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								86.64 4				65.59 1			90.06 3	86.78 39

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 17	62.73 12	86.09 8	85.03 14	65.51 15	83.81 1	90.51 11	63.71 2	89.23 6	81.51 1	88.44 12	85.45 79

DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 42	60.32 38	83.03 37	85.33 11	62.86 43	80.17 4	90.03 21	61.76 3	88.95 10	74.21 19	88.67 11	88.12 7

CSCG		76.92 25	76.75 23	77.41 36	83.96 49	59.60 44	82.95 38	86.50 5	60.78 71	75.27 20	84.83 91	60.76 4	86.56 50	67.86 51	87.87 26	86.06 56

TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 38	61.41 27	84.03 26	83.82 38	59.34 113	79.37 6	89.76 26	59.84 5	87.62 28	76.69 11	86.74 36	87.68 16

canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 110	50.56 165	80.23 79	83.87 36	60.30 82	77.15 12	86.56 66	59.65 6	82.00 157	66.01 64	82.12 64	88.58 4

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 10	60.02 40	88.12 1	86.42 6	62.94 40	82.40 3	92.12 1	59.64 7	89.76 3	78.70 5	88.32 16	86.79 38

DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 138	52.25 140	75.59 168	84.17 26	63.76 32	73.15 47	82.79 121	59.58 8	86.80 41	67.24 56	86.04 42	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 14	66.96 6	77.58 11	90.06 20	59.47 9	89.13 8	78.67 6	89.73 4	87.03 33

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 9	58.36 60	83.22 35	86.93 1	56.91 141	74.91 25	88.19 44	59.15 10	87.68 27	73.67 25	87.45 27	86.57 41

nrg030		72.96 60	73.01 55	72.84 128	75.41 183	50.24 174	80.02 82	82.89 62	58.36 127	74.44 30	86.73 57	58.90 11	80.83 177	65.84 66	74.46 138	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 40	65.37 16	78.78 8	90.64 7	58.63 12	87.24 31	79.00 4	90.37 2	85.26 92

DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 50	61.62 24	84.17 24	86.85 2	63.23 35	73.84 38	90.25 18	57.68 13	89.96 2	74.62 18	89.03 7	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	79.20 104	44.13 237	76.02 164	82.60 64	66.48 14	68.20 111	84.60 97	56.82 14	82.82 139	54.62 152	70.43 193	87.36 27

Regformer-1		75.47 40	74.93 40	77.09 40	80.43 88	57.70 67	79.50 92	82.13 67	67.84 1	75.73 19	80.75 180	56.50 15	86.07 58	71.07 39	80.38 80	87.50 20

Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 137	53.64 118	79.62 90	79.61 124	61.63 61	72.02 61	82.61 126	56.44 16	85.97 64	63.99 89	79.07 102	87.25 29

alignmvs		73.86 53	73.99 46	73.45 107	78.20 127	50.50 167	78.57 101	82.43 65	59.40 111	76.57 13	86.71 59	56.42 17	81.23 170	65.84 66	81.79 66	88.62 2

test_prior3		76.89 27	76.96 21	76.69 44	84.20 47	57.27 72	81.75 59	84.88 16	60.37 78	75.01 21	89.06 33	56.22 18	86.43 54	72.19 32	88.96 9	86.38 42

test_prior2							81.75 59		60.37 78	75.01 21	89.06 33	56.22 18		72.19 32	88.96 9

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 63	58.77 56	81.80 58	78.57 154	58.58 121	74.32 31	84.51 100	55.94 20	87.22 32	67.11 57	84.48 50	85.52 74

Regformer-2		75.63 39	74.99 38	77.54 34	80.43 88	58.32 61	79.50 92	82.92 59	67.84 1	75.94 16	80.75 180	55.73 21	86.80 41	71.44 38	80.38 80	87.50 20

MVS_Test		72.45 66	72.46 60	72.42 145	74.88 188	48.50 204	76.28 155	83.14 57	59.40 111	72.46 56	84.68 93	55.66 22	81.12 171	65.98 65	79.66 91	87.63 17

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 14	60.81 34	85.52 15	84.36 22	60.61 73	79.05 7	90.30 16	55.54 23	88.32 16	73.48 28	87.03 31	84.83 104
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 4	62.61 16	85.20 17	84.42 21	66.73 10	74.67 29	89.38 30	55.30 24	89.18 7	74.19 20	87.34 28	86.38 42

FIs		70.82 86	71.43 68	68.98 196	78.33 124	38.14 280	76.96 144	83.59 42	61.02 68	67.33 129	86.73 57	55.07 25	81.64 162	54.61 154	79.22 99	87.14 31

SD-MVS		77.70 17	77.62 15	77.93 32	84.47 45	61.88 23	84.55 19	83.87 36	60.37 78	79.89 5	89.38 30	54.97 26	85.58 70	76.12 12	84.94 46	86.33 48

CANet		76.46 31	75.93 32	78.06 29	81.29 74	57.53 69	82.35 50	83.31 52	67.78 3	70.09 74	86.34 69	54.92 27	88.90 11	72.68 30	84.55 48	87.76 14

Regformer-3		73.89 52	73.28 54	75.71 58	79.75 97	55.48 104	78.54 103	79.93 119	66.58 12	73.62 41	80.30 190	54.87 28	84.54 98	69.09 45	76.84 124	87.10 32

MP-MVS		78.35 11	78.26 11	78.64 21	86.54 12	63.47 5	86.02 9	83.55 43	63.89 31	73.60 42	90.60 8	54.85 29	86.72 44	77.20 10	88.06 21	85.74 67

mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 249	44.57 234	71.99 227	75.23 198	51.67 212	67.06 131	82.57 127	54.68 30	77.94 223	56.56 140	75.71 132	86.26 52

test12				77.76 33	84.52 44	58.41 59		83.36 50		72.93 51		54.61 31	88.05 21		88.12 20	86.81 37

FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 148	34.73 305	76.05 162	83.19 55	60.84 69	65.88 146	86.46 67	54.52 32	80.76 181	52.52 166	78.12 112	86.91 34

SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 56	62.18 19	87.60 2	85.83 8	66.69 11	78.03 10	90.98 4	54.26 33	90.06 1	78.42 7	89.02 8	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 34

MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 56	60.63 36	77.29 138	78.40 163	61.18 67	70.58 68	85.97 76	54.18 35	84.00 111	67.52 55	82.98 58	82.45 171

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 120	51.50 149	75.01 181	79.46 137	56.16 160	68.59 104	79.55 208	53.97 36	84.05 106	53.34 162	77.53 116	85.65 71

agg_prior1		75.94 36	76.01 31	75.72 57	85.04 35	59.96 41	81.44 67	81.04 95	56.14 161	74.68 27	88.90 37	53.91 37	84.04 107	75.01 17	87.92 25	83.16 159

UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 119	45.29 227	75.94 165	82.92 59	63.68 34	68.16 113	83.59 112	53.89 38	83.49 120	53.97 156	71.12 185	86.89 35

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 97	56.54 84	78.54 103	81.49 81	66.93 8	73.90 36	80.30 190	53.84 39	85.98 63	69.76 41	76.84 124	87.17 30

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 19	60.86 33	84.71 18	84.85 18	61.98 59	73.06 49	88.88 38	53.72 40	89.06 9	68.27 47	88.04 22	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 28	61.59 25	81.62 63	81.26 89	55.65 169	74.93 23	88.81 39	53.70 41	84.68 95

train_agg		76.27 33	76.15 28	76.64 47	85.58 28	61.59 25	81.62 63	81.26 89	55.86 163	74.93 23	88.81 39	53.70 41	84.68 95	75.24 15	88.33 14	83.65 148

test_8					85.40 31	60.96 32	81.54 66	81.18 92	55.86 163	74.81 26	88.80 41	53.70 41	84.45 100

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 33	59.14 50	81.31 69	84.02 29	56.32 156	74.05 32	88.98 36	53.34 44	87.92 24	69.23 44	88.42 13	87.59 18

agg_prior3		76.13 34	75.89 34	76.85 42	85.76 24	62.02 21	81.65 61	81.01 97	55.51 172	73.73 39	88.60 43	53.23 45	84.90 88	75.24 15	88.33 14	83.65 148

HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 7	62.81 10	86.29 5	84.32 23	62.82 44	73.96 33	90.50 12	53.20 46	88.35 14	74.02 21	87.05 29	86.13 53

#test#		77.83 15	77.41 17	79.10 11	86.71 7	62.81 10	85.69 14	84.32 23	61.61 62	73.96 33	90.50 12	53.20 46	88.35 14	73.68 24	87.05 29	86.13 53

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 122	54.02 114	77.05 142	79.33 141	65.03 20	71.68 63	79.35 212	52.75 48	84.89 89	66.46 61	74.23 141	85.83 61

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 30	61.04 31	83.84 28	85.16 12	62.88 42	78.10 9	91.26 3	52.51 49	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	77.63 143	55.71 98	76.04 163	81.81 75	50.30 229	69.66 85	85.40 88	52.51 49	84.89 89	51.82 170	80.24 84	85.45 79
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 38	59.52 45	82.93 39	85.39 10	62.15 53	76.41 15	91.51 2	52.47 51	86.78 43	80.66 2	89.64 6	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 112	53.33 124	78.23 109	84.79 19	61.30 66	70.41 70	81.04 168	52.41 52	87.12 36	64.61 76	82.49 63	85.41 86
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	78.05 133	43.81 241	74.20 193	80.86 101	65.18 17	62.76 179	84.52 98	52.35 53	83.59 118	50.96 176	70.78 187	87.37 25

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 135	53.56 120	76.62 148	79.16 143	64.40 26	71.18 65	78.95 217	52.19 54	84.66 97	65.47 69	73.57 150	85.32 89

PAPM_NR		72.63 63	71.80 64	75.13 68	81.72 66	53.42 123	79.91 84	83.28 54	59.14 115	66.31 141	85.90 77	51.86 55	86.06 59	57.45 137	80.62 74	85.91 59

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 26	49.69 193	81.59 65	81.29 88	61.45 63	71.05 66	88.11 45	51.77 56	87.73 26	61.05 121	83.09 55	85.05 98

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 117	49.70 191	82.10 56	81.65 77	60.40 77	65.94 144	85.84 78	51.74 57	86.37 56	55.93 143	79.55 94	88.07 8

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 118	42.97 248	76.53 150	81.16 93	66.95 7	68.53 107	85.42 87	51.61 58	83.07 130	52.32 167	69.70 216	87.46 22

EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 196	49.39 197	75.20 177	78.45 160	59.60 103	69.16 100	76.51 257	51.29 59	82.50 150	59.86 130	71.45 183	83.30 152

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 198	49.40 196	76.23 157	77.55 173	59.60 103	65.85 147	81.59 156	51.28 60	81.58 165	59.87 129	69.90 211	83.30 152
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	65.87 232	75.22 185	38.56 277	74.66 188	75.08 204	58.90 118	61.79 201	82.63 125	51.18 61	78.07 222	43.63 231	55.87 302	80.99 200

VNet		69.68 112	70.19 86	68.16 204	79.73 100	41.63 257	70.53 241	77.38 177	60.37 78	70.69 67	86.63 62	51.08 62	77.09 233	53.61 160	81.69 70	85.75 66

VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 151	41.00 261	74.04 194	79.68 122	60.06 85	69.26 98	84.81 92	51.06 63	77.58 227	54.44 155	74.43 139	84.48 115

PAPR		71.72 77	70.82 79	74.41 81	81.20 78	51.17 150	79.55 91	83.33 51	55.81 166	66.93 133	84.61 96	50.95 64	86.06 59	55.79 146	79.20 100	86.00 57

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 86	55.91 96	84.28 21	85.78 9	56.08 162	73.41 44	86.58 65	50.94 65	88.54 12	70.79 40	89.71 5	87.79 13

HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 37	60.81 34	82.91 40	85.08 13	62.57 47	73.09 48	89.97 23	50.90 66	87.48 29	75.30 13	86.85 34	87.33 28

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 136	37.75 283	77.57 131	82.11 69	62.03 58	62.65 182	82.48 129	50.57 67	79.46 193	42.91 238	64.01 260	84.79 107

EPNet		73.09 59	72.16 61	75.90 53	75.95 176	56.28 87	83.05 36	72.39 221	66.53 13	65.27 152	87.00 55	50.40 68	85.47 74	62.48 103	86.32 41	85.94 58
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 93	39.84 264	73.75 200	76.07 189	64.68 21	68.11 115	83.63 111	50.39 69	79.14 204	49.78 182	69.66 217	86.34 47

region2R		77.67 18	77.18 20	79.15 7	86.76 5	62.95 8	86.29 5	84.16 27	62.81 46	73.30 45	90.58 9	49.90 70	88.21 18	73.78 23	87.03 31	86.29 51

UA-Net		73.13 58	72.93 56	73.76 91	83.58 51	51.66 146	78.75 96	77.66 171	67.75 4	72.61 54	89.42 28	49.82 71	83.29 124	53.61 160	83.14 54	86.32 49

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 6	62.93 9	86.29 5	84.24 25	62.82 44	73.55 43	90.56 10	49.80 72	88.24 17	74.02 21	87.03 31	86.32 49

API-MVS		72.17 71	71.41 69	74.45 80	81.95 64	57.22 74	84.03 26	80.38 115	59.89 92	68.40 108	82.33 132	49.64 73	87.83 25	51.87 169	84.16 52	78.30 228

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 73	58.89 54	82.50 49	84.07 28	67.73 5	72.08 60	87.28 54	49.49 74	89.57 4	73.52 27	86.40 40	87.87 11

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 172	41.73 255	70.41 244	76.14 188	53.99 191	65.98 143	83.51 114	49.48 75	76.24 243	48.60 193	73.46 152	84.14 131

v8		70.33 102	69.28 105	73.49 105	73.15 224	50.22 175	78.62 100	80.78 102	60.79 70	66.45 138	82.11 140	49.35 76	84.98 82	63.58 95	68.71 224	85.28 90

v17		68.37 141	67.00 147	72.48 139	73.22 220	50.31 171	78.10 115	79.58 131	59.71 98	61.67 203	77.60 239	49.31 77	82.89 135	62.37 105	61.48 280	84.23 124

v18		68.33 142	66.96 148	72.42 145	73.13 228	50.16 180	77.97 120	79.57 133	59.57 107	61.80 200	77.50 244	49.30 78	82.90 134	62.31 107	61.50 278	84.20 130

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 111	45.63 226	80.22 80	78.69 152	64.14 29	66.46 137	87.36 51	49.30 78	85.60 68	50.26 180	83.71 53	88.59 3

v1neww		70.66 88	69.70 91	73.53 102	73.15 224	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v7new		70.66 88	69.70 91	73.53 102	73.15 224	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v6		70.66 88	69.70 91	73.53 102	73.14 227	50.21 178	78.11 112	80.67 105	59.65 100	69.82 83	81.65 152	49.29 80	84.96 84	64.55 77	70.39 195	85.42 82

v16		68.38 140	67.01 146	72.47 143	73.22 220	50.29 172	78.10 115	79.59 129	59.71 98	61.72 202	77.60 239	49.28 83	82.89 135	62.36 106	61.54 277	84.23 124

XXY-MVS		60.68 234	61.67 217	57.70 286	70.43 268	38.45 278	64.19 282	66.47 261	48.05 249	63.22 174	80.86 175	49.28 83	60.47 303	45.25 222	67.28 240	74.19 277

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 235	50.00 185	77.69 126	80.59 108	59.50 108	69.60 87	81.43 159	49.26 85	84.77 92	64.48 81	70.31 201	85.47 76

v1		70.50 95	69.53 96	73.42 110	72.91 235	50.00 185	77.69 126	80.59 108	59.50 108	69.59 89	81.42 161	49.26 85	84.77 92	64.49 80	70.30 202	85.47 76

cdsmvs_eth3d_5k		17.50 324	23.34 321	0.00 341	0.00 355	0.00 355	0.00 346	78.63 153	0.00 350	0.00 352	82.18 135	49.25 87	0.00 351	0.00 350	0.00 349	0.00 350

v15		68.22 150	66.81 154	72.47 143	73.25 219	50.40 169	77.92 122	79.60 126	59.77 97	61.28 211	77.52 243	49.25 87	82.77 141	62.16 112	60.51 287	84.24 123

diffmvs		67.72 157	66.73 155	70.70 176	69.74 278	47.69 213	73.33 204	74.74 205	53.30 195	64.51 165	81.80 148	49.25 87	79.02 206	59.15 132	74.75 136	85.39 87

v1141		70.50 95	69.53 96	73.41 111	72.92 234	50.00 185	77.69 126	80.60 107	59.50 108	69.60 87	81.43 159	49.24 90	84.77 92	64.48 81	70.30 202	85.46 78

V14		68.25 147	66.82 153	72.52 138	73.33 218	50.53 166	78.02 118	79.60 126	59.83 94	61.16 213	77.57 241	49.19 91	82.77 141	62.18 108	60.50 288	84.26 122

V9		68.27 145	66.84 150	72.56 136	73.39 217	50.63 161	78.10 115	79.60 126	59.94 89	61.05 215	77.62 238	49.18 92	82.77 141	62.17 110	60.48 289	84.27 121

PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 62	58.82 55	79.93 83	80.35 117	55.09 177	65.82 148	82.16 138	49.17 93	82.64 149	60.34 125	78.62 109	82.50 170

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 162	50.57 163	74.50 190	81.52 78	53.66 193	64.22 170	79.72 202	49.13 94	82.87 137	55.82 144	73.92 145	79.77 217

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 162	50.57 163	72.51 216	81.52 78	51.91 207	64.22 170	77.77 233	49.13 94	82.87 137	55.82 144	79.58 92	80.14 211

v12		68.28 144	66.83 152	72.60 135	73.43 214	50.74 158	78.18 110	79.59 129	60.01 88	60.89 217	77.66 237	49.12 96	82.77 141	62.18 108	60.46 290	84.29 120

DU-MVS		70.01 107	69.53 96	71.44 159	78.05 133	44.13 237	75.01 181	81.51 80	64.37 27	68.20 111	84.52 98	49.12 96	82.82 139	54.62 152	70.43 193	87.37 25

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 239	75.65 178	37.70 284	75.42 171	74.65 207	59.90 90	68.14 114	83.15 120	49.12 96	77.20 231	52.23 168	69.78 213	81.60 181

v13		68.29 143	66.84 150	72.63 133	73.50 212	50.83 156	78.25 108	79.58 131	60.05 86	60.76 218	77.68 236	49.11 99	82.77 141	62.17 110	60.45 291	84.30 119

VPNet		67.52 158	68.11 121	65.74 233	79.18 105	36.80 292	72.17 221	72.83 219	62.04 57	67.79 124	85.83 79	48.88 100	76.60 239	51.30 174	72.97 162	83.81 138

MPTG		77.61 19	77.36 18	78.35 24	86.08 21	63.57 2	83.37 33	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

MTAPA		76.90 26	76.42 27	78.35 24	86.08 21	63.57 2	74.92 184	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

原ACMM1				74.69 73	85.39 32	59.40 46		83.42 47	51.47 218	70.27 73	86.61 63	48.61 103	86.51 52	53.85 157	87.96 23	78.16 230

v148		68.24 149	67.19 144	71.40 162	70.43 268	47.77 212	75.76 167	77.03 181	58.91 117	67.36 128	80.10 194	48.60 104	81.89 158	60.01 127	66.52 243	84.53 113

PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 20	62.73 12	82.55 47	83.74 39	61.71 60	72.45 57	90.34 15	48.48 105	88.13 19	72.32 31	86.85 34	85.78 62

Test By Simon												48.33 106

v11		68.15 153	66.73 155	72.42 145	73.43 214	50.28 173	77.94 121	79.65 123	59.88 93	61.11 214	77.55 242	48.25 107	82.75 146	61.88 117	60.85 284	84.23 124

CP-MVS		77.12 24	76.68 24	78.43 23	86.05 23	63.18 7	87.55 3	83.45 46	62.44 50	72.68 53	90.50 12	48.18 108	87.34 30	73.59 26	85.71 43	84.76 109

MVS		67.37 159	66.33 165	70.51 178	75.46 182	50.94 151	73.95 195	81.85 74	41.57 300	62.54 185	78.57 223	47.98 109	85.47 74	52.97 164	82.05 65	75.14 260

XVS		77.17 23	76.56 26	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	90.01 22	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	6.49 346	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 65	59.39 47	81.38 68	82.90 61	54.90 181	68.08 116	78.70 218	47.73 112	85.51 73	51.68 173	84.17 51	81.88 179
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	78.09 131	49.02 200	77.09 141	81.24 91	51.04 224	60.91 216	83.98 108	47.71 113	84.99 80	40.81 250	79.32 98	80.90 201

v10		70.21 104	69.02 107	73.81 88	73.51 211	50.92 153	78.74 97	81.39 84	60.05 86	66.39 139	81.83 147	47.58 114	85.41 77	62.80 100	68.86 223	85.09 97

v7		70.57 92	69.48 101	73.85 86	73.50 212	50.92 153	78.27 107	81.43 82	58.93 116	69.61 86	81.49 158	47.56 115	85.43 76	63.94 90	70.62 189	85.21 93

v1144		70.42 100	69.31 104	73.76 91	73.22 220	50.64 160	77.83 123	81.43 82	58.58 121	69.40 94	81.16 165	47.53 116	85.29 79	64.01 88	70.64 188	85.34 88

v2v482		70.50 95	69.45 103	73.66 97	72.62 241	50.03 184	77.58 130	80.51 112	59.90 90	69.52 90	82.14 139	47.53 116	84.88 91	65.07 72	70.17 205	86.09 55

pm-mvs1		65.24 192	64.97 182	66.04 227	72.38 244	39.40 269	72.62 214	75.63 193	55.53 171	62.35 195	83.18 119	47.45 118	76.47 240	49.06 190	66.54 242	82.24 173

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 192	41.02 259	69.96 248	74.43 208	49.29 235	61.66 204	80.92 173	47.43 119	76.68 238	44.91 223	71.69 180	81.94 177

OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 82	59.01 52	82.60 46	83.64 41	63.74 33	72.52 55	87.49 49	47.18 120	85.88 66	69.47 43	80.78 72	83.66 147

pcd_1.5k_mvsjas		3.92 330	5.23 331	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	0.00 352	47.05 121	0.00 351	0.00 350	0.00 349	0.00 350

PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 120	55.93 95	81.63 62	82.12 68	56.24 159	70.02 78	85.68 82	47.05 121	84.34 102	65.27 70	74.41 140	85.67 69

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 68	55.79 97	74.92 184	79.00 146	55.04 179	69.88 80	78.66 219	47.05 121	82.19 154	61.61 119	79.58 92	80.83 202

WTY-MVS		59.75 239	60.39 227	57.85 284	72.32 246	37.83 282	61.05 296	64.18 279	45.95 269	61.91 198	79.11 215	47.01 124	60.88 302	42.50 240	69.49 219	74.83 266

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 77	55.63 101	75.11 179	78.92 147	54.92 180	69.96 79	79.68 203	47.00 125	82.09 156	61.60 120	79.37 95	80.81 203

v144192		69.71 111	68.51 113	73.33 115	73.10 229	50.13 182	77.54 132	80.64 106	56.65 147	68.57 106	80.55 183	46.87 126	84.96 84	62.98 98	69.66 217	84.89 103

PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 160	36.56 294	77.03 143	80.42 114	62.95 39	62.51 187	84.03 107	46.69 127	79.07 205	44.22 224	63.08 268	85.51 75

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 13	63.50 4	85.74 13	82.28 66	62.90 41	71.77 62	90.26 17	46.61 128	86.55 51	71.71 35	85.66 44	84.97 101

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 142	36.33 296	76.83 147	79.52 135	62.45 49	62.54 185	83.47 116	46.32 129	78.37 217	45.47 219	63.43 265	85.45 79

V42		68.65 132	67.35 138	72.56 136	68.93 281	50.18 179	72.90 209	79.47 136	56.92 140	69.45 93	80.26 192	46.29 130	82.99 131	64.07 86	67.82 235	84.53 113

1112_ss		64.00 203	63.36 195	65.93 230	79.28 103	42.58 250	71.35 231	72.36 222	46.41 262	60.55 220	77.89 230	46.27 131	73.28 258	46.18 207	69.97 209	81.92 178

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 55	59.29 49	82.30 55	81.88 72	59.34 113	71.59 64	86.83 56	45.94 132	83.65 117	65.09 71	85.22 45	81.06 198

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 149	36.30 298	76.94 145	79.61 124	62.36 51	62.43 193	83.66 110	45.69 133	78.37 217	45.35 221	63.26 266	85.42 82

APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 61	58.24 62	83.67 29	83.29 53	58.41 125	73.71 40	90.14 19	45.62 134	85.99 62	69.64 42	82.85 61	85.78 62

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 175	34.79 303	76.43 152	79.38 140	62.55 48	61.66 204	83.83 109	45.60 135	79.15 203	41.64 248	60.88 283	85.00 99

BH-w/o		66.85 171	65.83 170	69.90 186	79.29 102	52.46 137	74.66 188	76.65 184	54.51 186	64.85 161	78.12 225	45.59 136	82.95 133	43.26 234	75.54 133	74.27 272

HQP2-MVS												45.46 137

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 83	54.94 107	82.31 52	83.90 34	62.10 54	67.85 118	85.54 85	45.46 137	86.93 39	67.04 58	80.35 82	84.32 117

ACMMP		76.02 35	75.33 37	78.07 28	85.20 34	61.91 22	85.49 16	84.44 20	63.04 38	69.80 84	89.74 27	45.43 139	87.16 35	72.01 34	82.87 60	85.14 94
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	76.60 168	53.15 126	79.74 88	79.78 120	58.37 126	68.75 103	86.45 68	45.43 139	80.60 182	62.58 101	77.73 114	87.58 19

BH-untuned		68.27 145	67.29 139	71.21 165	79.74 99	53.22 125	76.06 161	77.46 176	57.19 135	66.10 142	81.61 154	45.37 141	83.50 119	45.42 220	76.68 128	76.91 246

v1192		69.97 109	68.68 111	73.85 86	73.19 223	50.94 151	77.68 129	81.36 85	57.51 133	68.95 102	80.85 176	45.28 142	85.33 78	62.97 99	70.37 198	85.27 91

HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 71	56.31 85	84.22 22	84.01 30	64.52 24	69.27 96	86.10 73	45.26 143	87.21 33	68.16 48	80.58 76	84.65 110

plane_prior6					81.20 78	56.24 89						45.26 143

v1921920		69.47 118	68.17 120	73.36 114	73.06 230	50.10 183	77.39 134	80.56 110	56.58 153	68.59 104	80.37 186	44.72 145	84.98 82	62.47 104	69.82 212	85.00 99

test_normal		69.26 122	67.90 124	73.32 116	70.84 265	50.38 170	75.30 173	79.17 142	54.23 189	62.00 196	80.61 182	44.69 146	83.89 113	64.33 83	79.95 88	85.69 68

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 262	50.14 181	75.49 170	79.16 143	54.57 185	62.45 189	80.76 179	44.67 147	84.20 103	64.23 84	79.81 89	85.54 73

Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 254	74.75 191	31.04 324	71.16 235	63.64 282	56.32 156	59.80 228	84.99 89	44.51 148	75.46 246	39.12 257	80.62 74	82.92 162

DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 11	57.99 64	81.15 71	82.96 58	57.03 138	66.78 134	85.56 83	44.50 149	88.11 20	51.77 171	80.23 85	83.10 160

TAMVS		66.78 173	65.27 177	71.33 164	79.16 107	53.67 117	73.84 199	69.59 237	52.32 204	65.28 151	81.72 149	44.49 150	77.40 230	42.32 241	78.66 108	82.92 162

v748		67.26 161	65.67 171	72.02 150	69.90 276	49.77 190	76.24 156	79.57 133	58.58 121	60.49 221	80.38 185	44.47 151	82.17 155	56.16 142	65.26 251	84.12 132

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 74	55.41 105	80.90 72	78.28 165	60.73 72	69.23 99	88.09 46	44.36 152	82.65 148	57.68 136	81.75 68	85.77 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 54	53.15 126		67.52 255			87.85 48	44.08 153			80.76 73	78.03 232

Test_1112_low_res		62.32 222	61.77 216	64.00 250	79.08 108	39.53 268	68.17 262	70.17 230	43.25 289	59.03 238	79.90 196	44.08 153	71.24 265	43.79 230	68.42 226	81.25 193

MVSFormer		71.50 80	70.38 84	74.88 70	78.76 114	57.15 79	82.79 41	78.48 158	51.26 221	69.49 91	83.22 117	43.99 155	83.24 125	66.06 62	79.37 95	84.23 124

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 114	57.15 79	76.57 149	73.29 217	46.19 264	69.49 91	82.18 135	43.99 155	79.23 197	64.66 74	79.37 95	83.93 133

v7n		69.01 126	67.36 137	73.98 84	72.51 243	52.65 132	78.54 103	81.30 87	60.26 83	62.67 181	81.62 153	43.61 157	84.49 99	57.01 139	68.70 225	84.79 107

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 109	53.13 128	73.27 205	71.07 226	52.15 205	64.72 162	80.23 193	43.56 158	77.10 232	45.48 218	78.88 103	83.05 161
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	78.27 126	56.88 81	77.12 140	73.71 215	46.53 260	69.34 95	83.22 117	43.37 159	79.18 198	64.77 73	79.20 100	84.23 124
jason: jason.
v1240		69.24 123	67.91 123	73.25 120	73.02 232	49.82 188	77.21 139	80.54 111	56.43 155	68.34 110	80.51 184	43.33 160	84.99 80	62.03 114	69.77 215	84.95 102

LCM-MVSNet-Re		61.88 228	61.35 220	63.46 251	74.58 194	31.48 323	61.42 292	58.14 305	58.71 120	53.02 290	79.55 208	43.07 161	76.80 236	45.69 213	77.96 113	82.11 176

BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 94	52.53 135	74.26 192	76.29 186	58.48 124	68.38 109	84.20 103	42.59 162	83.83 114	46.53 204	75.91 130	82.56 167

LFMVS		71.78 76	71.59 66	72.32 148	83.40 52	46.38 222	79.75 87	71.08 225	64.18 28	72.80 52	88.64 42	42.58 163	83.72 115	57.41 138	84.49 49	86.86 36

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 140	58.99 53	80.66 76	83.15 56	62.24 52	65.46 149	86.59 64	42.38 164	85.52 72	59.59 131	84.72 47	82.85 165

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 67	49.69 193	77.76 125	77.63 172	63.21 36	73.21 46	89.02 35	42.14 165	83.32 123	61.72 118	82.50 62	88.25 6

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 60	59.92 43	85.83 10	86.32 7	66.92 9	67.80 123	89.24 32	42.03 166	89.38 5	64.07 86	86.50 39	89.69 1

MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 123	59.33 48	74.82 186	70.11 231	58.08 129	67.83 122	84.68 93	41.96 167	76.34 242	65.62 68	77.54 115	79.30 222

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 43	61.41 27	84.15 25	77.86 167	55.27 174	67.51 127	88.08 47	41.93 168	81.85 159	69.04 46	80.01 86	81.35 192

abl_6		74.34 46	73.50 49	76.86 41	82.43 59	60.16 39	83.48 32	81.86 73	58.81 119	73.95 35	89.86 24	41.87 169	86.62 47	67.98 50	81.23 71	83.80 141

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

test1		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 143	42.98 247	76.11 159	77.47 174	56.62 150	65.22 156	82.17 137	41.85 170	80.18 188	47.05 202	72.72 167	83.20 155

CostFormer		64.04 202	62.51 203	68.61 201	71.88 253	45.77 225	71.30 232	70.60 229	47.55 253	64.31 168	76.61 255	41.63 173	79.62 192	49.74 184	69.00 221	80.42 206

AdaColmap		69.99 108	68.66 112	73.97 85	84.94 40	57.83 65	82.63 45	78.71 151	56.28 158	64.34 166	84.14 104	41.57 174	87.06 38	46.45 205	78.88 103	77.02 243

Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 173	62.29 18	80.20 81	76.06 190	59.83 94	65.26 153	77.09 247	41.56 175	84.02 110	60.60 123	71.09 186	81.53 182

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 173	63.22 6	80.59 77	76.06 190	59.83 94	66.32 140	79.87 197	41.56 175	85.53 71	60.60 123	72.77 163	82.80 166

QAPM		70.05 106	68.81 109	73.78 89	76.54 170	53.43 122	83.23 34	83.48 44	52.89 198	65.90 145	86.29 70	41.55 177	86.49 53	51.01 175	78.40 111	81.42 184

VDDNet		71.81 75	71.33 72	73.26 119	82.80 58	47.60 214	78.74 97	75.27 197	59.59 106	72.94 50	89.40 29	41.51 178	83.91 112	58.75 133	82.99 57	88.26 5

1121		68.53 138	67.16 145	72.63 133	85.64 27	61.14 29	73.95 195	66.46 262	44.61 278	70.28 72	86.68 60	41.42 179	80.78 179	53.62 158	81.79 66	75.97 250

CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 70	56.26 88	66.32 269	74.20 211	40.53 306	63.16 176	78.65 220	41.30 180	77.80 225	45.80 212	74.09 142	81.40 185

新几何1				70.76 173	85.66 25	61.13 30		66.43 263	44.68 277	70.29 71	86.64 61	41.29 181	75.23 252	49.72 185	81.75 68	75.93 253

tpmrst		58.24 253	58.70 241	56.84 288	66.97 293	34.32 307	69.57 251	61.14 296	47.17 257	58.58 243	71.60 287	41.28 182	60.41 304	49.20 189	62.84 269	75.78 255

tfpnnormal		62.47 220	61.63 218	64.99 244	74.81 190	39.01 271	71.22 233	73.72 214	55.22 176	60.21 222	80.09 195	41.26 183	76.98 235	30.02 302	68.09 232	78.97 226

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 46	59.04 51	83.65 30	81.05 94	60.15 84	70.43 69	89.84 25	41.09 184	85.59 69	67.61 54	82.90 59	85.77 64

V4		67.09 165	65.16 179	72.87 126	66.76 297	51.60 147	73.69 201	79.45 138	57.88 130	62.45 189	78.58 222	40.96 185	83.34 121	61.99 115	64.71 254	83.68 144

v52		67.09 165	65.16 179	72.87 126	66.77 296	51.60 147	73.69 201	79.45 138	57.88 130	62.46 188	78.57 223	40.95 186	83.34 121	61.99 115	64.70 256	83.68 144

114514_t		70.83 85	69.56 95	74.64 76	86.21 17	54.63 111	82.34 51	81.81 75	48.22 246	63.01 177	85.83 79	40.92 187	87.10 37	57.91 135	79.79 90	82.18 174

HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 96	55.65 100	69.07 255	75.52 194	42.68 294	63.53 172	77.95 227	40.43 188	81.64 162	46.01 210	71.91 178	83.73 142

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 171	42.34 251	74.98 183	77.15 180	55.83 165	65.04 157	81.16 165	39.91 189	80.14 189	47.18 199	72.76 164	82.90 164

MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 147	55.53 102	76.30 154	78.53 156	50.59 228	56.47 262	78.65 220	39.84 190	82.68 147	44.10 227	72.12 177	72.44 291
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	79.18 105	49.63 195	73.48 203	75.20 199	52.95 197	67.90 117	80.33 189	39.81 191	83.68 116	43.20 235	73.56 151	80.20 209

pmmvs6		63.69 204	62.82 201	66.27 223	70.63 266	39.27 270	73.13 206	75.47 195	52.69 200	59.75 229	82.30 133	39.71 192	77.03 234	47.40 198	64.35 259	82.53 168

pcd1.5k->3k		30.06 317	30.56 318	28.55 332	78.81 113	0.00 355	0.00 346	82.07 70	0.00 350	0.00 352	0.00 352	39.61 193	0.00 351	0.00 350	74.56 137	85.66 70

XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 199	56.87 82	70.59 240	79.04 145	54.77 182	66.99 132	86.01 75	39.57 194	78.21 220	62.54 102	73.33 154	83.37 151

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 233	57.78 66	77.47 133	76.88 182	57.60 132	61.97 197	76.85 251	39.31 195	80.49 183	54.72 151	70.28 204	82.17 175

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 90	57.54 68	82.74 43	82.84 63	60.58 74	65.24 154	86.18 71	39.25 196	86.03 61	66.95 60	76.79 126	83.22 154
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 91	57.51 70	82.55 47	83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

LGP-MVS_train				75.76 55	80.22 91	57.51 70		83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 167	48.75 202	76.52 151	80.04 118	50.64 227	65.24 154	84.93 90	39.15 197	78.54 212	36.77 267	76.88 123	85.14 94
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	74.26 201	53.99 115	81.21 70	81.34 86	52.70 199	62.75 180	85.55 84	38.86 200	84.14 105	48.41 195	83.01 56	79.97 212

sss		56.17 266	56.57 256	54.96 295	66.93 294	36.32 297	57.94 304	61.69 295	41.67 298	58.64 242	75.32 268	38.72 201	56.25 322	42.04 243	66.19 244	72.31 295

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 87	58.59 58	82.68 44	82.02 71	55.46 173	67.18 130	84.39 102	38.51 202	83.17 127	60.65 122	76.10 129	80.30 208
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	70.37 270	49.70 191	70.25 246	78.45 160	51.52 216	69.16 100	80.37 186	38.45 203	82.50 150	60.19 126	71.46 182	83.44 150

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 195	54.92 109	82.79 41	78.48 158	51.26 221	65.41 150	83.49 115	38.37 204	83.24 125	66.06 62	69.25 220	85.56 72

tpm2		62.07 225	60.10 229	67.99 205	72.79 238	43.86 240	71.05 236	66.85 260	43.14 291	62.77 178	75.39 267	38.32 205	80.80 178	41.69 245	68.88 222	79.32 221

tpm cat1		59.25 247	56.95 254	66.15 225	72.19 247	46.96 218	68.09 263	65.76 264	40.03 309	57.81 253	70.56 294	38.32 205	74.51 256	38.26 260	61.50 278	77.00 244

CNLPA		65.43 189	64.02 186	69.68 187	78.73 116	58.07 63	77.82 124	70.71 228	51.49 217	61.57 206	83.58 113	38.23 207	70.82 266	43.90 228	70.10 207	80.16 210

1314		64.61 199	63.21 196	68.80 198	71.87 254	47.46 215	73.95 195	78.39 164	42.88 293	59.97 224	76.60 256	38.11 208	79.39 195	54.84 150	72.32 174	79.55 218

testdata				64.66 246	81.52 68	52.93 129		65.29 268	46.09 265	73.88 37	87.46 50	38.08 209	66.26 285	53.31 163	78.48 110	74.78 268

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 150	43.33 244	77.31 135	77.83 168	56.45 154	64.60 164	82.70 122	38.08 209	80.33 185	46.08 209	72.31 175	83.92 134

tpmp4_e23		62.71 219	60.13 228	70.45 179	73.40 216	48.39 205	72.82 210	69.49 239	44.88 274	59.91 225	74.99 269	37.79 211	81.47 167	40.22 252	67.71 237	81.48 183

EPNet_dtu		61.90 226	61.97 211	61.68 262	72.89 237	39.78 265	75.85 166	65.62 265	55.09 177	54.56 277	79.36 211	37.59 212	67.02 281	39.80 256	76.95 122	78.25 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		59.75 239	58.00 248	64.98 245	74.14 207	48.06 208	63.35 284	63.23 285	49.13 237	59.33 234	71.46 288	37.45 213	69.59 271	41.39 249	62.57 271	77.30 237

IterMVS		62.79 214	61.27 221	67.35 211	69.37 279	52.04 144	71.17 234	68.24 250	52.63 201	59.82 227	76.91 250	37.32 214	72.36 261	52.80 165	63.19 267	77.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 211	62.18 209	66.21 224	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	79.83 214

thres400		63.31 207	62.18 209	66.72 215	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	81.36 186

tpm		57.34 259	58.16 245	54.86 296	71.80 255	34.77 304	67.47 265	56.04 317	48.20 247	60.10 223	76.92 249	37.17 217	53.41 330	40.76 251	65.01 252	76.40 249

test222					83.14 53	58.68 57	72.57 215	63.45 283	41.78 296	67.56 126	86.12 72	37.13 218			78.73 107	74.98 264

thres200		62.20 223	61.16 223	65.34 241	75.38 184	39.99 263	69.60 250	69.29 242	55.64 170	61.87 199	76.99 248	37.07 219	78.96 211	31.28 297	73.28 155	77.06 242

conf200view11		63.38 206	62.41 205	66.29 222	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	81.03 199

thres100view900		63.28 209	62.41 205	65.89 231	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	79.83 214

thres600view7		63.30 208	62.27 207	66.41 219	77.18 159	38.87 272	72.35 218	69.11 244	56.98 139	62.37 194	80.96 172	37.01 220	79.00 210	31.43 296	73.05 161	81.36 186

view600		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

view800		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

conf0.05thres1000		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

tfpn		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

PatchFormer-LS_test		62.20 223	60.59 226	67.04 213	72.18 248	46.82 220	70.36 245	68.62 248	51.92 206	59.19 235	70.23 296	36.86 227	75.07 253	50.23 181	65.68 248	79.23 223

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 41	56.87 82	80.74 75	73.16 218	53.06 196	59.09 237	82.35 131	36.79 228	85.94 65	32.82 284	69.96 210	72.45 290

semantic-postprocess				65.40 240	71.99 251	50.80 157		69.63 236	45.71 271	60.61 219	77.93 228	36.56 229	65.99 287	55.67 147	63.50 264	79.42 220

XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 200	57.22 74	70.09 247	78.81 149	55.24 175	67.79 124	85.81 81	36.54 230	78.28 219	62.04 113	75.74 131	83.19 156

pmmvs4		61.48 231	59.39 231	67.76 207	71.57 256	53.86 116	71.42 230	65.34 267	44.20 282	59.46 230	77.92 229	35.90 231	74.71 255	43.87 229	64.87 253	74.71 269

CR-MVSNet		59.91 237	57.90 249	65.96 228	69.96 274	52.07 142	65.31 276	63.15 286	42.48 295	59.36 231	74.84 270	35.83 232	70.75 267	45.50 217	64.65 257	75.06 261

Patchmtry		57.16 260	56.47 257	59.23 275	69.17 280	34.58 306	62.98 285	63.15 286	44.53 279	56.83 259	74.84 270	35.83 232	68.71 275	40.03 254	60.91 282	74.39 271

thresconf0.02		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tfpn_n400		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tfpnconf		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tfpnview11		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

RPMNet		58.70 250	56.29 260	65.96 228	69.96 274	52.07 142	65.31 276	62.15 293	43.20 290	59.36 231	70.15 298	35.37 238	70.75 267	36.42 273	64.65 257	75.06 261

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 189	46.24 223	78.66 99	76.85 183	59.60 103	63.45 173	82.09 141	35.25 239	77.41 229	59.88 128	78.76 106	85.14 94

test_0402		63.25 210	61.01 224	69.96 184	80.00 95	54.37 113	76.86 146	72.02 223	54.58 184	58.71 240	80.79 178	35.00 240	84.36 101	26.41 322	64.71 254	71.15 303

tfpn_ndepth		59.57 242	59.02 234	61.23 267	73.81 208	35.60 300	69.40 253	65.59 266	50.96 225	57.96 252	77.72 234	34.81 241	75.91 245	30.36 300	70.57 191	72.18 296

sam_mvs1												34.74 242

tfpn1000		59.24 248	58.70 241	60.86 273	73.75 209	33.99 309	68.86 256	63.98 280	51.25 223	57.29 257	79.51 210	34.58 243	75.26 251	29.08 314	69.99 208	73.32 282

pmmvs5		56.47 262	55.68 263	58.86 278	61.41 319	36.71 293	66.37 268	62.75 289	40.38 307	53.70 284	76.62 254	34.56 244	67.05 280	40.02 255	65.27 250	72.83 285

patchmatchnet-post											64.03 315	34.50 245	74.27 257

PatchmatchNet		59.84 238	58.24 244	64.65 247	73.05 231	46.70 221	69.42 252	62.18 292	47.55 253	58.88 239	71.96 286	34.49 246	69.16 273	42.99 237	63.60 263	78.07 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 291	48.28 291	51.50 309	64.40 309	30.85 325	45.68 330	48.46 336	35.60 321	46.10 313	72.10 285	34.47 247	46.37 336	27.08 320	60.65 286	77.27 239

MS-PatchMatch		62.42 221	61.46 219	65.31 242	75.21 186	52.10 141	72.05 226	74.05 212	46.41 262	57.42 256	74.36 274	34.35 248	77.57 228	45.62 215	73.67 147	66.26 314

tpmvs		58.47 251	56.95 254	63.03 256	70.20 271	41.21 258	67.90 264	67.23 258	49.62 234	54.73 275	70.84 292	34.14 249	76.24 243	36.64 270	61.29 281	71.64 299

PMMVS		53.96 275	53.26 279	56.04 290	62.60 315	50.92 153	61.17 295	56.09 316	32.81 325	53.51 288	66.84 308	34.04 250	59.93 306	44.14 226	68.18 231	57.27 329

Patchmatch-RL test		58.16 254	55.49 264	66.15 225	67.92 289	48.89 201	60.66 297	51.07 330	47.86 251	59.36 231	62.71 321	34.02 251	72.27 262	56.41 141	59.40 294	77.30 237

test_post											3.55 348	33.90 252	66.52 283

Test4		67.77 156	65.97 168	73.19 121	68.64 282	50.58 162	74.80 187	80.48 113	54.13 190	59.11 236	79.07 216	33.89 253	83.12 129	63.61 94	79.98 87	85.87 60

PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 80	54.87 110	78.57 101	77.47 174	48.51 242	55.71 265	81.89 146	33.71 254	79.71 190	41.66 246	70.37 198	77.58 234
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	70.87 264	48.10 207	70.48 242	74.40 209	56.69 146	64.70 163	76.77 252	33.66 255	81.10 172	55.42 149	70.32 200	83.87 137

LS3D		64.71 197	62.50 204	71.34 163	79.72 101	55.71 98	79.82 85	74.72 206	48.50 243	56.62 260	84.62 95	33.59 256	82.34 153	29.65 308	75.23 134	75.97 250

sam_mvs												33.43 257

PatchT		53.17 281	53.44 278	52.33 307	68.29 287	25.34 338	58.21 303	54.41 323	44.46 280	54.56 277	69.05 301	33.32 258	60.94 301	36.93 266	61.76 276	70.73 305

test20.03		53.87 277	54.02 274	53.41 302	61.47 318	28.11 330	61.30 293	59.21 301	51.34 220	52.09 292	77.43 245	33.29 259	58.55 310	29.76 307	60.27 292	73.58 281

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 272	56.13 90	76.35 153	77.35 178	48.43 244	64.99 160	80.84 177	33.01 260	80.34 184	64.66 74	67.64 238	84.23 124

MDTV_nov1_ep13_2view						25.89 335	61.22 294		40.10 308	51.10 295		32.97 261		38.49 259		78.61 227

IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 187	52.78 131	73.09 207	75.13 200	55.69 168	58.48 245	73.73 278	32.86 262	86.32 57	50.63 177	70.11 206	81.10 197
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	78.19 128	57.19 76	75.28 174	75.09 201	51.61 213	70.04 75	81.41 162	32.79 263	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 213	70.04 75	81.41 162	32.79 263	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 213	70.04 75	81.41 162	32.79 263	79.02 206	63.81 91	77.31 117	81.22 194

Anonymous20231206		55.10 273	55.30 265	54.48 298	69.81 277	33.94 310	62.91 286	62.13 294	41.08 301	55.18 272	75.65 265	32.75 266	56.59 319	30.32 301	67.86 234	72.91 284

UGNet		68.81 128	67.39 135	73.06 122	78.33 124	54.47 112	79.77 86	75.40 196	60.45 76	63.22 174	84.40 101	32.71 267	80.91 176	51.71 172	80.56 78	83.81 138
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		58.15 255	58.13 247	58.22 281	68.57 283	44.80 230	65.46 273	57.92 306	50.08 231	55.44 268	69.82 299	32.62 268	57.44 313	49.66 186	73.62 148	72.41 292

test0.0.03 1		53.32 280	53.59 277	52.50 306	62.81 314	29.45 327	59.51 299	54.11 325	50.08 231	54.40 279	74.31 275	32.62 268	55.92 324	30.50 299	63.95 261	72.15 298

MDTV_nov1_ep13			57.00 253		72.73 239	38.26 279	65.02 279	64.73 272	44.74 276	55.46 267	72.48 283	32.61 270	70.47 269	37.47 263	67.75 236

cascas		65.98 184	63.42 194	73.64 99	77.26 158	52.58 134	72.26 220	77.21 179	48.56 241	61.21 212	74.60 273	32.57 271	85.82 67	50.38 179	76.75 127	82.52 169

test_post1							68.67 257				3.64 347	32.39 272	69.49 272	44.17 225

CVMVSNet		59.63 241	59.14 233	61.08 272	74.47 196	38.84 273	75.20 177	68.74 247	31.15 328	58.24 246	76.51 257	32.39 272	68.58 276	49.77 183	65.84 246	75.81 254

MIMVSNet		57.35 258	57.07 252	58.22 281	74.21 202	37.18 285	62.46 287	60.88 297	48.88 239	55.29 271	75.99 263	31.68 274	62.04 299	31.87 287	72.35 173	75.43 259

PVSNet_0	43.31 20	47.46 296	45.64 296	52.92 304	67.60 291	44.65 232	54.06 313	54.64 321	41.59 299	46.15 311	58.75 327	30.99 275	58.66 309	32.18 285	24.81 338	55.46 330

testing_2		66.02 183	63.77 190	72.76 131	66.03 302	50.48 168	72.93 208	80.36 116	54.41 187	54.25 281	76.76 253	30.89 276	83.16 128	64.19 85	74.08 143	84.65 110

gg-mvs-nofinetune		57.86 256	56.43 258	62.18 260	72.62 241	35.35 302	66.57 266	56.33 314	50.65 226	57.64 254	57.10 328	30.65 277	76.36 241	37.38 264	78.88 103	74.82 267

GG-mvs-BLEND				62.34 259	71.36 261	37.04 287	69.20 254	57.33 309		54.73 275	65.48 314	30.37 278	77.82 224	34.82 277	74.93 135	72.17 297

MDA-MVSNet-bldmvs		53.87 277	50.81 284	63.05 255	66.25 299	48.58 203	56.93 307	63.82 281	48.09 248	41.22 325	70.48 295	30.34 279	68.00 277	34.24 279	45.92 327	72.57 288

EPMVS		53.96 275	53.69 275	54.79 297	66.12 301	31.96 322	62.34 289	49.05 333	44.42 281	55.54 266	71.33 290	30.22 280	56.70 317	41.65 247	62.54 272	75.71 256

DWT-MVSNet_test		61.90 226	59.93 230	67.83 206	71.98 252	46.09 224	71.03 237	69.71 233	50.09 230	58.51 244	70.62 293	30.21 281	77.63 226	49.28 188	67.91 233	79.78 216

YYNet1		50.73 288	48.96 287	56.03 291	61.10 321	41.78 254	51.94 320	56.44 313	40.94 303	44.84 314	67.80 305	30.08 282	55.08 328	36.77 267	50.71 317	71.22 301

MDA-MVSNet_test_wron		50.71 289	48.95 288	56.00 292	61.17 320	41.84 253	51.90 321	56.45 312	40.96 302	44.79 315	67.84 304	30.04 283	55.07 329	36.71 269	50.69 318	71.11 304

MSDG		61.81 229	59.23 232	69.55 192	72.64 240	52.63 133	70.45 243	75.81 192	51.38 219	53.70 284	76.11 260	29.52 284	81.08 173	37.70 262	65.79 247	74.93 265

CMPMVS	42.80 21	57.81 257	55.97 261	63.32 252	60.98 322	47.38 216	64.66 280	69.50 238	32.06 327	46.83 309	77.80 232	29.50 285	71.36 264	48.68 192	73.75 146	71.21 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 212	61.23 222	68.92 197	76.57 169	47.80 210	59.92 298	76.39 185	54.35 188	58.67 241	82.46 130	29.44 286	81.49 166	42.12 242	71.14 184	77.46 235
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		53.16 282	52.47 280	55.23 293	59.45 329	33.39 317	59.43 300	69.13 243	45.98 266	50.35 301	72.32 284	29.30 287	58.26 311	42.02 244	44.30 329	74.05 278

CHOSEN 280x420		47.83 294	46.36 295	52.24 308	67.37 292	49.78 189	38.91 338	43.11 342	35.00 322	43.27 322	63.30 320	28.95 288	49.19 335	36.53 271	60.80 285	57.76 328

pmmvs-eth3d		58.81 249	56.31 259	66.30 221	67.61 290	52.42 139	72.30 219	64.76 271	43.55 287	54.94 273	74.19 276	28.95 288	72.60 260	43.31 232	57.21 299	73.88 280

dp		51.89 285	51.60 283	52.77 305	68.44 286	32.45 319	62.36 288	54.57 322	44.16 283	49.31 302	67.91 303	28.87 290	56.61 318	33.89 280	54.89 305	69.24 311

jajsoiax		68.25 147	66.45 160	73.66 97	75.62 179	55.49 103	80.82 73	78.51 157	52.33 203	64.33 167	84.11 105	28.28 291	81.81 161	63.48 96	70.62 189	83.67 146

RPSCF		55.80 269	54.22 273	60.53 274	65.13 306	42.91 249	64.30 281	57.62 308	36.84 319	58.05 247	82.28 134	28.01 292	56.24 323	37.14 265	58.61 297	82.44 172

F-COLMAP		63.05 213	60.87 225	69.58 191	76.99 164	53.63 119	78.12 111	76.16 187	47.97 250	52.41 291	81.61 154	27.87 293	78.11 221	40.07 253	66.66 241	77.00 244

K. test v3		60.47 235	57.11 251	70.56 177	73.74 210	48.22 206	75.10 180	62.55 290	58.27 128	53.62 286	76.31 259	27.81 294	81.59 164	47.42 197	39.18 332	81.88 179

ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 139	52.83 130	80.39 78	78.03 166	57.30 134	57.47 255	82.55 128	27.68 295	84.17 104	45.54 216	69.78 213	79.90 213

UnsupCasMVSNet_bld		50.07 290	48.87 289	53.66 300	60.97 323	33.67 315	57.62 305	64.56 273	39.47 311	47.38 306	64.02 317	27.47 296	59.32 307	34.69 278	43.68 330	67.98 313

mvs_tets		68.18 151	66.36 164	73.63 100	75.61 180	55.35 106	80.77 74	78.56 155	52.48 202	64.27 169	84.10 106	27.45 297	81.84 160	63.45 97	70.56 192	83.69 143

lessismore_v0				69.91 185	71.42 259	47.80 210		50.90 331		50.39 300	75.56 266	27.43 298	81.33 168	45.91 211	34.10 335	80.59 204

ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 132	52.01 145	79.48 94	79.69 121	55.75 167	56.59 261	80.98 171	27.12 299	80.94 174	42.90 239	71.58 181	77.25 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 230	58.80 240	70.20 182	75.80 177	47.22 217	75.59 168	69.68 235	54.61 183	54.11 282	79.26 213	27.07 300	82.96 132	43.27 233	49.79 320	80.41 207

PVSNet	50.76 19	58.40 252	57.39 250	61.42 264	75.53 181	44.04 239	61.43 291	63.45 283	47.04 258	56.91 258	73.61 279	27.00 301	64.76 290	39.12 257	72.40 172	75.47 258

OpenMVS_ROB	52.78 18	60.03 236	58.14 246	65.69 234	70.47 267	44.82 229	75.33 172	70.86 227	45.04 273	56.06 263	76.00 261	26.89 302	79.65 191	35.36 276	67.29 239	72.60 287

ADS-MVSNet2		51.33 287	48.76 290	59.07 277	66.02 303	44.60 233	50.90 322	59.76 300	36.90 317	50.74 297	66.18 312	26.38 303	63.11 294	27.17 318	54.76 306	69.50 308

ADS-MVSNet		48.48 293	47.77 292	50.63 310	66.02 303	29.92 326	50.90 322	50.87 332	36.90 317	50.74 297	66.18 312	26.38 303	52.47 332	27.17 318	54.76 306	69.50 308

N_pmnet		39.35 310	40.28 307	36.54 327	63.76 311	1.62 353	49.37 325	0.76 355	34.62 323	43.61 321	66.38 311	26.25 305	42.57 342	26.02 324	51.77 313	65.44 315

MVS-HIRNet		45.52 299	44.48 301	48.65 314	68.49 285	34.05 308	59.41 301	44.50 341	27.03 332	37.96 331	50.47 335	26.16 306	64.10 291	26.74 321	59.52 293	47.82 333

FMVSNet5		55.86 268	54.93 266	58.66 280	71.05 263	36.35 295	64.18 283	62.48 291	46.76 259	50.66 299	74.73 272	25.80 307	64.04 292	33.11 283	65.57 249	75.59 257

new-patchmatchnet		47.56 295	47.73 293	47.06 316	58.81 330	9.37 349	48.78 326	59.21 301	43.28 288	44.22 317	68.66 302	25.67 308	57.20 316	31.57 295	49.35 323	74.62 270

MIMVSNet1		55.17 272	54.31 271	57.77 285	70.03 273	32.01 321	65.68 271	64.81 270	49.19 236	46.75 310	76.00 261	25.53 309	64.04 292	28.65 316	62.13 274	77.26 240

PatchMatch-RL		56.25 265	54.55 269	61.32 266	77.06 161	56.07 92	65.57 272	54.10 326	44.13 284	53.49 289	71.27 291	25.20 310	66.78 282	36.52 272	63.66 262	61.12 323

JIA-IIPM		51.56 286	47.68 294	63.21 253	64.61 308	50.73 159	47.71 327	58.77 303	42.90 292	48.46 304	51.72 332	24.97 311	70.24 270	36.06 275	53.89 309	68.64 312

EU-MVSNet		55.61 270	54.41 270	59.19 276	65.41 305	33.42 316	72.44 217	71.91 224	28.81 330	51.27 294	73.87 277	24.76 312	69.08 274	43.04 236	58.20 298	75.06 261

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 141	53.48 121	77.99 119	78.82 148	53.37 194	56.03 264	77.41 246	24.75 313	84.04 107	46.37 206	73.42 153	73.14 283

TESTMET0.1,1		55.28 271	54.90 267	56.42 289	66.56 298	43.67 242	65.46 273	56.27 315	39.18 312	53.83 283	67.44 306	24.21 314	55.46 327	48.04 196	73.11 160	70.13 306

LP		48.51 292	45.51 297	57.52 287	62.86 313	44.53 236	52.38 319	59.84 299	38.11 314	42.81 323	61.02 322	23.23 315	63.02 295	24.10 325	45.24 328	65.02 317

COLMAP_ROB	52.97 17	61.27 233	58.81 235	68.64 200	74.63 193	52.51 136	78.42 106	73.30 216	49.92 233	50.96 296	81.51 157	23.06 316	79.40 194	31.63 293	65.85 245	74.01 279
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 284	52.37 281	50.51 311	60.39 325	23.55 341	58.42 302	58.15 304	49.03 238	51.83 293	79.21 214	22.39 317	55.59 325	29.24 313	62.64 270	72.40 294

DSMNet-mixed		39.30 311	38.72 310	41.03 324	51.22 337	19.66 343	45.53 331	31.35 348	15.83 342	39.80 330	67.42 307	22.19 318	45.13 339	22.43 326	52.69 311	58.31 327

test-mter		56.42 263	55.82 262	58.22 281	68.57 283	44.80 230	65.46 273	57.92 306	39.94 310	55.44 268	69.82 299	21.92 319	57.44 313	49.66 186	73.62 148	72.41 292

OurMVSNet-221017-0		61.37 232	58.63 243	69.61 188	72.05 250	48.06 208	73.93 198	72.51 220	47.23 256	54.74 274	80.92 173	21.49 320	81.24 169	48.57 194	56.22 301	79.53 219

Anonymous20231211		55.92 267	53.63 276	62.77 257	68.22 288	35.56 301	74.48 191	69.89 232	46.42 261	49.07 303	73.45 280	21.13 321	76.77 237	28.74 315	51.30 316	75.97 250

ITE_SJBPF				62.09 261	66.16 300	44.55 235		64.32 274	47.36 255	55.31 270	80.34 188	19.27 322	62.68 297	36.29 274	62.39 273	79.04 224

AllTest		57.08 261	54.65 268	64.39 248	71.44 257	49.03 198	69.92 249	67.30 256	45.97 267	47.16 307	79.77 200	17.47 323	67.56 278	33.65 281	59.16 295	76.57 247

TestCases				64.39 248	71.44 257	49.03 198		67.30 256	45.97 267	47.16 307	79.77 200	17.47 323	67.56 278	33.65 281	59.16 295	76.57 247

testpf		44.11 303	45.40 298	40.26 325	60.52 324	27.34 332	33.26 340	54.33 324	45.87 270	41.08 326	60.26 324	16.46 325	59.14 308	46.09 208	50.68 319	34.31 339

XVG-ACMP-BASELINE		64.36 201	62.23 208	70.74 174	72.35 245	52.45 138	70.80 239	78.45 160	53.84 192	59.87 226	81.10 167	16.24 326	79.32 196	55.64 148	71.76 179	80.47 205

tmp_tt		9.43 326	11.14 327	4.30 338	2.38 352	4.40 352	13.62 345	16.08 352	0.39 347	15.89 343	13.06 345	15.80 327	5.54 350	12.63 341	10.46 347	2.95 345

USDC		56.35 264	54.24 272	62.69 258	64.74 307	40.31 262	65.05 278	73.83 213	43.93 285	47.58 305	77.71 235	15.36 328	75.05 254	38.19 261	61.81 275	72.70 286

test2356		45.61 298	44.66 300	48.47 315	60.15 326	28.08 331	52.44 318	52.83 329	38.01 315	46.13 312	60.98 323	15.08 329	55.54 326	20.43 334	55.85 303	61.78 321

test1235678		45.66 297	44.46 302	49.26 312	59.88 327	28.68 329	56.36 309	55.54 320	39.12 313	40.89 327	63.40 319	14.41 330	57.32 315	21.05 331	49.47 322	61.78 321

1111		44.40 302	45.00 299	42.61 323	57.55 332	17.33 346	53.82 316	57.05 310	40.78 304	44.11 318	66.57 309	13.37 331	45.77 337	22.15 327	49.58 321	64.73 318

.test1245		34.88 314	39.49 309	21.04 335	57.55 332	17.33 346	53.82 316	57.05 310	40.78 304	44.11 318	66.57 309	13.37 331	45.77 337	22.15 327	0.00 349	0.03 348

ANet_high		41.38 307	37.47 311	53.11 303	39.73 346	24.45 340	56.94 306	69.69 234	47.65 252	26.04 337	52.32 331	12.44 333	62.38 298	21.80 330	10.61 346	72.49 289

no-one		40.85 308	36.09 312	55.14 294	48.55 339	38.72 274	42.15 336	62.92 288	34.60 324	23.55 338	49.74 336	12.21 334	66.16 286	26.27 323	24.84 337	60.54 324

FPMVS		42.18 306	41.11 306	45.39 318	58.03 331	41.01 260	49.50 324	53.81 327	30.07 329	33.71 332	64.03 315	11.69 335	52.08 333	14.01 340	55.11 304	43.09 336

TinyColmap		54.14 274	51.72 282	61.40 265	66.84 295	41.97 252	66.52 267	68.51 249	44.81 275	42.69 324	75.77 264	11.66 336	72.94 259	31.96 286	56.77 300	69.27 310

TDRefinement		53.44 279	50.72 285	61.60 263	64.31 310	46.96 218	70.89 238	65.27 269	41.78 296	44.61 316	77.98 226	11.52 337	66.36 284	28.57 317	51.59 314	71.49 300

ambc				65.13 243	63.72 312	37.07 286	47.66 328	78.78 150		54.37 280	71.42 289	11.24 338	80.94 174	45.64 214	53.85 310	77.38 236

pmmvs3		44.92 300	41.95 305	53.86 299	52.58 336	43.55 243	62.11 290	46.90 340	26.05 334	40.63 328	60.19 325	11.08 339	57.91 312	31.83 292	46.15 326	60.11 325

new_pmnet		34.13 316	34.29 315	33.64 328	52.63 335	18.23 345	44.43 334	33.90 346	22.81 337	30.89 334	53.18 330	10.48 340	35.72 346	20.77 332	39.51 331	46.98 334

LF4IMVS		42.95 304	42.26 304	45.04 319	48.30 340	32.50 318	54.80 311	48.49 335	28.03 331	40.51 329	70.16 297	9.24 341	43.89 340	31.63 293	49.18 324	58.72 326

PM-MVS		52.33 283	50.19 286	58.75 279	62.10 316	45.14 228	65.75 270	40.38 343	43.60 286	53.52 287	72.65 282	9.16 342	65.87 288	50.41 178	54.18 308	65.24 316

testus		44.59 301	43.87 303	46.76 317	59.85 328	24.65 339	53.86 314	55.82 318	36.26 320	43.97 320	63.42 318	8.39 343	53.14 331	20.70 333	52.52 312	62.51 319

testmv		42.25 305	40.11 308	48.66 313	53.23 334	27.02 333	56.62 308	55.74 319	37.25 316	33.10 333	59.52 326	7.78 344	56.58 320	19.61 335	38.13 334	62.40 320

EMVS		22.97 322	21.84 324	26.36 334	40.20 345	19.53 344	41.95 337	34.64 345	17.09 341	9.73 347	22.83 344	7.29 345	42.22 344	9.18 345	13.66 343	17.32 343

test12356		36.16 313	35.94 313	36.83 326	50.82 338	8.52 350	44.84 333	53.49 328	32.72 326	30.11 335	55.08 329	7.11 346	49.47 334	16.60 337	32.68 336	52.50 331

E-PMN		23.77 321	22.73 322	26.90 333	42.02 344	20.67 342	42.66 335	35.70 344	17.43 340	10.28 346	25.05 342	6.42 347	42.39 343	10.28 343	14.71 341	17.63 342

Gipuma		34.77 315	31.91 317	43.33 322	62.05 317	37.87 281	20.39 343	67.03 259	23.23 336	18.41 341	25.84 341	4.24 348	62.73 296	14.71 339	51.32 315	29.38 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 320	25.91 320	31.87 331	39.46 347	6.57 351	31.17 341	28.52 349	23.96 335	20.45 340	48.94 337	4.20 349	37.94 345	16.51 338	19.97 339	51.09 332

LCM-MVSNet		40.30 309	35.88 314	53.57 301	42.24 343	29.15 328	45.21 332	60.53 298	22.23 338	28.02 336	50.98 334	3.72 350	61.78 300	31.22 298	38.76 333	69.78 307

DeepMVS_CX				12.03 337	17.97 351	10.91 348		10.60 353	7.46 345	11.07 345	28.36 340	3.28 351	11.29 349	8.01 346	9.74 348	13.89 344

PMVS	28.69 22	36.22 312	33.29 316	45.02 320	36.82 348	35.98 299	54.68 312	48.74 334	26.31 333	21.02 339	51.61 333	2.88 352	60.10 305	9.99 344	47.58 325	38.99 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 323	17.77 325	32.34 330	34.34 350	25.44 337	16.11 344	24.11 350	11.19 344	13.22 344	31.92 339	1.58 353	30.95 347	10.47 342	17.03 340	40.62 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		27.88 319	25.99 319	33.55 329	47.54 341	25.89 335	47.24 329	32.91 347	21.44 339	15.90 342	38.09 338	0.85 354	42.76 341	16.90 336	13.03 344	32.00 340

wuyk23d		13.32 325	12.52 326	15.71 336	47.54 341	26.27 334	31.06 342	1.98 354	4.93 346	5.18 349	1.94 349	0.45 355	18.54 348	6.81 347	12.83 345	2.33 346

wuykxyi23d		28.12 318	22.54 323	44.87 321	34.97 349	32.11 320	37.96 339	47.31 338	13.32 343	9.29 348	23.72 343	0.45 355	56.58 320	21.85 329	13.98 342	45.93 335

test123		4.73 328	6.30 329	0.02 339	0.01 353	0.01 354	56.36 309	0.00 356	0.01 348	0.04 350	0.21 351	0.01 357	0.00 351	0.03 349	0.00 349	0.04 347

sosnet-low-res		0.00 331	0.00 332	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	0.00 352	0.00 358	0.00 351	0.00 350	0.00 349	0.00 350

sosnet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	0.00 352	0.00 358	0.00 351	0.00 350	0.00 349	0.00 350

uncertanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	0.00 352	0.00 358	0.00 351	0.00 350	0.00 349	0.00 350

Regformer		0.00 331	0.00 332	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	0.00 352	0.00 358	0.00 351	0.00 350	0.00 349	0.00 350

testmvs		4.52 329	6.03 330	0.01 340	0.01 353	0.00 355	53.86 314	0.00 356	0.01 348	0.04 350	0.27 350	0.00 358	0.00 351	0.04 348	0.00 349	0.03 348

ab-mvs-re		6.49 327	8.65 328	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	77.89 230	0.00 358	0.00 351	0.00 350	0.00 349	0.00 350

uanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 355	0.00 346	0.00 356	0.00 350	0.00 352	0.00 352	0.00 358	0.00 351	0.00 350	0.00 349	0.00 350

test_part2					87.58 3	60.47 37				83.42 2

test_all								86.76 3

MTGPA								80.97 98

MTMP								17.08 351

gm-plane-assit					71.40 260	41.72 256			48.85 240		73.31 281		82.48 152	48.90 191

test9_res														75.28 14	88.31 17	83.81 138

agg_prior2														73.09 29	87.93 24	84.33 116

agg_prior					85.04 35	59.96 41		81.04 95		74.68 27			84.04 107

test_prior4						62.51 17	82.08 57

test_prior				76.69 44	84.20 47	57.27 72		84.88 16					86.43 54			86.38 42

旧先验2							76.08 160		45.32 272	76.55 14			65.56 289	58.75 133

新几何2							76.12 158

无先验							79.66 89	74.30 210	48.40 245				80.78 179	53.62 158		79.03 225

原ACMM2							79.02 95

testdata2													72.18 263	46.95 203

testdata1							72.65 211		60.50 75

plane_prior7					81.41 71	55.96 94

plane_prior5								84.01 30					87.21 33	68.16 48	80.58 76	84.65 110

plane_prior4											86.10 73

plane_prior3						56.09 91			63.92 30	69.27 96

plane_prior2							84.22 22		64.52 24

plane_prior1					81.27 76

plane_prior						56.31 85	83.58 31		63.19 37						80.48 79

n2								0.00 356

nn								0.00 356

door-mid								47.19 339

test11								83.47 45

door								47.60 337

HQP5-MVS						54.94 107

HQP-NCC					80.66 83		82.31 52		62.10 54	67.85 118

ACMP_Plane					80.66 83		82.31 52		62.10 54	67.85 118

BP-MVS														67.04 58

HQP4-MVS										67.85 118			86.93 39			84.32 117

HQP3-MVS								83.90 34							80.35 82

NP-MVS					80.98 81	56.05 93					85.54 85

ACMMP++_ref															74.07 144

ACMMP++															72.16 176

High-resolution multi-view benchmark