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	68.29 205	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	86.77 179	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	79.28 202	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	68.32 204	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	76.46 235	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	86.86 176	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	76.49 234	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	88.74 201

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	84.45 207	57.97 249	82.59 243	87.62 198	67.40 215	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	85.06 229	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	12.47 345	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	87.80 154	63.34 199	86.31 168	91.09 97	79.01 26	72.17 213	89.07 115	67.20 67	92.81 158	66.08 176	75.65 244	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	84.28 208	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	85.89 186	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	76.86 226	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	73.55 268	90.06 157

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 159	68.99 83	83.65 232	91.46 88	63.00 252	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	84.22 211	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	63.96 247	81.09 79	91.57 66	66.06 76	95.45 45	67.19 167	94.82 30	88.81 198

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	87.39 236

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 117	65.40 147	84.43 218	92.00 63	67.62 210	78.11 118	85.05 230	66.02 77	94.27 85	71.52 138	89.50 80	89.01 192

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	87.51 234

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	75.24 253	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	87.80 154	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	76.46 235	92.20 86

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 248	85.79 187	56.21 278	86.78 154	85.76 221	73.60 109	77.93 122	87.57 152	65.02 84	88.99 249	67.14 168	75.33 250	87.63 231

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	83.31 243	62.66 212	84.17 224	88.11 187	67.64 208	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	85.66 189	66.99 122	84.66 208	90.47 111	55.08 307	72.02 218	85.27 225	63.83 93	94.11 95	66.10 175	89.80 78	84.24 286

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	77.22 218	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	57.37 297	81.76 72	92.02 55	63.41 95	94.12 93	67.28 165	92.93 49	87.26 241

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	79.22 203	91.23 109

新几何1				83.42 112	93.13 36	70.71 55		85.48 222	57.43 296	81.80 71	91.98 56	63.28 97	92.27 171	64.60 188	92.99 48	87.27 240

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 165	60.21 231	83.37 240	87.78 196	66.11 224	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	74.96 274	83.59 237	57.82 253	80.59 260	83.87 236	66.54 221	74.93 186	88.31 134	63.24 99	80.09 302	62.16 203	76.85 227	86.97 248

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	88.57 212

pcd_1.5k_mvsjas		5.26 329	7.02 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	63.15 102	0.00 352	0.00 349	0.00 351	0.00 349

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 181	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	85.62 218	63.15 102	94.29 83	75.62 96	88.87 86	88.59 210

WTY-MVS		75.65 215	75.68 191	75.57 270	86.40 182	56.82 266	77.92 283	82.40 255	65.10 234	76.18 156	87.72 147	63.13 105	80.90 298	60.31 219	81.96 168	89.00 194

TransMVSNet (Re)		75.39 219	74.56 210	77.86 243	85.50 193	57.10 262	86.78 154	86.09 219	72.17 143	71.53 223	87.34 158	63.01 106	89.31 240	56.84 249	61.83 315	87.17 243

v8		79.97 128	79.02 127	82.80 146	84.09 221	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	75.40 249	90.00 159

v18		77.67 177	76.35 179	81.64 176	84.09 221	64.47 178	87.27 133	89.01 162	72.59 135	69.39 250	82.04 263	62.85 108	91.80 182	72.72 123	67.20 297	88.63 204

v6		80.40 113	79.54 108	82.98 132	84.09 221	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	77.02 221	90.58 133

v1neww		80.40 113	79.54 108	82.98 132	84.10 219	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	77.02 221	90.58 133

v7new		80.40 113	79.54 108	82.98 132	84.10 219	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	77.02 221	90.58 133

v17		77.68 175	76.35 179	81.69 173	84.15 216	64.65 167	87.33 130	88.99 164	72.70 133	69.25 254	82.07 262	62.82 112	91.79 183	72.69 125	67.15 298	88.63 204

v16		77.69 174	76.36 178	81.68 174	84.15 216	64.63 169	87.33 130	88.99 164	72.69 134	69.31 253	82.08 261	62.80 113	91.79 183	72.70 124	67.23 296	88.63 204

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	84.09 221	64.65 167	87.14 137	88.96 168	72.76 131	68.90 255	81.91 270	62.74 115	91.73 187	72.32 129	66.29 303	88.61 207

v1		80.19 122	79.31 118	82.85 141	83.83 233	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	76.82 230	90.68 124

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 231	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	76.82 230	90.68 124

V14		77.52 180	76.12 182	81.70 172	84.15 216	64.77 165	87.21 136	88.95 169	72.80 130	68.79 256	81.94 269	62.69 117	91.72 189	72.31 130	66.27 304	88.60 208

v1141		80.19 122	79.31 118	82.85 141	83.84 231	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	76.83 229	90.68 124

V9		77.52 180	76.11 185	81.73 171	84.19 215	64.89 162	87.26 134	88.94 172	72.87 129	68.65 259	81.96 268	62.65 120	91.72 189	72.27 131	66.24 305	88.60 208

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	87.29 171	61.85 221	83.78 231	89.59 142	64.74 237	71.23 225	88.70 121	62.59 122	93.66 122	52.66 266	87.03 112	89.01 192

1112_ss		77.40 188	76.43 173	80.32 200	89.11 114	60.41 230	83.65 232	87.72 197	62.13 263	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	84.22 211	64.99 159	87.30 132	88.93 173	72.92 126	68.48 263	81.97 266	62.54 124	91.70 192	72.24 132	66.21 307	88.58 211

v13		77.50 184	76.07 187	81.77 168	84.23 210	65.07 158	87.34 129	88.91 174	72.92 126	68.35 264	81.97 266	62.53 125	91.69 193	72.20 133	66.22 306	88.56 213

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 251	87.20 172	51.60 307	80.06 263	80.46 278	75.20 82	67.69 268	86.72 176	62.48 126	88.98 250	63.44 192	89.25 83	91.51 102

v148		78.72 151	77.80 152	81.47 181	82.73 259	61.96 220	86.30 169	88.08 191	73.26 116	76.18 156	85.47 222	62.46 127	92.36 169	71.92 136	73.82 266	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	89.77 174
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
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	87.80 154	67.79 111	84.72 207	85.05 227	67.63 209	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	87.83 152	54.54 291	87.94 111	91.17 95	77.65 33	73.48 194	88.49 129	62.24 131	88.43 258	62.19 202	74.07 261	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	84.22 211	64.52 170	87.11 142	89.02 160	72.76 131	68.76 257	81.90 271	62.09 133	91.71 191	71.98 134	66.73 299	88.56 213

testdata				79.97 206	90.90 67	64.21 182		84.71 228	59.27 283	85.40 23	92.91 44	62.02 134	89.08 247	68.95 154	91.37 62	86.63 256

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 166	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	87.35 166	70.21 60	85.55 192	86.41 212	62.85 255	81.32 74	88.61 125	61.68 135	92.24 173	78.41 67	90.26 72	91.83 95

cdsmvs_eth3d_5k		19.96 323	26.61 323	0.00 340	0.00 354	0.00 355	0.00 345	89.26 154	0.00 349	0.00 351	88.61 125	61.62 137	0.00 352	0.00 349	0.00 351	0.00 349

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 161	68.23 103	84.40 220	86.20 216	67.49 213	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	84.06 226	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	75.55 246	89.90 167

v7		80.24 119	79.26 122	83.15 122	84.07 225	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	77.41 215	90.49 138

v1144		80.03 126	79.03 126	83.01 130	83.78 234	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	77.23 217	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	84.74 236	61.34 142	93.11 145	58.24 238	85.84 126	84.27 285

Test_1112_low_res		76.40 202	75.44 195	79.27 219	89.28 106	58.09 245	81.69 251	87.07 206	59.53 281	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	51.78 306	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	87.07 174	53.91 295	87.91 113	91.18 94	77.56 37	73.14 198	88.82 120	61.23 145	89.17 245	59.95 221	72.37 274	90.43 141

pm-mvs1		77.25 189	76.68 170	78.93 228	84.22 211	58.62 240	86.41 165	88.36 185	71.37 153	73.31 195	88.01 141	61.22 146	89.15 246	64.24 189	73.01 270	89.03 191

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

v2v482		80.23 120	79.29 121	83.05 128	83.62 236	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	76.78 232	91.18 110

jason		81.39 91	80.29 95	84.70 76	86.63 180	69.90 67	85.95 176	86.77 208	63.24 249	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	65.77 228	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	87.71 159	54.39 293	88.02 107	91.22 92	77.50 40	73.26 196	88.64 124	60.73 152	88.41 259	61.88 206	73.88 265	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	85.60 191	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	83.35 241	63.96 189	86.96 146	90.36 116	69.99 171	77.50 128	85.67 215	60.66 155	93.77 114	74.27 107	76.58 233	90.62 128

V42		79.38 141	78.24 145	82.83 144	81.10 282	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	75.46 248	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	86.24 183	53.06 303	87.52 124	90.66 105	77.08 46	72.50 204	88.67 123	60.48 158	89.52 235	57.33 246	70.74 285	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	77.05 220	90.88 117

v1192		79.59 134	78.43 140	83.07 127	83.55 238	64.52 170	86.93 148	90.58 107	70.83 158	77.78 124	85.90 209	59.15 165	93.94 100	73.96 110	77.19 219	90.76 120

test222					91.50 60	68.26 102	84.16 225	83.20 248	54.63 308	79.74 86	91.63 64	58.97 166			91.42 61	86.77 252

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 81	68.58 97	78.70 277	87.50 201	56.38 302	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	83.30 244	63.94 190	86.80 152	90.33 118	69.91 172	77.48 129	85.53 220	58.44 169	93.75 116	73.60 114	76.85 227	90.71 123

v748		77.97 167	76.65 171	81.92 166	82.29 266	63.28 201	87.53 123	90.35 117	73.50 113	70.76 229	85.55 219	58.28 170	92.81 158	68.81 156	72.76 273	89.67 176

114514_t		80.68 106	79.51 111	84.20 89	94.09 22	67.27 119	89.64 61	91.11 96	58.75 287	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	83.45 240	65.51 145	88.32 99	91.21 93	73.69 107	72.41 210	86.32 200	57.93 172	93.81 110	69.18 153	75.65 244	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	66.63 220	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	80.36 261	88.64 181	56.29 303	76.45 146	85.17 226	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	66.03 227	72.38 211	89.64 102	57.56 175	86.04 276	59.61 224	83.35 153	88.79 199

sss		73.60 231	73.64 219	73.51 285	82.80 257	55.01 288	76.12 289	81.69 267	62.47 260	74.68 188	85.85 212	57.32 176	78.11 310	60.86 216	80.93 178	87.39 236

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 198	68.74 90	88.77 82	88.10 189	74.99 84	74.97 185	83.49 248	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	85.13 198	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	82.89 256	65.32 151	86.12 172	89.55 143	69.64 178	70.55 230	85.82 213	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	87.50 235

test_normal		79.81 130	78.45 137	83.89 103	82.70 260	65.40 147	85.82 182	89.48 146	69.39 179	70.12 239	85.66 216	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	83.21 245	63.54 193	86.62 159	90.30 120	69.74 177	77.33 131	85.68 214	57.04 182	93.76 115	73.13 120	76.92 224	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	83.98 228	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		34.07 319	35.26 319	30.50 334	86.92 175	0.00 355	0.00 345	91.58 82	0.00 349	0.00 351	0.00 351	56.23 185	0.00 352	0.00 349	82.60 163	91.49 104

EPNet_dtu		75.46 217	74.86 206	77.23 254	82.57 263	54.60 290	86.89 149	83.09 249	71.64 147	66.25 282	85.86 211	55.99 186	88.04 263	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
V4		77.95 168	76.37 175	82.67 151	79.40 299	65.52 143	86.43 163	89.94 134	72.28 139	72.14 216	84.95 231	55.72 187	93.44 130	73.64 112	72.86 271	89.05 189

v52		77.94 170	76.37 175	82.67 151	79.39 300	65.52 143	86.43 163	89.94 134	72.28 139	72.15 215	84.94 232	55.70 188	93.44 130	73.64 112	72.84 272	89.06 185

CostFormer		75.24 220	73.90 218	79.27 219	82.65 262	58.27 244	80.80 256	82.73 253	61.57 266	75.33 177	83.13 250	55.52 189	91.07 215	64.98 185	78.34 209	88.45 216

tpmrst		72.39 248	72.13 235	73.18 287	80.54 287	49.91 317	79.91 266	79.08 290	63.11 250	71.69 221	79.95 286	55.32 190	82.77 293	65.66 180	73.89 264	86.87 249

1314		76.53 197	75.30 200	80.21 203	83.93 229	62.32 216	84.66 208	88.81 175	60.23 275	70.16 238	84.07 241	55.30 191	90.73 220	67.37 164	83.21 155	87.59 233

tfpnnormal		74.39 223	73.16 223	78.08 241	86.10 185	58.05 246	84.65 211	87.53 200	70.32 167	71.22 226	85.63 217	54.97 192	89.86 229	43.03 313	75.02 254	86.32 261

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 161	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	79.57 197	90.09 153

test1		78.40 155	77.40 159	81.40 183	87.60 161	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	79.57 197	90.09 153

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 161	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	79.38 200	89.61 177

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 249	66.96 125	86.94 147	87.45 203	72.45 136	71.49 224	84.17 239	54.79 196	91.58 200	67.61 161	80.31 189	89.30 181

MVSTER		79.01 147	77.88 150	82.38 157	83.07 250	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	85.17 195	69.91 66	90.57 38	90.97 98	66.70 216	72.17 213	91.91 57	54.70 197	93.96 98	61.81 208	90.95 66	88.41 218

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 190	69.02 81	83.03 242	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	67.55 212	77.81 123	86.48 194	54.10 202	93.15 142	57.75 242	82.72 162	87.20 242

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 177	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	79.57 197	89.45 179

DP-MVS		76.78 194	74.57 209	83.42 112	93.29 32	69.46 77	88.55 91	83.70 237	63.98 246	70.20 235	88.89 118	54.01 204	94.80 73	46.66 296	81.88 170	86.01 269

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	64.33 242	69.87 245	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
CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 191	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
MVP-Stereo		76.12 209	74.46 213	81.13 189	85.37 194	69.79 68	84.42 219	87.95 193	65.03 235	67.46 270	85.33 224	53.28 209	91.73 187	58.01 240	83.27 154	81.85 305
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	80.51 288	67.08 121	87.24 135	89.53 144	65.66 230	75.16 180	87.19 165	52.52 210	92.25 172	77.17 79	79.34 201	89.61 177

CR-MVSNet		73.37 238	71.27 244	79.67 212	81.32 280	65.19 155	75.92 291	80.30 280	59.92 278	72.73 202	81.19 274	52.50 211	86.69 270	59.84 222	77.71 211	87.11 246

Patchmtry		70.74 258	69.16 257	75.49 271	80.72 284	54.07 294	74.94 300	80.30 280	58.34 288	70.01 240	81.19 274	52.50 211	86.54 272	53.37 263	71.09 283	85.87 271

pmmvs4		74.03 227	71.91 236	80.39 197	81.96 269	68.32 100	81.45 254	82.14 259	59.32 282	69.87 245	85.13 227	52.40 213	88.13 262	60.21 220	74.74 257	84.73 283

PatchFormer-LS_test		74.50 222	73.05 224	78.86 229	82.95 254	59.55 236	81.65 252	82.30 257	67.44 214	71.62 222	78.15 297	52.34 214	88.92 254	65.05 184	75.90 241	88.12 221

RPMNet		71.62 252	68.94 259	79.67 212	81.32 280	65.19 155	75.92 291	78.30 297	57.60 295	72.73 202	76.45 307	52.30 215	86.69 270	48.14 287	77.71 211	87.11 246

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	89.07 183

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

thres200		75.55 216	74.47 212	78.82 230	87.78 157	57.85 252	83.07 241	83.51 241	72.44 138	75.84 161	84.42 238	52.08 220	91.75 186	47.41 290	83.64 147	86.86 250

PMMVS		69.34 269	68.67 260	71.35 295	75.67 315	62.03 219	75.17 295	73.46 321	50.00 324	68.68 258	79.05 291	52.07 221	78.13 309	61.16 214	82.77 160	73.90 326

tpm cat1		70.57 260	68.31 263	77.35 252	82.41 265	57.95 250	78.08 282	80.22 283	52.04 319	68.54 262	77.66 302	52.00 222	87.84 265	51.77 267	72.07 278	86.25 262

Patchmatch-test1		73.49 232	71.85 238	78.41 238	84.05 227	62.17 218	79.96 265	79.29 289	66.30 223	72.38 211	79.58 290	51.95 223	85.08 283	55.46 254	77.67 213	87.99 223

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

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		73.26 241	71.46 241	78.63 232	83.34 242	56.71 269	80.65 259	80.40 279	56.63 301	73.55 193	82.02 264	51.80 231	91.24 206	56.35 251	78.42 208	87.95 224

LS3D		76.95 192	74.82 207	83.37 115	90.45 71	67.36 118	89.15 72	86.94 207	61.87 265	69.52 248	90.61 86	51.71 232	94.53 78	46.38 299	86.71 115	88.21 220

IterMVS		74.29 224	72.94 225	78.35 239	81.53 274	63.49 195	81.58 253	82.49 254	68.06 206	69.99 242	83.69 246	51.66 233	85.54 279	65.85 178	71.64 280	86.01 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 250	71.71 240	74.35 280	82.19 267	52.00 304	79.22 272	77.29 302	64.56 239	72.95 200	83.68 247	51.35 234	83.26 292	58.33 237	75.80 242	87.81 228

sam_mvs1												51.32 235

PatchmatchNet		73.12 243	71.33 243	78.49 237	83.18 247	60.85 226	79.63 267	78.57 295	64.13 243	71.73 220	79.81 289	51.20 236	85.97 277	57.40 245	76.36 237	88.66 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		73.45 233	71.17 246	80.31 201	83.55 238	59.56 235	81.88 247	82.33 256	57.94 292	70.51 232	81.62 272	51.19 237	91.63 198	53.96 260	77.51 214	89.75 175

patchmatchnet-post											74.00 314	51.12 238	88.60 257

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 166	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	87.35 166	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	87.35 166	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

Patchmatch-test		64.82 289	63.24 287	69.57 301	79.42 298	49.82 318	63.49 330	69.05 334	51.98 320	59.95 307	80.13 285	50.91 239	70.98 333	40.66 318	73.57 267	87.90 226

semantic-postprocess				80.11 204	82.69 261	64.85 163		83.47 242	69.16 186	70.49 233	84.15 240	50.83 243	88.15 261	69.23 152	72.14 277	87.34 238

Patchmatch-RL test		70.24 264	67.78 273	77.61 248	77.43 307	59.57 234	71.16 306	70.33 327	62.94 254	68.65 259	72.77 317	50.62 244	85.49 280	69.58 150	66.58 301	87.77 229

tfpn_n400		73.39 235	72.42 231	76.31 260	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	86.48 257

tfpnconf		73.39 235	72.42 231	76.31 260	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	86.48 257

tfpnview11		73.39 235	72.42 231	76.31 260	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	86.48 257

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	50.54 248	93.49 127	73.40 117	88.25 98	94.54 10

pmmvs6		74.69 221	73.39 220	78.61 233	81.38 277	57.48 258	86.64 158	87.95 193	64.99 236	70.18 236	86.61 187	50.43 249	89.52 235	62.12 204	70.18 287	88.83 197

test_post											5.46 346	50.36 250	84.24 286

tfpn_ndepth		73.70 229	72.75 226	76.52 258	87.78 157	54.92 289	84.32 222	80.28 282	67.57 211	72.50 204	84.82 233	50.12 251	89.44 238	45.73 302	81.66 172	85.20 275

sam_mvs												50.01 252

tfpn1000		73.44 234	72.49 229	76.29 263	87.81 153	53.69 297	84.05 229	78.81 294	67.99 207	72.09 217	86.27 201	49.95 253	89.04 248	44.09 310	81.38 174	86.15 264

PatchT		68.46 274	67.85 270	70.29 299	80.70 285	43.93 327	72.47 304	74.88 312	60.15 276	70.55 230	76.57 306	49.94 254	81.59 296	50.58 271	74.83 256	85.34 274

tpmvs		71.09 256	69.29 256	76.49 259	82.04 268	56.04 279	78.92 275	81.37 271	64.05 244	67.18 274	78.28 296	49.74 255	89.77 230	49.67 278	72.37 274	83.67 290

CVMVSNet		72.99 245	72.58 228	74.25 281	84.28 208	50.85 313	86.41 165	83.45 243	44.56 328	73.23 197	87.54 154	49.38 256	85.70 278	65.90 177	78.44 207	86.19 263

MDTV_nov1_ep13_2view						37.79 337	75.16 296		55.10 306	66.53 279		49.34 257		53.98 259		87.94 225

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	49.30 258	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		71.55 253	70.20 253	75.61 269	77.83 305	56.39 274	81.74 250	80.89 272	57.76 293	67.46 270	84.49 237	49.26 259	85.32 282	57.08 248	75.29 251	85.11 279

LTVRE_ROB	69.57 13	76.25 204	74.54 211	81.41 182	88.60 128	64.38 181	79.24 271	89.12 158	70.76 161	69.79 247	87.86 142	49.09 260	93.20 139	56.21 252	80.16 190	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
FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 178	63.01 207	88.39 96	89.28 151	70.49 165	74.39 190	87.28 159	49.06 261	91.11 209	60.91 215	78.52 205	90.09 153

MDTV_nov1_ep13			69.97 254		83.18 247	53.48 298	77.10 287	80.18 284	60.45 272	69.33 252	80.44 282	48.89 262	86.90 269	51.60 268	78.51 206

test_post1							78.90 276				5.43 347	48.81 263	85.44 281	59.25 228

test-LLR		72.94 246	72.43 230	74.48 278	81.35 278	58.04 247	78.38 278	77.46 300	66.66 217	69.95 243	79.00 293	48.06 264	79.24 304	66.13 173	84.83 130	86.15 264

test0.0.03 1		68.00 275	67.69 274	68.90 304	77.55 306	47.43 321	75.70 294	72.95 323	66.66 217	66.56 278	82.29 258	48.06 264	75.87 319	44.97 306	74.51 259	83.41 292

MS-PatchMatch		73.83 228	72.67 227	77.30 253	83.87 230	66.02 135	81.82 248	84.66 229	61.37 269	68.61 261	82.82 253	47.29 266	88.21 260	59.27 227	84.32 137	77.68 318

cascas		76.72 195	74.64 208	82.99 131	85.78 188	65.88 139	82.33 245	89.21 156	60.85 271	72.74 201	81.02 278	47.28 267	93.75 116	67.48 163	85.02 128	89.34 180

test20.03		67.45 277	66.95 276	68.94 303	75.48 318	44.84 325	77.50 284	77.67 299	66.66 217	63.01 298	83.80 243	47.02 268	78.40 308	42.53 315	68.86 293	83.58 291

test_0402		72.79 247	70.44 250	79.84 208	88.13 141	65.99 136	85.93 177	84.29 232	65.57 231	67.40 272	85.49 221	46.92 269	92.61 161	35.88 324	74.38 260	80.94 308

F-COLMAP		76.38 203	74.33 214	82.50 155	89.28 106	66.95 126	88.41 95	89.03 159	64.05 244	66.83 276	88.61 125	46.78 270	92.89 153	57.48 243	78.55 204	87.67 230

Anonymous20231206		68.60 271	67.80 272	71.02 297	80.23 291	50.75 314	78.30 281	80.47 277	56.79 300	66.11 283	82.63 255	46.35 271	78.95 306	43.62 312	75.70 243	83.36 293

CHOSEN 280x420		66.51 283	64.71 282	71.90 290	81.45 275	63.52 194	57.98 335	68.95 335	53.57 314	62.59 300	76.70 305	46.22 272	75.29 322	55.25 255	79.68 193	76.88 324

GA-MVS		76.87 193	75.17 205	81.97 164	82.75 258	62.58 213	81.44 255	86.35 215	72.16 144	74.74 187	82.89 251	46.20 273	92.02 176	68.85 155	81.09 177	91.30 108

MDA-MVSNet_test_wron		65.03 287	62.92 288	71.37 293	75.93 313	56.73 267	69.09 318	74.73 315	57.28 298	54.03 323	77.89 299	45.88 274	74.39 325	49.89 277	61.55 316	82.99 299

YYNet1		65.03 287	62.91 289	71.38 292	75.85 314	56.60 271	69.12 317	74.66 318	57.28 298	54.12 322	77.87 300	45.85 275	74.48 324	49.95 276	61.52 317	83.05 297

EPMVS		69.02 270	68.16 265	71.59 291	79.61 296	49.80 319	77.40 285	66.93 337	62.82 256	70.01 240	79.05 291	45.79 276	77.86 312	56.58 250	75.26 252	87.13 245

IB-MVS	68.01 15	75.85 213	73.36 221	83.31 116	84.76 202	66.03 134	83.38 236	85.06 226	70.21 170	69.40 249	81.05 277	45.76 277	94.66 76	65.10 183	75.49 247	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
jajsoiax		79.29 142	77.96 148	83.27 118	84.68 204	66.57 129	89.25 67	90.16 126	69.20 185	75.46 169	89.49 105	45.75 278	93.13 144	76.84 84	80.80 181	90.11 151

PatchMatch-RL		72.38 249	70.90 248	76.80 257	88.60 128	67.38 117	79.53 268	76.17 306	62.75 257	69.36 251	82.00 265	45.51 279	84.89 284	53.62 262	80.58 184	78.12 316

RPSCF		73.23 242	71.46 241	78.54 235	82.50 264	59.85 232	82.18 246	82.84 252	58.96 284	71.15 227	89.41 112	45.48 280	84.77 285	58.82 232	71.83 279	91.02 114

MSDG		73.36 240	70.99 247	80.49 196	84.51 206	65.80 140	80.71 258	86.13 218	65.70 229	65.46 285	83.74 245	44.60 281	90.91 217	51.13 270	76.89 225	84.74 282

PVSNet_0	57.27 20	61.67 295	59.27 296	68.85 305	79.61 296	57.44 259	68.01 321	73.44 322	55.93 304	58.54 310	70.41 322	44.58 282	77.55 313	47.01 291	35.91 336	71.55 328

DWT-MVSNet_test		73.70 229	71.86 237	79.21 221	82.91 255	58.94 238	82.34 244	82.17 258	65.21 232	71.05 228	78.31 295	44.21 283	90.17 227	63.29 194	77.28 216	88.53 215

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 203	66.37 131	89.17 68	90.19 125	69.38 181	75.40 172	89.46 108	44.17 284	93.15 142	76.78 85	80.70 183	90.14 149

MDA-MVSNet-bldmvs		66.68 281	63.66 285	75.75 267	79.28 301	60.56 229	73.92 302	78.35 296	64.43 240	50.13 330	79.87 288	44.02 285	83.67 288	46.10 300	56.86 324	83.03 298

gg-mvs-nofinetune		69.95 266	67.96 268	75.94 266	83.07 250	54.51 292	77.23 286	70.29 328	63.11 250	70.32 234	62.33 329	43.62 286	88.69 256	53.88 261	87.76 101	84.62 284

GG-mvs-BLEND				75.38 272	81.59 273	55.80 285	79.32 270	69.63 330		67.19 273	73.67 316	43.24 287	88.90 255	50.41 272	84.50 134	81.45 307

CMPMVS	51.72 21	70.19 265	68.16 265	76.28 264	73.15 324	57.55 257	79.47 269	83.92 235	48.02 326	56.48 319	84.81 234	43.13 288	86.42 274	62.67 199	81.81 171	84.89 280
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Test4		77.83 172	75.90 189	83.62 106	80.24 290	65.25 153	85.27 198	90.67 103	69.03 191	66.48 280	83.75 244	43.07 289	93.00 151	75.93 90	88.66 90	92.62 74

dp		66.80 280	65.43 280	70.90 298	79.74 295	48.82 320	75.12 298	74.77 314	59.61 280	64.08 294	77.23 303	42.89 290	80.72 299	48.86 280	66.58 301	83.16 295

PVSNet	64.34 18	72.08 251	70.87 249	75.69 268	86.21 184	56.44 273	74.37 301	80.73 275	62.06 264	70.17 237	82.23 259	42.86 291	83.31 291	54.77 257	84.45 136	87.32 239

pmmvs-eth3d		70.50 262	67.83 271	78.52 236	77.37 308	66.18 133	81.82 248	81.51 269	58.90 285	63.90 295	80.42 283	42.69 292	86.28 275	58.56 234	65.30 309	83.11 296

UnsupCasMVSNet_eth		67.33 278	65.99 279	71.37 293	73.48 321	51.47 309	75.16 296	85.19 225	65.20 233	60.78 303	80.93 281	42.35 293	77.20 314	57.12 247	53.69 329	85.44 273

ADS-MVSNet2		66.20 286	63.33 286	74.82 276	79.92 293	58.75 239	67.55 323	75.19 310	53.37 315	65.25 287	75.86 308	42.32 294	80.53 300	41.57 316	68.91 291	85.18 276

ADS-MVSNet		64.36 291	62.88 290	68.78 306	79.92 293	47.17 322	67.55 323	71.18 326	53.37 315	65.25 287	75.86 308	42.32 294	73.99 327	41.57 316	68.91 291	85.18 276

SixPastTwentyTwo		73.37 238	71.26 245	79.70 210	85.08 200	57.89 251	85.57 188	83.56 240	71.03 157	65.66 284	85.88 210	42.10 296	92.57 162	59.11 229	63.34 312	88.65 203

JIA-IIPM		66.32 285	62.82 291	76.82 256	77.09 311	61.72 222	65.34 327	75.38 308	58.04 291	64.51 291	62.32 330	42.05 297	86.51 273	51.45 269	69.22 290	82.21 303

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	66.78 277	86.70 181	41.95 298	91.51 201	55.64 253	78.14 210	87.17 243
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	67.75 267	87.47 156	41.27 299	93.19 140	58.37 236	75.94 240	87.60 232

MIMVSNet		70.69 259	69.30 255	74.88 275	84.52 205	56.35 276	75.87 293	79.42 288	64.59 238	67.76 266	82.41 256	41.10 300	81.54 297	46.64 298	81.34 175	86.75 253

N_pmnet		52.79 308	53.26 306	51.40 327	78.99 303	7.68 352	69.52 313	3.89 353	51.63 322	57.01 317	74.98 311	40.83 301	65.96 340	37.78 322	64.67 310	80.56 311

testing_2		75.73 214	73.34 222	82.89 140	77.37 308	65.22 154	84.10 227	90.54 109	69.09 187	60.46 304	81.15 276	40.48 302	92.84 157	76.36 86	80.54 187	90.60 130

EU-MVSNet		68.53 273	67.61 275	71.31 296	78.51 304	47.01 323	84.47 214	84.27 233	42.27 329	66.44 281	84.79 235	40.44 303	83.76 287	58.76 233	68.54 295	83.17 294

DSMNet-mixed		57.77 302	56.90 302	60.38 318	67.70 334	35.61 338	69.18 315	53.97 344	32.30 339	57.49 315	79.88 287	40.39 304	68.57 337	38.78 320	72.37 274	76.97 321

OurMVSNet-221017-0		74.26 225	72.42 231	79.80 209	83.76 235	59.59 233	85.92 178	86.64 209	66.39 222	66.96 275	87.58 151	39.46 305	91.60 199	65.76 179	69.27 289	88.22 219

K. test v3		71.19 255	68.51 261	79.21 221	83.04 252	57.78 254	84.35 221	76.91 304	72.90 128	62.99 299	82.86 252	39.27 306	91.09 214	61.65 209	52.66 330	88.75 200

lessismore_v0				78.97 227	81.01 283	57.15 261		65.99 338		61.16 302	82.82 253	39.12 307	91.34 204	59.67 223	46.92 334	88.43 217

UnsupCasMVSNet_bld		63.70 293	61.53 295	70.21 300	73.69 320	51.39 310	72.82 303	81.89 265	55.63 305	57.81 313	71.80 319	38.67 308	78.61 307	49.26 279	52.21 331	80.63 309

new-patchmatchnet		61.73 294	61.73 294	61.70 317	72.74 325	24.50 348	69.16 316	78.03 298	61.40 267	56.72 318	75.53 310	38.42 309	76.48 317	45.95 301	57.67 322	84.13 288

MVS-HIRNet		59.14 298	57.67 300	63.57 314	81.65 272	43.50 328	71.73 305	65.06 340	39.59 333	51.43 328	57.73 333	38.34 310	82.58 294	39.53 319	73.95 263	64.62 333

COLMAP_ROB	66.92 17	73.01 244	70.41 251	80.81 193	87.13 173	65.63 142	88.30 100	84.19 234	62.96 253	63.80 296	87.69 149	38.04 311	92.56 163	46.66 296	74.91 255	84.24 286
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 267	69.00 258	72.55 288	79.27 302	56.85 265	78.38 278	74.71 316	57.64 294	68.09 265	77.19 304	37.75 312	76.70 315	63.92 190	84.09 138	84.10 289

LP		61.36 296	57.78 299	72.09 289	75.54 317	58.53 241	67.16 325	75.22 309	51.90 321	54.13 321	69.97 323	37.73 313	80.45 301	32.74 328	55.63 326	77.29 320

OpenMVS_ROB	64.09 19	70.56 261	68.19 264	77.65 247	80.26 289	59.41 237	85.01 203	82.96 251	58.76 286	65.43 286	82.33 257	37.63 314	91.23 207	45.34 305	76.03 239	82.32 302

FMVSNet5		69.50 268	67.96 268	74.15 282	82.97 253	55.35 287	80.01 264	82.12 260	62.56 259	63.02 297	81.53 273	36.92 315	81.92 295	48.42 281	74.06 262	85.17 278

MIMVSNet1		68.58 272	66.78 277	73.98 283	80.07 292	51.82 305	80.77 257	84.37 231	64.40 241	59.75 308	82.16 260	36.47 316	83.63 289	42.73 314	70.33 286	86.48 257

ITE_SJBPF				78.22 240	81.77 271	60.57 228		83.30 244	69.25 184	67.54 269	87.20 164	36.33 317	87.28 268	54.34 258	74.62 258	86.80 251

test-mter		71.41 254	70.39 252	74.48 278	81.35 278	58.04 247	78.38 278	77.46 300	60.32 274	69.95 243	79.00 293	36.08 318	79.24 304	66.13 173	84.83 130	86.15 264

testgi		66.67 282	66.53 278	67.08 309	75.62 316	41.69 332	75.93 290	76.50 305	66.11 224	65.20 289	86.59 188	35.72 319	74.71 323	43.71 311	73.38 269	84.84 281

EG-PatchMatch MVS		74.04 226	71.82 239	80.71 195	84.92 201	67.42 115	85.86 179	88.08 191	66.04 226	64.22 293	83.85 242	35.10 320	92.56 163	57.44 244	80.83 180	82.16 304

XVG-ACMP-BASELINE		76.11 210	74.27 215	81.62 177	83.20 246	64.67 166	83.60 234	89.75 139	69.75 175	71.85 219	87.09 169	32.78 321	92.11 175	69.99 147	80.43 188	88.09 222

AllTest		70.96 257	68.09 267	79.58 214	85.15 196	63.62 191	84.58 213	79.83 285	62.31 261	60.32 305	86.73 174	32.02 322	88.96 252	50.28 273	71.57 281	86.15 264

TestCases				79.58 214	85.15 196	63.62 191		79.83 285	62.31 261	60.32 305	86.73 174	32.02 322	88.96 252	50.28 273	71.57 281	86.15 264

USDC		70.33 263	68.37 262	76.21 265	80.60 286	56.23 277	79.19 273	86.49 211	60.89 270	61.29 301	85.47 222	31.78 324	89.47 237	53.37 263	76.21 238	82.94 301

tmp_tt		18.61 324	21.40 325	10.23 337	4.82 351	10.11 351	34.70 342	30.74 351	1.48 346	23.91 342	26.07 344	28.42 325	13.41 349	27.12 337	15.35 346	7.17 344

testpf		56.51 304	57.58 301	53.30 324	71.99 327	41.19 333	46.89 340	69.32 333	58.06 290	52.87 327	69.45 325	27.99 326	72.73 329	59.59 225	62.07 314	45.98 338

Anonymous20231211		64.82 289	61.79 293	73.91 284	77.11 310	50.92 312	85.29 197	81.53 268	54.19 309	57.98 312	78.03 298	26.90 327	87.83 266	37.92 321	57.12 323	82.99 299

test2356		59.50 297	58.08 297	63.74 313	71.23 328	41.88 330	67.59 322	72.42 325	53.72 313	57.65 314	70.74 321	26.31 328	72.40 330	32.03 331	71.06 284	76.93 322

TDRefinement		67.49 276	64.34 283	76.92 255	73.47 322	61.07 223	84.86 206	82.98 250	59.77 279	58.30 311	85.13 227	26.06 329	87.89 264	47.92 289	60.59 320	81.81 306

test1235678		58.74 300	56.89 303	64.30 311	69.70 330	41.87 331	71.05 307	74.87 313	54.06 310	50.63 329	71.53 320	25.30 330	74.10 326	31.80 332	63.10 313	76.93 322

TinyColmap		67.30 279	64.81 281	74.76 277	81.92 270	56.68 270	80.29 262	81.49 270	60.33 273	56.27 320	83.22 249	24.77 331	87.66 267	45.52 303	69.47 288	79.95 312

LF4IMVS		64.02 292	62.19 292	69.50 302	70.90 329	53.29 299	76.13 288	77.18 303	52.65 318	58.59 309	80.98 279	23.55 332	76.52 316	53.06 265	66.66 300	78.68 315

1111		57.11 303	56.82 304	57.97 321	69.10 331	28.28 343	68.90 319	74.54 319	54.01 311	53.71 324	74.51 312	23.09 333	67.90 338	32.28 329	61.26 318	77.73 317

.test1245		45.55 313	50.02 310	32.14 333	69.10 331	28.28 343	68.90 319	74.54 319	54.01 311	53.71 324	74.51 312	23.09 333	67.90 338	32.28 329	0.02 348	0.25 347

new_pmnet		50.91 310	50.29 309	52.78 325	68.58 333	34.94 341	63.71 329	56.63 343	39.73 332	44.95 331	65.47 328	21.93 335	58.48 342	34.98 325	56.62 325	64.92 332

pmmvs3		57.79 301	54.26 305	68.37 307	64.02 336	56.72 268	75.12 298	65.17 339	40.20 331	52.93 326	69.86 324	20.36 336	75.48 321	45.45 304	55.25 328	72.90 327

PM-MVS		66.41 284	64.14 284	73.20 286	73.92 319	56.45 272	78.97 274	64.96 341	63.88 248	64.72 290	80.24 284	19.84 337	83.44 290	66.24 172	64.52 311	79.71 313

no-one		51.08 309	45.79 314	66.95 310	57.92 341	50.49 316	59.63 334	76.04 307	48.04 325	31.85 336	56.10 336	19.12 338	80.08 303	36.89 323	26.52 338	70.29 329

testus		59.00 299	57.91 298	62.25 316	72.25 326	39.09 335	69.74 311	75.02 311	53.04 317	57.21 316	73.72 315	18.76 339	70.33 334	32.86 327	68.57 294	77.35 319

ambc				75.24 273	73.16 323	50.51 315	63.05 331	87.47 202		64.28 292	77.81 301	17.80 340	89.73 232	57.88 241	60.64 319	85.49 272

ANet_high		50.57 311	46.10 313	63.99 312	48.67 346	39.13 334	70.99 309	80.85 273	61.39 268	31.18 338	57.70 334	17.02 341	73.65 328	31.22 333	15.89 345	79.18 314

FPMVS		53.68 307	51.64 307	59.81 319	65.08 335	51.03 311	69.48 314	69.58 331	41.46 330	40.67 333	72.32 318	16.46 342	70.00 335	24.24 339	65.42 308	58.40 335

testmv		53.85 306	51.03 308	62.31 315	61.46 338	38.88 336	70.95 310	74.69 317	51.11 323	41.26 332	66.85 326	14.28 343	72.13 331	29.19 334	49.51 333	75.93 325

test12356		49.28 312	48.51 312	51.59 326	62.06 337	19.11 349	60.40 332	72.45 324	47.60 327	40.64 334	65.68 327	13.84 344	68.72 336	27.29 336	46.67 335	66.94 331

EMVS		30.81 321	29.65 322	34.27 332	50.96 345	25.95 347	56.58 338	46.80 348	24.01 342	15.53 346	30.68 343	12.47 345	54.43 345	12.81 345	17.05 344	22.43 343

E-PMN		31.77 320	30.64 321	35.15 331	52.87 344	27.67 345	57.09 337	47.86 347	24.64 340	16.40 345	33.05 342	11.23 346	54.90 344	14.46 344	18.15 342	22.87 342

DeepMVS_CX				27.40 335	40.17 350	26.90 346		24.59 352	17.44 344	23.95 341	48.61 337	9.77 347	26.48 347	18.06 341	24.47 339	28.83 341

Gipuma		45.18 314	41.86 315	55.16 323	77.03 312	51.52 308	32.50 343	80.52 276	32.46 337	27.12 339	35.02 340	9.52 348	75.50 320	22.31 340	60.21 321	38.45 340
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 305	49.68 311	67.97 308	53.73 343	45.28 324	66.85 326	80.78 274	35.96 335	39.45 335	62.23 331	8.70 349	78.06 311	48.24 286	51.20 332	80.57 310

PMMVS2		40.82 316	38.86 317	46.69 329	53.84 342	16.45 350	48.61 339	49.92 346	37.49 334	31.67 337	60.97 332	8.14 350	56.42 343	28.42 335	30.72 337	67.19 330

PMVS	37.38 22	44.16 315	40.28 316	55.82 322	40.82 349	42.54 329	65.12 328	63.99 342	34.43 336	24.48 340	57.12 335	3.92 351	76.17 318	17.10 342	55.52 327	48.75 336
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 322	25.89 324	43.81 330	44.55 348	35.46 340	28.87 344	39.07 349	18.20 343	18.58 344	40.18 339	2.68 352	47.37 346	17.07 343	23.78 340	48.60 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		38.26 318	35.42 318	46.79 328	58.74 339	35.48 339	59.65 333	51.25 345	32.45 338	23.44 343	47.53 338	2.04 353	58.96 341	25.60 338	18.09 343	45.92 339

wuyk23d		16.82 325	15.94 326	19.46 336	58.74 339	31.45 342	39.22 341	3.74 354	6.84 345	6.04 348	2.70 348	1.27 354	24.29 348	10.54 346	14.40 347	2.63 345

wuykxyi23d		39.76 317	33.18 320	59.51 320	46.98 347	44.01 326	57.70 336	67.74 336	24.13 341	13.98 347	34.33 341	1.27 354	71.33 332	34.23 326	18.23 341	63.18 334

test123		6.12 327	8.11 328	0.14 338	0.06 353	0.09 353	71.05 307	0.03 356	0.04 348	0.25 350	1.30 350	0.05 356	0.03 351	0.21 348	0.01 350	0.29 346

testmvs		6.04 328	8.02 329	0.10 339	0.08 352	0.03 354	69.74 311	0.04 355	0.05 347	0.31 349	1.68 349	0.02 357	0.04 350	0.24 347	0.02 348	0.25 347

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

ab-mvs-re		7.23 326	9.64 327	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	86.72 176	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

test_part2					95.06 1	72.65 26				91.80 1

test_full								94.22 1

MTGPA								92.02 60

MTMP								32.83 350

gm-plane-assit					81.40 276	53.83 296			62.72 258		80.94 280		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		58.10 289	87.04 14			88.98 250	74.07 109

新几何2							86.29 170

无先验							87.48 125	88.98 166	60.00 277				94.12 93	67.28 165		88.97 195

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								0.00 357

nn								0.00 357

door-mid								69.98 329

test11								92.23 52

door								69.44 332

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