High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 16	62.73 12	86.09 8	85.03 13	65.51 15	83.81 1	90.51 11	63.71 1	89.23 6	81.51 1	88.44 11	85.45 78

DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 41	60.32 37	83.03 37	85.33 10	62.86 43	80.17 3	90.03 21	61.76 2	88.95 10	74.21 19	88.67 10	88.12 7

CSCG		76.92 25	76.75 23	77.41 36	83.96 48	59.60 43	82.95 38	86.50 4	60.78 71	75.27 19	84.83 91	60.76 3	86.56 50	67.86 51	87.87 25	86.06 55

TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 37	61.41 27	84.03 26	83.82 37	59.34 113	79.37 5	89.76 26	59.84 4	87.62 28	76.69 11	86.74 35	87.68 16

canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 109	50.56 164	80.23 79	83.87 35	60.30 82	77.15 11	86.56 66	59.65 5	82.00 157	66.01 64	82.12 63	88.58 4

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 9	60.02 39	88.12 1	86.42 5	62.94 40	82.40 2	92.12 1	59.64 6	89.76 3	78.70 5	88.32 15	86.79 38

DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 137	52.25 139	75.59 168	84.17 25	63.76 32	73.15 46	82.79 121	59.58 7	86.80 41	67.24 56	86.04 41	87.89 9
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
CNVR-MVS		79.84 4	79.97 4	79.45 4	87.90 2	62.17 20	84.37 20	85.03 13	66.96 6	77.58 10	90.06 20	59.47 8	89.13 8	78.67 6	89.73 3	87.03 33

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 8	58.36 59	83.22 35	86.93 1	56.91 139	74.91 24	88.19 44	59.15 9	87.68 27	73.67 25	87.45 26	86.57 40

nrg030		72.96 60	73.01 55	72.84 128	75.41 180	50.24 173	80.02 82	82.89 61	58.36 127	74.44 29	86.73 57	58.90 10	80.83 177	65.84 66	74.46 137	87.44 23

HPM-MVS++		79.88 3	80.14 3	79.10 11	88.17 1	64.80 1	86.59 4	83.70 39	65.37 16	78.78 7	90.64 7	58.63 11	87.24 31	79.00 4	90.37 2	85.26 91

DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 49	61.62 24	84.17 24	86.85 2	63.23 35	73.84 37	90.25 18	57.68 12	89.96 2	74.62 18	89.03 6	87.89 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_NR-MVSNet		71.11 83	71.00 77	71.44 159	79.20 103	44.13 236	76.02 164	82.60 63	66.48 14	68.20 110	84.60 97	56.82 13	82.82 139	54.62 152	70.43 189	87.36 27

Regformer-1		75.47 40	74.93 40	77.09 40	80.43 87	57.70 66	79.50 92	82.13 66	67.84 1	75.73 18	80.75 178	56.50 14	86.07 58	71.07 39	80.38 79	87.50 20

Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 136	53.64 117	79.62 90	79.61 123	61.63 61	72.02 60	82.61 126	56.44 15	85.97 64	63.99 89	79.07 101	87.25 29

alignmvs		73.86 53	73.99 46	73.45 107	78.20 126	50.50 166	78.57 101	82.43 64	59.40 111	76.57 12	86.71 59	56.42 16	81.23 170	65.84 66	81.79 65	88.62 2

test_prior3		76.89 27	76.96 21	76.69 44	84.20 46	57.27 71	81.75 59	84.88 15	60.37 78	75.01 20	89.06 33	56.22 17	86.43 54	72.19 32	88.96 8	86.38 41

test_prior2							81.75 59		60.37 78	75.01 20	89.06 33	56.22 17		72.19 32	88.96 8

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 62	58.77 55	81.80 58	78.57 153	58.58 121	74.32 30	84.51 100	55.94 19	87.22 32	67.11 57	84.48 49	85.52 73

Regformer-2		75.63 39	74.99 38	77.54 34	80.43 87	58.32 60	79.50 92	82.92 58	67.84 1	75.94 15	80.75 178	55.73 20	86.80 41	71.44 38	80.38 79	87.50 20

MVS_Test		72.45 66	72.46 60	72.42 145	74.88 185	48.50 203	76.28 155	83.14 56	59.40 111	72.46 55	84.68 93	55.66 21	81.12 171	65.98 65	79.66 90	87.63 17

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 13	60.81 34	85.52 15	84.36 21	60.61 73	79.05 6	90.30 16	55.54 22	88.32 16	73.48 28	87.03 30	84.83 103
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		78.58 8	78.31 9	79.39 5	87.51 3	62.61 16	85.20 17	84.42 20	66.73 10	74.67 28	89.38 30	55.30 23	89.18 7	74.19 20	87.34 27	86.38 41

FIs		70.82 86	71.43 68	68.98 196	78.33 123	38.14 276	76.96 144	83.59 41	61.02 68	67.33 128	86.73 57	55.07 24	81.64 162	54.61 154	79.22 98	87.14 31

SD-MVS		77.70 17	77.62 15	77.93 32	84.47 44	61.88 23	84.55 19	83.87 35	60.37 78	79.89 4	89.38 30	54.97 25	85.58 70	76.12 12	84.94 45	86.33 47

CANet		76.46 31	75.93 32	78.06 29	81.29 73	57.53 68	82.35 50	83.31 51	67.78 3	70.09 73	86.34 69	54.92 26	88.90 11	72.68 30	84.55 47	87.76 14

Regformer-3		73.89 52	73.28 54	75.71 58	79.75 96	55.48 103	78.54 103	79.93 118	66.58 12	73.62 40	80.30 188	54.87 27	84.54 98	69.09 45	76.84 123	87.10 32

MP-MVS		78.35 11	78.26 11	78.64 21	86.54 11	63.47 5	86.02 9	83.55 42	63.89 31	73.60 41	90.60 8	54.85 28	86.72 44	77.20 10	88.06 20	85.74 66

mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 239	44.57 233	71.99 225	75.23 197	51.67 205	67.06 130	82.57 127	54.68 29	77.94 221	56.56 140	75.71 131	86.26 51

test12				77.76 33	84.52 43	58.41 58		83.36 49		72.93 50		54.61 30	88.05 21		88.12 19	86.81 37

FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 147	34.73 300	76.05 162	83.19 54	60.84 69	65.88 145	86.46 67	54.52 31	80.76 181	52.52 166	78.12 111	86.91 34

SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 55	62.18 19	87.60 2	85.83 7	66.69 11	78.03 9	90.98 4	54.26 32	90.06 1	78.42 7	89.02 7	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 33

MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 55	60.63 36	77.29 138	78.40 162	61.18 67	70.58 67	85.97 76	54.18 34	84.00 111	67.52 55	82.98 57	82.45 170

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 119	51.50 148	75.01 181	79.46 136	56.16 158	68.59 103	79.55 201	53.97 35	84.05 106	53.34 162	77.53 115	85.65 70

agg_prior1		75.94 36	76.01 31	75.72 57	85.04 34	59.96 40	81.44 67	81.04 94	56.14 159	74.68 26	88.90 37	53.91 36	84.04 107	75.01 17	87.92 24	83.16 158

UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 118	45.29 226	75.94 165	82.92 58	63.68 34	68.16 112	83.59 112	53.89 37	83.49 120	53.97 156	71.12 182	86.89 35

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 96	56.54 83	78.54 103	81.49 80	66.93 8	73.90 35	80.30 188	53.84 38	85.98 63	69.76 41	76.84 123	87.17 30

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 18	60.86 33	84.71 18	84.85 17	61.98 59	73.06 48	88.88 38	53.72 39	89.06 9	68.27 47	88.04 21	87.42 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 27	61.59 25	81.62 63	81.26 88	55.65 167	74.93 22	88.81 39	53.70 40	84.68 95

train_agg		76.27 33	76.15 28	76.64 47	85.58 27	61.59 25	81.62 63	81.26 88	55.86 161	74.93 22	88.81 39	53.70 40	84.68 95	75.24 15	88.33 13	83.65 147

test_8					85.40 30	60.96 32	81.54 66	81.18 91	55.86 161	74.81 25	88.80 41	53.70 40	84.45 100

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 32	59.14 49	81.31 69	84.02 28	56.32 154	74.05 31	88.98 36	53.34 43	87.92 24	69.23 44	88.42 12	87.59 18

agg_prior3		76.13 34	75.89 34	76.85 42	85.76 23	62.02 21	81.65 61	81.01 96	55.51 170	73.73 38	88.60 43	53.23 44	84.90 88	75.24 15	88.33 13	83.65 147

HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 6	62.81 10	86.29 5	84.32 22	62.82 44	73.96 32	90.50 12	53.20 45	88.35 14	74.02 21	87.05 28	86.13 52

#test#		77.83 15	77.41 17	79.10 11	86.71 6	62.81 10	85.69 14	84.32 22	61.61 62	73.96 32	90.50 12	53.20 45	88.35 14	73.68 24	87.05 28	86.13 52

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 121	54.02 113	77.05 142	79.33 140	65.03 20	71.68 62	79.35 204	52.75 47	84.89 89	66.46 61	74.23 140	85.83 60

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 29	61.04 31	83.84 28	85.16 11	62.88 42	78.10 8	91.26 3	52.51 48	88.39 13	79.34 3	90.52 1	86.78 39

PCF-MVS	61.88 8	70.95 84	69.49 100	75.35 65	77.63 142	55.71 97	76.04 163	81.81 74	50.30 220	69.66 84	85.40 88	52.51 48	84.89 89	51.82 170	80.24 83	85.45 78
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MP-MVS-pluss		78.35 11	78.46 8	78.03 30	84.96 37	59.52 44	82.93 39	85.39 9	62.15 53	76.41 14	91.51 2	52.47 50	86.78 43	80.66 2	89.64 5	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 111	53.33 123	78.23 109	84.79 18	61.30 66	70.41 69	81.04 168	52.41 51	87.12 36	64.61 76	82.49 62	85.41 85
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NR-MVSNet		69.54 116	68.85 108	71.59 158	78.05 132	43.81 240	74.20 193	80.86 100	65.18 17	62.76 178	84.52 98	52.35 52	83.59 118	50.96 176	70.78 184	87.37 25

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 134	53.56 119	76.62 148	79.16 142	64.40 26	71.18 64	78.95 209	52.19 53	84.66 97	65.47 69	73.57 149	85.32 88

PAPM_NR		72.63 63	71.80 64	75.13 68	81.72 65	53.42 122	79.91 84	83.28 53	59.14 115	66.31 140	85.90 77	51.86 54	86.06 59	57.45 137	80.62 73	85.91 58

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 25	49.69 192	81.59 65	81.29 87	61.45 63	71.05 65	88.11 45	51.77 55	87.73 26	61.05 121	83.09 54	85.05 97

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 116	49.70 190	82.10 56	81.65 76	60.40 77	65.94 143	85.84 78	51.74 56	86.37 56	55.93 143	79.55 93	88.07 8

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 117	42.97 247	76.53 150	81.16 92	66.95 7	68.53 106	85.42 87	51.61 57	83.07 130	52.32 167	69.70 211	87.46 22

EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 192	49.39 196	75.20 177	78.45 159	59.60 103	69.16 99	76.51 248	51.29 58	82.50 150	59.86 130	71.45 180	83.30 151

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 194	49.40 195	76.23 157	77.55 172	59.60 103	65.85 146	81.59 156	51.28 59	81.58 165	59.87 129	69.90 206	83.30 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		64.72 196	64.33 184	65.87 230	75.22 182	38.56 273	74.66 188	75.08 203	58.90 118	61.79 198	82.63 125	51.18 60	78.07 220	43.63 231	55.87 292	80.99 198

VNet		69.68 112	70.19 86	68.16 204	79.73 99	41.63 256	70.53 238	77.38 176	60.37 78	70.69 66	86.63 62	51.08 61	77.09 231	53.61 160	81.69 69	85.75 65

VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 150	41.00 260	74.04 194	79.68 121	60.06 85	69.26 97	84.81 92	51.06 62	77.58 225	54.44 155	74.43 138	84.48 114

PAPR		71.72 77	70.82 79	74.41 81	81.20 77	51.17 149	79.55 91	83.33 50	55.81 164	66.93 132	84.61 96	50.95 63	86.06 59	55.79 146	79.20 99	86.00 56

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 85	55.91 95	84.28 21	85.78 8	56.08 160	73.41 43	86.58 65	50.94 64	88.54 12	70.79 40	89.71 4	87.79 13

HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 36	60.81 34	82.91 40	85.08 12	62.57 47	73.09 47	89.97 23	50.90 65	87.48 29	75.30 13	86.85 33	87.33 28

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 135	37.75 279	77.57 131	82.11 68	62.03 58	62.65 181	82.48 129	50.57 66	79.46 193	42.91 238	64.01 250	84.79 106

EPNet		73.09 59	72.16 61	75.90 53	75.95 173	56.28 86	83.05 36	72.39 219	66.53 13	65.27 151	87.00 55	50.40 67	85.47 74	62.48 103	86.32 40	85.94 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WR-MVS		68.47 139	68.47 115	68.44 203	80.20 92	39.84 263	73.75 200	76.07 188	64.68 21	68.11 114	83.63 111	50.39 68	79.14 202	49.78 182	69.66 212	86.34 46

region2R		77.67 18	77.18 20	79.15 7	86.76 4	62.95 8	86.29 5	84.16 26	62.81 46	73.30 44	90.58 9	49.90 69	88.21 18	73.78 23	87.03 30	86.29 50

UA-Net		73.13 58	72.93 56	73.76 91	83.58 50	51.66 145	78.75 96	77.66 170	67.75 4	72.61 53	89.42 28	49.82 70	83.29 124	53.61 160	83.14 53	86.32 48

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 5	62.93 9	86.29 5	84.24 24	62.82 44	73.55 42	90.56 10	49.80 71	88.24 17	74.02 21	87.03 30	86.32 48

API-MVS		72.17 71	71.41 69	74.45 80	81.95 63	57.22 73	84.03 26	80.38 114	59.89 92	68.40 107	82.33 132	49.64 72	87.83 25	51.87 169	84.16 51	78.30 224

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 72	58.89 53	82.50 49	84.07 27	67.73 5	72.08 59	87.28 54	49.49 73	89.57 4	73.52 27	86.40 39	87.87 11

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 169	41.73 254	70.41 241	76.14 187	53.99 188	65.98 142	83.51 114	49.48 74	76.24 240	48.60 193	73.46 151	84.14 130

v8		70.33 102	69.28 105	73.49 105	73.15 214	50.22 174	78.62 100	80.78 101	60.79 70	66.45 137	82.11 140	49.35 75	84.98 82	63.58 95	68.71 219	85.28 89

v17		68.37 141	67.00 147	72.48 139	73.22 210	50.31 170	78.10 115	79.58 130	59.71 98	61.67 200	77.60 230	49.31 76	82.89 135	62.37 105	61.48 270	84.23 123

v18		68.33 142	66.96 148	72.42 145	73.13 218	50.16 179	77.97 120	79.57 132	59.57 107	61.80 197	77.50 235	49.30 77	82.90 134	62.31 107	61.50 268	84.20 129

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 110	45.63 225	80.22 80	78.69 151	64.14 29	66.46 136	87.36 51	49.30 77	85.60 68	50.26 180	83.71 52	88.59 3

v1neww		70.66 88	69.70 91	73.53 102	73.15 214	50.22 174	78.11 112	80.68 102	59.65 100	69.83 80	81.67 150	49.29 79	84.96 84	64.55 77	70.38 192	85.42 81

v7new		70.66 88	69.70 91	73.53 102	73.15 214	50.22 174	78.11 112	80.68 102	59.65 100	69.83 80	81.67 150	49.29 79	84.96 84	64.55 77	70.38 192	85.42 81

v6		70.66 88	69.70 91	73.53 102	73.14 217	50.21 177	78.11 112	80.67 104	59.65 100	69.82 82	81.65 152	49.29 79	84.96 84	64.55 77	70.39 191	85.42 81

v16		68.38 140	67.01 146	72.47 143	73.22 210	50.29 171	78.10 115	79.59 128	59.71 98	61.72 199	77.60 230	49.28 82	82.89 135	62.36 106	61.54 267	84.23 123

XXY-MVS		60.68 231	61.67 215	57.70 277	70.43 258	38.45 274	64.19 273	66.47 257	48.05 240	63.22 173	80.86 173	49.28 82	60.47 294	45.25 222	67.28 230	74.19 269

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 225	50.00 184	77.69 126	80.59 107	59.50 108	69.60 86	81.43 159	49.26 84	84.77 92	64.48 81	70.31 197	85.47 75

v1		70.50 95	69.53 96	73.42 110	72.91 225	50.00 184	77.69 126	80.59 107	59.50 108	69.59 88	81.42 161	49.26 84	84.77 92	64.49 80	70.30 198	85.47 75

cdsmvs_eth3d_5k		17.50 315	23.34 312	0.00 332	0.00 345	0.00 345	0.00 337	78.63 152	0.00 341	0.00 342	82.18 135	49.25 86	0.00 342	0.00 341	0.00 339	0.00 340

v15		68.22 150	66.81 154	72.47 143	73.25 209	50.40 168	77.92 122	79.60 125	59.77 97	61.28 208	77.52 234	49.25 86	82.77 141	62.16 112	60.51 277	84.24 122

diffmvs		67.72 157	66.73 155	70.70 176	69.74 268	47.69 212	73.33 204	74.74 204	53.30 192	64.51 164	81.80 148	49.25 86	79.02 204	59.15 132	74.75 135	85.39 86

v1141		70.50 95	69.53 96	73.41 111	72.92 224	50.00 184	77.69 126	80.60 106	59.50 108	69.60 86	81.43 159	49.24 89	84.77 92	64.48 81	70.30 198	85.46 77

V14		68.25 147	66.82 153	72.52 138	73.33 208	50.53 165	78.02 118	79.60 125	59.83 94	61.16 210	77.57 232	49.19 90	82.77 141	62.18 108	60.50 278	84.26 121

V9		68.27 145	66.84 150	72.56 136	73.39 207	50.63 160	78.10 115	79.60 125	59.94 89	61.05 212	77.62 229	49.18 91	82.77 141	62.17 110	60.48 279	84.27 120

PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 61	58.82 54	79.93 83	80.35 116	55.09 174	65.82 147	82.16 138	49.17 92	82.64 149	60.34 125	78.62 108	82.50 169

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 159	50.57 162	74.50 190	81.52 77	53.66 190	64.22 169	79.72 199	49.13 93	82.87 137	55.82 144	73.92 144	79.77 214

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 159	50.57 162	72.51 214	81.52 77	51.91 204	64.22 169	77.77 225	49.13 93	82.87 137	55.82 144	79.58 91	80.14 209

v12		68.28 144	66.83 152	72.60 135	73.43 204	50.74 157	78.18 110	79.59 128	60.01 88	60.89 214	77.66 228	49.12 95	82.77 141	62.18 108	60.46 280	84.29 119

DU-MVS		70.01 107	69.53 96	71.44 159	78.05 132	44.13 236	75.01 181	81.51 79	64.37 27	68.20 110	84.52 98	49.12 95	82.82 139	54.62 152	70.43 189	87.37 25

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 237	75.65 175	37.70 280	75.42 171	74.65 206	59.90 90	68.14 113	83.15 120	49.12 95	77.20 229	52.23 168	69.78 208	81.60 180

v13		68.29 143	66.84 150	72.63 133	73.50 202	50.83 155	78.25 108	79.58 130	60.05 86	60.76 215	77.68 227	49.11 98	82.77 141	62.17 110	60.45 281	84.30 118

VPNet		67.52 158	68.11 121	65.74 231	79.18 104	36.80 288	72.17 219	72.83 217	62.04 57	67.79 123	85.83 79	48.88 99	76.60 236	51.30 174	72.97 159	83.81 137

MPTG		77.61 19	77.36 18	78.35 24	86.08 20	63.57 2	83.37 33	80.97 97	65.13 18	75.77 16	90.88 5	48.63 100	86.66 45	77.23 8	88.17 17	84.81 104

MTAPA		76.90 26	76.42 27	78.35 24	86.08 20	63.57 2	74.92 184	80.97 97	65.13 18	75.77 16	90.88 5	48.63 100	86.66 45	77.23 8	88.17 17	84.81 104

原ACMM1				74.69 73	85.39 31	59.40 45		83.42 46	51.47 211	70.27 72	86.61 63	48.61 102	86.51 52	53.85 157	87.96 22	78.16 226

v148		68.24 149	67.19 144	71.40 162	70.43 258	47.77 211	75.76 167	77.03 180	58.91 117	67.36 127	80.10 192	48.60 103	81.89 158	60.01 127	66.52 233	84.53 112

PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 19	62.73 12	82.55 47	83.74 38	61.71 60	72.45 56	90.34 15	48.48 104	88.13 19	72.32 31	86.85 33	85.78 61

Test By Simon												48.33 105

v11		68.15 153	66.73 155	72.42 145	73.43 204	50.28 172	77.94 121	79.65 122	59.88 93	61.11 211	77.55 233	48.25 106	82.75 146	61.88 117	60.85 274	84.23 123

CP-MVS		77.12 24	76.68 24	78.43 23	86.05 22	63.18 7	87.55 3	83.45 45	62.44 50	72.68 52	90.50 12	48.18 107	87.34 30	73.59 26	85.71 42	84.76 108

MVS		67.37 159	66.33 165	70.51 178	75.46 179	50.94 150	73.95 195	81.85 73	41.57 291	62.54 184	78.57 215	47.98 108	85.47 74	52.97 164	82.05 64	75.14 256

XVS		77.17 23	76.56 26	79.00 14	86.32 14	62.62 14	85.83 10	83.92 31	64.55 22	72.17 57	90.01 22	47.95 109	88.01 22	71.55 36	86.74 35	86.37 44

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 14	62.62 14	85.83 10	83.92 31	64.55 22	72.17 57	6.49 337	47.95 109	88.01 22	71.55 36	86.74 35	86.37 44

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 64	59.39 46	81.38 68	82.90 60	54.90 178	68.08 115	78.70 210	47.73 111	85.51 73	51.68 173	84.17 50	81.88 178
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
PAPM		67.92 155	66.69 157	71.63 157	78.09 130	49.02 199	77.09 141	81.24 90	51.04 216	60.91 213	83.98 108	47.71 112	84.99 80	40.81 250	79.32 97	80.90 199

v10		70.21 104	69.02 107	73.81 88	73.51 201	50.92 152	78.74 97	81.39 83	60.05 86	66.39 138	81.83 147	47.58 113	85.41 77	62.80 100	68.86 218	85.09 96

v7		70.57 92	69.48 101	73.85 86	73.50 202	50.92 152	78.27 107	81.43 81	58.93 116	69.61 85	81.49 158	47.56 114	85.43 76	63.94 90	70.62 186	85.21 92

v1144		70.42 100	69.31 104	73.76 91	73.22 210	50.64 159	77.83 123	81.43 81	58.58 121	69.40 93	81.16 165	47.53 115	85.29 79	64.01 88	70.64 185	85.34 87

v2v482		70.50 95	69.45 103	73.66 97	72.62 231	50.03 183	77.58 130	80.51 111	59.90 90	69.52 89	82.14 139	47.53 115	84.88 91	65.07 72	70.17 201	86.09 54

pm-mvs1		65.24 192	64.97 182	66.04 226	72.38 234	39.40 268	72.62 212	75.63 192	55.53 169	62.35 192	83.18 119	47.45 117	76.47 237	49.06 190	66.54 232	82.24 172

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 188	41.02 258	69.96 245	74.43 207	49.29 226	61.66 201	80.92 171	47.43 118	76.68 235	44.91 223	71.69 177	81.94 176

OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 81	59.01 51	82.60 46	83.64 40	63.74 33	72.52 54	87.49 49	47.18 119	85.88 66	69.47 43	80.78 71	83.66 146

pcd_1.5k_mvsjas		3.92 321	5.23 322	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	0.00 343	47.05 120	0.00 342	0.00 341	0.00 339	0.00 340

PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 119	55.93 94	81.63 62	82.12 67	56.24 157	70.02 77	85.68 82	47.05 120	84.34 102	65.27 70	74.41 139	85.67 68

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 67	55.79 96	74.92 184	79.00 145	55.04 176	69.88 79	78.66 211	47.05 120	82.19 154	61.61 119	79.58 91	80.83 200

WTY-MVS		59.75 236	60.39 224	57.85 275	72.32 236	37.83 278	61.05 287	64.18 270	45.95 260	61.91 195	79.11 207	47.01 123	60.88 293	42.50 240	69.49 214	74.83 262

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 76	55.63 100	75.11 179	78.92 146	54.92 177	69.96 78	79.68 200	47.00 124	82.09 156	61.60 120	79.37 94	80.81 201

v144192		69.71 111	68.51 113	73.33 115	73.10 219	50.13 181	77.54 132	80.64 105	56.65 145	68.57 105	80.55 181	46.87 125	84.96 84	62.98 98	69.66 212	84.89 102

PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 157	36.56 290	77.03 143	80.42 113	62.95 39	62.51 186	84.03 107	46.69 126	79.07 203	44.22 224	63.08 258	85.51 74

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 12	63.50 4	85.74 13	82.28 65	62.90 41	71.77 61	90.26 17	46.61 127	86.55 51	71.71 35	85.66 43	84.97 100

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 141	36.33 292	76.83 147	79.52 134	62.45 49	62.54 184	83.47 116	46.32 128	78.37 215	45.47 219	63.43 255	85.45 78

V42		68.65 132	67.35 138	72.56 136	68.93 271	50.18 178	72.90 209	79.47 135	56.92 138	69.45 92	80.26 190	46.29 129	82.99 131	64.07 86	67.82 225	84.53 112

1112_ss		64.00 203	63.36 195	65.93 229	79.28 102	42.58 249	71.35 229	72.36 220	46.41 253	60.55 217	77.89 222	46.27 130	73.28 249	46.18 207	69.97 204	81.92 177

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 54	59.29 48	82.30 55	81.88 71	59.34 113	71.59 63	86.83 56	45.94 131	83.65 117	65.09 71	85.22 44	81.06 197

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 148	36.30 294	76.94 145	79.61 123	62.36 51	62.43 190	83.66 110	45.69 132	78.37 215	45.35 221	63.26 256	85.42 81

APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 60	58.24 61	83.67 29	83.29 52	58.41 125	73.71 39	90.14 19	45.62 133	85.99 62	69.64 42	82.85 60	85.78 61

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 172	34.79 298	76.43 152	79.38 139	62.55 48	61.66 201	83.83 109	45.60 134	79.15 201	41.64 248	60.88 273	85.00 98

BH-w/o		66.85 171	65.83 170	69.90 186	79.29 101	52.46 136	74.66 188	76.65 183	54.51 183	64.85 160	78.12 217	45.59 135	82.95 133	43.26 234	75.54 132	74.27 268

HQP2-MVS												45.46 136

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 82	54.94 106	82.31 52	83.90 33	62.10 54	67.85 117	85.54 85	45.46 136	86.93 39	67.04 58	80.35 81	84.32 116

ACMMP		76.02 35	75.33 37	78.07 28	85.20 33	61.91 22	85.49 16	84.44 19	63.04 38	69.80 83	89.74 27	45.43 138	87.16 35	72.01 34	82.87 59	85.14 93
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
OMC-MVS		71.40 82	70.60 81	73.78 89	76.60 165	53.15 125	79.74 88	79.78 119	58.37 126	68.75 102	86.45 68	45.43 138	80.60 182	62.58 101	77.73 113	87.58 19

BH-untuned		68.27 145	67.29 139	71.21 165	79.74 98	53.22 124	76.06 161	77.46 175	57.19 135	66.10 141	81.61 154	45.37 140	83.50 119	45.42 220	76.68 127	76.91 242

v1192		69.97 109	68.68 111	73.85 86	73.19 213	50.94 150	77.68 129	81.36 84	57.51 133	68.95 101	80.85 174	45.28 141	85.33 78	62.97 99	70.37 194	85.27 90

HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 70	56.31 84	84.22 22	84.01 29	64.52 24	69.27 95	86.10 73	45.26 142	87.21 33	68.16 48	80.58 75	84.65 109

plane_prior6					81.20 77	56.24 88						45.26 142

v1921920		69.47 118	68.17 120	73.36 114	73.06 220	50.10 182	77.39 134	80.56 109	56.58 151	68.59 103	80.37 184	44.72 144	84.98 82	62.47 104	69.82 207	85.00 98

test_normal		69.26 122	67.90 124	73.32 116	70.84 255	50.38 169	75.30 173	79.17 141	54.23 186	62.00 193	80.61 180	44.69 145	83.89 113	64.33 83	79.95 87	85.69 67

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 252	50.14 180	75.49 170	79.16 142	54.57 182	62.45 188	80.76 177	44.67 146	84.20 103	64.23 84	79.81 88	85.54 72

Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 251	74.75 187	31.04 314	71.16 232	63.64 272	56.32 154	59.80 224	84.99 89	44.51 147	75.46 242	39.12 257	80.62 73	82.92 161

DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 10	57.99 63	81.15 71	82.96 57	57.03 136	66.78 133	85.56 83	44.50 148	88.11 20	51.77 171	80.23 84	83.10 159

TAMVS		66.78 173	65.27 177	71.33 164	79.16 106	53.67 116	73.84 199	69.59 235	52.32 201	65.28 150	81.72 149	44.49 149	77.40 228	42.32 241	78.66 107	82.92 161

v748		67.26 161	65.67 171	72.02 150	69.90 266	49.77 189	76.24 156	79.57 132	58.58 121	60.49 218	80.38 183	44.47 150	82.17 155	56.16 142	65.26 241	84.12 131

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 73	55.41 104	80.90 72	78.28 164	60.73 72	69.23 98	88.09 46	44.36 151	82.65 148	57.68 136	81.75 67	85.77 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 53	53.15 125		67.52 251			87.85 48	44.08 152			80.76 72	78.03 228

Test_1112_low_res		62.32 219	61.77 214	64.00 247	79.08 107	39.53 267	68.17 253	70.17 228	43.25 280	59.03 234	79.90 193	44.08 152	71.24 256	43.79 230	68.42 221	81.25 192

MVSFormer		71.50 80	70.38 84	74.88 70	78.76 113	57.15 78	82.79 41	78.48 157	51.26 214	69.49 90	83.22 117	43.99 154	83.24 125	66.06 62	79.37 94	84.23 123

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 113	57.15 78	76.57 149	73.29 215	46.19 255	69.49 90	82.18 135	43.99 154	79.23 197	64.66 74	79.37 94	83.93 132

v7n		69.01 126	67.36 137	73.98 84	72.51 233	52.65 131	78.54 103	81.30 86	60.26 83	62.67 180	81.62 153	43.61 156	84.49 99	57.01 139	68.70 220	84.79 106

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 108	53.13 127	73.27 205	71.07 224	52.15 202	64.72 161	80.23 191	43.56 157	77.10 230	45.48 218	78.88 102	83.05 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		69.65 113	68.39 117	73.43 109	78.27 125	56.88 80	77.12 140	73.71 213	46.53 251	69.34 94	83.22 117	43.37 158	79.18 198	64.77 73	79.20 99	84.23 123
jason: jason.
v1240		69.24 123	67.91 123	73.25 120	73.02 222	49.82 187	77.21 139	80.54 110	56.43 153	68.34 109	80.51 182	43.33 159	84.99 80	62.03 114	69.77 210	84.95 101

LCM-MVSNet-Re		61.88 225	61.35 217	63.46 248	74.58 190	31.48 313	61.42 283	58.14 295	58.71 120	53.02 280	79.55 201	43.07 160	76.80 233	45.69 213	77.96 112	82.11 175

BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 93	52.53 134	74.26 192	76.29 185	58.48 124	68.38 108	84.20 103	42.59 161	83.83 114	46.53 204	75.91 129	82.56 166

LFMVS		71.78 76	71.59 66	72.32 148	83.40 51	46.38 221	79.75 87	71.08 223	64.18 28	72.80 51	88.64 42	42.58 162	83.72 115	57.41 138	84.49 48	86.86 36

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 139	58.99 52	80.66 76	83.15 55	62.24 52	65.46 148	86.59 64	42.38 163	85.52 72	59.59 131	84.72 46	82.85 164

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 66	49.69 192	77.76 125	77.63 171	63.21 36	73.21 45	89.02 35	42.14 164	83.32 123	61.72 118	82.50 61	88.25 6

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 59	59.92 42	85.83 10	86.32 6	66.92 9	67.80 122	89.24 32	42.03 165	89.38 5	64.07 86	86.50 38	89.69 1

MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 122	59.33 47	74.82 186	70.11 229	58.08 129	67.83 121	84.68 93	41.96 166	76.34 239	65.62 68	77.54 114	79.30 219

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 42	61.41 27	84.15 25	77.86 166	55.27 172	67.51 126	88.08 47	41.93 167	81.85 159	69.04 46	80.01 85	81.35 191

abl_6		74.34 46	73.50 49	76.86 41	82.43 58	60.16 38	83.48 32	81.86 72	58.81 119	73.95 34	89.86 24	41.87 168	86.62 47	67.98 50	81.23 70	83.80 140

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 142	43.33 243	77.31 135	77.83 167	56.62 148	65.04 156	82.70 122	41.85 169	80.33 185	47.18 199	72.76 161	83.92 133

test1		67.21 162	66.55 158	69.19 193	77.63 142	43.33 243	77.31 135	77.83 167	56.62 148	65.04 156	82.70 122	41.85 169	80.33 185	47.18 199	72.76 161	83.92 133

FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 142	42.98 246	76.11 159	77.47 173	56.62 148	65.22 155	82.17 137	41.85 169	80.18 188	47.05 202	72.72 164	83.20 154

CostFormer		64.04 202	62.51 203	68.61 201	71.88 243	45.77 224	71.30 230	70.60 227	47.55 244	64.31 167	76.61 246	41.63 172	79.62 192	49.74 184	69.00 216	80.42 204

AdaColmap		69.99 108	68.66 112	73.97 85	84.94 39	57.83 64	82.63 45	78.71 150	56.28 156	64.34 165	84.14 104	41.57 173	87.06 38	46.45 205	78.88 102	77.02 239

Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 170	62.29 18	80.20 81	76.06 189	59.83 94	65.26 152	77.09 238	41.56 174	84.02 110	60.60 123	71.09 183	81.53 181

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 170	63.22 6	80.59 77	76.06 189	59.83 94	66.32 139	79.87 194	41.56 174	85.53 71	60.60 123	72.77 160	82.80 165

QAPM		70.05 106	68.81 109	73.78 89	76.54 167	53.43 121	83.23 34	83.48 43	52.89 195	65.90 144	86.29 70	41.55 176	86.49 53	51.01 175	78.40 110	81.42 183

VDDNet		71.81 75	71.33 72	73.26 119	82.80 57	47.60 213	78.74 97	75.27 196	59.59 106	72.94 49	89.40 29	41.51 177	83.91 112	58.75 133	82.99 56	88.26 5

1121		68.53 138	67.16 145	72.63 133	85.64 26	61.14 29	73.95 195	66.46 258	44.61 269	70.28 71	86.68 60	41.42 178	80.78 179	53.62 158	81.79 65	75.97 246

CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 69	56.26 87	66.32 260	74.20 210	40.53 297	63.16 175	78.65 212	41.30 179	77.80 223	45.80 212	74.09 141	81.40 184

新几何1				70.76 173	85.66 24	61.13 30		66.43 259	44.68 268	70.29 70	86.64 61	41.29 180	75.23 243	49.72 185	81.75 67	75.93 249

tpmrst		58.24 244	58.70 233	56.84 279	66.97 283	34.32 302	69.57 248	61.14 286	47.17 248	58.58 239	71.60 278	41.28 181	60.41 295	49.20 189	62.84 259	75.78 251

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 45	59.04 50	83.65 30	81.05 93	60.15 84	70.43 68	89.84 25	41.09 182	85.59 69	67.61 54	82.90 58	85.77 63

V4		67.09 165	65.16 179	72.87 126	66.76 287	51.60 146	73.69 201	79.45 137	57.88 130	62.45 188	78.58 214	40.96 183	83.34 121	61.99 115	64.71 244	83.68 143

v52		67.09 165	65.16 179	72.87 126	66.77 286	51.60 146	73.69 201	79.45 137	57.88 130	62.46 187	78.57 215	40.95 184	83.34 121	61.99 115	64.70 246	83.68 143

114514_t		70.83 85	69.56 95	74.64 76	86.21 16	54.63 110	82.34 51	81.81 74	48.22 237	63.01 176	85.83 79	40.92 185	87.10 37	57.91 135	79.79 89	82.18 173

HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 95	55.65 99	69.07 251	75.52 193	42.68 285	63.53 171	77.95 219	40.43 186	81.64 162	46.01 210	71.91 175	83.73 141

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 168	42.34 250	74.98 183	77.15 179	55.83 163	65.04 156	81.16 165	39.91 187	80.14 189	47.18 199	72.76 161	82.90 163

MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 146	55.53 101	76.30 154	78.53 155	50.59 219	56.47 252	78.65 212	39.84 188	82.68 147	44.10 227	72.12 174	72.44 282
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TR-MVS		66.59 178	65.07 181	71.17 167	79.18 104	49.63 194	73.48 203	75.20 198	52.95 194	67.90 116	80.33 187	39.81 189	83.68 116	43.20 235	73.56 150	80.20 207

pmmvs6		63.69 204	62.82 201	66.27 222	70.63 256	39.27 269	73.13 206	75.47 194	52.69 197	59.75 225	82.30 133	39.71 190	77.03 232	47.40 198	64.35 249	82.53 167

pcd1.5k->3k		30.06 308	30.56 309	28.55 323	78.81 112	0.00 345	0.00 337	82.07 69	0.00 341	0.00 342	0.00 343	39.61 191	0.00 342	0.00 341	74.56 136	85.66 69

XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 195	56.87 81	70.59 237	79.04 144	54.77 179	66.99 131	86.01 75	39.57 192	78.21 218	62.54 102	73.33 153	83.37 150

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 223	57.78 65	77.47 133	76.88 181	57.60 132	61.97 194	76.85 242	39.31 193	80.49 183	54.72 151	70.28 200	82.17 174

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 89	57.54 67	82.74 43	82.84 62	60.58 74	65.24 153	86.18 71	39.25 194	86.03 61	66.95 60	76.79 125	83.22 153
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 62	71.74 65	75.76 55	80.22 90	57.51 69	82.55 47	83.40 47	61.32 64	66.67 134	87.33 52	39.15 195	86.59 48	67.70 52	77.30 119	83.19 155

LGP-MVS_train				75.76 55	80.22 90	57.51 69		83.40 47	61.32 64	66.67 134	87.33 52	39.15 195	86.59 48	67.70 52	77.30 119	83.19 155

TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 164	48.75 201	76.52 151	80.04 117	50.64 218	65.24 153	84.93 90	39.15 195	78.54 210	36.77 267	76.88 122	85.14 93
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	61.03 9	68.85 127	67.56 129	72.70 132	74.26 197	53.99 114	81.21 70	81.34 85	52.70 196	62.75 179	85.55 84	38.86 198	84.14 105	48.41 195	83.01 55	79.97 210

sss		56.17 257	56.57 247	54.96 286	66.93 284	36.32 293	57.94 295	61.69 285	41.67 289	58.64 238	75.32 259	38.72 199	56.25 313	42.04 243	66.19 234	72.31 286

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 86	58.59 57	82.68 44	82.02 70	55.46 171	67.18 129	84.39 102	38.51 200	83.17 127	60.65 122	76.10 128	80.30 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 164	65.58 173	71.88 152	70.37 260	49.70 190	70.25 243	78.45 159	51.52 209	69.16 99	80.37 184	38.45 201	82.50 150	60.19 126	71.46 179	83.44 149

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 191	54.92 108	82.79 41	78.48 157	51.26 214	65.41 149	83.49 115	38.37 202	83.24 125	66.06 62	69.25 215	85.56 71

tpm2		62.07 222	60.10 226	67.99 205	72.79 228	43.86 239	71.05 233	66.85 256	43.14 282	62.77 177	75.39 258	38.32 203	80.80 178	41.69 245	68.88 217	79.32 218

tpm cat1		59.25 239	56.95 245	66.15 224	72.19 237	46.96 217	68.09 254	65.76 260	40.03 300	57.81 244	70.56 285	38.32 203	74.51 247	38.26 260	61.50 268	77.00 240

CNLPA		65.43 189	64.02 186	69.68 187	78.73 115	58.07 62	77.82 124	70.71 226	51.49 210	61.57 203	83.58 113	38.23 205	70.82 257	43.90 228	70.10 203	80.16 208

1314		64.61 199	63.21 196	68.80 198	71.87 244	47.46 214	73.95 195	78.39 163	42.88 284	59.97 220	76.60 247	38.11 206	79.39 195	54.84 150	72.32 171	79.55 215

testdata				64.66 243	81.52 67	52.93 128		65.29 263	46.09 256	73.88 36	87.46 50	38.08 207	66.26 276	53.31 163	78.48 109	74.78 264

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 149	43.33 243	77.31 135	77.83 167	56.45 152	64.60 163	82.70 122	38.08 207	80.33 185	46.08 209	72.31 172	83.92 133

tpmp4_e23		62.71 217	60.13 225	70.45 179	73.40 206	48.39 204	72.82 210	69.49 237	44.88 265	59.91 221	74.99 260	37.79 209	81.47 167	40.22 252	67.71 227	81.48 182

EPNet_dtu		61.90 223	61.97 209	61.68 259	72.89 227	39.78 264	75.85 166	65.62 261	55.09 174	54.56 267	79.36 203	37.59 210	67.02 272	39.80 256	76.95 121	78.25 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		59.75 236	58.00 239	64.98 242	74.14 199	48.06 207	63.35 275	63.23 275	49.13 228	59.33 230	71.46 279	37.45 211	69.59 262	41.39 249	62.57 261	77.30 233

IterMVS		62.79 212	61.27 218	67.35 211	69.37 269	52.04 143	71.17 231	68.24 246	52.63 198	59.82 223	76.91 241	37.32 212	72.36 252	52.80 165	63.19 257	77.66 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 209	62.18 207	66.21 223	76.85 162	39.62 265	71.96 226	69.44 238	56.63 146	62.61 182	79.83 195	37.18 213	79.17 199	31.84 288	73.25 155	79.83 212

thres400		63.31 206	62.18 207	66.72 215	76.85 162	39.62 265	71.96 226	69.44 238	56.63 146	62.61 182	79.83 195	37.18 213	79.17 199	31.84 288	73.25 155	81.36 185

tpm		57.34 250	58.16 236	54.86 287	71.80 245	34.77 299	67.47 256	56.04 307	48.20 238	60.10 219	76.92 240	37.17 215	53.41 321	40.76 251	65.01 242	76.40 245

test222					83.14 52	58.68 56	72.57 213	63.45 273	41.78 287	67.56 125	86.12 72	37.13 216			78.73 106	74.98 260

thres200		62.20 220	61.16 220	65.34 239	75.38 181	39.99 262	69.60 247	69.29 240	55.64 168	61.87 196	76.99 239	37.07 217	78.96 209	31.28 295	73.28 154	77.06 238

thres600view7		63.30 207	62.27 205	66.41 219	77.18 156	38.87 270	72.35 216	69.11 242	56.98 137	62.37 191	80.96 170	37.01 218	79.00 208	31.43 294	73.05 158	81.36 185

view600		62.77 213	61.84 210	65.55 233	77.28 151	36.87 284	72.15 220	67.78 247	56.79 140	61.46 204	81.92 142	36.88 219	78.42 211	29.86 299	72.46 165	81.36 185

view800		62.77 213	61.84 210	65.55 233	77.28 151	36.87 284	72.15 220	67.78 247	56.79 140	61.46 204	81.92 142	36.88 219	78.42 211	29.86 299	72.46 165	81.36 185

conf0.05thres1000		62.77 213	61.84 210	65.55 233	77.28 151	36.87 284	72.15 220	67.78 247	56.79 140	61.46 204	81.92 142	36.88 219	78.42 211	29.86 299	72.46 165	81.36 185

tfpn		62.77 213	61.84 210	65.55 233	77.28 151	36.87 284	72.15 220	67.78 247	56.79 140	61.46 204	81.92 142	36.88 219	78.42 211	29.86 299	72.46 165	81.36 185

PatchFormer-LS_test		62.20 220	60.59 223	67.04 213	72.18 238	46.82 219	70.36 242	68.62 244	51.92 203	59.19 231	70.23 287	36.86 223	75.07 244	50.23 181	65.68 238	79.23 220

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 40	56.87 81	80.74 75	73.16 216	53.06 193	59.09 233	82.35 131	36.79 224	85.94 65	32.82 284	69.96 205	72.45 281

semantic-postprocess				65.40 238	71.99 241	50.80 156		69.63 234	45.71 262	60.61 216	77.93 220	36.56 225	65.99 278	55.67 147	63.50 254	79.42 217

XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 196	57.22 73	70.09 244	78.81 148	55.24 173	67.79 123	85.81 81	36.54 226	78.28 217	62.04 113	75.74 130	83.19 155

pmmvs4		61.48 228	59.39 228	67.76 207	71.57 246	53.86 115	71.42 228	65.34 262	44.20 273	59.46 226	77.92 221	35.90 227	74.71 246	43.87 229	64.87 243	74.71 265

CR-MVSNet		59.91 234	57.90 240	65.96 227	69.96 264	52.07 141	65.31 267	63.15 276	42.48 286	59.36 227	74.84 261	35.83 228	70.75 258	45.50 217	64.65 247	75.06 257

Patchmtry		57.16 251	56.47 248	59.23 266	69.17 270	34.58 301	62.98 276	63.15 276	44.53 270	56.83 249	74.84 261	35.83 228	68.71 266	40.03 254	60.91 272	74.39 267

RPMNet		58.70 241	56.29 251	65.96 227	69.96 264	52.07 141	65.31 267	62.15 283	43.20 281	59.36 227	70.15 289	35.37 230	70.75 258	36.42 273	64.65 247	75.06 257

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 186	46.24 222	78.66 99	76.85 182	59.60 103	63.45 172	82.09 141	35.25 231	77.41 227	59.88 128	78.76 105	85.14 93

test_0402		63.25 208	61.01 221	69.96 184	80.00 94	54.37 112	76.86 146	72.02 221	54.58 181	58.71 236	80.79 176	35.00 232	84.36 101	26.41 313	64.71 244	71.15 293

sam_mvs1												34.74 233

pmmvs5		56.47 253	55.68 254	58.86 269	61.41 309	36.71 289	66.37 259	62.75 279	40.38 298	53.70 274	76.62 245	34.56 234	67.05 271	40.02 255	65.27 240	72.83 276

patchmatchnet-post											64.03 306	34.50 235	74.27 248

PatchmatchNet		59.84 235	58.24 235	64.65 244	73.05 221	46.70 220	69.42 249	62.18 282	47.55 244	58.88 235	71.96 277	34.49 236	69.16 264	42.99 237	63.60 253	78.07 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 282	48.28 282	51.50 300	64.40 299	30.85 315	45.68 321	48.46 326	35.60 312	46.10 303	72.10 276	34.47 237	46.37 327	27.08 311	60.65 276	77.27 235

MS-PatchMatch		62.42 218	61.46 216	65.31 240	75.21 183	52.10 140	72.05 224	74.05 211	46.41 253	57.42 247	74.36 265	34.35 238	77.57 226	45.62 215	73.67 146	66.26 304

tpmvs		58.47 242	56.95 245	63.03 253	70.20 261	41.21 257	67.90 255	67.23 254	49.62 225	54.73 265	70.84 283	34.14 239	76.24 240	36.64 270	61.29 271	71.64 289

PMMVS		53.96 266	53.26 270	56.04 281	62.60 305	50.92 152	61.17 286	56.09 306	32.81 316	53.51 278	66.84 299	34.04 240	59.93 297	44.14 226	68.18 222	57.27 319

Patchmatch-RL test		58.16 245	55.49 255	66.15 224	67.92 279	48.89 200	60.66 288	51.07 320	47.86 242	59.36 227	62.71 312	34.02 241	72.27 253	56.41 141	59.40 284	77.30 233

test_post											3.55 339	33.90 242	66.52 274

Test4		67.77 156	65.97 168	73.19 121	68.64 272	50.58 161	74.80 187	80.48 112	54.13 187	59.11 232	79.07 208	33.89 243	83.12 129	63.61 94	79.98 86	85.87 59

PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 79	54.87 109	78.57 101	77.47 173	48.51 233	55.71 255	81.89 146	33.71 244	79.71 190	41.66 246	70.37 194	77.58 230
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 188	63.70 191	71.02 171	70.87 254	48.10 206	70.48 239	74.40 208	56.69 144	64.70 162	76.77 243	33.66 245	81.10 172	55.42 149	70.32 196	83.87 136

LS3D		64.71 197	62.50 204	71.34 163	79.72 100	55.71 97	79.82 85	74.72 205	48.50 234	56.62 250	84.62 95	33.59 246	82.34 153	29.65 304	75.23 133	75.97 246

sam_mvs												33.43 247

PatchT		53.17 272	53.44 269	52.33 298	68.29 277	25.34 328	58.21 294	54.41 313	44.46 271	54.56 267	69.05 292	33.32 248	60.94 292	36.93 266	61.76 266	70.73 295

test20.03		53.87 268	54.02 265	53.41 293	61.47 308	28.11 320	61.30 284	59.21 291	51.34 213	52.09 282	77.43 236	33.29 249	58.55 301	29.76 303	60.27 282	73.58 273

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 262	56.13 89	76.35 153	77.35 177	48.43 235	64.99 159	80.84 175	33.01 250	80.34 184	64.66 74	67.64 228	84.23 123

MDTV_nov1_ep13_2view						25.89 325	61.22 285		40.10 299	51.10 285		32.97 251		38.49 259		78.61 223

IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 184	52.78 130	73.09 207	75.13 199	55.69 166	58.48 241	73.73 269	32.86 252	86.32 57	50.63 177	70.11 202	81.10 196
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
xiu_mvs_v1_base_debu		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 206	70.04 74	81.41 162	32.79 253	79.02 204	63.81 91	77.31 116	81.22 193

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 206	70.04 74	81.41 162	32.79 253	79.02 204	63.81 91	77.31 116	81.22 193

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 206	70.04 74	81.41 162	32.79 253	79.02 204	63.81 91	77.31 116	81.22 193

Anonymous20231206		55.10 264	55.30 256	54.48 289	69.81 267	33.94 304	62.91 277	62.13 284	41.08 292	55.18 262	75.65 256	32.75 256	56.59 310	30.32 298	67.86 224	72.91 275

UGNet		68.81 128	67.39 135	73.06 122	78.33 123	54.47 111	79.77 86	75.40 195	60.45 76	63.22 173	84.40 101	32.71 257	80.91 176	51.71 172	80.56 77	83.81 137
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
test-LLR		58.15 246	58.13 238	58.22 272	68.57 273	44.80 229	65.46 264	57.92 296	50.08 222	55.44 258	69.82 290	32.62 258	57.44 304	49.66 186	73.62 147	72.41 283

test0.0.03 1		53.32 271	53.59 268	52.50 297	62.81 304	29.45 317	59.51 290	54.11 315	50.08 222	54.40 269	74.31 266	32.62 258	55.92 315	30.50 297	63.95 251	72.15 288

MDTV_nov1_ep13			57.00 244		72.73 229	38.26 275	65.02 270	64.73 267	44.74 267	55.46 257	72.48 274	32.61 260	70.47 260	37.47 263	67.75 226

cascas		65.98 184	63.42 194	73.64 99	77.26 155	52.58 133	72.26 218	77.21 178	48.56 232	61.21 209	74.60 264	32.57 261	85.82 67	50.38 179	76.75 126	82.52 168

test_post1							68.67 252				3.64 338	32.39 262	69.49 263	44.17 225

CVMVSNet		59.63 238	59.14 230	61.08 264	74.47 192	38.84 271	75.20 177	68.74 243	31.15 319	58.24 242	76.51 248	32.39 262	68.58 267	49.77 183	65.84 236	75.81 250

MIMVSNet		57.35 249	57.07 243	58.22 272	74.21 198	37.18 281	62.46 278	60.88 287	48.88 230	55.29 261	75.99 254	31.68 264	62.04 290	31.87 287	72.35 170	75.43 255

PVSNet_0	43.31 20	47.46 287	45.64 287	52.92 295	67.60 281	44.65 231	54.06 304	54.64 311	41.59 290	46.15 301	58.75 318	30.99 265	58.66 300	32.18 285	24.81 328	55.46 320

testing_2		66.02 183	63.77 190	72.76 131	66.03 292	50.48 167	72.93 208	80.36 115	54.41 184	54.25 271	76.76 244	30.89 266	83.16 128	64.19 85	74.08 142	84.65 109

gg-mvs-nofinetune		57.86 247	56.43 249	62.18 257	72.62 231	35.35 297	66.57 257	56.33 304	50.65 217	57.64 245	57.10 319	30.65 267	76.36 238	37.38 264	78.88 102	74.82 263

GG-mvs-BLEND				62.34 256	71.36 251	37.04 283	69.20 250	57.33 299		54.73 265	65.48 305	30.37 268	77.82 222	34.82 277	74.93 134	72.17 287

MDA-MVSNet-bldmvs		53.87 268	50.81 275	63.05 252	66.25 289	48.58 202	56.93 298	63.82 271	48.09 239	41.22 315	70.48 286	30.34 269	68.00 268	34.24 279	45.92 317	72.57 279

EPMVS		53.96 266	53.69 266	54.79 288	66.12 291	31.96 312	62.34 280	49.05 323	44.42 272	55.54 256	71.33 281	30.22 270	56.70 308	41.65 247	62.54 262	75.71 252

DWT-MVSNet_test		61.90 223	59.93 227	67.83 206	71.98 242	46.09 223	71.03 234	69.71 231	50.09 221	58.51 240	70.62 284	30.21 271	77.63 224	49.28 188	67.91 223	79.78 213

YYNet1		50.73 279	48.96 278	56.03 282	61.10 311	41.78 253	51.94 311	56.44 303	40.94 294	44.84 304	67.80 296	30.08 272	55.08 319	36.77 267	50.71 307	71.22 291

MDA-MVSNet_test_wron		50.71 280	48.95 279	56.00 283	61.17 310	41.84 252	51.90 312	56.45 302	40.96 293	44.79 305	67.84 295	30.04 273	55.07 320	36.71 269	50.69 308	71.11 294

MSDG		61.81 226	59.23 229	69.55 192	72.64 230	52.63 132	70.45 240	75.81 191	51.38 212	53.70 274	76.11 251	29.52 274	81.08 173	37.70 262	65.79 237	74.93 261

CMPMVS	42.80 21	57.81 248	55.97 252	63.32 249	60.98 312	47.38 215	64.66 271	69.50 236	32.06 318	46.83 299	77.80 224	29.50 275	71.36 255	48.68 192	73.75 145	71.21 292
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 210	61.23 219	68.92 197	76.57 166	47.80 209	59.92 289	76.39 184	54.35 185	58.67 237	82.46 130	29.44 276	81.49 166	42.12 242	71.14 181	77.46 231
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
UnsupCasMVSNet_eth		53.16 273	52.47 271	55.23 284	59.45 319	33.39 307	59.43 291	69.13 241	45.98 257	50.35 291	72.32 275	29.30 277	58.26 302	42.02 244	44.30 319	74.05 270

CHOSEN 280x420		47.83 285	46.36 286	52.24 299	67.37 282	49.78 188	38.91 329	43.11 332	35.00 313	43.27 312	63.30 311	28.95 278	49.19 326	36.53 271	60.80 275	57.76 318

pmmvs-eth3d		58.81 240	56.31 250	66.30 221	67.61 280	52.42 138	72.30 217	64.76 266	43.55 278	54.94 263	74.19 267	28.95 278	72.60 251	43.31 232	57.21 289	73.88 272

dp		51.89 276	51.60 274	52.77 296	68.44 276	32.45 309	62.36 279	54.57 312	44.16 274	49.31 292	67.91 294	28.87 280	56.61 309	33.89 280	54.89 295	69.24 301

jajsoiax		68.25 147	66.45 160	73.66 97	75.62 176	55.49 102	80.82 73	78.51 156	52.33 200	64.33 166	84.11 105	28.28 281	81.81 161	63.48 96	70.62 186	83.67 145

RPSCF		55.80 260	54.22 264	60.53 265	65.13 296	42.91 248	64.30 272	57.62 298	36.84 310	58.05 243	82.28 134	28.01 282	56.24 314	37.14 265	58.61 287	82.44 171

F-COLMAP		63.05 211	60.87 222	69.58 191	76.99 161	53.63 118	78.12 111	76.16 186	47.97 241	52.41 281	81.61 154	27.87 283	78.11 219	40.07 253	66.66 231	77.00 240

K. test v3		60.47 232	57.11 242	70.56 177	73.74 200	48.22 205	75.10 180	62.55 280	58.27 128	53.62 276	76.31 250	27.81 284	81.59 164	47.42 197	39.18 322	81.88 178

ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 138	52.83 129	80.39 78	78.03 165	57.30 134	57.47 246	82.55 128	27.68 285	84.17 104	45.54 216	69.78 208	79.90 211

UnsupCasMVSNet_bld		50.07 281	48.87 280	53.66 291	60.97 313	33.67 305	57.62 296	64.56 268	39.47 302	47.38 296	64.02 308	27.47 286	59.32 298	34.69 278	43.68 320	67.98 303

mvs_tets		68.18 151	66.36 164	73.63 100	75.61 177	55.35 105	80.77 74	78.56 154	52.48 199	64.27 168	84.10 106	27.45 287	81.84 160	63.45 97	70.56 188	83.69 142

lessismore_v0				69.91 185	71.42 249	47.80 209		50.90 321		50.39 290	75.56 257	27.43 288	81.33 168	45.91 211	34.10 325	80.59 202

ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 131	52.01 144	79.48 94	79.69 120	55.75 165	56.59 251	80.98 169	27.12 289	80.94 174	42.90 239	71.58 178	77.25 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 227	58.80 232	70.20 182	75.80 174	47.22 216	75.59 168	69.68 233	54.61 180	54.11 272	79.26 205	27.07 290	82.96 132	43.27 233	49.79 310	80.41 205

PVSNet	50.76 19	58.40 243	57.39 241	61.42 261	75.53 178	44.04 238	61.43 282	63.45 273	47.04 249	56.91 248	73.61 270	27.00 291	64.76 281	39.12 257	72.40 169	75.47 254

OpenMVS_ROB	52.78 18	60.03 233	58.14 237	65.69 232	70.47 257	44.82 228	75.33 172	70.86 225	45.04 264	56.06 253	76.00 252	26.89 292	79.65 191	35.36 276	67.29 229	72.60 278

ADS-MVSNet2		51.33 278	48.76 281	59.07 268	66.02 293	44.60 232	50.90 313	59.76 290	36.90 308	50.74 287	66.18 303	26.38 293	63.11 285	27.17 309	54.76 296	69.50 298

ADS-MVSNet		48.48 284	47.77 283	50.63 301	66.02 293	29.92 316	50.90 313	50.87 322	36.90 308	50.74 287	66.18 303	26.38 293	52.47 323	27.17 309	54.76 296	69.50 298

N_pmnet		39.35 301	40.28 298	36.54 318	63.76 301	1.62 343	49.37 316	0.76 345	34.62 314	43.61 311	66.38 302	26.25 295	42.57 333	26.02 315	51.77 303	65.44 305

MVS-HIRNet		45.52 290	44.48 292	48.65 305	68.49 275	34.05 303	59.41 292	44.50 331	27.03 323	37.96 321	50.47 326	26.16 296	64.10 282	26.74 312	59.52 283	47.82 323

FMVSNet5		55.86 259	54.93 257	58.66 271	71.05 253	36.35 291	64.18 274	62.48 281	46.76 250	50.66 289	74.73 263	25.80 297	64.04 283	33.11 283	65.57 239	75.59 253

new-patchmatchnet		47.56 286	47.73 284	47.06 307	58.81 320	9.37 339	48.78 317	59.21 291	43.28 279	44.22 307	68.66 293	25.67 298	57.20 307	31.57 293	49.35 313	74.62 266

MIMVSNet1		55.17 263	54.31 262	57.77 276	70.03 263	32.01 311	65.68 262	64.81 265	49.19 227	46.75 300	76.00 252	25.53 299	64.04 283	28.65 307	62.13 264	77.26 236

PatchMatch-RL		56.25 256	54.55 260	61.32 263	77.06 158	56.07 91	65.57 263	54.10 316	44.13 275	53.49 279	71.27 282	25.20 300	66.78 273	36.52 272	63.66 252	61.12 313

JIA-IIPM		51.56 277	47.68 285	63.21 250	64.61 298	50.73 158	47.71 318	58.77 293	42.90 283	48.46 294	51.72 323	24.97 301	70.24 261	36.06 275	53.89 299	68.64 302

EU-MVSNet		55.61 261	54.41 261	59.19 267	65.41 295	33.42 306	72.44 215	71.91 222	28.81 321	51.27 284	73.87 268	24.76 302	69.08 265	43.04 236	58.20 288	75.06 257

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 140	53.48 120	77.99 119	78.82 147	53.37 191	56.03 254	77.41 237	24.75 303	84.04 107	46.37 206	73.42 152	73.14 274

TESTMET0.1,1		55.28 262	54.90 258	56.42 280	66.56 288	43.67 241	65.46 264	56.27 305	39.18 303	53.83 273	67.44 297	24.21 304	55.46 318	48.04 196	73.11 157	70.13 296

LP		48.51 283	45.51 288	57.52 278	62.86 303	44.53 235	52.38 310	59.84 289	38.11 305	42.81 313	61.02 313	23.23 305	63.02 286	24.10 316	45.24 318	65.02 307

COLMAP_ROB	52.97 17	61.27 230	58.81 231	68.64 200	74.63 189	52.51 135	78.42 106	73.30 214	49.92 224	50.96 286	81.51 157	23.06 306	79.40 194	31.63 291	65.85 235	74.01 271
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 275	52.37 272	50.51 302	60.39 315	23.55 331	58.42 293	58.15 294	49.03 229	51.83 283	79.21 206	22.39 307	55.59 316	29.24 305	62.64 260	72.40 285

DSMNet-mixed		39.30 302	38.72 301	41.03 315	51.22 327	19.66 333	45.53 322	31.35 338	15.83 333	39.80 320	67.42 298	22.19 308	45.13 330	22.43 317	52.69 301	58.31 317

test-mter		56.42 254	55.82 253	58.22 272	68.57 273	44.80 229	65.46 264	57.92 296	39.94 301	55.44 258	69.82 290	21.92 309	57.44 304	49.66 186	73.62 147	72.41 283

OurMVSNet-221017-0		61.37 229	58.63 234	69.61 188	72.05 240	48.06 207	73.93 198	72.51 218	47.23 247	54.74 264	80.92 171	21.49 310	81.24 169	48.57 194	56.22 291	79.53 216

Anonymous20231211		55.92 258	53.63 267	62.77 254	68.22 278	35.56 296	74.48 191	69.89 230	46.42 252	49.07 293	73.45 271	21.13 311	76.77 234	28.74 306	51.30 306	75.97 246

ITE_SJBPF				62.09 258	66.16 290	44.55 234		64.32 269	47.36 246	55.31 260	80.34 186	19.27 312	62.68 288	36.29 274	62.39 263	79.04 221

AllTest		57.08 252	54.65 259	64.39 245	71.44 247	49.03 197	69.92 246	67.30 252	45.97 258	47.16 297	79.77 197	17.47 313	67.56 269	33.65 281	59.16 285	76.57 243

TestCases				64.39 245	71.44 247	49.03 197		67.30 252	45.97 258	47.16 297	79.77 197	17.47 313	67.56 269	33.65 281	59.16 285	76.57 243

testpf		44.11 294	45.40 289	40.26 316	60.52 314	27.34 322	33.26 331	54.33 314	45.87 261	41.08 316	60.26 315	16.46 315	59.14 299	46.09 208	50.68 309	34.31 329

XVG-ACMP-BASELINE		64.36 201	62.23 206	70.74 174	72.35 235	52.45 137	70.80 236	78.45 159	53.84 189	59.87 222	81.10 167	16.24 316	79.32 196	55.64 148	71.76 176	80.47 203

tmp_tt		9.43 317	11.14 318	4.30 329	2.38 342	4.40 342	13.62 336	16.08 342	0.39 338	15.89 333	13.06 336	15.80 317	5.54 341	12.63 332	10.46 337	2.95 335

USDC		56.35 255	54.24 263	62.69 255	64.74 297	40.31 261	65.05 269	73.83 212	43.93 276	47.58 295	77.71 226	15.36 318	75.05 245	38.19 261	61.81 265	72.70 277

test2356		45.61 289	44.66 291	48.47 306	60.15 316	28.08 321	52.44 309	52.83 319	38.01 306	46.13 302	60.98 314	15.08 319	55.54 317	20.43 325	55.85 293	61.78 311

test1235678		45.66 288	44.46 293	49.26 303	59.88 317	28.68 319	56.36 300	55.54 310	39.12 304	40.89 317	63.40 310	14.41 320	57.32 306	21.05 322	49.47 312	61.78 311

1111		44.40 293	45.00 290	42.61 314	57.55 322	17.33 336	53.82 307	57.05 300	40.78 295	44.11 308	66.57 300	13.37 321	45.77 328	22.15 318	49.58 311	64.73 308

.test1245		34.88 305	39.49 300	21.04 326	57.55 322	17.33 336	53.82 307	57.05 300	40.78 295	44.11 308	66.57 300	13.37 321	45.77 328	22.15 318	0.00 339	0.03 338

ANet_high		41.38 298	37.47 302	53.11 294	39.73 336	24.45 330	56.94 297	69.69 232	47.65 243	26.04 327	52.32 322	12.44 323	62.38 289	21.80 321	10.61 336	72.49 280

no-one		40.85 299	36.09 303	55.14 285	48.55 329	38.72 272	42.15 327	62.92 278	34.60 315	23.55 328	49.74 327	12.21 324	66.16 277	26.27 314	24.84 327	60.54 314

FPMVS		42.18 297	41.11 297	45.39 309	58.03 321	41.01 259	49.50 315	53.81 317	30.07 320	33.71 322	64.03 306	11.69 325	52.08 324	14.01 331	55.11 294	43.09 326

TinyColmap		54.14 265	51.72 273	61.40 262	66.84 285	41.97 251	66.52 258	68.51 245	44.81 266	42.69 314	75.77 255	11.66 326	72.94 250	31.96 286	56.77 290	69.27 300

TDRefinement		53.44 270	50.72 276	61.60 260	64.31 300	46.96 217	70.89 235	65.27 264	41.78 287	44.61 306	77.98 218	11.52 327	66.36 275	28.57 308	51.59 304	71.49 290

ambc				65.13 241	63.72 302	37.07 282	47.66 319	78.78 149		54.37 270	71.42 280	11.24 328	80.94 174	45.64 214	53.85 300	77.38 232

pmmvs3		44.92 291	41.95 296	53.86 290	52.58 326	43.55 242	62.11 281	46.90 330	26.05 325	40.63 318	60.19 316	11.08 329	57.91 303	31.83 290	46.15 316	60.11 315

new_pmnet		34.13 307	34.29 306	33.64 319	52.63 325	18.23 335	44.43 325	33.90 336	22.81 328	30.89 324	53.18 321	10.48 330	35.72 337	20.77 323	39.51 321	46.98 324

LF4IMVS		42.95 295	42.26 295	45.04 310	48.30 330	32.50 308	54.80 302	48.49 325	28.03 322	40.51 319	70.16 288	9.24 331	43.89 331	31.63 291	49.18 314	58.72 316

PM-MVS		52.33 274	50.19 277	58.75 270	62.10 306	45.14 227	65.75 261	40.38 333	43.60 277	53.52 277	72.65 273	9.16 332	65.87 279	50.41 178	54.18 298	65.24 306

testus		44.59 292	43.87 294	46.76 308	59.85 318	24.65 329	53.86 305	55.82 308	36.26 311	43.97 310	63.42 309	8.39 333	53.14 322	20.70 324	52.52 302	62.51 309

testmv		42.25 296	40.11 299	48.66 304	53.23 324	27.02 323	56.62 299	55.74 309	37.25 307	33.10 323	59.52 317	7.78 334	56.58 311	19.61 326	38.13 324	62.40 310

EMVS		22.97 313	21.84 315	26.36 325	40.20 335	19.53 334	41.95 328	34.64 335	17.09 332	9.73 337	22.83 335	7.29 335	42.22 335	9.18 336	13.66 333	17.32 333

test12356		36.16 304	35.94 304	36.83 317	50.82 328	8.52 340	44.84 324	53.49 318	32.72 317	30.11 325	55.08 320	7.11 336	49.47 325	16.60 328	32.68 326	52.50 321

E-PMN		23.77 312	22.73 313	26.90 324	42.02 334	20.67 332	42.66 326	35.70 334	17.43 331	10.28 336	25.05 333	6.42 337	42.39 334	10.28 334	14.71 331	17.63 332

Gipuma		34.77 306	31.91 308	43.33 313	62.05 307	37.87 277	20.39 334	67.03 255	23.23 327	18.41 331	25.84 332	4.24 338	62.73 287	14.71 330	51.32 305	29.38 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 311	25.91 311	31.87 322	39.46 337	6.57 341	31.17 332	28.52 339	23.96 326	20.45 330	48.94 328	4.20 339	37.94 336	16.51 329	19.97 329	51.09 322

LCM-MVSNet		40.30 300	35.88 305	53.57 292	42.24 333	29.15 318	45.21 323	60.53 288	22.23 329	28.02 326	50.98 325	3.72 340	61.78 291	31.22 296	38.76 323	69.78 297

DeepMVS_CX				12.03 328	17.97 341	10.91 338		10.60 343	7.46 336	11.07 335	28.36 331	3.28 341	11.29 340	8.01 337	9.74 338	13.89 334

PMVS	28.69 22	36.22 303	33.29 307	45.02 311	36.82 338	35.98 295	54.68 303	48.74 324	26.31 324	21.02 329	51.61 324	2.88 342	60.10 296	9.99 335	47.58 315	38.99 328
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 314	17.77 316	32.34 321	34.34 340	25.44 327	16.11 335	24.11 340	11.19 335	13.22 334	31.92 330	1.58 343	30.95 338	10.47 333	17.03 330	40.62 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		27.88 310	25.99 310	33.55 320	47.54 331	25.89 325	47.24 320	32.91 337	21.44 330	15.90 332	38.09 329	0.85 344	42.76 332	16.90 327	13.03 334	32.00 330

wuyk23d		13.32 316	12.52 317	15.71 327	47.54 331	26.27 324	31.06 333	1.98 344	4.93 337	5.18 339	1.94 340	0.45 345	18.54 339	6.81 338	12.83 335	2.33 336

wuykxyi23d		28.12 309	22.54 314	44.87 312	34.97 339	32.11 310	37.96 330	47.31 328	13.32 334	9.29 338	23.72 334	0.45 345	56.58 311	21.85 320	13.98 332	45.93 325

test123		4.73 319	6.30 320	0.02 330	0.01 343	0.01 344	56.36 300	0.00 346	0.01 339	0.04 340	0.21 342	0.01 347	0.00 342	0.03 340	0.00 339	0.04 337

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	0.00 343	0.00 348	0.00 342	0.00 341	0.00 339	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	0.00 343	0.00 348	0.00 342	0.00 341	0.00 339	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	0.00 343	0.00 348	0.00 342	0.00 341	0.00 339	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	0.00 343	0.00 348	0.00 342	0.00 341	0.00 339	0.00 340

testmvs		4.52 320	6.03 321	0.01 331	0.01 343	0.00 345	53.86 305	0.00 346	0.01 339	0.04 340	0.27 341	0.00 348	0.00 342	0.04 339	0.00 339	0.03 338

ab-mvs-re		6.49 318	8.65 319	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	77.89 222	0.00 348	0.00 342	0.00 341	0.00 339	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 345	0.00 337	0.00 346	0.00 341	0.00 342	0.00 343	0.00 348	0.00 342	0.00 341	0.00 339	0.00 340

ESAPD								86.76 3

MTGPA								80.97 97

MTMP								17.08 341

gm-plane-assit					71.40 250	41.72 255			48.85 231		73.31 272		82.48 152	48.90 191

test9_res														75.28 14	88.31 16	83.81 137

agg_prior2														73.09 29	87.93 23	84.33 115

agg_prior					85.04 34	59.96 40		81.04 94		74.68 26			84.04 107

test_prior4						62.51 17	82.08 57

test_prior				76.69 44	84.20 46	57.27 71		84.88 15					86.43 54			86.38 41

旧先验2							76.08 160		45.32 263	76.55 13			65.56 280	58.75 133

新几何2							76.12 158

无先验							79.66 89	74.30 209	48.40 236				80.78 179	53.62 158		79.03 222

原ACMM2							79.02 95

testdata2													72.18 254	46.95 203

testdata1							72.65 211		60.50 75

plane_prior7					81.41 70	55.96 93

plane_prior5								84.01 29					87.21 33	68.16 48	80.58 75	84.65 109

plane_prior4											86.10 73

plane_prior3						56.09 90			63.92 30	69.27 95

plane_prior2							84.22 22		64.52 24

plane_prior1					81.27 75

plane_prior						56.31 84	83.58 31		63.19 37						80.48 78

n2								0.00 346

nn								0.00 346

door-mid								47.19 329

test11								83.47 44

door								47.60 327

HQP5-MVS						54.94 106

HQP-NCC					80.66 82		82.31 52		62.10 54	67.85 117

ACMP_Plane					80.66 82		82.31 52		62.10 54	67.85 117

BP-MVS														67.04 58

HQP4-MVS										67.85 117			86.93 39			84.32 116

HQP3-MVS								83.90 33							80.35 81

NP-MVS					80.98 80	56.05 92					85.54 85

ACMMP++_ref															74.07 143

ACMMP++															72.16 173

High-resolution multi-view benchmark