	85.15 198	63.62 191		79.83 285	62.31 263	60.32 307	86.73 174	32.02 324	88.96 254	50.28 273	71.57 283	86.15 267

USDC		70.33 265	68.37 264	76.21 267	80.60 288	56.23 277	79.19 275	86.49 211	60.89 272	61.29 303	85.47 224	31.78 326	89.47 237	53.37 263	76.21 240	82.94 304

tmp_tt		18.61 326	21.40 327	10.23 339	4.82 353	10.11 353	34.70 344	30.74 353	1.48 348	23.91 344	26.07 346	28.42 327	13.41 351	27.12 339	15.35 348	7.17 347

testpf		56.51 306	57.58 303	53.30 326	71.99 329	41.19 335	46.89 342	69.32 335	58.06 292	52.87 329	69.45 327	27.99 328	72.73 331	59.59 225	62.07 316	45.98 341

Anonymous20231211		64.82 291	61.79 295	73.91 286	77.11 312	50.92 314	85.29 197	81.53 268	54.19 311	57.98 314	78.03 300	26.90 329	87.83 268	37.92 323	57.12 325	82.99 302

test2356		59.50 299	58.08 299	63.74 315	71.23 330	41.88 332	67.59 324	72.42 327	53.72 315	57.65 316	70.74 323	26.31 330	72.40 332	32.03 333	71.06 286	76.93 325

TDRefinement		67.49 278	64.34 285	76.92 256	73.47 324	61.07 223	84.86 206	82.98 250	59.77 281	58.30 313	85.13 229	26.06 331	87.89 266	47.92 289	60.59 322	81.81 309

test1235678		58.74 302	56.89 305	64.30 313	69.70 332	41.87 333	71.05 309	74.87 315	54.06 312	50.63 331	71.53 322	25.30 332	74.10 328	31.80 334	63.10 315	76.93 325

TinyColmap		67.30 281	64.81 283	74.76 279	81.92 272	56.68 270	80.29 264	81.49 270	60.33 275	56.27 322	83.22 251	24.77 333	87.66 269	45.52 303	69.47 290	79.95 315

LF4IMVS		64.02 294	62.19 294	69.50 304	70.90 331	53.29 299	76.13 290	77.18 305	52.65 320	58.59 311	80.98 281	23.55 334	76.52 318	53.06 265	66.66 302	78.68 318

1111		57.11 305	56.82 306	57.97 323	69.10 333	28.28 345	68.90 321	74.54 321	54.01 313	53.71 326	74.51 314	23.09 335	67.90 340	32.28 331	61.26 320	77.73 320

.test1245		45.55 315	50.02 312	32.14 335	69.10 333	28.28 345	68.90 321	74.54 321	54.01 313	53.71 326	74.51 314	23.09 335	67.90 340	32.28 331	0.02 350	0.25 350

new_pmnet		50.91 312	50.29 311	52.78 327	68.58 335	34.94 343	63.71 331	56.63 345	39.73 334	44.95 333	65.47 330	21.93 337	58.48 344	34.98 327	56.62 327	64.92 335

pmmvs3		57.79 303	54.26 307	68.37 309	64.02 338	56.72 268	75.12 300	65.17 341	40.20 333	52.93 328	69.86 326	20.36 338	75.48 323	45.45 304	55.25 330	72.90 330

PM-MVS		66.41 286	64.14 286	73.20 288	73.92 321	56.45 272	78.97 276	64.96 343	63.88 250	64.72 292	80.24 286	19.84 339	83.44 292	66.24 172	64.52 313	79.71 316

no-one		51.08 311	45.79 316	66.95 312	57.92 343	50.49 318	59.63 336	76.04 309	48.04 327	31.85 338	56.10 338	19.12 340	80.08 305	36.89 325	26.52 340	70.29 332

testus		59.00 301	57.91 300	62.25 318	72.25 328	39.09 337	69.74 313	75.02 313	53.04 319	57.21 318	73.72 317	18.76 341	70.33 336	32.86 329	68.57 296	77.35 322

ambc				75.24 275	73.16 325	50.51 317	63.05 333	87.47 202		64.28 294	77.81 303	17.80 342	89.73 232	57.88 241	60.64 321	85.49 275

ANet_high		50.57 313	46.10 315	63.99 314	48.67 348	39.13 336	70.99 311	80.85 273	61.39 270	31.18 340	57.70 336	17.02 343	73.65 330	31.22 335	15.89 347	79.18 317

FPMVS		53.68 309	51.64 309	59.81 321	65.08 337	51.03 313	69.48 316	69.58 333	41.46 332	40.67 335	72.32 320	16.46 344	70.00 337	24.24 341	65.42 310	58.40 338

testmv		53.85 308	51.03 310	62.31 317	61.46 340	38.88 338	70.95 312	74.69 319	51.11 325	41.26 334	66.85 328	14.28 345	72.13 333	29.19 336	49.51 335	75.93 328

test12356		49.28 314	48.51 314	51.59 328	62.06 339	19.11 351	60.40 334	72.45 326	47.60 329	40.64 336	65.68 329	13.84 346	68.72 338	27.29 338	46.67 337	66.94 334

EMVS		30.81 323	29.65 324	34.27 334	50.96 347	25.95 349	56.58 340	46.80 350	24.01 344	15.53 348	30.68 345	12.47 347	54.43 347	12.81 347	17.05 346	22.43 346

E-PMN		31.77 322	30.64 323	35.15 333	52.87 346	27.67 347	57.09 339	47.86 349	24.64 342	16.40 347	33.05 344	11.23 348	54.90 346	14.46 346	18.15 344	22.87 345

DeepMVS_CX				27.40 337	40.17 352	26.90 348		24.59 354	17.44 346	23.95 343	48.61 339	9.77 349	26.48 349	18.06 343	24.47 341	28.83 344

Gipuma		45.18 316	41.86 317	55.16 325	77.03 314	51.52 310	32.50 345	80.52 276	32.46 339	27.12 341	35.02 342	9.52 350	75.50 322	22.31 342	60.21 323	38.45 343
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 307	49.68 313	67.97 310	53.73 345	45.28 326	66.85 328	80.78 274	35.96 337	39.45 337	62.23 333	8.70 351	78.06 313	48.24 286	51.20 334	80.57 313

PMMVS2		40.82 318	38.86 319	46.69 331	53.84 344	16.45 352	48.61 341	49.92 348	37.49 336	31.67 339	60.97 334	8.14 352	56.42 345	28.42 337	30.72 339	67.19 333

PMVS	37.38 22	44.16 317	40.28 318	55.82 324	40.82 351	42.54 331	65.12 330	63.99 344	34.43 338	24.48 342	57.12 337	3.92 353	76.17 320	17.10 344	55.52 329	48.75 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 324	25.89 326	43.81 332	44.55 350	35.46 342	28.87 346	39.07 351	18.20 345	18.58 346	40.18 341	2.68 354	47.37 348	17.07 345	23.78 342	48.60 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		38.26 320	35.42 320	46.79 330	58.74 341	35.48 341	59.65 335	51.25 347	32.45 340	23.44 345	47.53 340	2.04 355	58.96 343	25.60 340	18.09 345	45.92 342

wuyk23d		16.82 327	15.94 328	19.46 338	58.74 341	31.45 344	39.22 343	3.74 356	6.84 347	6.04 350	2.70 350	1.27 356	24.29 350	10.54 348	14.40 349	2.63 348

wuykxyi23d		39.76 319	33.18 322	59.51 322	46.98 349	44.01 328	57.70 338	67.74 338	24.13 343	13.98 349	34.33 343	1.27 356	71.33 334	34.23 328	18.23 343	63.18 337

test123		6.12 329	8.11 330	0.14 340	0.06 355	0.09 355	71.05 309	0.03 358	0.04 350	0.25 352	1.30 352	0.05 358	0.03 353	0.21 350	0.01 352	0.29 349

testmvs		6.04 330	8.02 331	0.10 341	0.08 354	0.03 356	69.74 313	0.04 357	0.05 349	0.31 351	1.68 351	0.02 359	0.04 352	0.24 349	0.02 350	0.25 350

sosnet-low-res		0.00 332	0.00 333	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	0.00 353	0.00 360	0.00 354	0.00 351	0.00 353	0.00 352

sosnet		0.00 332	0.00 333	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	0.00 353	0.00 360	0.00 354	0.00 351	0.00 353	0.00 352

uncertanet		0.00 332	0.00 333	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	0.00 353	0.00 360	0.00 354	0.00 351	0.00 353	0.00 352

Regformer		0.00 332	0.00 333	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	0.00 353	0.00 360	0.00 354	0.00 351	0.00 353	0.00 352

ab-mvs-re		7.23 328	9.64 329	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	86.72 176	0.00 360	0.00 354	0.00 351	0.00 353	0.00 352

uanet		0.00 332	0.00 333	0.00 342	0.00 356	0.00 357	0.00 347	0.00 359	0.00 351	0.00 353	0.00 353	0.00 360	0.00 354	0.00 351	0.00 353	0.00 352

test_part2					95.06 1	72.65 26				91.80 1

ESAPD								94.22 1

MTGPA								92.02 60

MTMP								32.83 352

gm-plane-assit					81.40 278	53.83 296			62.72 260		80.94 282		92.39 167	63.40 193

test9_res														84.90 17	95.70 13	92.87 69

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

test_prior4						72.60 28	89.01 75

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		58.10 291	87.04 14			88.98 252	74.07 109

新几何2							86.29 170

无先验							87.48 125	88.98 166	60.00 279				94.12 93	67.28 165		88.97 196

原ACMM2							86.86 150

testdata2													91.01 216	62.37 201

testdata1							84.14 226		75.71 71

plane_prior7					90.08 79	68.51 98

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								0.00 359

nn								0.00 359

door-mid								69.98 331

test11								92.23 52

door								69.44 334

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

NP-MVS					89.62 90	68.32 100					90.24 90

ACMMP++_ref															81.95 169

ACMMP++															81.25 176

High-resolution multi-view benchmark