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 3				65.59 1			90.06 4	86.78 40

ESAPD		80.72 1	81.17 2	79.38 7	87.58 3	60.47 38	86.37 5	86.64 3	63.49 35	83.42 2	91.40 4	65.59 1	90.90 1	75.98 14	90.06 4	86.78 40

HSP-MVS		80.69 2	81.20 1	79.14 10	86.21 19	62.73 12	86.09 11	85.03 15	65.51 15	83.81 1	90.51 14	63.71 3	89.23 9	81.51 1	88.44 14	85.45 81

DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 44	60.32 40	83.03 40	85.33 12	62.86 45	80.17 6	90.03 24	61.76 4	88.95 13	74.21 22	88.67 13	88.12 8

CSCG		76.92 27	76.75 25	77.41 38	83.96 51	59.60 46	82.95 41	86.50 5	60.78 74	75.27 22	84.83 94	60.76 5	86.56 53	67.86 54	87.87 28	86.06 58

TSAR-MVS + MP.		78.44 12	78.28 12	78.90 18	84.96 40	61.41 28	84.03 29	83.82 39	59.34 116	79.37 8	89.76 29	59.84 6	87.62 31	76.69 12	86.74 38	87.68 17

canonicalmvs		74.67 47	74.98 41	73.71 97	78.94 112	50.56 167	80.23 82	83.87 37	60.30 85	77.15 14	86.56 69	59.65 7	82.00 160	66.01 67	82.12 66	88.58 4

APDe-MVS		80.16 4	80.59 3	78.86 20	86.64 11	60.02 42	88.12 1	86.42 6	62.94 42	82.40 4	92.12 2	59.64 8	89.76 5	78.70 6	88.32 18	86.79 39

DELS-MVS		74.76 45	74.46 44	75.65 62	77.84 140	52.25 142	75.59 171	84.17 27	63.76 32	73.15 49	82.79 125	59.58 9	86.80 44	67.24 59	86.04 44	87.89 10
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 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 23	85.03 15	66.96 6	77.58 13	90.06 23	59.47 10	89.13 11	78.67 7	89.73 6	87.03 34

MCST-MVS		77.48 22	77.45 18	77.54 36	86.67 10	58.36 62	83.22 38	86.93 1	56.91 145	74.91 27	88.19 47	59.15 11	87.68 30	73.67 28	87.45 29	86.57 43

nrg030		72.96 62	73.01 57	72.84 130	75.41 186	50.24 176	80.02 85	82.89 63	58.36 130	74.44 32	86.73 60	58.90 12	80.83 180	65.84 69	74.46 140	87.44 24

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 41	65.37 16	78.78 10	90.64 10	58.63 13	87.24 34	79.00 5	90.37 3	85.26 94

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 52	61.62 25	84.17 27	86.85 2	63.23 37	73.84 40	90.25 21	57.68 14	89.96 4	74.62 21	89.03 9	87.89 10
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 85	71.00 79	71.44 161	79.20 106	44.13 241	76.02 167	82.60 65	66.48 14	68.20 113	84.60 100	56.82 15	82.82 142	54.62 155	70.43 197	87.36 28

Regformer-1		75.47 42	74.93 42	77.09 42	80.43 90	57.70 69	79.50 95	82.13 68	67.84 1	75.73 21	80.75 185	56.50 16	86.07 61	71.07 42	80.38 82	87.50 21

Effi-MVS+		73.31 59	72.54 61	75.62 63	77.87 139	53.64 120	79.62 93	79.61 125	61.63 64	72.02 63	82.61 130	56.44 17	85.97 67	63.99 92	79.07 104	87.25 30

alignmvs		73.86 55	73.99 48	73.45 109	78.20 129	50.50 169	78.57 104	82.43 66	59.40 114	76.57 15	86.71 62	56.42 18	81.23 173	65.84 69	81.79 68	88.62 2

SMA-MVS		80.22 3	80.31 4	79.95 2	86.60 12	61.97 22	86.33 7	85.70 10	62.39 53	81.75 5	92.28 1	56.41 19	89.70 6	79.85 3	91.51 1	88.19 7

test_prior3		76.89 29	76.96 23	76.69 46	84.20 49	57.27 74	81.75 62	84.88 17	60.37 81	75.01 23	89.06 36	56.22 20	86.43 57	72.19 35	88.96 11	86.38 44

test_prior2							81.75 62		60.37 81	75.01 23	89.06 36	56.22 20		72.19 35	88.96 11

TSAR-MVS + GP.		74.90 44	74.15 47	77.17 41	82.00 65	58.77 58	81.80 61	78.57 156	58.58 124	74.32 33	84.51 103	55.94 22	87.22 35	67.11 60	84.48 52	85.52 76

Regformer-2		75.63 41	74.99 40	77.54 36	80.43 90	58.32 63	79.50 95	82.92 60	67.84 1	75.94 18	80.75 185	55.73 23	86.80 44	71.44 41	80.38 82	87.50 21

MVS_Test		72.45 68	72.46 62	72.42 147	74.88 191	48.50 207	76.28 158	83.14 58	59.40 114	72.46 58	84.68 96	55.66 24	81.12 174	65.98 68	79.66 93	87.63 18

APD-MVS		78.02 15	78.04 15	77.98 33	86.44 16	60.81 35	85.52 18	84.36 23	60.61 76	79.05 9	90.30 19	55.54 25	88.32 19	73.48 31	87.03 33	84.83 106
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 20	84.42 22	66.73 10	74.67 31	89.38 33	55.30 26	89.18 10	74.19 23	87.34 30	86.38 44

FIs		70.82 88	71.43 70	68.98 198	78.33 126	38.14 285	76.96 147	83.59 43	61.02 71	67.33 131	86.73 60	55.07 27	81.64 165	54.61 157	79.22 101	87.14 32

SD-MVS		77.70 19	77.62 17	77.93 34	84.47 47	61.88 24	84.55 22	83.87 37	60.37 81	79.89 7	89.38 33	54.97 28	85.58 73	76.12 13	84.94 48	86.33 50

CANet		76.46 33	75.93 34	78.06 31	81.29 76	57.53 71	82.35 53	83.31 53	67.78 3	70.09 76	86.34 72	54.92 29	88.90 14	72.68 33	84.55 50	87.76 15

Regformer-3		73.89 54	73.28 56	75.71 60	79.75 99	55.48 106	78.54 106	79.93 120	66.58 12	73.62 43	80.30 195	54.87 30	84.54 101	69.09 48	76.84 126	87.10 33

MP-MVS		78.35 13	78.26 13	78.64 23	86.54 14	63.47 5	86.02 12	83.55 44	63.89 31	73.60 44	90.60 11	54.85 31	86.72 47	77.20 11	88.06 23	85.74 69
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		68.03 156	67.51 134	69.59 191	72.08 255	44.57 238	71.99 232	75.23 200	51.67 219	67.06 133	82.57 131	54.68 32	77.94 228	56.56 143	75.71 134	86.26 54

test12				77.76 35	84.52 46	58.41 61		83.36 51		72.93 53		54.61 33	88.05 24		88.12 22	86.81 38

FC-MVSNet-test		69.80 112	70.58 84	67.46 211	77.61 150	34.73 311	76.05 165	83.19 56	60.84 72	65.88 148	86.46 70	54.52 34	80.76 184	52.52 169	78.12 114	86.91 35

SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 58	62.18 19	87.60 2	85.83 8	66.69 11	78.03 12	90.98 7	54.26 35	90.06 3	78.42 8	89.02 10	87.69 16
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 36

MVS_111021_HR		74.02 52	73.46 54	75.69 61	83.01 58	60.63 37	77.29 141	78.40 165	61.18 70	70.58 70	85.97 79	54.18 37	84.00 114	67.52 58	82.98 60	82.45 174

Fast-Effi-MVS+		70.28 105	69.12 108	73.73 96	78.50 122	51.50 151	75.01 184	79.46 138	56.16 165	68.59 106	79.55 215	53.97 38	84.05 109	53.34 165	77.53 118	85.65 73

agg_prior1		75.94 38	76.01 33	75.72 59	85.04 37	59.96 43	81.44 70	81.04 96	56.14 166	74.68 29	88.90 40	53.91 39	84.04 110	75.01 20	87.92 27	83.16 162

UniMVSNet (Re)		70.63 93	70.20 87	71.89 153	78.55 121	45.29 231	75.94 168	82.92 60	63.68 34	68.16 115	83.59 115	53.89 40	83.49 123	53.97 159	71.12 188	86.89 36

Regformer-4		74.25 51	73.48 52	76.57 50	79.75 99	56.54 86	78.54 106	81.49 82	66.93 8	73.90 38	80.30 195	53.84 41	85.98 66	69.76 44	76.84 126	87.17 31

DeepC-MVS_fast	68.24 3	77.25 24	76.63 27	79.12 12	86.15 21	60.86 34	84.71 21	84.85 19	61.98 62	73.06 51	88.88 41	53.72 42	89.06 12	68.27 50	88.04 24	87.42 25
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 30	61.59 26	81.62 66	81.26 90	55.65 174	74.93 25	88.81 42	53.70 43	84.68 98

train_agg		76.27 35	76.15 30	76.64 49	85.58 30	61.59 26	81.62 66	81.26 90	55.86 168	74.93 25	88.81 42	53.70 43	84.68 98	75.24 18	88.33 16	83.65 150

test_8					85.40 33	60.96 33	81.54 69	81.18 93	55.86 168	74.81 28	88.80 44	53.70 43	84.45 103

CDPH-MVS		76.31 34	75.67 37	78.22 29	85.35 35	59.14 52	81.31 72	84.02 30	56.32 161	74.05 34	88.98 39	53.34 46	87.92 27	69.23 47	88.42 15	87.59 19

agg_prior3		76.13 36	75.89 36	76.85 44	85.76 26	62.02 21	81.65 64	81.01 98	55.51 177	73.73 41	88.60 46	53.23 47	84.90 91	75.24 18	88.33 16	83.65 150

HFP-MVS		78.01 16	77.65 16	79.10 13	86.71 8	62.81 10	86.29 8	84.32 24	62.82 46	73.96 35	90.50 15	53.20 48	88.35 17	74.02 24	87.05 31	86.13 55

#test#		77.83 17	77.41 19	79.10 13	86.71 8	62.81 10	85.69 17	84.32 24	61.61 65	73.96 35	90.50 15	53.20 48	88.35 17	73.68 27	87.05 31	86.13 55

EI-MVSNet-Vis-set		72.42 69	71.59 68	74.91 71	78.47 124	54.02 116	77.05 145	79.33 143	65.03 20	71.68 65	79.35 219	52.75 50	84.89 92	66.46 64	74.23 143	85.83 63

ACMMP_Plus		78.77 9	78.78 9	78.74 22	85.44 32	61.04 32	83.84 31	85.16 13	62.88 44	78.10 11	91.26 6	52.51 51	88.39 16	79.34 4	90.52 2	86.78 40

PCF-MVS	61.88 8	70.95 86	69.49 102	75.35 67	77.63 145	55.71 100	76.04 166	81.81 76	50.30 236	69.66 87	85.40 91	52.51 51	84.89 92	51.82 173	80.24 86	85.45 81
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MP-MVS-pluss		78.35 13	78.46 10	78.03 32	84.96 40	59.52 47	82.93 42	85.39 11	62.15 56	76.41 17	91.51 3	52.47 53	86.78 46	80.66 2	89.64 8	87.80 13
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 58	72.68 60	75.29 69	78.82 114	53.33 126	78.23 112	84.79 20	61.30 69	70.41 72	81.04 172	52.41 54	87.12 39	64.61 79	82.49 65	85.41 88
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NR-MVSNet		69.54 118	68.85 110	71.59 160	78.05 135	43.81 245	74.20 196	80.86 102	65.18 17	62.76 181	84.52 101	52.35 55	83.59 121	50.96 179	70.78 190	87.37 26

EI-MVSNet-UG-set		71.92 76	71.06 78	74.52 81	77.98 137	53.56 122	76.62 151	79.16 145	64.40 26	71.18 67	78.95 224	52.19 56	84.66 100	65.47 72	73.57 152	85.32 91

PAPM_NR		72.63 65	71.80 66	75.13 70	81.72 68	53.42 125	79.91 87	83.28 55	59.14 118	66.31 143	85.90 80	51.86 57	86.06 62	57.45 140	80.62 76	85.91 61

MG-MVS		73.96 53	73.89 49	74.16 84	85.65 28	49.69 195	81.59 68	81.29 89	61.45 66	71.05 68	88.11 48	51.77 58	87.73 29	61.05 124	83.09 57	85.05 100

EPP-MVSNet		72.16 74	71.31 75	74.71 74	78.68 119	49.70 193	82.10 59	81.65 78	60.40 80	65.94 146	85.84 81	51.74 59	86.37 59	55.93 146	79.55 96	88.07 9

TranMVSNet+NR-MVSNet		70.36 103	70.10 90	71.17 169	78.64 120	42.97 252	76.53 153	81.16 94	66.95 7	68.53 109	85.42 90	51.61 60	83.07 133	52.32 170	69.70 220	87.46 23

EI-MVSNet		69.27 123	68.44 118	71.73 157	74.47 200	49.39 199	75.20 180	78.45 162	59.60 106	69.16 102	76.51 264	51.29 61	82.50 153	59.86 133	71.45 186	83.30 155

IterMVS-LS		69.22 126	68.48 116	71.43 163	74.44 202	49.40 198	76.23 160	77.55 175	59.60 106	65.85 149	81.59 160	51.28 62	81.58 168	59.87 132	69.90 215	83.30 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		64.72 198	64.33 186	65.87 237	75.22 188	38.56 282	74.66 191	75.08 206	58.90 121	61.79 204	82.63 129	51.18 63	78.07 227	43.63 234	55.87 310	80.99 206

VNet		69.68 114	70.19 88	68.16 206	79.73 102	41.63 261	70.53 246	77.38 179	60.37 81	70.69 69	86.63 65	51.08 64	77.09 238	53.61 163	81.69 72	85.75 68

VPA-MVSNet		69.02 127	69.47 104	67.69 210	77.42 153	41.00 265	74.04 198	79.68 123	60.06 88	69.26 100	84.81 95	51.06 65	77.58 232	54.44 158	74.43 141	84.48 117

PAPR		71.72 79	70.82 81	74.41 83	81.20 80	51.17 152	79.55 94	83.33 52	55.81 171	66.93 135	84.61 99	50.95 66	86.06 62	55.79 149	79.20 102	86.00 59

PHI-MVS		75.87 39	75.36 38	77.41 38	80.62 88	55.91 98	84.28 24	85.78 9	56.08 167	73.41 46	86.58 68	50.94 67	88.54 15	70.79 43	89.71 7	87.79 14

HPM-MVS		77.28 23	76.85 24	78.54 24	85.00 39	60.81 35	82.91 43	85.08 14	62.57 49	73.09 50	89.97 26	50.90 68	87.48 32	75.30 16	86.85 36	87.33 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
WR-MVS_H		67.02 170	66.92 151	67.33 214	77.95 138	37.75 288	77.57 134	82.11 70	62.03 61	62.65 184	82.48 133	50.57 69	79.46 197	42.91 241	64.01 266	84.79 109

EPNet		73.09 61	72.16 63	75.90 55	75.95 179	56.28 89	83.05 39	72.39 223	66.53 13	65.27 154	87.00 58	50.40 70	85.47 77	62.48 106	86.32 43	85.94 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WR-MVS		68.47 141	68.47 117	68.44 205	80.20 95	39.84 268	73.75 204	76.07 191	64.68 21	68.11 117	83.63 114	50.39 71	79.14 209	49.78 185	69.66 221	86.34 49

region2R		77.67 20	77.18 22	79.15 9	86.76 6	62.95 8	86.29 8	84.16 28	62.81 48	73.30 47	90.58 12	49.90 72	88.21 21	73.78 26	87.03 33	86.29 53

UA-Net		73.13 60	72.93 58	73.76 93	83.58 53	51.66 148	78.75 99	77.66 173	67.75 4	72.61 56	89.42 31	49.82 73	83.29 127	53.61 163	83.14 56	86.32 51

ACMMPR		77.71 18	77.23 21	79.16 8	86.75 7	62.93 9	86.29 8	84.24 26	62.82 46	73.55 45	90.56 13	49.80 74	88.24 20	74.02 24	87.03 33	86.32 51

API-MVS		72.17 73	71.41 71	74.45 82	81.95 66	57.22 76	84.03 29	80.38 116	59.89 95	68.40 110	82.33 136	49.64 75	87.83 28	51.87 172	84.16 54	78.30 234

MVS_0304		76.73 31	76.04 32	78.78 21	81.32 75	58.89 56	82.50 52	84.07 29	67.73 5	72.08 62	87.28 57	49.49 76	89.57 7	73.52 30	86.40 42	87.87 12

ab-mvs		66.65 177	66.42 164	67.37 212	76.17 175	41.73 259	70.41 249	76.14 190	53.99 196	65.98 145	83.51 117	49.48 77	76.24 248	48.60 196	73.46 154	84.14 133

v8		70.33 104	69.28 107	73.49 107	73.15 230	50.22 177	78.62 103	80.78 103	60.79 73	66.45 140	82.11 144	49.35 78	84.98 85	63.58 98	68.71 228	85.28 92

v17		68.37 143	67.00 149	72.48 141	73.22 226	50.31 173	78.10 118	79.58 132	59.71 101	61.67 206	77.60 246	49.31 79	82.89 138	62.37 108	61.48 286	84.23 126

v18		68.33 144	66.96 150	72.42 147	73.13 234	50.16 182	77.97 123	79.57 134	59.57 110	61.80 203	77.50 251	49.30 80	82.90 137	62.31 110	61.50 284	84.20 132

IS-MVSNet		71.57 80	71.00 79	73.27 120	78.86 113	45.63 229	80.22 83	78.69 154	64.14 29	66.46 139	87.36 54	49.30 80	85.60 71	50.26 183	83.71 55	88.59 3

v1neww		70.66 90	69.70 93	73.53 104	73.15 230	50.22 177	78.11 115	80.68 104	59.65 103	69.83 83	81.67 154	49.29 82	84.96 87	64.55 80	70.38 200	85.42 84

v7new		70.66 90	69.70 93	73.53 104	73.15 230	50.22 177	78.11 115	80.68 104	59.65 103	69.83 83	81.67 154	49.29 82	84.96 87	64.55 80	70.38 200	85.42 84

v6		70.66 90	69.70 93	73.53 104	73.14 233	50.21 180	78.11 115	80.67 106	59.65 103	69.82 85	81.65 156	49.29 82	84.96 87	64.55 80	70.39 199	85.42 84

v16		68.38 142	67.01 148	72.47 145	73.22 226	50.29 174	78.10 118	79.59 130	59.71 101	61.72 205	77.60 246	49.28 85	82.89 138	62.36 109	61.54 283	84.23 126

XXY-MVS		60.68 238	61.67 221	57.70 294	70.43 274	38.45 283	64.19 291	66.47 264	48.05 256	63.22 176	80.86 180	49.28 85	60.47 312	45.25 225	67.28 246	74.19 287

divwei89l23v2f112		70.50 97	69.53 98	73.41 113	72.91 241	50.00 187	77.69 129	80.59 109	59.50 111	69.60 89	81.43 163	49.26 87	84.77 95	64.48 84	70.31 205	85.47 78

v1		70.50 97	69.53 98	73.42 112	72.91 241	50.00 187	77.69 129	80.59 109	59.50 111	69.59 91	81.42 165	49.26 87	84.77 95	64.49 83	70.30 206	85.47 78

cdsmvs_eth3d_5k		17.50 332	23.34 329	0.00 349	0.00 363	0.00 363	0.00 355	78.63 155	0.00 359	0.00 360	82.18 139	49.25 89	0.00 360	0.00 359	0.00 357	0.00 360

v15		68.22 152	66.81 156	72.47 145	73.25 225	50.40 171	77.92 125	79.60 127	59.77 100	61.28 214	77.52 250	49.25 89	82.77 144	62.16 115	60.51 293	84.24 125

diffmvs		67.72 159	66.73 157	70.70 178	69.74 284	47.69 216	73.33 208	74.74 207	53.30 200	64.51 167	81.80 152	49.25 89	79.02 211	59.15 135	74.75 138	85.39 89

v1141		70.50 97	69.53 98	73.41 113	72.92 240	50.00 187	77.69 129	80.60 108	59.50 111	69.60 89	81.43 163	49.24 92	84.77 95	64.48 84	70.30 206	85.46 80

V14		68.25 149	66.82 155	72.52 140	73.33 224	50.53 168	78.02 121	79.60 127	59.83 97	61.16 216	77.57 248	49.19 93	82.77 144	62.18 111	60.50 294	84.26 124

V9		68.27 147	66.84 152	72.56 138	73.39 223	50.63 163	78.10 118	79.60 127	59.94 92	61.05 218	77.62 245	49.18 94	82.77 144	62.17 113	60.48 295	84.27 123

PVSNet_Blended_VisFu		71.45 83	70.39 85	74.65 77	82.01 64	58.82 57	79.93 86	80.35 118	55.09 182	65.82 150	82.16 142	49.17 95	82.64 152	60.34 128	78.62 111	82.50 173

PVSNet_BlendedMVS		68.56 139	67.72 128	71.07 172	77.03 165	50.57 165	74.50 193	81.52 79	53.66 198	64.22 172	79.72 207	49.13 96	82.87 140	55.82 147	73.92 147	79.77 223

PVSNet_Blended		68.59 135	67.72 128	71.19 168	77.03 165	50.57 165	72.51 221	81.52 79	51.91 212	64.22 172	77.77 240	49.13 96	82.87 140	55.82 147	79.58 94	80.14 217

v12		68.28 146	66.83 154	72.60 137	73.43 220	50.74 160	78.18 113	79.59 130	60.01 91	60.89 220	77.66 244	49.12 98	82.77 144	62.18 111	60.46 296	84.29 122

DU-MVS		70.01 109	69.53 98	71.44 161	78.05 135	44.13 241	75.01 184	81.51 81	64.37 27	68.20 113	84.52 101	49.12 98	82.82 142	54.62 155	70.43 197	87.37 26

Baseline_NR-MVSNet		67.05 169	67.56 131	65.50 244	75.65 181	37.70 289	75.42 174	74.65 209	59.90 93	68.14 116	83.15 124	49.12 98	77.20 236	52.23 171	69.78 217	81.60 184

v13		68.29 145	66.84 152	72.63 135	73.50 218	50.83 158	78.25 111	79.58 132	60.05 89	60.76 221	77.68 243	49.11 101	82.77 144	62.17 113	60.45 297	84.30 121

VPNet		67.52 160	68.11 123	65.74 238	79.18 107	36.80 297	72.17 226	72.83 221	62.04 60	67.79 126	85.83 82	48.88 102	76.60 244	51.30 177	72.97 165	83.81 140

zzz-MVS		77.61 21	77.36 20	78.35 26	86.08 23	63.57 2	83.37 36	80.97 99	65.13 18	75.77 19	90.88 8	48.63 103	86.66 48	77.23 9	88.17 20	84.81 107

MTAPA		76.90 28	76.42 29	78.35 26	86.08 23	63.57 2	74.92 187	80.97 99	65.13 18	75.77 19	90.88 8	48.63 103	86.66 48	77.23 9	88.17 20	84.81 107

原ACMM1				74.69 75	85.39 34	59.40 48		83.42 48	51.47 225	70.27 75	86.61 66	48.61 105	86.51 55	53.85 160	87.96 25	78.16 236

v148		68.24 151	67.19 146	71.40 164	70.43 274	47.77 215	75.76 170	77.03 183	58.91 120	67.36 130	80.10 199	48.60 106	81.89 161	60.01 130	66.52 249	84.53 115

PGM-MVS		76.77 30	76.06 31	78.88 19	86.14 22	62.73 12	82.55 50	83.74 40	61.71 63	72.45 59	90.34 18	48.48 107	88.13 22	72.32 34	86.85 36	85.78 64

Test By Simon												48.33 108

v11		68.15 155	66.73 157	72.42 147	73.43 220	50.28 175	77.94 124	79.65 124	59.88 96	61.11 217	77.55 249	48.25 109	82.75 149	61.88 120	60.85 290	84.23 126

CP-MVS		77.12 26	76.68 26	78.43 25	86.05 25	63.18 7	87.55 3	83.45 47	62.44 52	72.68 55	90.50 15	48.18 110	87.34 33	73.59 29	85.71 45	84.76 111

MVS		67.37 161	66.33 167	70.51 180	75.46 185	50.94 153	73.95 199	81.85 75	41.57 309	62.54 187	78.57 230	47.98 111	85.47 77	52.97 167	82.05 67	75.14 269

XVS		77.17 25	76.56 28	79.00 16	86.32 17	62.62 14	85.83 13	83.92 33	64.55 22	72.17 60	90.01 25	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

X-MVStestdata		70.21 106	67.28 142	79.00 16	86.32 17	62.62 14	85.83 13	83.92 33	64.55 22	72.17 60	6.49 355	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

MAR-MVS		71.51 81	70.15 89	75.60 64	81.84 67	59.39 49	81.38 71	82.90 62	54.90 186	68.08 118	78.70 225	47.73 114	85.51 76	51.68 176	84.17 53	81.88 182
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		67.92 157	66.69 159	71.63 159	78.09 133	49.02 202	77.09 144	81.24 92	51.04 231	60.91 219	83.98 111	47.71 115	84.99 83	40.81 254	79.32 100	80.90 207

v10		70.21 106	69.02 109	73.81 90	73.51 217	50.92 155	78.74 100	81.39 85	60.05 89	66.39 141	81.83 151	47.58 116	85.41 80	62.80 103	68.86 227	85.09 99

v7		70.57 94	69.48 103	73.85 88	73.50 218	50.92 155	78.27 110	81.43 83	58.93 119	69.61 88	81.49 162	47.56 117	85.43 79	63.94 93	70.62 192	85.21 95

v1144		70.42 102	69.31 106	73.76 93	73.22 226	50.64 162	77.83 126	81.43 83	58.58 124	69.40 96	81.16 169	47.53 118	85.29 82	64.01 91	70.64 191	85.34 90

v2v482		70.50 97	69.45 105	73.66 99	72.62 247	50.03 186	77.58 133	80.51 113	59.90 93	69.52 92	82.14 143	47.53 118	84.88 94	65.07 75	70.17 209	86.09 57

pm-mvs1		65.24 194	64.97 184	66.04 232	72.38 250	39.40 273	72.62 219	75.63 195	55.53 176	62.35 198	83.18 123	47.45 120	76.47 245	49.06 193	66.54 248	82.24 176

HY-MVS	56.14 13	64.55 202	63.89 190	66.55 220	74.73 196	41.02 263	69.96 253	74.43 210	49.29 242	61.66 207	80.92 178	47.43 121	76.68 243	44.91 226	71.69 183	81.94 180

OPM-MVS		74.73 46	74.25 46	76.19 52	80.81 84	59.01 54	82.60 49	83.64 42	63.74 33	72.52 57	87.49 52	47.18 122	85.88 69	69.47 46	80.78 74	83.66 149

pcd_1.5k_mvsjas		3.92 338	5.23 339	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	0.00 361	47.05 123	0.00 360	0.00 359	0.00 357	0.00 360

PS-MVSNAJss		72.24 71	71.21 76	75.31 68	78.50 122	55.93 97	81.63 65	82.12 69	56.24 164	70.02 80	85.68 85	47.05 123	84.34 105	65.27 73	74.41 142	85.67 71

PS-MVSNAJ		70.51 96	69.70 93	72.93 126	81.52 70	55.79 99	74.92 187	79.00 148	55.04 184	69.88 82	78.66 226	47.05 123	82.19 157	61.61 122	79.58 94	80.83 208

WTY-MVS		59.75 245	60.39 231	57.85 292	72.32 252	37.83 287	61.05 305	64.18 285	45.95 276	61.91 201	79.11 222	47.01 126	60.88 311	42.50 243	69.49 223	74.83 275

xiu_mvs_v2_base		70.52 95	69.75 91	72.84 130	81.21 79	55.63 103	75.11 182	78.92 149	54.92 185	69.96 81	79.68 208	47.00 127	82.09 159	61.60 123	79.37 97	80.81 209

v144192		69.71 113	68.51 115	73.33 117	73.10 235	50.13 184	77.54 135	80.64 107	56.65 151	68.57 108	80.55 188	46.87 128	84.96 87	62.98 101	69.66 221	84.89 105

PEN-MVS		66.60 178	66.45 162	67.04 215	77.11 163	36.56 299	77.03 146	80.42 115	62.95 41	62.51 189	84.03 110	46.69 129	79.07 210	44.22 227	63.08 274	85.51 77

mPP-MVS		76.54 32	75.93 34	78.34 28	86.47 15	63.50 4	85.74 16	82.28 67	62.90 43	71.77 64	90.26 20	46.61 130	86.55 54	71.71 38	85.66 46	84.97 103

CP-MVSNet		66.49 181	66.41 165	66.72 217	77.67 144	36.33 301	76.83 150	79.52 136	62.45 51	62.54 187	83.47 119	46.32 131	78.37 222	45.47 222	63.43 271	85.45 81

V42		68.65 134	67.35 140	72.56 138	68.93 289	50.18 181	72.90 213	79.47 137	56.92 144	69.45 95	80.26 197	46.29 132	82.99 134	64.07 89	67.82 241	84.53 115

1112_ss		64.00 206	63.36 198	65.93 235	79.28 105	42.58 254	71.35 236	72.36 224	46.41 269	60.55 223	77.89 237	46.27 133	73.28 265	46.18 210	69.97 213	81.92 181

MSLP-MVS++		73.77 56	73.47 53	74.66 76	83.02 57	59.29 51	82.30 58	81.88 73	59.34 116	71.59 66	86.83 59	45.94 134	83.65 120	65.09 74	85.22 47	81.06 201

PS-CasMVS		66.42 182	66.32 168	66.70 219	77.60 151	36.30 303	76.94 148	79.61 125	62.36 54	62.43 196	83.66 113	45.69 135	78.37 222	45.35 224	63.26 272	85.42 84

APD-MVS_3200maxsize		74.96 43	74.39 45	76.67 48	82.20 63	58.24 64	83.67 32	83.29 54	58.41 128	73.71 42	90.14 22	45.62 136	85.99 65	69.64 45	82.85 63	85.78 64

DTE-MVSNet		65.58 189	65.34 177	66.31 222	76.06 178	34.79 309	76.43 155	79.38 141	62.55 50	61.66 207	83.83 112	45.60 137	79.15 208	41.64 251	60.88 289	85.00 101

BH-w/o		66.85 173	65.83 172	69.90 188	79.29 104	52.46 139	74.66 191	76.65 186	54.51 191	64.85 163	78.12 232	45.59 138	82.95 136	43.26 237	75.54 135	74.27 282

HQP2-MVS												45.46 139

HQP-MVS		73.45 57	72.80 59	75.40 66	80.66 85	54.94 109	82.31 55	83.90 35	62.10 57	67.85 120	85.54 88	45.46 139	86.93 42	67.04 61	80.35 84	84.32 119

ACMMP		76.02 37	75.33 39	78.07 30	85.20 36	61.91 23	85.49 19	84.44 21	63.04 40	69.80 86	89.74 30	45.43 141	87.16 38	72.01 37	82.87 62	85.14 96
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 84	70.60 83	73.78 91	76.60 171	53.15 128	79.74 91	79.78 121	58.37 129	68.75 105	86.45 71	45.43 141	80.60 185	62.58 104	77.73 116	87.58 20

BH-untuned		68.27 147	67.29 141	71.21 167	79.74 101	53.22 127	76.06 164	77.46 178	57.19 138	66.10 144	81.61 158	45.37 143	83.50 122	45.42 223	76.68 130	76.91 255

v1192		69.97 111	68.68 113	73.85 88	73.19 229	50.94 153	77.68 132	81.36 86	57.51 136	68.95 104	80.85 181	45.28 144	85.33 81	62.97 102	70.37 202	85.27 93

HQP_MVS		74.31 49	73.73 50	76.06 53	81.41 73	56.31 87	84.22 25	84.01 31	64.52 24	69.27 98	86.10 76	45.26 145	87.21 36	68.16 51	80.58 78	84.65 112

plane_prior6					81.20 80	56.24 91						45.26 145

v1921920		69.47 120	68.17 122	73.36 116	73.06 236	50.10 185	77.39 137	80.56 111	56.58 157	68.59 106	80.37 191	44.72 147	84.98 85	62.47 107	69.82 216	85.00 101

test_normal		69.26 124	67.90 126	73.32 118	70.84 271	50.38 172	75.30 176	79.17 144	54.23 194	62.00 199	80.61 187	44.69 148	83.89 116	64.33 86	79.95 90	85.69 70

DI_MVS_plusplus_test		69.35 122	68.03 124	73.30 119	71.11 268	50.14 183	75.49 173	79.16 145	54.57 190	62.45 191	80.76 184	44.67 149	84.20 106	64.23 87	79.81 91	85.54 75

Vis-MVSNet (Re-imp)		63.69 207	63.88 191	63.14 261	74.75 195	31.04 332	71.16 240	63.64 288	56.32 161	59.80 231	84.99 92	44.51 150	75.46 251	39.12 261	80.62 76	82.92 165

DP-MVS Recon		72.15 75	70.73 82	76.40 51	86.57 13	57.99 66	81.15 74	82.96 59	57.03 142	66.78 136	85.56 86	44.50 151	88.11 23	51.77 174	80.23 87	83.10 163

TAMVS		66.78 175	65.27 179	71.33 166	79.16 109	53.67 119	73.84 203	69.59 239	52.32 209	65.28 153	81.72 153	44.49 152	77.40 235	42.32 244	78.66 110	82.92 165

v748		67.26 163	65.67 173	72.02 152	69.90 282	49.77 192	76.24 159	79.57 134	58.58 124	60.49 224	80.38 190	44.47 153	82.17 158	56.16 145	65.26 257	84.12 134

Vis-MVSNet		72.18 72	71.37 73	74.61 79	81.29 76	55.41 107	80.90 75	78.28 167	60.73 75	69.23 101	88.09 49	44.36 154	82.65 151	57.68 139	81.75 70	85.77 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 56	53.15 128		67.52 258			87.85 51	44.08 155			80.76 75	78.03 240

Test_1112_low_res		62.32 226	61.77 220	64.00 255	79.08 110	39.53 272	68.17 269	70.17 232	43.25 298	59.03 241	79.90 201	44.08 155	71.24 273	43.79 233	68.42 230	81.25 196

MVSFormer		71.50 82	70.38 86	74.88 72	78.76 116	57.15 81	82.79 44	78.48 160	51.26 228	69.49 93	83.22 121	43.99 157	83.24 128	66.06 65	79.37 97	84.23 126

lupinMVS		69.57 117	68.28 120	73.44 110	78.76 116	57.15 81	76.57 152	73.29 219	46.19 271	69.49 93	82.18 139	43.99 157	79.23 201	64.66 77	79.37 97	83.93 135

v7n		69.01 128	67.36 139	73.98 86	72.51 249	52.65 134	78.54 106	81.30 88	60.26 86	62.67 183	81.62 157	43.61 159	84.49 102	57.01 142	68.70 229	84.79 109

CDS-MVSNet		66.80 174	65.37 176	71.10 171	78.98 111	53.13 130	73.27 209	71.07 228	52.15 210	64.72 164	80.23 198	43.56 160	77.10 237	45.48 221	78.88 105	83.05 164
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		69.65 115	68.39 119	73.43 111	78.27 128	56.88 83	77.12 143	73.71 217	46.53 267	69.34 97	83.22 121	43.37 161	79.18 203	64.77 76	79.20 102	84.23 126
jason: jason.
v1240		69.24 125	67.91 125	73.25 122	73.02 238	49.82 190	77.21 142	80.54 112	56.43 160	68.34 112	80.51 189	43.33 162	84.99 83	62.03 117	69.77 219	84.95 104

LCM-MVSNet-Re		61.88 232	61.35 224	63.46 256	74.58 198	31.48 331	61.42 301	58.14 312	58.71 123	53.02 297	79.55 215	43.07 163	76.80 241	45.69 216	77.96 115	82.11 179

BH-RMVSNet		68.81 130	67.42 136	72.97 125	80.11 96	52.53 137	74.26 195	76.29 188	58.48 127	68.38 111	84.20 106	42.59 164	83.83 117	46.53 207	75.91 132	82.56 170

LFMVS		71.78 78	71.59 68	72.32 150	83.40 54	46.38 225	79.75 90	71.08 227	64.18 28	72.80 54	88.64 45	42.58 165	83.72 118	57.41 141	84.49 51	86.86 37

3Dnovator	64.47 5	72.49 67	71.39 72	75.79 56	77.70 142	58.99 55	80.66 79	83.15 57	62.24 55	65.46 151	86.59 67	42.38 166	85.52 75	59.59 134	84.72 49	82.85 168

VDD-MVS		72.50 66	72.09 64	73.75 95	81.58 69	49.69 195	77.76 128	77.63 174	63.21 38	73.21 48	89.02 38	42.14 167	83.32 126	61.72 121	82.50 64	88.25 6

3Dnovator+	66.72 4	75.84 40	74.57 43	79.66 4	82.40 62	59.92 45	85.83 13	86.32 7	66.92 9	67.80 125	89.24 35	42.03 168	89.38 8	64.07 89	86.50 41	89.69 1

MVS_111021_LR		69.50 119	68.78 112	71.65 158	78.38 125	59.33 50	74.82 189	70.11 233	58.08 132	67.83 124	84.68 96	41.96 169	76.34 247	65.62 71	77.54 117	79.30 228

CPTT-MVS		72.78 63	72.08 65	74.87 73	84.88 45	61.41 28	84.15 28	77.86 169	55.27 179	67.51 129	88.08 50	41.93 170	81.85 162	69.04 49	80.01 88	81.35 195

abl_6		74.34 48	73.50 51	76.86 43	82.43 61	60.16 41	83.48 35	81.86 74	58.81 122	73.95 37	89.86 27	41.87 171	86.62 50	67.98 53	81.23 73	83.80 143

GBi-Net		67.21 164	66.55 160	69.19 195	77.63 145	43.33 248	77.31 138	77.83 170	56.62 154	65.04 159	82.70 126	41.85 172	80.33 188	47.18 202	72.76 167	83.92 136

test1		67.21 164	66.55 160	69.19 195	77.63 145	43.33 248	77.31 138	77.83 170	56.62 154	65.04 159	82.70 126	41.85 172	80.33 188	47.18 202	72.76 167	83.92 136

FMVSNet2		66.93 172	66.31 169	68.79 201	77.63 145	42.98 251	76.11 162	77.47 176	56.62 154	65.22 158	82.17 141	41.85 172	80.18 191	47.05 205	72.72 170	83.20 158

CostFormer		64.04 205	62.51 206	68.61 203	71.88 259	45.77 228	71.30 237	70.60 231	47.55 260	64.31 170	76.61 262	41.63 175	79.62 196	49.74 187	69.00 225	80.42 212

AdaColmap		69.99 110	68.66 114	73.97 87	84.94 42	57.83 67	82.63 48	78.71 153	56.28 163	64.34 168	84.14 107	41.57 176	87.06 41	46.45 208	78.88 105	77.02 251

Effi-MVS+-dtu		69.64 116	67.53 133	75.95 54	76.10 176	62.29 18	80.20 84	76.06 192	59.83 97	65.26 155	77.09 254	41.56 177	84.02 113	60.60 126	71.09 189	81.53 185

mvs-test1		70.44 101	68.19 121	77.18 40	76.10 176	63.22 6	80.59 80	76.06 192	59.83 97	66.32 142	79.87 202	41.56 177	85.53 74	60.60 126	72.77 166	82.80 169

QAPM		70.05 108	68.81 111	73.78 91	76.54 173	53.43 124	83.23 37	83.48 45	52.89 203	65.90 147	86.29 73	41.55 179	86.49 56	51.01 178	78.40 113	81.42 187

VDDNet		71.81 77	71.33 74	73.26 121	82.80 60	47.60 217	78.74 100	75.27 199	59.59 109	72.94 52	89.40 32	41.51 180	83.91 115	58.75 136	82.99 59	88.26 5

1121		68.53 140	67.16 147	72.63 135	85.64 29	61.14 30	73.95 199	66.46 265	44.61 285	70.28 74	86.68 63	41.42 181	80.78 182	53.62 161	81.79 68	75.97 259

CHOSEN 1792x2688		65.08 197	62.84 203	71.82 155	81.49 72	56.26 90	66.32 277	74.20 213	40.53 315	63.16 178	78.65 227	41.30 182	77.80 230	45.80 215	74.09 144	81.40 188

新几何1				70.76 175	85.66 27	61.13 31		66.43 266	44.68 284	70.29 73	86.64 64	41.29 183	75.23 259	49.72 188	81.75 70	75.93 262

tpmrst		58.24 259	58.70 247	56.84 296	66.97 301	34.32 313	69.57 256	61.14 303	47.17 264	58.58 247	71.60 294	41.28 184	60.41 313	49.20 192	62.84 275	75.78 264

tfpnnormal		62.47 224	61.63 222	64.99 249	74.81 194	39.01 275	71.22 238	73.72 216	55.22 181	60.21 225	80.09 200	41.26 185	76.98 240	30.02 309	68.09 238	78.97 232

HPM-MVS_fast		74.30 50	73.46 54	76.80 45	84.45 48	59.04 53	83.65 33	81.05 95	60.15 87	70.43 71	89.84 28	41.09 186	85.59 72	67.61 57	82.90 61	85.77 66

V4		67.09 167	65.16 181	72.87 128	66.76 305	51.60 149	73.69 205	79.45 139	57.88 133	62.45 191	78.58 229	40.96 187	83.34 124	61.99 118	64.71 260	83.68 146

v52		67.09 167	65.16 181	72.87 128	66.77 304	51.60 149	73.69 205	79.45 139	57.88 133	62.46 190	78.57 230	40.95 188	83.34 124	61.99 118	64.70 262	83.68 146

114514_t		70.83 87	69.56 97	74.64 78	86.21 19	54.63 113	82.34 54	81.81 76	48.22 253	63.01 179	85.83 82	40.92 189	87.10 40	57.91 138	79.79 92	82.18 177

HyFIR lowres test		65.67 188	63.01 201	73.67 98	79.97 98	55.65 102	69.07 260	75.52 196	42.68 303	63.53 174	77.95 234	40.43 190	81.64 165	46.01 213	71.91 181	83.73 144

FMVSNet3		66.32 183	65.61 174	68.46 204	76.48 174	42.34 255	74.98 186	77.15 182	55.83 170	65.04 159	81.16 169	39.91 191	80.14 192	47.18 202	72.76 167	82.90 167

MVP-Stereo		65.41 192	63.80 192	70.22 182	77.62 149	55.53 104	76.30 157	78.53 158	50.59 235	56.47 268	78.65 227	39.84 192	82.68 150	44.10 230	72.12 180	72.44 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TR-MVS		66.59 180	65.07 183	71.17 169	79.18 107	49.63 197	73.48 207	75.20 201	52.95 202	67.90 119	80.33 194	39.81 193	83.68 119	43.20 238	73.56 153	80.20 215

pmmvs6		63.69 207	62.82 204	66.27 226	70.63 272	39.27 274	73.13 210	75.47 197	52.69 205	59.75 232	82.30 137	39.71 194	77.03 239	47.40 201	64.35 265	82.53 171

pcd1.5k->3k		30.06 325	30.56 326	28.55 340	78.81 115	0.00 363	0.00 355	82.07 71	0.00 359	0.00 360	0.00 361	39.61 195	0.00 360	0.00 359	74.56 139	85.66 72

XVG-OURS-SEG-HR		68.81 130	67.47 135	72.82 132	74.40 203	56.87 84	70.59 245	79.04 147	54.77 187	66.99 134	86.01 78	39.57 196	78.21 225	62.54 105	73.33 156	83.37 154

Fast-Effi-MVS+-dtu		67.37 161	65.33 178	73.48 108	72.94 239	57.78 68	77.47 136	76.88 184	57.60 135	61.97 200	76.85 258	39.31 197	80.49 186	54.72 154	70.28 208	82.17 178

ACMP	63.53 6	72.30 70	71.20 77	75.59 65	80.28 92	57.54 70	82.74 46	82.84 64	60.58 77	65.24 156	86.18 74	39.25 198	86.03 64	66.95 63	76.79 128	83.22 157
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 64	71.74 67	75.76 57	80.22 93	57.51 72	82.55 50	83.40 49	61.32 67	66.67 137	87.33 55	39.15 199	86.59 51	67.70 55	77.30 122	83.19 159

LGP-MVS_train				75.76 57	80.22 93	57.51 72		83.40 49	61.32 67	66.67 137	87.33 55	39.15 199	86.59 51	67.70 55	77.30 122	83.19 159

TAPA-MVS	59.36 10	66.60 178	65.20 180	70.81 174	76.63 170	48.75 205	76.52 154	80.04 119	50.64 234	65.24 156	84.93 93	39.15 199	78.54 217	36.77 272	76.88 125	85.14 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	61.03 9	68.85 129	67.56 131	72.70 134	74.26 205	53.99 117	81.21 73	81.34 87	52.70 204	62.75 182	85.55 87	38.86 202	84.14 108	48.41 198	83.01 58	79.97 218

sss		56.17 274	56.57 262	54.96 303	66.93 302	36.32 302	57.94 313	61.69 301	41.67 307	58.64 246	75.32 275	38.72 203	56.25 331	42.04 246	66.19 250	72.31 305

ACMM	61.98 7	70.80 89	69.73 92	74.02 85	80.59 89	58.59 60	82.68 47	82.02 72	55.46 178	67.18 132	84.39 105	38.51 204	83.17 130	60.65 125	76.10 131	80.30 214
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 166	65.58 175	71.88 154	70.37 276	49.70 193	70.25 251	78.45 162	51.52 223	69.16 102	80.37 191	38.45 205	82.50 153	60.19 129	71.46 185	83.44 153

test_djsdf		69.45 121	67.74 127	74.58 80	74.57 199	54.92 111	82.79 44	78.48 160	51.26 228	65.41 152	83.49 118	38.37 206	83.24 128	66.06 65	69.25 224	85.56 74

tpm2		62.07 229	60.10 233	67.99 207	72.79 244	43.86 244	71.05 241	66.85 263	43.14 300	62.77 180	75.39 274	38.32 207	80.80 181	41.69 248	68.88 226	79.32 227

tpm cat1		59.25 253	56.95 260	66.15 228	72.19 253	46.96 221	68.09 270	65.76 268	40.03 318	57.81 259	70.56 302	38.32 207	74.51 263	38.26 265	61.50 284	77.00 252

CNLPA		65.43 191	64.02 188	69.68 189	78.73 118	58.07 65	77.82 127	70.71 230	51.49 224	61.57 209	83.58 116	38.23 209	70.82 274	43.90 231	70.10 211	80.16 216

1314		64.61 201	63.21 199	68.80 200	71.87 260	47.46 218	73.95 199	78.39 166	42.88 302	59.97 227	76.60 263	38.11 210	79.39 199	54.84 153	72.32 177	79.55 224

testdata				64.66 251	81.52 70	52.93 131		65.29 272	46.09 272	73.88 39	87.46 53	38.08 211	66.26 294	53.31 166	78.48 112	74.78 277

FMVSNet1		66.70 176	65.87 171	69.19 195	77.49 152	43.33 248	77.31 138	77.83 170	56.45 159	64.60 166	82.70 126	38.08 211	80.33 188	46.08 212	72.31 178	83.92 136

tpmp4_e23		62.71 223	60.13 232	70.45 181	73.40 222	48.39 208	72.82 214	69.49 241	44.88 281	59.91 228	74.99 276	37.79 213	81.47 170	40.22 256	67.71 243	81.48 186

EPNet_dtu		61.90 230	61.97 215	61.68 270	72.89 243	39.78 269	75.85 169	65.62 269	55.09 182	54.56 283	79.36 218	37.59 214	67.02 290	39.80 260	76.95 124	78.25 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		59.75 245	58.00 254	64.98 250	74.14 213	48.06 211	63.35 293	63.23 291	49.13 244	59.33 237	71.46 295	37.45 215	69.59 279	41.39 252	62.57 277	77.30 245

IterMVS		62.79 218	61.27 225	67.35 213	69.37 287	52.04 146	71.17 239	68.24 253	52.63 206	59.82 230	76.91 257	37.32 216	72.36 268	52.80 168	63.19 273	77.66 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20240521		64.54 203	64.00 189	66.15 228	74.87 192	34.97 308	74.13 197	79.35 142	56.54 158	58.65 245	83.31 120	37.31 217	80.04 193	41.39 252	70.44 196	83.45 152

tfpn200view9		63.18 215	62.18 213	66.21 227	76.85 168	39.62 270	71.96 233	69.44 242	56.63 152	62.61 185	79.83 203	37.18 218	79.17 204	31.84 294	73.25 158	79.83 220

thres400		63.31 211	62.18 213	66.72 217	76.85 168	39.62 270	71.96 233	69.44 242	56.63 152	62.61 185	79.83 203	37.18 218	79.17 204	31.84 294	73.25 158	81.36 189

tpm		57.34 266	58.16 251	54.86 304	71.80 261	34.77 310	67.47 273	56.04 324	48.20 254	60.10 226	76.92 256	37.17 220	53.41 339	40.76 255	65.01 258	76.40 258

test222					83.14 55	58.68 59	72.57 220	63.45 289	41.78 305	67.56 128	86.12 75	37.13 221			78.73 109	74.98 273

thres200		62.20 227	61.16 227	65.34 246	75.38 187	39.99 267	69.60 255	69.29 244	55.64 175	61.87 202	76.99 255	37.07 222	78.96 216	31.28 304	73.28 157	77.06 250

tfpn111		63.33 210	62.34 210	66.30 223	77.31 154	38.66 279	72.65 215	69.11 246	57.07 139	62.45 191	81.03 173	37.01 223	79.23 201	31.38 303	73.09 163	81.03 202

conf200view11		63.38 209	62.41 208	66.29 225	77.31 154	38.66 279	72.65 215	69.11 246	57.07 139	62.45 191	81.03 173	37.01 223	79.17 204	31.84 294	73.25 158	81.03 202

thres100view900		63.28 213	62.41 208	65.89 236	77.31 154	38.66 279	72.65 215	69.11 246	57.07 139	62.45 191	81.03 173	37.01 223	79.17 204	31.84 294	73.25 158	79.83 220

thres600view7		63.30 212	62.27 211	66.41 221	77.18 162	38.87 276	72.35 223	69.11 246	56.98 143	62.37 197	80.96 177	37.01 223	79.00 215	31.43 302	73.05 164	81.36 189

view600		62.77 219	61.84 216	65.55 240	77.28 157	36.87 293	72.15 227	67.78 254	56.79 146	61.46 210	81.92 146	36.88 227	78.42 218	29.86 310	72.46 171	81.36 189

view800		62.77 219	61.84 216	65.55 240	77.28 157	36.87 293	72.15 227	67.78 254	56.79 146	61.46 210	81.92 146	36.88 227	78.42 218	29.86 310	72.46 171	81.36 189

conf0.05thres1000		62.77 219	61.84 216	65.55 240	77.28 157	36.87 293	72.15 227	67.78 254	56.79 146	61.46 210	81.92 146	36.88 227	78.42 218	29.86 310	72.46 171	81.36 189

tfpn		62.77 219	61.84 216	65.55 240	77.28 157	36.87 293	72.15 227	67.78 254	56.79 146	61.46 210	81.92 146	36.88 227	78.42 218	29.86 310	72.46 171	81.36 189

PatchFormer-LS_test		62.20 227	60.59 230	67.04 215	72.18 254	46.82 223	70.36 250	68.62 251	51.92 211	59.19 238	70.23 304	36.86 231	75.07 260	50.23 184	65.68 254	79.23 229

DP-MVS		65.68 187	63.66 195	71.75 156	84.93 43	56.87 84	80.74 78	73.16 220	53.06 201	59.09 240	82.35 135	36.79 232	85.94 68	32.82 290	69.96 214	72.45 300

semantic-postprocess				65.40 245	71.99 257	50.80 159		69.63 238	45.71 278	60.61 222	77.93 235	36.56 233	65.99 296	55.67 150	63.50 270	79.42 226

XVG-OURS		68.76 133	67.37 138	72.90 127	74.32 204	57.22 76	70.09 252	78.81 151	55.24 180	67.79 126	85.81 84	36.54 234	78.28 224	62.04 116	75.74 133	83.19 159

pmmvs4		61.48 235	59.39 235	67.76 209	71.57 262	53.86 118	71.42 235	65.34 271	44.20 290	59.46 233	77.92 236	35.90 235	74.71 262	43.87 232	64.87 259	74.71 278

CR-MVSNet		59.91 243	57.90 255	65.96 233	69.96 280	52.07 144	65.31 285	63.15 292	42.48 304	59.36 234	74.84 277	35.83 236	70.75 275	45.50 220	64.65 263	75.06 270

Patchmtry		57.16 267	56.47 263	59.23 283	69.17 288	34.58 312	62.98 294	63.15 292	44.53 286	56.83 265	74.84 277	35.83 236	68.71 283	40.03 258	60.91 288	74.39 281

conf0.01		59.97 241	58.81 239	63.42 257	74.15 207	33.83 317	68.32 263	64.22 279	51.79 213	58.04 252	79.57 209	35.41 238	75.41 252	29.57 316	68.26 231	81.03 202

conf0.002		59.97 241	58.81 239	63.42 257	74.15 207	33.83 317	68.32 263	64.22 279	51.79 213	58.04 252	79.57 209	35.41 238	75.41 252	29.57 316	68.26 231	81.03 202

thresconf0.02		59.40 249	58.81 239	61.17 276	74.15 207	33.83 317	68.32 263	64.22 279	51.79 213	58.04 252	79.57 209	35.41 238	75.41 252	29.57 316	68.26 231	74.25 283

tfpn_n400		59.40 249	58.81 239	61.17 276	74.15 207	33.83 317	68.32 263	64.22 279	51.79 213	58.04 252	79.57 209	35.41 238	75.41 252	29.57 316	68.26 231	74.25 283

tfpnconf		59.40 249	58.81 239	61.17 276	74.15 207	33.83 317	68.32 263	64.22 279	51.79 213	58.04 252	79.57 209	35.41 238	75.41 252	29.57 316	68.26 231	74.25 283

tfpnview11		59.40 249	58.81 239	61.17 276	74.15 207	33.83 317	68.32 263	64.22 279	51.79 213	58.04 252	79.57 209	35.41 238	75.41 252	29.57 316	68.26 231	74.25 283

RPMNet		58.70 256	56.29 266	65.96 233	69.96 280	52.07 144	65.31 285	62.15 299	43.20 299	59.36 234	70.15 306	35.37 244	70.75 275	36.42 279	64.65 263	75.06 270

CANet_DTU		68.18 153	67.71 130	69.59 191	74.83 193	46.24 226	78.66 102	76.85 185	59.60 106	63.45 175	82.09 145	35.25 245	77.41 234	59.88 131	78.76 108	85.14 96

test_0402		63.25 214	61.01 228	69.96 186	80.00 97	54.37 115	76.86 149	72.02 225	54.58 189	58.71 243	80.79 183	35.00 246	84.36 104	26.41 331	64.71 260	71.15 313

tfpn_ndepth		59.57 248	59.02 238	61.23 275	73.81 214	35.60 305	69.40 258	65.59 270	50.96 232	57.96 258	77.72 241	34.81 247	75.91 250	30.36 307	70.57 194	72.18 306

sam_mvs1												34.74 248				78.05 238

tfpn1000		59.24 254	58.70 247	60.86 281	73.75 215	33.99 315	68.86 261	63.98 286	51.25 230	57.29 263	79.51 217	34.58 249	75.26 258	29.08 323	69.99 212	73.32 292

pmmvs5		56.47 270	55.68 269	58.86 286	61.41 327	36.71 298	66.37 276	62.75 295	40.38 316	53.70 291	76.62 261	34.56 250	67.05 289	40.02 259	65.27 256	72.83 295

patchmatchnet-post											64.03 324	34.50 251	74.27 264

PatchmatchNet		59.84 244	58.24 250	64.65 252	73.05 237	46.70 224	69.42 257	62.18 298	47.55 260	58.88 242	71.96 293	34.49 252	69.16 281	42.99 240	63.60 269	78.07 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 299	48.28 299	51.50 317	64.40 317	30.85 333	45.68 339	48.46 343	35.60 330	46.10 321	72.10 292	34.47 253	46.37 345	27.08 329	60.65 292	77.27 247

MS-PatchMatch		62.42 225	61.46 223	65.31 247	75.21 189	52.10 143	72.05 231	74.05 214	46.41 269	57.42 262	74.36 281	34.35 254	77.57 233	45.62 218	73.67 149	66.26 324

tpmvs		58.47 257	56.95 260	63.03 263	70.20 277	41.21 262	67.90 272	67.23 261	49.62 241	54.73 281	70.84 299	34.14 255	76.24 248	36.64 276	61.29 287	71.64 309

PMMVS		53.96 283	53.26 287	56.04 298	62.60 323	50.92 155	61.17 304	56.09 323	32.81 334	53.51 295	66.84 317	34.04 256	59.93 315	44.14 229	68.18 237	57.27 339

Patchmatch-RL test		58.16 260	55.49 270	66.15 228	67.92 297	48.89 204	60.66 306	51.07 337	47.86 258	59.36 234	62.71 330	34.02 257	72.27 270	56.41 144	59.40 300	77.30 245

test_post											3.55 357	33.90 258	66.52 292

Test4		67.77 158	65.97 170	73.19 123	68.64 290	50.58 164	74.80 190	80.48 114	54.13 195	59.11 239	79.07 223	33.89 259	83.12 132	63.61 97	79.98 89	85.87 62

PLC	56.13 14	65.09 196	63.21 199	70.72 177	81.04 82	54.87 112	78.57 104	77.47 176	48.51 249	55.71 271	81.89 150	33.71 260	79.71 194	41.66 249	70.37 202	77.58 242
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 190	63.70 194	71.02 173	70.87 270	48.10 210	70.48 247	74.40 211	56.69 150	64.70 165	76.77 259	33.66 261	81.10 175	55.42 152	70.32 204	83.87 139

LS3D		64.71 199	62.50 207	71.34 165	79.72 103	55.71 100	79.82 88	74.72 208	48.50 250	56.62 266	84.62 98	33.59 262	82.34 156	29.65 315	75.23 136	75.97 259

sam_mvs												33.43 263

PatchT		53.17 289	53.44 286	52.33 315	68.29 295	25.34 346	58.21 312	54.41 330	44.46 288	54.56 283	69.05 310	33.32 264	60.94 310	36.93 271	61.76 282	70.73 315

test20.03		53.87 285	54.02 282	53.41 310	61.47 326	28.11 338	61.30 302	59.21 308	51.34 227	52.09 299	77.43 252	33.29 265	58.55 319	29.76 314	60.27 298	73.58 291

our_test_3		56.49 269	54.42 277	62.68 266	69.51 285	45.48 230	66.08 278	61.49 302	44.11 293	50.73 306	69.60 309	33.05 266	68.15 285	38.38 264	56.86 306	74.40 280

anonymousdsp		67.00 171	64.82 185	73.57 103	70.09 278	56.13 92	76.35 156	77.35 180	48.43 251	64.99 162	80.84 182	33.01 267	80.34 187	64.66 77	67.64 244	84.23 126

MDTV_nov1_ep13_2view						25.89 343	61.22 303		40.10 317	51.10 302		32.97 268		38.49 263		78.61 233

IB-MVS	56.42 12	65.40 193	62.73 205	73.40 115	74.89 190	52.78 133	73.09 211	75.13 202	55.69 173	58.48 249	73.73 285	32.86 269	86.32 60	50.63 180	70.11 210	81.10 200
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		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 203	51.61 220	70.04 77	81.41 166	32.79 270	79.02 211	63.81 94	77.31 119	81.22 197

xiu_mvs_v1_base		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 203	51.61 220	70.04 77	81.41 166	32.79 270	79.02 211	63.81 94	77.31 119	81.22 197

xiu_mvs_v1_base_debi		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 203	51.61 220	70.04 77	81.41 166	32.79 270	79.02 211	63.81 94	77.31 119	81.22 197

Anonymous20231206		55.10 281	55.30 272	54.48 306	69.81 283	33.94 316	62.91 295	62.13 300	41.08 310	55.18 278	75.65 272	32.75 273	56.59 328	30.32 308	67.86 240	72.91 294

UGNet		68.81 130	67.39 137	73.06 124	78.33 126	54.47 114	79.77 89	75.40 198	60.45 79	63.22 176	84.40 104	32.71 274	80.91 179	51.71 175	80.56 80	83.81 140
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		58.15 261	58.13 253	58.22 289	68.57 291	44.80 234	65.46 282	57.92 313	50.08 238	55.44 274	69.82 307	32.62 275	57.44 322	49.66 189	73.62 150	72.41 302

test0.0.03 1		53.32 288	53.59 285	52.50 314	62.81 322	29.45 335	59.51 308	54.11 332	50.08 238	54.40 285	74.31 282	32.62 275	55.92 333	30.50 306	63.95 267	72.15 308

MDTV_nov1_ep13			57.00 259		72.73 245	38.26 284	65.02 288	64.73 276	44.74 283	55.46 273	72.48 290	32.61 277	70.47 277	37.47 268	67.75 242

cascas		65.98 186	63.42 197	73.64 101	77.26 161	52.58 136	72.26 225	77.21 181	48.56 248	61.21 215	74.60 280	32.57 278	85.82 70	50.38 182	76.75 129	82.52 172

test_post1							68.67 262				3.64 356	32.39 279	69.49 280	44.17 228

CVMVSNet		59.63 247	59.14 237	61.08 280	74.47 200	38.84 277	75.20 180	68.74 250	31.15 337	58.24 250	76.51 264	32.39 279	68.58 284	49.77 186	65.84 252	75.81 263

ppachtmachnet_test		58.06 262	55.38 271	66.10 231	69.51 285	48.99 203	68.01 271	66.13 267	44.50 287	54.05 289	70.74 300	32.09 281	72.34 269	36.68 275	56.71 308	76.99 254

MIMVSNet		57.35 265	57.07 258	58.22 289	74.21 206	37.18 290	62.46 296	60.88 304	48.88 246	55.29 277	75.99 270	31.68 282	62.04 308	31.87 293	72.35 176	75.43 268

PVSNet_0	43.31 20	47.46 304	45.64 304	52.92 312	67.60 299	44.65 236	54.06 322	54.64 328	41.59 308	46.15 319	58.75 336	30.99 283	58.66 318	32.18 291	24.81 346	55.46 340

testing_2		66.02 185	63.77 193	72.76 133	66.03 310	50.48 170	72.93 212	80.36 117	54.41 192	54.25 287	76.76 260	30.89 284	83.16 131	64.19 88	74.08 145	84.65 112

gg-mvs-nofinetune		57.86 263	56.43 264	62.18 268	72.62 247	35.35 307	66.57 274	56.33 321	50.65 233	57.64 260	57.10 337	30.65 285	76.36 246	37.38 269	78.88 105	74.82 276

GG-mvs-BLEND				62.34 267	71.36 267	37.04 292	69.20 259	57.33 316		54.73 281	65.48 323	30.37 286	77.82 229	34.82 283	74.93 137	72.17 307

MDA-MVSNet-bldmvs		53.87 285	50.81 292	63.05 262	66.25 307	48.58 206	56.93 316	63.82 287	48.09 255	41.22 333	70.48 303	30.34 287	68.00 286	34.24 285	45.92 335	72.57 298

EPMVS		53.96 283	53.69 283	54.79 305	66.12 309	31.96 330	62.34 298	49.05 340	44.42 289	55.54 272	71.33 297	30.22 288	56.70 326	41.65 250	62.54 278	75.71 265

DWT-MVSNet_test		61.90 230	59.93 234	67.83 208	71.98 258	46.09 227	71.03 242	69.71 235	50.09 237	58.51 248	70.62 301	30.21 289	77.63 231	49.28 191	67.91 239	79.78 222

YYNet1		50.73 296	48.96 295	56.03 299	61.10 329	41.78 258	51.94 329	56.44 320	40.94 312	44.84 322	67.80 314	30.08 290	55.08 337	36.77 272	50.71 325	71.22 311

MDA-MVSNet_test_wron		50.71 297	48.95 296	56.00 300	61.17 328	41.84 257	51.90 330	56.45 319	40.96 311	44.79 323	67.84 313	30.04 291	55.07 338	36.71 274	50.69 326	71.11 314

MSDG		61.81 233	59.23 236	69.55 194	72.64 246	52.63 135	70.45 248	75.81 194	51.38 226	53.70 291	76.11 267	29.52 292	81.08 176	37.70 267	65.79 253	74.93 274

CMPMVS	42.80 21	57.81 264	55.97 267	63.32 259	60.98 330	47.38 219	64.66 289	69.50 240	32.06 336	46.83 317	77.80 239	29.50 293	71.36 272	48.68 195	73.75 148	71.21 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 216	61.23 226	68.92 199	76.57 172	47.80 213	59.92 307	76.39 187	54.35 193	58.67 244	82.46 134	29.44 294	81.49 169	42.12 245	71.14 187	77.46 243
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		53.16 290	52.47 288	55.23 301	59.45 337	33.39 325	59.43 309	69.13 245	45.98 273	50.35 309	72.32 291	29.30 295	58.26 320	42.02 247	44.30 337	74.05 288

CHOSEN 280x420		47.83 302	46.36 303	52.24 316	67.37 300	49.78 191	38.91 347	43.11 349	35.00 331	43.27 330	63.30 329	28.95 296	49.19 344	36.53 277	60.80 291	57.76 338

pmmvs-eth3d		58.81 255	56.31 265	66.30 223	67.61 298	52.42 141	72.30 224	64.76 275	43.55 296	54.94 279	74.19 283	28.95 296	72.60 267	43.31 235	57.21 305	73.88 290

dp		51.89 293	51.60 291	52.77 313	68.44 294	32.45 327	62.36 297	54.57 329	44.16 291	49.31 310	67.91 312	28.87 298	56.61 327	33.89 286	54.89 313	69.24 321

jajsoiax		68.25 149	66.45 162	73.66 99	75.62 182	55.49 105	80.82 76	78.51 159	52.33 208	64.33 169	84.11 108	28.28 299	81.81 164	63.48 99	70.62 192	83.67 148

RPSCF		55.80 277	54.22 281	60.53 282	65.13 314	42.91 253	64.30 290	57.62 315	36.84 328	58.05 251	82.28 138	28.01 300	56.24 332	37.14 270	58.61 303	82.44 175

F-COLMAP		63.05 217	60.87 229	69.58 193	76.99 167	53.63 121	78.12 114	76.16 189	47.97 257	52.41 298	81.61 158	27.87 301	78.11 226	40.07 257	66.66 247	77.00 252

K. test v3		60.47 239	57.11 257	70.56 179	73.74 216	48.22 209	75.10 183	62.55 296	58.27 131	53.62 293	76.31 266	27.81 302	81.59 167	47.42 200	39.18 340	81.88 182

ACMH+	57.40 11	66.12 184	64.06 187	72.30 151	77.79 141	52.83 132	80.39 81	78.03 168	57.30 137	57.47 261	82.55 132	27.68 303	84.17 107	45.54 219	69.78 217	79.90 219

UnsupCasMVSNet_bld		50.07 298	48.87 297	53.66 308	60.97 331	33.67 323	57.62 314	64.56 277	39.47 320	47.38 314	64.02 326	27.47 304	59.32 316	34.69 284	43.68 338	67.98 323

mvs_tets		68.18 153	66.36 166	73.63 102	75.61 183	55.35 108	80.77 77	78.56 157	52.48 207	64.27 171	84.10 109	27.45 305	81.84 163	63.45 100	70.56 195	83.69 145

lessismore_v0				69.91 187	71.42 265	47.80 213		50.90 338		50.39 308	75.56 273	27.43 306	81.33 171	45.91 214	34.10 343	80.59 210

ACMH	55.70 15	65.20 195	63.57 196	70.07 185	78.07 134	52.01 147	79.48 97	79.69 122	55.75 172	56.59 267	80.98 176	27.12 307	80.94 177	42.90 242	71.58 184	77.25 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 234	58.80 246	70.20 184	75.80 180	47.22 220	75.59 171	69.68 237	54.61 188	54.11 288	79.26 220	27.07 308	82.96 135	43.27 236	49.79 328	80.41 213

PVSNet	50.76 19	58.40 258	57.39 256	61.42 272	75.53 184	44.04 243	61.43 300	63.45 289	47.04 265	56.91 264	73.61 286	27.00 309	64.76 299	39.12 261	72.40 175	75.47 267

OpenMVS_ROB	52.78 18	60.03 240	58.14 252	65.69 239	70.47 273	44.82 233	75.33 175	70.86 229	45.04 280	56.06 269	76.00 268	26.89 310	79.65 195	35.36 282	67.29 245	72.60 297

ADS-MVSNet2		51.33 295	48.76 298	59.07 285	66.02 311	44.60 237	50.90 331	59.76 307	36.90 326	50.74 304	66.18 321	26.38 311	63.11 303	27.17 327	54.76 314	69.50 318

ADS-MVSNet		48.48 301	47.77 300	50.63 318	66.02 311	29.92 334	50.90 331	50.87 339	36.90 326	50.74 304	66.18 321	26.38 311	52.47 341	27.17 327	54.76 314	69.50 318

N_pmnet		39.35 318	40.28 315	36.54 335	63.76 319	1.62 361	49.37 334	0.76 362	34.62 332	43.61 329	66.38 320	26.25 313	42.57 351	26.02 333	51.77 321	65.44 325

MVS-HIRNet		45.52 307	44.48 309	48.65 322	68.49 293	34.05 314	59.41 310	44.50 348	27.03 341	37.96 339	50.47 344	26.16 314	64.10 300	26.74 330	59.52 299	47.82 343

FMVSNet5		55.86 276	54.93 273	58.66 288	71.05 269	36.35 300	64.18 292	62.48 297	46.76 266	50.66 307	74.73 279	25.80 315	64.04 301	33.11 289	65.57 255	75.59 266

new-patchmatchnet		47.56 303	47.73 301	47.06 324	58.81 338	9.37 357	48.78 335	59.21 308	43.28 297	44.22 325	68.66 311	25.67 316	57.20 325	31.57 301	49.35 331	74.62 279

MIMVSNet1		55.17 280	54.31 279	57.77 293	70.03 279	32.01 329	65.68 280	64.81 274	49.19 243	46.75 318	76.00 268	25.53 317	64.04 301	28.65 325	62.13 280	77.26 248

PatchMatch-RL		56.25 273	54.55 276	61.32 274	77.06 164	56.07 94	65.57 281	54.10 333	44.13 292	53.49 296	71.27 298	25.20 318	66.78 291	36.52 278	63.66 268	61.12 333

JIA-IIPM		51.56 294	47.68 302	63.21 260	64.61 316	50.73 161	47.71 336	58.77 310	42.90 301	48.46 312	51.72 341	24.97 319	70.24 278	36.06 281	53.89 317	68.64 322

EU-MVSNet		55.61 278	54.41 278	59.19 284	65.41 313	33.42 324	72.44 222	71.91 226	28.81 339	51.27 301	73.87 284	24.76 320	69.08 282	43.04 239	58.20 304	75.06 270

EG-PatchMatch MVS		64.71 199	62.87 202	70.22 182	77.68 143	53.48 123	77.99 122	78.82 150	53.37 199	56.03 270	77.41 253	24.75 321	84.04 110	46.37 209	73.42 155	73.14 293

TESTMET0.1,1		55.28 279	54.90 274	56.42 297	66.56 306	43.67 246	65.46 282	56.27 322	39.18 321	53.83 290	67.44 315	24.21 322	55.46 336	48.04 199	73.11 162	70.13 316

LP		48.51 300	45.51 305	57.52 295	62.86 321	44.53 240	52.38 328	59.84 306	38.11 323	42.81 331	61.02 331	23.23 323	63.02 304	24.10 334	45.24 336	65.02 327

COLMAP_ROB	52.97 17	61.27 237	58.81 239	68.64 202	74.63 197	52.51 138	78.42 109	73.30 218	49.92 240	50.96 303	81.51 161	23.06 324	79.40 198	31.63 299	65.85 251	74.01 289
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 292	52.37 289	50.51 319	60.39 333	23.55 349	58.42 311	58.15 311	49.03 245	51.83 300	79.21 221	22.39 325	55.59 334	29.24 322	62.64 276	72.40 304

DSMNet-mixed		39.30 319	38.72 318	41.03 332	51.22 345	19.66 351	45.53 340	31.35 355	15.83 351	39.80 338	67.42 316	22.19 326	45.13 348	22.43 335	52.69 319	58.31 337

test-mter		56.42 271	55.82 268	58.22 289	68.57 291	44.80 234	65.46 282	57.92 313	39.94 319	55.44 274	69.82 307	21.92 327	57.44 322	49.66 189	73.62 150	72.41 302

OurMVSNet-221017-0		61.37 236	58.63 249	69.61 190	72.05 256	48.06 211	73.93 202	72.51 222	47.23 263	54.74 280	80.92 178	21.49 328	81.24 172	48.57 197	56.22 309	79.53 225

Anonymous20231211		55.92 275	53.63 284	62.77 264	68.22 296	35.56 306	74.48 194	69.89 234	46.42 268	49.07 311	73.45 287	21.13 329	76.77 242	28.74 324	51.30 324	75.97 259

ITE_SJBPF				62.09 269	66.16 308	44.55 239		64.32 278	47.36 262	55.31 276	80.34 193	19.27 330	62.68 306	36.29 280	62.39 279	79.04 230

AllTest		57.08 268	54.65 275	64.39 253	71.44 263	49.03 200	69.92 254	67.30 259	45.97 274	47.16 315	79.77 205	17.47 331	67.56 287	33.65 287	59.16 301	76.57 256

TestCases				64.39 253	71.44 263	49.03 200		67.30 259	45.97 274	47.16 315	79.77 205	17.47 331	67.56 287	33.65 287	59.16 301	76.57 256

testpf		44.11 311	45.40 306	40.26 333	60.52 332	27.34 340	33.26 349	54.33 331	45.87 277	41.08 334	60.26 333	16.46 333	59.14 317	46.09 211	50.68 327	34.31 349

XVG-ACMP-BASELINE		64.36 204	62.23 212	70.74 176	72.35 251	52.45 140	70.80 244	78.45 162	53.84 197	59.87 229	81.10 171	16.24 334	79.32 200	55.64 151	71.76 182	80.47 211

tmp_tt		9.43 334	11.14 335	4.30 346	2.38 360	4.40 360	13.62 354	16.08 359	0.39 356	15.89 351	13.06 354	15.80 335	5.54 359	12.63 350	10.46 355	2.95 355

USDC		56.35 272	54.24 280	62.69 265	64.74 315	40.31 266	65.05 287	73.83 215	43.93 294	47.58 313	77.71 242	15.36 336	75.05 261	38.19 266	61.81 281	72.70 296

test2356		45.61 306	44.66 308	48.47 323	60.15 334	28.08 339	52.44 327	52.83 336	38.01 324	46.13 320	60.98 332	15.08 337	55.54 335	20.43 343	55.85 311	61.78 331

test1235678		45.66 305	44.46 310	49.26 320	59.88 335	28.68 337	56.36 318	55.54 327	39.12 322	40.89 335	63.40 328	14.41 338	57.32 324	21.05 340	49.47 330	61.78 331

1111		44.40 310	45.00 307	42.61 331	57.55 340	17.33 354	53.82 325	57.05 317	40.78 313	44.11 326	66.57 318	13.37 339	45.77 346	22.15 336	49.58 329	64.73 328

.test1245		34.88 322	39.49 317	21.04 343	57.55 340	17.33 354	53.82 325	57.05 317	40.78 313	44.11 326	66.57 318	13.37 339	45.77 346	22.15 336	0.00 357	0.03 358

ANet_high		41.38 315	37.47 319	53.11 311	39.73 354	24.45 348	56.94 315	69.69 236	47.65 259	26.04 345	52.32 340	12.44 341	62.38 307	21.80 339	10.61 354	72.49 299

no-one		40.85 316	36.09 320	55.14 302	48.55 347	38.72 278	42.15 345	62.92 294	34.60 333	23.55 346	49.74 345	12.21 342	66.16 295	26.27 332	24.84 345	60.54 334

FPMVS		42.18 314	41.11 314	45.39 326	58.03 339	41.01 264	49.50 333	53.81 334	30.07 338	33.71 340	64.03 324	11.69 343	52.08 342	14.01 349	55.11 312	43.09 346

TinyColmap		54.14 282	51.72 290	61.40 273	66.84 303	41.97 256	66.52 275	68.51 252	44.81 282	42.69 332	75.77 271	11.66 344	72.94 266	31.96 292	56.77 307	69.27 320

TDRefinement		53.44 287	50.72 293	61.60 271	64.31 318	46.96 221	70.89 243	65.27 273	41.78 305	44.61 324	77.98 233	11.52 345	66.36 293	28.57 326	51.59 322	71.49 310

ambc				65.13 248	63.72 320	37.07 291	47.66 337	78.78 152		54.37 286	71.42 296	11.24 346	80.94 177	45.64 217	53.85 318	77.38 244

pmmvs3		44.92 308	41.95 313	53.86 307	52.58 344	43.55 247	62.11 299	46.90 347	26.05 343	40.63 336	60.19 334	11.08 347	57.91 321	31.83 298	46.15 334	60.11 335

new_pmnet		34.13 324	34.29 323	33.64 336	52.63 343	18.23 353	44.43 343	33.90 353	22.81 346	30.89 342	53.18 339	10.48 348	35.72 355	20.77 341	39.51 339	46.98 344

LF4IMVS		42.95 312	42.26 312	45.04 327	48.30 348	32.50 326	54.80 320	48.49 342	28.03 340	40.51 337	70.16 305	9.24 349	43.89 349	31.63 299	49.18 332	58.72 336

PM-MVS		52.33 291	50.19 294	58.75 287	62.10 324	45.14 232	65.75 279	40.38 350	43.60 295	53.52 294	72.65 289	9.16 350	65.87 297	50.41 181	54.18 316	65.24 326

testus		44.59 309	43.87 311	46.76 325	59.85 336	24.65 347	53.86 323	55.82 325	36.26 329	43.97 328	63.42 327	8.39 351	53.14 340	20.70 342	52.52 320	62.51 329

testmv		42.25 313	40.11 316	48.66 321	53.23 342	27.02 341	56.62 317	55.74 326	37.25 325	33.10 341	59.52 335	7.78 352	56.58 329	19.61 344	38.13 342	62.40 330

EMVS		22.97 330	21.84 332	26.36 342	40.20 353	19.53 352	41.95 346	34.64 352	17.09 350	9.73 355	22.83 353	7.29 353	42.22 353	9.18 354	13.66 351	17.32 353

test12356		36.16 321	35.94 321	36.83 334	50.82 346	8.52 358	44.84 342	53.49 335	32.72 335	30.11 343	55.08 338	7.11 354	49.47 343	16.60 346	32.68 344	52.50 341

E-PMN		23.77 329	22.73 330	26.90 341	42.02 352	20.67 350	42.66 344	35.70 351	17.43 349	10.28 354	25.05 351	6.42 355	42.39 352	10.28 352	14.71 349	17.63 352

Gipuma		34.77 323	31.91 325	43.33 330	62.05 325	37.87 286	20.39 352	67.03 262	23.23 345	18.41 349	25.84 350	4.24 356	62.73 305	14.71 348	51.32 323	29.38 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 328	25.91 328	31.87 339	39.46 355	6.57 359	31.17 350	28.52 356	23.96 344	20.45 348	48.94 346	4.20 357	37.94 354	16.51 347	19.97 347	51.09 342

LCM-MVSNet		40.30 317	35.88 322	53.57 309	42.24 351	29.15 336	45.21 341	60.53 305	22.23 347	28.02 344	50.98 343	3.72 358	61.78 309	31.22 305	38.76 341	69.78 317

DeepMVS_CX				12.03 345	17.97 359	10.91 356		10.60 360	7.46 354	11.07 353	28.36 349	3.28 359	11.29 358	8.01 355	9.74 356	13.89 354

PMVS	28.69 22	36.22 320	33.29 324	45.02 328	36.82 356	35.98 304	54.68 321	48.74 341	26.31 342	21.02 347	51.61 342	2.88 360	60.10 314	9.99 353	47.58 333	38.99 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 331	17.77 333	32.34 338	34.34 358	25.44 345	16.11 353	24.11 357	11.19 353	13.22 352	31.92 348	1.58 361	30.95 356	10.47 351	17.03 348	40.62 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		27.88 327	25.99 327	33.55 337	47.54 349	25.89 343	47.24 338	32.91 354	21.44 348	15.90 350	38.09 347	0.85 362	42.76 350	16.90 345	13.03 352	32.00 350

wuyk23d		13.32 333	12.52 334	15.71 344	47.54 349	26.27 342	31.06 351	1.98 361	4.93 355	5.18 357	1.94 358	0.45 363	18.54 357	6.81 356	12.83 353	2.33 356

wuykxyi23d		28.12 326	22.54 331	44.87 329	34.97 357	32.11 328	37.96 348	47.31 345	13.32 352	9.29 356	23.72 352	0.45 363	56.58 329	21.85 338	13.98 350	45.93 345

test123		4.73 336	6.30 337	0.02 347	0.01 361	0.01 362	56.36 318	0.00 363	0.01 357	0.04 358	0.21 360	0.01 365	0.00 360	0.03 358	0.00 357	0.04 357

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	0.00 361	0.00 366	0.00 360	0.00 359	0.00 357	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	0.00 361	0.00 366	0.00 360	0.00 359	0.00 357	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	0.00 361	0.00 366	0.00 360	0.00 359	0.00 357	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	0.00 361	0.00 366	0.00 360	0.00 359	0.00 357	0.00 360

testmvs		4.52 337	6.03 338	0.01 348	0.01 361	0.00 363	53.86 323	0.00 363	0.01 357	0.04 358	0.27 359	0.00 366	0.00 360	0.04 357	0.00 357	0.03 358

ab-mvs-re		6.49 335	8.65 336	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	77.89 237	0.00 366	0.00 360	0.00 359	0.00 357	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 363	0.00 355	0.00 363	0.00 359	0.00 360	0.00 361	0.00 366	0.00 360	0.00 359	0.00 357	0.00 360

GSMVS																78.05 238

test_part3							86.37 5		63.49 35		91.40 4		90.90 1	75.98 14

test_part2					87.58 3	60.47 38				83.42 2

MTGPA								80.97 99

MTMP								17.08 358

gm-plane-assit					71.40 266	41.72 260			48.85 247		73.31 288		82.48 155	48.90 194

test9_res														75.28 17	88.31 19	83.81 140

agg_prior2														73.09 32	87.93 26	84.33 118

agg_prior					85.04 37	59.96 43		81.04 96		74.68 29			84.04 110

test_prior4						62.51 17	82.08 60

test_prior				76.69 46	84.20 49	57.27 74		84.88 17					86.43 57			86.38 44

旧先验2							76.08 163		45.32 279	76.55 16			65.56 298	58.75 136

新几何2							76.12 161

无先验							79.66 92	74.30 212	48.40 252				80.78 182	53.62 161		79.03 231

原ACMM2							79.02 98

testdata2													72.18 271	46.95 206

testdata1							72.65 215		60.50 78

plane_prior7					81.41 73	55.96 96

plane_prior5								84.01 31					87.21 36	68.16 51	80.58 78	84.65 112

plane_prior4											86.10 76

plane_prior3						56.09 93			63.92 30	69.27 98

plane_prior2							84.22 25		64.52 24

plane_prior1					81.27 78

plane_prior						56.31 87	83.58 34		63.19 39						80.48 81

n2								0.00 363

nn								0.00 363

door-mid								47.19 346

test11								83.47 46

door								47.60 344

HQP5-MVS						54.94 109

HQP-NCC					80.66 85		82.31 55		62.10 57	67.85 120

ACMP_Plane					80.66 85		82.31 55		62.10 57	67.85 120

BP-MVS														67.04 61

HQP4-MVS										67.85 120			86.93 42			84.32 119

HQP3-MVS								83.90 35							80.35 84

NP-MVS					80.98 83	56.05 95					85.54 88

ACMMP++_ref															74.07 146

ACMMP++															72.16 179

High-resolution multi-view benchmark