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.

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 32	95.17 2	92.11 48	98.46 11	87.33 8	99.97 1	97.21 6	99.31 1	99.63 2

HSP-MVS		95.55 4	96.51 2	92.66 85	98.31 39	80.10 147	97.42 68	96.46 88	92.20 13	97.11 3	98.29 14	93.46 1	99.10 80	96.01 13	99.30 2	98.77 22

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 24	86.04 27	97.81 39	96.93 45	92.45 11	95.69 11	98.50 9	85.38 14	99.85 10	94.75 24	99.18 3	98.65 28

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 25	94.66 3	96.79 4	98.78 4	86.42 12	99.95 2	97.59 4	99.18 3	99.00 14

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 43	99.16 3	96.96 42	94.11 6	95.59 12	98.64 7	85.07 15	99.91 3	95.61 19	99.10 5	99.00 14

MSLP-MVS++		94.28 16	94.39 17	93.97 33	98.30 40	84.06 63	98.64 13	96.93 45	90.71 22	93.08 40	98.70 5	79.98 60	99.21 66	94.12 31	99.07 6	98.63 29

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 45	97.66 56	84.10 62	95.85 173	96.42 92	91.26 17	97.49 2	96.80 89	86.50 11	98.49 104	95.54 20	99.03 7	98.33 40

test9_res														96.00 14	99.03 7	98.31 42

agg_prior2														94.30 28	99.00 9	98.57 31

train_agg		94.28 16	94.45 15	93.74 40	98.64 21	83.71 69	97.82 37	96.65 67	84.50 114	95.16 15	98.09 29	84.33 22	99.36 56	95.91 15	98.96 10	98.16 50

agg_prior3		94.10 21	94.29 21	93.53 53	98.62 24	83.03 83	97.80 41	96.64 70	84.28 123	95.01 19	98.03 33	83.40 31	99.41 53	95.91 15	98.96 10	98.16 50

test_part1								96.77 53				89.33 6			98.95 12	99.18 10

ESAPD		95.32 5	95.52 6	94.70 19	98.90 7	85.14 44	98.15 25	96.77 53	84.95 102	96.07 8	98.83 2	89.33 6	99.80 14	97.78 2	98.95 12	99.18 10

SMA-MVS		94.64 11	94.66 11	94.58 21	98.02 48	85.42 38	97.47 61	96.74 57	85.49 88	98.01 1	98.70 5	82.85 35	99.84 12	95.79 17	98.92 14	98.49 35

MG-MVS		94.25 18	93.72 25	95.85 7	99.38 3	89.35 6	97.98 32	98.09 14	89.99 29	92.34 47	96.97 82	81.30 48	98.99 86	88.54 83	98.88 15	99.20 8

test_prior3		94.03 24	94.34 18	93.09 69	98.68 15	81.91 102	98.37 18	96.40 95	86.08 75	94.57 26	98.02 34	83.14 33	99.06 82	95.05 21	98.79 16	98.29 44

test_prior2							98.37 18		86.08 75	94.57 26	98.02 34	83.14 33		95.05 21	98.79 16

APDe-MVS		94.56 13	94.75 10	93.96 34	98.84 11	83.40 76	98.04 30	96.41 93	85.79 80	95.00 20	98.28 15	84.32 25	99.18 73	97.35 5	98.77 18	99.28 5

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 33	98.06 29	96.64 70	93.64 8	91.74 53	98.54 8	80.17 58	99.90 4	92.28 51	98.75 19	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		94.10 21	94.31 19	93.48 56	98.59 26	83.13 79	97.77 42	96.56 78	84.38 118	94.19 29	98.13 24	84.66 19	99.16 75	95.74 18	98.74 20	98.15 52

CDPH-MVS		93.12 36	92.91 38	93.74 40	98.65 20	83.88 64	97.67 50	96.26 107	83.00 148	93.22 39	98.24 16	81.31 47	99.21 66	89.12 79	98.74 20	98.14 53

DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 80	90.59 3	99.21 2	97.02 37	94.40 5	91.46 55	97.08 79	83.32 32	99.69 28	92.83 44	98.70 22	99.04 12
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
DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 170	97.09 75	70.21 291	98.99 8	96.69 64	95.57 1	95.08 18	99.23 1	86.40 13	99.87 8	97.84 1	98.66 23	99.65 1

PHI-MVS		93.59 31	93.63 26	93.48 56	98.05 47	81.76 108	98.64 13	97.13 24	82.60 156	94.09 32	98.49 10	80.35 53	99.85 10	94.74 25	98.62 24	98.83 19

ACMMP_Plus		93.46 32	93.23 32	94.17 29	97.16 73	84.28 61	96.82 112	96.65 67	86.24 72	94.27 28	97.99 37	77.94 83	99.83 13	93.39 36	98.57 25	98.39 38

原ACMM1				91.22 132	97.77 55	78.10 214		96.61 74	81.05 176	91.28 61	97.42 67	77.92 84	98.98 87	79.85 152	98.51 26	96.59 133

SD-MVS		94.84 9	95.02 9	94.29 26	97.87 54	84.61 53	97.76 45	96.19 113	89.59 32	96.66 5	98.17 22	84.33 22	99.60 37	96.09 12	98.50 27	98.66 27

新几何1				93.12 67	97.44 64	81.60 114		96.71 61	74.54 275	91.22 63	97.57 57	79.13 69	99.51 47	77.40 174	98.46 28	98.26 47

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 64	95.41 108	81.35 118	99.02 7	96.59 76	89.50 33	94.18 31	98.36 13	83.68 30	99.45 51	94.77 23	98.45 29	98.81 20
Skip Steuart: Steuart Systems R&D Blog.
1121		90.66 74	89.82 80	93.16 66	97.39 66	81.71 112	93.33 235	96.66 66	74.45 276	91.38 56	97.55 61	79.27 65	99.52 44	79.95 150	98.43 30	98.26 47

HFP-MVS		92.89 40	92.86 39	92.98 72	98.71 13	81.12 121	97.58 54	96.70 62	85.20 95	91.75 51	97.97 41	78.47 76	99.71 24	90.95 60	98.41 31	98.12 55

#test#		92.99 38	92.99 36	92.98 72	98.71 13	81.12 121	97.77 42	96.70 62	85.75 81	91.75 51	97.97 41	78.47 76	99.71 24	91.36 56	98.41 31	98.12 55

ACMMPR		92.69 46	92.67 43	92.75 81	98.66 18	80.57 133	97.58 54	96.69 64	85.20 95	91.57 54	97.92 43	77.01 94	99.67 32	90.95 60	98.41 31	98.00 66

MP-MVS-pluss		92.58 50	92.35 47	93.29 61	97.30 71	82.53 91	96.44 137	96.04 122	84.68 109	89.12 84	98.37 12	77.48 88	99.74 21	93.31 40	98.38 34	97.59 93
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		92.72 45	92.70 42	92.79 79	98.68 15	80.53 136	97.53 58	96.51 83	85.22 93	91.94 49	97.98 39	77.26 90	99.67 32	90.83 63	98.37 35	98.18 49

APD-MVS		93.61 30	93.59 27	93.69 43	98.76 12	83.26 77	97.21 75	96.09 118	82.41 158	94.65 25	98.21 17	81.96 40	98.81 96	94.65 26	98.36 36	99.01 13
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test12				94.25 27	98.34 37	85.55 35		96.35 101		92.36 46		80.84 49	99.22 64		98.31 37	97.98 68

MP-MVS		92.61 49	92.67 43	92.42 93	98.13 44	79.73 155	97.33 72	96.20 111	85.63 83	90.53 67	97.66 51	78.14 81	99.70 27	92.12 53	98.30 38	97.85 76

test222					96.15 84	78.41 205	95.87 171	96.46 88	71.97 292	89.66 76	97.45 63	76.33 105			98.24 39	98.30 43

CP-MVS		92.54 51	92.60 45	92.34 96	98.50 31	79.90 150	98.40 17	96.40 95	84.75 107	90.48 69	98.09 29	77.40 89	99.21 66	91.15 59	98.23 40	97.92 72

zzz-MVS		92.74 42	92.71 40	92.86 76	97.90 50	80.85 127	96.47 132	96.33 103	87.92 52	90.20 71	98.18 18	76.71 100	99.76 16	92.57 48	98.09 41	97.96 69

MTAPA		92.45 52	92.31 48	92.86 76	97.90 50	80.85 127	92.88 248	96.33 103	87.92 52	90.20 71	98.18 18	76.71 100	99.76 16	92.57 48	98.09 41	97.96 69

XVS		92.69 46	92.71 40	92.63 88	98.52 29	80.29 140	97.37 70	96.44 90	87.04 67	91.38 56	97.83 47	77.24 92	99.59 38	90.46 66	98.07 43	98.02 61

X-MVStestdata		86.26 155	84.14 172	92.63 88	98.52 29	80.29 140	97.37 70	96.44 90	87.04 67	91.38 56	20.73 354	77.24 92	99.59 38	90.46 66	98.07 43	98.02 61

MVS		90.60 76	88.64 94	96.50 1	94.25 141	90.53 4	93.33 235	97.21 22	77.59 234	78.88 191	97.31 69	71.52 157	99.69 28	89.60 74	98.03 45	99.27 6

mPP-MVS		91.88 57	91.82 53	92.07 106	98.38 36	78.63 196	97.29 73	96.09 118	85.12 97	88.45 90	97.66 51	75.53 114	99.68 30	89.83 72	98.02 46	97.88 73

HPM-MVS		91.62 62	91.53 59	91.89 112	97.88 53	79.22 173	96.99 100	95.73 136	82.07 162	89.50 81	97.19 75	75.59 113	98.93 93	90.91 62	97.94 47	97.54 94
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		93.41 33	93.39 30	93.47 59	97.34 70	82.83 87	97.56 56	98.27 12	89.16 36	89.71 74	97.14 76	79.77 62	99.56 42	93.65 33	97.94 47	98.02 61

PGM-MVS		91.93 56	91.80 54	92.32 98	98.27 41	79.74 154	95.28 186	97.27 20	83.83 133	90.89 66	97.78 49	76.12 108	99.56 42	88.82 81	97.93 49	97.66 87

3Dnovator	82.32 10	89.33 95	87.64 108	94.42 23	93.73 154	85.70 32	97.73 47	96.75 56	86.73 70	76.21 224	95.93 100	62.17 229	99.68 30	81.67 140	97.81 50	97.88 73

PAPM		92.87 41	92.40 46	94.30 25	92.25 183	87.85 14	96.40 143	96.38 99	91.07 18	88.72 88	96.90 83	82.11 39	97.37 148	90.05 70	97.70 51	97.67 86

CANet		94.89 8	94.64 12	95.63 8	97.55 61	88.12 11	99.06 5	96.39 98	94.07 7	95.34 14	97.80 48	76.83 97	99.87 8	97.08 7	97.64 52	98.89 17

testdata				90.13 160	95.92 92	74.17 258		96.49 87	73.49 283	94.82 23	97.99 37	78.80 73	97.93 118	83.53 128	97.52 53	98.29 44

MVSFormer		91.36 66	90.57 68	93.73 42	93.00 166	88.08 12	94.80 205	94.48 195	80.74 184	94.90 21	97.13 77	78.84 71	95.10 257	83.77 120	97.46 54	98.02 61

lupinMVS		93.87 28	93.58 28	94.75 18	93.00 166	88.08 12	99.15 4	95.50 147	91.03 19	94.90 21	97.66 51	78.84 71	97.56 138	94.64 27	97.46 54	98.62 30

HPM-MVS_fast		90.38 82	90.17 72	91.03 136	97.61 57	77.35 231	97.15 84	95.48 148	79.51 212	88.79 87	96.90 83	71.64 156	98.81 96	87.01 100	97.44 56	96.94 120

GG-mvs-BLEND				93.49 55	94.94 120	86.26 25	81.62 323	97.00 38		88.32 93	94.30 140	91.23 2	96.21 195	88.49 85	97.43 57	98.00 66

旧先验1					97.39 66	79.58 165		96.54 81			98.08 32	84.00 26			97.42 58	97.62 91

MVS_0304		93.82 29	93.11 35	95.95 5	96.79 77	89.15 7	98.56 15	95.30 159	93.61 9	94.82 23	98.02 34	66.60 197	99.88 7	96.94 8	97.39 59	98.81 20

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 102	92.35 1	98.21 23	95.79 134	92.42 12	96.24 6	98.18 18	71.04 162	99.17 74	96.77 9	97.39 59	96.79 126

CSCG		92.02 55	91.65 57	93.12 67	98.53 28	80.59 132	97.47 61	97.18 23	77.06 243	84.64 123	97.98 39	83.98 27	99.52 44	90.72 64	97.33 61	99.23 7

gg-mvs-nofinetune		85.48 172	82.90 188	93.24 63	94.51 137	85.82 30	79.22 327	96.97 41	61.19 324	87.33 101	53.01 341	90.58 3	96.07 199	86.07 103	97.23 62	97.81 79

MAR-MVS		90.63 75	90.22 70	91.86 117	98.47 33	78.20 212	97.18 79	96.61 74	83.87 132	88.18 95	98.18 18	68.71 175	99.75 19	83.66 125	97.15 63	97.63 90
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
3Dnovator+	82.88 8	89.63 91	87.85 103	94.99 15	94.49 138	86.76 23	97.84 36	95.74 135	86.10 74	75.47 233	96.02 99	65.00 214	99.51 47	82.91 135	97.07 64	98.72 26

Regformer-1		94.00 25	94.04 23	93.87 36	98.41 34	84.29 60	97.43 66	97.04 36	89.50 33	92.75 44	98.13 24	82.60 37	99.26 61	93.55 34	96.99 65	98.06 58

Regformer-2		93.92 26	94.01 24	93.67 44	98.41 34	83.75 68	97.43 66	97.00 38	89.43 35	92.69 45	98.13 24	82.48 38	99.22 64	93.51 35	96.99 65	98.04 59

DeepC-MVS	86.58 3	91.53 64	91.06 64	92.94 74	94.52 135	81.89 104	95.95 164	95.98 124	90.76 21	83.76 133	96.76 90	73.24 144	99.71 24	91.67 55	96.96 67	97.22 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CPTT-MVS		89.72 90	89.87 79	89.29 180	98.33 38	73.30 262	97.70 48	95.35 157	75.68 255	87.40 99	97.44 66	70.43 167	98.25 111	89.56 76	96.90 68	96.33 142

APD-MVS_3200maxsize		91.23 69	91.35 60	90.89 141	97.89 52	76.35 242	96.30 151	95.52 146	79.82 207	91.03 65	97.88 46	74.70 136	98.54 101	92.11 54	96.89 69	97.77 81

MVP-Stereo		82.65 214	81.67 207	85.59 244	86.10 283	78.29 207	93.33 235	92.82 263	77.75 232	69.17 273	87.98 223	59.28 249	95.76 221	71.77 216	96.88 70	82.73 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM_NR		91.46 65	90.82 66	93.37 60	98.50 31	81.81 107	95.03 200	96.13 115	84.65 110	86.10 111	97.65 55	79.24 67	99.75 19	83.20 131	96.88 70	98.56 32

jason		92.73 44	92.23 50	94.21 28	90.50 214	87.30 21	98.65 12	95.09 164	90.61 23	92.76 43	97.13 77	75.28 130	97.30 151	93.32 39	96.75 72	98.02 61
jason: jason.
xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 118	92.02 2	98.19 24	95.68 138	92.06 14	96.01 10	98.14 23	70.83 165	98.96 88	96.74 10	96.57 73	96.76 129

MVS_111021_LR		91.60 63	91.64 58	91.47 125	95.74 100	78.79 193	96.15 156	96.77 53	88.49 43	88.64 89	97.07 80	72.33 150	99.19 71	93.13 42	96.48 74	96.43 137

PAPR		92.74 42	92.17 51	94.45 22	98.89 9	84.87 51	97.20 77	96.20 111	87.73 57	88.40 91	98.12 27	78.71 74	99.76 16	87.99 91	96.28 75	98.74 23

Vis-MVSNet		88.67 109	87.82 104	91.24 131	92.68 169	78.82 190	96.95 106	93.85 228	87.55 58	87.07 104	95.13 126	63.43 223	97.21 156	77.58 172	96.15 76	97.70 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet		94.06 23	94.15 22	93.76 39	97.27 72	84.35 58	98.29 20	97.64 17	94.57 4	95.36 13	96.88 85	79.96 61	99.12 79	91.30 57	96.11 77	97.82 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
API-MVS		90.18 84	88.97 90	93.80 38	98.66 18	82.95 86	97.50 60	95.63 141	75.16 263	86.31 108	97.69 50	72.49 148	99.90 4	81.26 142	96.07 78	98.56 32

QAPM		86.88 143	84.51 159	93.98 32	94.04 147	85.89 29	97.19 78	96.05 121	73.62 281	75.12 236	95.62 107	62.02 232	99.74 21	70.88 226	96.06 79	96.30 144

1314		88.94 101	87.20 119	94.17 29	93.21 161	85.73 31	93.33 235	96.64 70	82.89 149	75.98 226	96.36 95	66.83 193	99.39 54	83.52 129	96.02 80	97.39 104

MS-PatchMatch		83.05 206	81.82 202	86.72 233	89.64 228	79.10 178	94.88 203	94.59 193	79.70 209	70.67 262	89.65 203	50.43 296	96.82 175	70.82 229	95.99 81	84.25 305

CHOSEN 280x420		91.71 60	91.85 52	91.29 128	94.94 120	82.69 89	87.89 298	96.17 114	85.94 77	87.27 102	94.31 139	90.27 4	95.65 232	94.04 32	95.86 82	95.53 157

OpenMVS	79.58 14	86.09 156	83.62 178	93.50 54	90.95 208	86.71 24	97.44 63	95.83 132	75.35 259	72.64 252	95.72 103	57.42 268	99.64 34	71.41 220	95.85 83	94.13 180

PVSNet_Blended		93.13 35	92.98 37	93.57 49	97.47 62	83.86 65	99.32 1	96.73 58	91.02 20	89.53 79	96.21 97	76.42 102	99.57 40	94.29 29	95.81 84	97.29 111

CHOSEN 1792x2688		91.07 70	90.21 71	93.64 45	95.18 114	83.53 73	96.26 153	96.13 115	88.92 38	84.90 117	93.10 163	72.86 146	99.62 36	88.86 80	95.67 85	97.79 80

abl_6		89.80 88	89.71 83	90.07 161	96.53 79	75.52 248	94.48 208	95.04 167	81.12 175	89.22 82	97.00 81	68.83 174	98.96 88	89.86 71	95.27 86	95.73 152

Regformer-3		93.19 34	93.19 33	93.19 65	98.10 45	83.01 84	97.08 96	96.98 40	88.98 37	91.35 60	97.89 44	80.80 50	99.23 62	92.30 50	95.20 87	97.32 106

Regformer-4		93.06 37	93.12 34	92.89 75	98.10 45	82.20 97	97.08 96	96.92 47	88.87 39	91.23 62	97.89 44	80.57 52	99.19 71	92.21 52	95.20 87	97.29 111

114514_t		88.79 107	87.57 112	92.45 92	98.21 42	81.74 109	96.99 100	95.45 152	75.16 263	82.48 145	95.69 105	68.59 176	98.50 103	80.33 145	95.18 89	97.10 116

CANet_DTU		90.98 71	90.04 73	93.83 37	94.76 124	86.23 26	96.32 147	93.12 260	93.11 10	93.71 34	96.82 88	63.08 225	99.48 49	84.29 115	95.12 90	95.77 151

DP-MVS Recon		91.72 59	90.85 65	94.34 24	99.50 1	85.00 46	98.51 16	95.96 125	80.57 188	88.08 96	97.63 56	76.84 96	99.89 6	85.67 105	94.88 91	98.13 54

BH-w/o		88.24 121	87.47 115	90.54 147	95.03 119	78.54 199	97.41 69	93.82 229	84.08 126	78.23 196	94.51 138	69.34 173	97.21 156	80.21 148	94.58 92	95.87 149

MVS_Test		90.29 83	89.18 89	93.62 47	95.23 112	84.93 47	94.41 211	94.66 187	84.31 120	90.37 70	91.02 184	75.13 131	97.82 127	83.11 133	94.42 93	98.12 55

Vis-MVSNet (Re-imp)		88.88 104	88.87 93	88.91 186	93.89 149	74.43 256	96.93 108	94.19 207	84.39 117	83.22 140	95.67 106	78.24 79	94.70 266	78.88 159	94.40 94	97.61 92

UGNet		87.73 130	86.55 132	91.27 129	95.16 115	79.11 177	96.35 145	96.23 109	88.14 49	87.83 98	90.48 192	50.65 294	99.09 81	80.13 149	94.03 95	95.60 156
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
PVSNet	82.34 9	89.02 99	87.79 105	92.71 83	95.49 105	81.50 115	97.70 48	97.29 19	87.76 56	85.47 113	95.12 127	56.90 270	98.90 94	80.33 145	94.02 96	97.71 84

TSAR-MVS + GP.		94.35 15	94.50 13	93.89 35	97.38 69	83.04 82	98.10 28	95.29 160	91.57 15	93.81 33	97.45 63	86.64 9	99.43 52	96.28 11	94.01 97	99.20 8

PVSNet_Blended_VisFu		91.24 68	90.77 67	92.66 85	95.09 116	82.40 93	97.77 42	95.87 131	88.26 47	86.39 107	93.94 146	76.77 98	99.27 59	88.80 82	94.00 98	96.31 143

PMMVS		89.46 93	89.92 77	88.06 203	94.64 125	69.57 297	96.22 154	94.95 170	87.27 61	91.37 59	96.54 94	65.88 202	97.39 147	88.54 83	93.89 99	97.23 114

BH-untuned		86.95 142	85.94 141	89.99 165	94.52 135	77.46 228	96.78 114	93.37 253	81.80 169	76.62 217	93.81 150	66.64 196	97.02 166	76.06 188	93.88 100	95.48 158

BH-RMVSNet		86.84 144	85.28 147	91.49 124	95.35 110	80.26 143	96.95 106	92.21 269	82.86 150	81.77 161	95.46 109	59.34 248	97.64 135	69.79 234	93.81 101	96.57 134

Effi-MVS+		90.70 73	89.90 78	93.09 69	93.61 155	83.48 74	95.20 189	92.79 264	83.22 143	91.82 50	95.70 104	71.82 153	97.48 145	91.25 58	93.67 102	98.32 41

IS-MVSNet		88.67 109	88.16 99	90.20 155	93.61 155	76.86 237	96.77 116	93.07 261	84.02 128	83.62 134	95.60 108	74.69 137	96.24 194	78.43 161	93.66 103	97.49 99

AdaColmap		88.81 105	87.61 111	92.39 95	99.33 4	79.95 148	96.70 121	95.58 142	77.51 235	83.05 142	96.69 92	61.90 236	99.72 23	84.29 115	93.47 104	97.50 98

xiu_mvs_v1_base_debu		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 265	98.32 108	92.72 45	93.46 105	94.74 173

xiu_mvs_v1_base		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 265	98.32 108	92.72 45	93.46 105	94.74 173

xiu_mvs_v1_base_debi		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 265	98.32 108	92.72 45	93.46 105	94.74 173

mvs_anonymous		88.68 108	87.62 110	91.86 117	94.80 123	81.69 113	93.53 230	94.92 171	82.03 163	78.87 192	90.43 194	75.77 112	95.34 248	85.04 110	93.16 108	98.55 34

LCM-MVSNet-Re		83.75 192	83.54 180	84.39 270	93.54 157	64.14 312	92.51 255	84.03 334	83.90 131	66.14 282	86.59 246	67.36 181	92.68 289	84.89 112	92.87 109	96.35 139

TAPA-MVS	81.61 12	85.02 176	83.67 176	89.06 182	96.79 77	73.27 264	95.92 166	94.79 180	74.81 270	80.47 171	96.83 87	71.07 161	98.19 114	49.82 324	92.57 110	95.71 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPMVS		87.47 133	85.90 142	92.18 103	95.41 108	82.26 96	87.00 306	96.28 106	85.88 79	84.23 126	85.57 263	75.07 133	96.26 192	71.14 225	92.50 111	98.03 60

LS3D		82.22 222	79.94 230	89.06 182	97.43 65	74.06 260	93.20 243	92.05 271	61.90 320	73.33 245	95.21 120	59.35 247	99.21 66	54.54 311	92.48 112	93.90 183

ACMMP		90.39 81	89.97 74	91.64 122	97.58 60	78.21 211	96.78 114	96.72 60	84.73 108	84.72 121	97.23 73	71.22 159	99.63 35	88.37 88	92.41 113	97.08 117
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
TESTMET0.1,1		89.83 87	89.34 87	91.31 126	92.54 174	80.19 145	97.11 90	96.57 77	86.15 73	86.85 106	91.83 175	79.32 63	96.95 168	81.30 141	92.35 114	96.77 128

DI_MVS_plusplus_test		85.92 158	83.61 179	92.86 76	86.43 268	83.20 78	95.57 181	95.46 149	85.10 100	65.99 283	86.84 240	56.70 272	97.89 125	88.10 90	92.33 115	97.48 100

test_normal		85.83 160	83.51 181	92.78 80	86.33 273	83.01 84	95.56 183	95.46 149	85.11 99	65.73 285	86.63 245	56.62 274	97.86 126	87.87 92	92.29 116	97.47 101

PLC	83.97 7	88.00 125	87.38 117	89.83 173	98.02 48	76.46 240	97.16 83	94.43 199	79.26 218	81.98 159	96.28 96	69.36 172	99.27 59	77.71 170	92.25 117	93.77 188
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchMatch-RL		85.00 177	83.66 177	89.02 184	95.86 97	74.55 255	92.49 256	93.60 242	79.30 217	79.29 189	91.47 176	58.53 254	98.45 106	70.22 230	92.17 118	94.07 181

Test4		82.30 221	79.15 236	91.78 119	81.84 310	81.74 109	94.04 221	94.20 205	84.86 104	59.75 316	83.88 280	37.14 328	96.28 191	84.60 113	92.00 119	97.30 109

test-LLR		88.48 113	87.98 101	89.98 166	92.26 181	77.23 233	97.11 90	95.96 125	83.76 135	86.30 109	91.38 178	72.30 151	96.78 178	80.82 143	91.92 120	95.94 147

test-mter		88.95 100	88.60 95	89.98 166	92.26 181	77.23 233	97.11 90	95.96 125	85.32 91	86.30 109	91.38 178	76.37 104	96.78 178	80.82 143	91.92 120	95.94 147

diffmvs		87.96 127	86.47 133	92.42 93	94.26 140	82.70 88	92.79 252	94.03 220	77.94 229	88.99 86	89.98 201	70.72 166	97.56 138	77.75 164	91.80 122	96.98 118

Fast-Effi-MVS+		87.93 128	86.94 128	90.92 140	94.04 147	79.16 175	98.26 21	93.72 237	81.29 173	83.94 131	92.90 164	69.83 171	96.68 181	76.70 182	91.74 123	96.93 121

UA-Net		88.92 102	88.48 97	90.24 153	94.06 146	77.18 235	93.04 245	94.66 187	87.39 60	91.09 64	93.89 147	74.92 134	98.18 115	75.83 190	91.43 124	95.35 161

PatchmatchNet		86.83 145	85.12 150	91.95 110	94.12 144	82.27 95	86.55 310	95.64 140	84.59 112	82.98 143	84.99 273	77.26 90	95.96 210	68.61 245	91.34 125	97.64 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PCF-MVS	84.09 5	86.77 148	85.00 153	92.08 105	92.06 190	83.07 81	92.14 264	94.47 197	79.63 211	76.90 214	94.78 133	71.15 160	99.20 70	72.87 208	91.05 126	93.98 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		91.84 58	91.77 55	92.04 108	97.60 58	81.17 120	96.61 126	96.87 49	88.20 48	89.19 83	97.55 61	78.69 75	99.14 77	90.29 68	90.94 127	95.80 150

CNLPA		86.96 141	85.37 146	91.72 121	97.59 59	79.34 168	97.21 75	91.05 284	74.22 277	78.90 190	96.75 91	67.21 183	98.95 90	74.68 200	90.77 128	96.88 124

mvs-test1		86.83 145	87.17 120	85.81 241	91.96 193	65.24 309	97.90 34	93.34 254	85.57 84	84.51 125	95.14 125	61.99 233	97.19 158	83.55 126	90.55 129	95.00 168

CVMVSNet		84.83 180	85.57 143	82.63 290	91.55 200	60.38 322	95.13 196	95.03 168	80.60 187	82.10 158	94.71 134	66.40 199	90.19 320	74.30 202	90.32 130	97.31 108

EPNet_dtu		87.65 131	87.89 102	86.93 230	94.57 127	71.37 282	96.72 117	96.50 85	88.56 41	87.12 103	95.02 130	75.91 111	94.01 279	66.62 256	90.00 131	95.42 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LFMVS		89.27 96	87.64 108	94.16 31	97.16 73	85.52 36	97.18 79	94.66 187	79.17 219	89.63 77	96.57 93	55.35 282	98.22 112	89.52 77	89.54 132	98.74 23

EI-MVSNet-UG-set		91.35 67	91.22 61	91.73 120	97.39 66	80.68 130	96.47 132	96.83 51	87.92 52	88.30 94	97.36 68	77.84 85	99.13 78	89.43 78	89.45 133	95.37 160

sss		90.87 72	89.96 75	93.60 48	94.15 143	83.84 67	97.14 85	98.13 13	85.93 78	89.68 75	96.09 98	71.67 154	99.30 58	87.69 93	89.16 134	97.66 87

HY-MVS	84.06 6	91.63 61	90.37 69	95.39 12	96.12 85	88.25 10	90.22 280	97.58 18	88.33 46	90.50 68	91.96 171	79.26 66	99.06 82	90.29 68	89.07 135	98.88 18

CDS-MVSNet		89.50 92	88.96 91	91.14 134	91.94 196	80.93 125	97.09 94	95.81 133	84.26 124	84.72 121	94.20 141	80.31 54	95.64 233	83.37 130	88.96 136	96.85 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VNet		92.11 54	91.22 61	94.79 17	96.91 76	86.98 22	97.91 33	97.96 15	86.38 71	93.65 35	95.74 102	70.16 170	98.95 90	93.39 36	88.87 137	98.43 36

alignmvs		92.97 39	92.26 49	95.12 13	95.54 104	87.77 15	98.67 11	96.38 99	88.04 50	93.01 41	97.45 63	79.20 68	98.60 98	93.25 41	88.76 138	98.99 16

WTY-MVS		92.65 48	91.68 56	95.56 9	96.00 90	88.90 8	98.23 22	97.65 16	88.57 40	89.82 73	97.22 74	79.29 64	99.06 82	89.57 75	88.73 139	98.73 25

DWT-MVSNet_test		90.52 80	89.80 81	92.70 84	95.73 101	82.20 97	93.69 226	96.55 80	88.34 45	87.04 105	95.34 111	86.53 10	97.55 140	76.32 186	88.66 140	98.34 39

canonicalmvs		92.27 53	91.22 61	95.41 11	95.80 99	88.31 9	97.09 94	94.64 190	88.49 43	92.99 42	97.31 69	72.68 147	98.57 100	93.38 38	88.58 141	99.36 4

HyFIR lowres test		89.36 94	88.60 95	91.63 123	94.91 122	80.76 129	95.60 180	95.53 144	82.56 157	84.03 127	91.24 181	78.03 82	96.81 176	87.07 99	88.41 142	97.32 106

TAMVS		88.48 113	87.79 105	90.56 146	91.09 206	79.18 174	96.45 135	95.88 130	83.64 138	83.12 141	93.33 159	75.94 110	95.74 225	82.40 136	88.27 143	96.75 130

EPP-MVSNet		89.76 89	89.72 82	89.87 171	93.78 150	76.02 245	97.22 74	96.51 83	79.35 214	85.11 115	95.01 131	84.82 16	97.10 163	87.46 96	88.21 144	96.50 135

MVS-HIRNet		71.36 298	67.00 301	84.46 268	90.58 213	69.74 295	79.15 328	87.74 316	46.09 340	61.96 309	50.50 342	45.14 311	95.64 233	53.74 313	88.11 145	88.00 266

TR-MVS		86.30 154	84.93 156	90.42 148	94.63 126	77.58 226	96.57 128	93.82 229	80.30 195	82.42 147	95.16 123	58.74 252	97.55 140	74.88 198	87.82 146	96.13 145

cascas		86.50 150	84.48 161	92.55 90	92.64 173	85.95 28	97.04 99	95.07 166	75.32 260	80.50 170	91.02 184	54.33 289	97.98 117	86.79 101	87.62 147	93.71 189

OMC-MVS		88.80 106	88.16 99	90.72 143	95.30 111	77.92 220	94.81 204	94.51 194	86.80 69	84.97 116	96.85 86	67.53 179	98.60 98	85.08 109	87.62 147	95.63 155

Patchmatch-test1		84.89 179	82.76 191	91.27 129	92.30 179	81.86 105	92.88 248	95.56 143	84.85 105	82.52 144	85.19 268	58.04 259	94.21 275	65.93 262	87.58 149	97.74 82

VDDNet		86.44 152	84.51 159	92.22 101	91.56 199	81.83 106	97.10 93	94.64 190	69.50 302	87.84 97	95.19 121	48.01 304	97.92 124	89.82 73	86.92 150	96.89 123

VDD-MVS		88.28 119	87.02 125	92.06 107	95.09 116	80.18 146	97.55 57	94.45 198	83.09 145	89.10 85	95.92 101	47.97 305	98.49 104	93.08 43	86.91 151	97.52 97

PatchFormer-LS_test		90.14 85	89.30 88	92.65 87	95.43 106	82.46 92	93.46 231	96.35 101	88.56 41	84.82 118	95.22 118	84.63 20	97.55 140	78.40 162	86.81 152	97.94 71

thres200		88.92 102	87.65 107	92.73 82	96.30 81	85.62 34	97.85 35	98.86 1	84.38 118	84.82 118	93.99 145	75.12 132	98.01 116	70.86 227	86.67 153	94.56 176

DP-MVS		81.47 229	78.28 239	91.04 135	98.14 43	78.48 201	95.09 199	86.97 320	61.14 325	71.12 259	92.78 166	59.59 243	99.38 55	53.11 315	86.61 154	95.27 162

F-COLMAP		84.50 185	83.44 183	87.67 215	95.22 113	72.22 269	95.95 164	93.78 234	75.74 253	76.30 222	95.18 122	59.50 245	98.45 106	72.67 210	86.59 155	92.35 201

CR-MVSNet		83.53 195	81.36 212	90.06 162	90.16 220	79.75 152	79.02 329	91.12 281	84.24 125	82.27 156	80.35 301	75.45 116	93.67 285	63.37 279	86.25 156	96.75 130

RPMNet		79.32 246	75.75 258	90.06 162	90.16 220	79.75 152	79.02 329	93.92 225	58.43 331	82.27 156	72.55 329	73.03 145	93.67 285	46.10 330	86.25 156	96.75 130

conf200view11		88.27 120	86.95 126	92.24 99	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.93 118	69.04 237	86.24 158	93.89 184

thres100view900		88.30 118	86.95 126	92.33 97	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.93 118	69.04 237	86.24 158	94.17 177

tfpn200view9		88.48 113	87.15 121	92.47 91	96.21 82	85.30 40	97.44 63	98.85 2	83.37 141	83.99 128	93.82 148	75.36 127	97.93 118	69.04 237	86.24 158	94.17 177

thres400		88.42 116	87.15 121	92.23 100	96.21 82	85.30 40	97.44 63	98.85 2	83.37 141	83.99 128	93.82 148	75.36 127	97.93 118	69.04 237	86.24 158	93.45 193

CostFormer		89.08 98	88.39 98	91.15 133	93.13 164	79.15 176	88.61 293	96.11 117	83.14 144	89.58 78	86.93 236	83.83 29	96.87 173	88.22 89	85.92 162	97.42 102

tfpn111		88.08 122	86.70 130	92.20 102	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.82 127	67.13 252	85.88 163	93.89 184

thres600view7		88.06 123	86.70 130	92.15 104	96.10 86	85.17 42	97.14 85	98.85 2	82.70 152	83.41 135	93.66 151	75.43 122	97.82 127	67.13 252	85.88 163	93.45 193

Effi-MVS+-dtu		84.61 183	84.90 157	83.72 280	91.96 193	63.14 316	94.95 201	93.34 254	85.57 84	79.79 179	87.12 234	61.99 233	95.61 236	83.55 126	85.83 165	92.41 200

JIA-IIPM		79.00 249	77.20 246	84.40 269	89.74 227	64.06 313	75.30 335	95.44 153	62.15 319	81.90 160	59.08 339	78.92 70	95.59 237	66.51 259	85.78 166	93.54 190

tpm2		87.35 138	86.26 134	90.62 145	92.93 168	78.67 194	88.06 297	95.99 123	79.33 215	87.40 99	86.43 252	80.28 55	96.40 186	80.23 147	85.73 167	96.79 126

1112_ss		88.60 112	87.47 115	92.00 109	93.21 161	80.97 124	96.47 132	92.46 267	83.64 138	80.86 167	97.30 71	80.24 56	97.62 136	77.60 171	85.49 168	97.40 103

Test_1112_low_res		88.03 124	86.73 129	91.94 111	93.15 163	80.88 126	96.44 137	92.41 268	83.59 140	80.74 169	91.16 182	80.18 57	97.59 137	77.48 173	85.40 169	97.36 105

view600		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

view800		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

conf0.05thres1000		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

tfpn		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

GA-MVS		85.79 166	84.04 173	91.02 137	89.47 232	80.27 142	96.90 109	94.84 176	85.57 84	80.88 166	89.08 207	56.56 275	96.47 185	77.72 169	85.35 174	96.34 140

tpmrst		88.36 117	87.38 117	91.31 126	94.36 139	79.92 149	87.32 302	95.26 162	85.32 91	88.34 92	86.13 256	80.60 51	96.70 180	83.78 119	85.34 175	97.30 109

tfpn_ndepth		87.25 139	86.00 136	91.01 138	95.86 97	81.46 116	96.53 129	97.09 33	77.35 238	81.36 162	95.07 129	84.74 18	95.86 215	60.88 286	85.14 176	95.72 153

MDTV_nov1_ep13			83.69 175		94.09 145	81.01 123	86.78 308	96.09 118	83.81 134	84.75 120	84.32 276	74.44 138	96.54 182	63.88 275	85.07 177

Fast-Effi-MVS+-dtu		83.33 204	82.60 193	85.50 245	89.55 230	69.38 298	96.09 161	91.38 278	82.30 159	75.96 227	91.41 177	56.71 271	95.58 238	75.13 197	84.90 178	91.54 202

tfpn1000		86.43 153	85.10 151	90.41 149	95.56 103	80.51 137	95.90 169	97.09 33	75.91 252	80.02 178	94.82 132	84.78 17	95.47 243	57.36 295	84.46 179	95.26 163

PatchT		79.75 241	76.85 251	88.42 194	89.55 230	75.49 249	77.37 333	94.61 192	63.07 314	82.46 146	73.32 328	75.52 115	93.41 288	51.36 318	84.43 180	96.36 138

XVG-OURS-SEG-HR		85.74 167	85.16 149	87.49 222	90.22 218	71.45 281	91.29 274	94.09 218	81.37 172	83.90 132	95.22 118	60.30 240	97.53 144	85.58 106	84.42 181	93.50 191

tpmp4_e23		86.46 151	84.95 154	90.98 139	93.74 153	78.60 198	88.13 296	95.90 129	79.65 210	85.42 114	85.67 258	80.08 59	97.06 164	71.71 217	84.26 182	97.28 113

tpm cat1		83.63 194	81.38 211	90.39 150	93.53 158	78.19 213	85.56 316	95.09 164	70.78 298	78.51 193	83.28 289	74.80 135	97.03 165	66.77 255	84.05 183	95.95 146

DSMNet-mixed		73.13 291	72.45 285	75.19 316	77.51 325	46.82 340	85.09 317	82.01 340	67.61 309	69.27 272	81.33 296	50.89 293	86.28 331	54.54 311	83.80 184	92.46 199

ADS-MVSNet2		79.57 242	77.53 244	85.71 242	93.78 150	72.13 271	79.48 325	86.11 325	73.09 285	80.14 176	79.99 303	62.15 230	90.14 321	59.49 289	83.52 185	94.85 170

ADS-MVSNet		81.26 232	78.36 238	89.96 168	93.78 150	79.78 151	79.48 325	93.60 242	73.09 285	80.14 176	79.99 303	62.15 230	95.24 253	59.49 289	83.52 185	94.85 170

XVG-OURS		85.18 175	84.38 163	87.59 218	90.42 216	71.73 278	91.06 277	94.07 219	82.00 164	83.29 139	95.08 128	56.42 276	97.55 140	83.70 124	83.42 187	93.49 192

dp		84.30 187	82.31 196	90.28 152	94.24 142	77.97 216	86.57 309	95.53 144	79.94 205	80.75 168	85.16 270	71.49 158	96.39 187	63.73 276	83.36 188	96.48 136

MSDG		80.62 238	77.77 243	89.14 181	93.43 159	77.24 232	91.89 268	90.18 298	69.86 301	68.02 274	91.94 173	52.21 292	98.84 95	59.32 291	83.12 189	91.35 203

MIMVSNet		79.18 248	75.99 257	88.72 191	87.37 253	80.66 131	79.96 324	91.82 274	77.38 237	74.33 238	81.87 294	41.78 320	90.74 316	66.36 261	83.10 190	94.76 172

HQP3-MVS								94.80 178							83.01 191

HQP-MVS		87.91 129	87.55 113	88.98 185	92.08 187	78.48 201	97.63 51	94.80 178	90.52 24	82.30 148	94.56 136	65.40 210	97.32 149	87.67 94	83.01 191	91.13 204

plane_prior						77.96 217	97.52 59		90.36 27						82.96 193

conf0.01		85.70 168	84.35 164	89.77 175	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	93.89 184

conf0.002		85.70 168	84.35 164	89.77 175	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	93.89 184

thresconf0.02		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

tfpn_n400		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

tfpnconf		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

tfpnview11		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

CLD-MVS		87.97 126	87.48 114	89.44 178	92.16 186	80.54 135	98.14 27	94.92 171	91.41 16	79.43 187	95.40 110	62.34 228	97.27 154	90.60 65	82.90 200	90.50 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		87.50 132	87.09 124	88.74 190	91.86 197	77.96 217	97.18 79	94.69 183	89.89 30	81.33 163	94.15 142	64.77 215	97.30 151	87.08 97	82.82 201	90.96 206

plane_prior5								94.69 183					97.30 151	87.08 97	82.82 201	90.96 206

OPM-MVS		85.84 159	85.10 151	88.06 203	88.34 244	77.83 223	95.72 176	94.20 205	87.89 55	80.45 172	94.05 144	58.57 253	97.26 155	83.88 118	82.76 203	89.09 236

ab-mvs		87.08 140	84.94 155	93.48 56	93.34 160	83.67 71	88.82 290	95.70 137	81.18 174	84.55 124	90.14 199	62.72 226	98.94 92	85.49 107	82.54 204	97.85 76

tpmvs		83.04 207	80.77 217	89.84 172	95.43 106	77.96 217	85.59 315	95.32 158	75.31 261	76.27 223	83.70 285	73.89 140	97.41 146	59.53 288	81.93 205	94.14 179

CMPMVS	54.94 21	75.71 280	74.56 274	79.17 307	79.69 319	55.98 329	89.59 283	93.30 256	60.28 327	53.85 328	89.07 208	47.68 307	96.33 189	76.55 183	81.02 206	85.22 297
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LPG-MVS_test		84.20 188	83.49 182	86.33 234	90.88 209	73.06 265	95.28 186	94.13 212	82.20 160	76.31 220	93.20 160	54.83 287	96.95 168	83.72 122	80.83 207	88.98 240

LGP-MVS_train				86.33 234	90.88 209	73.06 265		94.13 212	82.20 160	76.31 220	93.20 160	54.83 287	96.95 168	83.72 122	80.83 207	88.98 240

ACMM	80.70 13	83.72 193	82.85 189	86.31 237	91.19 205	72.12 272	95.88 170	94.29 202	80.44 190	77.02 212	91.96 171	55.24 283	97.14 162	79.30 155	80.38 209	89.67 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		82.12 223	81.15 214	85.03 249	84.19 302	70.70 287	94.22 219	93.95 223	83.07 146	73.48 242	89.75 202	49.66 299	95.37 247	82.24 138	79.76 210	89.02 239

test_djsdf		83.00 209	82.45 195	84.64 261	84.07 304	69.78 294	94.80 205	94.48 195	80.74 184	75.41 234	87.70 225	61.32 238	95.10 257	83.77 120	79.76 210	89.04 238

ACMP	81.66 11	84.00 189	83.22 185	86.33 234	91.53 202	72.95 267	95.91 168	93.79 233	83.70 137	73.79 240	92.22 168	54.31 290	96.89 172	83.98 117	79.74 212	89.16 235
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		90.05 86	89.96 75	90.33 151	97.47 62	83.86 65	98.02 31	96.73 58	87.98 51	89.53 79	89.61 204	76.42 102	99.57 40	94.29 29	79.59 213	87.57 274

Patchmatch-test		78.25 255	74.72 270	88.83 188	91.20 204	74.10 259	73.91 339	88.70 312	59.89 329	66.82 278	85.12 272	78.38 78	94.54 269	48.84 326	79.58 214	97.86 75

pcd1.5k->3k		34.11 326	35.46 326	30.05 342	86.70 257	0.00 361	0.00 352	94.74 182	0.00 356	0.00 357	0.00 358	58.13 257	0.00 359	0.00 356	79.56 215	90.14 217

mvs_tets		81.74 226	80.71 219	84.84 253	84.22 301	70.29 290	93.91 222	93.78 234	82.77 151	73.37 243	89.46 205	47.36 308	95.31 250	81.99 139	79.55 216	88.92 244

FIs		86.73 149	86.10 135	88.61 192	90.05 222	80.21 144	96.14 157	96.95 43	85.56 87	78.37 195	92.30 167	76.73 99	95.28 251	79.51 153	79.27 217	90.35 213

ACMMP++															79.05 218

PS-MVSNAJss		84.91 178	84.30 170	86.74 231	85.89 287	74.40 257	94.95 201	94.16 211	83.93 130	76.45 219	90.11 200	71.04 162	95.77 220	83.16 132	79.02 219	90.06 223

FC-MVSNet-test		85.96 157	85.39 145	87.66 216	89.38 234	78.02 215	95.65 179	96.87 49	85.12 97	77.34 207	91.94 173	76.28 106	94.74 265	77.09 178	78.82 220	90.21 216

EG-PatchMatch MVS		74.92 282	72.02 286	83.62 281	83.76 307	73.28 263	93.62 227	92.04 272	68.57 304	58.88 318	83.80 281	31.87 337	95.57 239	56.97 298	78.67 221	82.00 329

EI-MVSNet		85.80 161	85.20 148	87.59 218	91.55 200	77.41 229	95.13 196	95.36 155	80.43 192	80.33 174	94.71 134	73.72 142	95.97 206	76.96 181	78.64 222	89.39 230

MVSTER		89.25 97	88.92 92	90.24 153	95.98 91	84.66 52	96.79 113	95.36 155	87.19 65	80.33 174	90.61 191	90.02 5	95.97 206	85.38 108	78.64 222	90.09 221

anonymousdsp		80.98 236	79.97 229	84.01 272	81.73 311	70.44 289	92.49 256	93.58 244	77.10 242	72.98 249	86.31 254	57.58 264	94.90 261	79.32 154	78.63 224	86.69 286

ACMMP++_ref															78.45 225

XVG-ACMP-BASELINE		79.38 245	77.90 242	83.81 276	84.98 296	67.14 306	89.03 289	93.18 257	80.26 198	72.87 250	88.15 221	38.55 325	96.26 192	76.05 189	78.05 226	88.02 265

tpm		85.55 170	84.47 162	88.80 189	90.19 219	75.39 250	88.79 291	94.69 183	84.83 106	83.96 130	85.21 267	78.22 80	94.68 267	76.32 186	78.02 227	96.34 140

test2356		74.41 285	74.53 275	74.07 318	76.13 330	54.45 333	94.74 207	92.08 270	71.96 293	65.51 286	83.05 291	56.96 269	83.71 338	52.74 316	77.58 228	84.06 307

test0.0.03 1		82.79 212	82.48 194	83.74 279	86.81 256	72.22 269	96.52 130	95.03 168	83.76 135	73.00 248	93.20 160	72.30 151	88.88 323	64.15 270	77.52 229	90.12 219

RPSCF		77.73 262	76.63 253	81.06 299	88.66 242	55.76 331	87.77 299	87.88 315	64.82 313	74.14 239	92.79 165	49.22 301	96.81 176	67.47 251	76.88 230	90.62 209

LTVRE_ROB	73.68 18	77.99 257	75.74 259	84.74 256	90.45 215	72.02 273	86.41 311	91.12 281	72.57 290	66.63 279	87.27 229	54.95 286	96.98 167	56.29 306	75.98 231	85.21 298
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
OpenMVS_ROB	68.52 20	73.02 292	69.57 296	83.37 284	80.54 317	71.82 275	93.60 228	88.22 313	62.37 318	61.98 308	83.15 290	35.31 332	95.47 243	45.08 331	75.88 232	82.82 321

USDC		78.65 253	76.25 255	85.85 240	87.58 251	74.60 254	89.58 284	90.58 297	84.05 127	63.13 296	88.23 219	40.69 324	96.86 174	66.57 258	75.81 233	86.09 293

COLMAP_ROB	73.24 19	75.74 279	73.00 284	83.94 275	92.38 175	69.08 299	91.85 269	86.93 321	61.48 323	65.32 287	90.27 196	42.27 319	96.93 171	50.91 321	75.63 234	85.80 295
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GBi-Net		82.42 217	80.43 222	88.39 196	92.66 170	81.95 99	94.30 215	93.38 250	79.06 221	75.82 229	85.66 259	56.38 277	93.84 281	71.23 222	75.38 235	89.38 232

test1		82.42 217	80.43 222	88.39 196	92.66 170	81.95 99	94.30 215	93.38 250	79.06 221	75.82 229	85.66 259	56.38 277	93.84 281	71.23 222	75.38 235	89.38 232

FMVSNet3		84.71 181	82.71 192	90.70 144	94.55 128	87.71 16	95.92 166	94.67 186	81.73 170	75.82 229	88.08 222	66.99 191	94.47 270	71.23 222	75.38 235	89.91 225

testing_2		76.96 272	73.18 282	88.30 199	75.87 331	79.64 163	89.92 282	94.21 204	80.16 199	51.23 330	75.94 321	33.94 333	95.81 218	82.28 137	75.12 238	89.46 229

FMVSNet2		82.79 212	80.44 221	89.83 173	92.66 170	85.43 37	95.42 185	94.35 200	79.06 221	74.46 237	87.28 228	56.38 277	94.31 273	69.72 235	74.68 239	89.76 226

ITE_SJBPF				82.38 292	87.00 255	65.59 308		89.55 302	79.99 204	69.37 271	91.30 180	41.60 322	95.33 249	62.86 281	74.63 240	86.24 290

ACMH	75.40 17	77.99 257	74.96 266	87.10 228	90.67 212	76.41 241	93.19 244	91.64 277	72.47 291	63.44 294	87.61 226	43.34 315	97.16 159	58.34 293	73.94 241	87.72 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		82.54 215	80.79 216	87.79 212	86.11 282	80.49 138	93.55 229	93.18 257	77.29 239	73.35 244	89.40 206	65.26 213	95.05 260	75.32 195	73.61 242	87.83 268

AllTest		75.92 278	73.06 283	84.47 266	92.18 184	67.29 303	91.07 276	84.43 331	67.63 305	63.48 292	90.18 197	38.20 326	97.16 159	57.04 296	73.37 243	88.97 242

TestCases				84.47 266	92.18 184	67.29 303		84.43 331	67.63 305	63.48 292	90.18 197	38.20 326	97.16 159	57.04 296	73.37 243	88.97 242

pmmvs5		81.34 231	79.54 232	86.73 232	85.02 295	76.91 236	96.22 154	91.65 276	77.65 233	73.55 241	88.61 213	55.70 280	94.43 271	74.12 204	73.35 245	88.86 245

XXY-MVS		83.84 191	82.00 197	89.35 179	87.13 254	81.38 117	95.72 176	94.26 203	80.15 200	75.92 228	90.63 190	61.96 235	96.52 183	78.98 158	73.28 246	90.14 217

testus		70.06 300	69.09 298	72.98 320	74.54 333	51.28 338	93.78 224	87.34 317	71.49 296	62.69 301	83.46 287	24.44 342	84.77 337	51.22 320	72.85 247	82.90 320

testpf		70.88 299	70.47 293	72.08 321	88.92 237	59.57 325	48.62 349	93.15 259	63.05 315	63.07 297	79.51 306	58.33 256	86.63 330	66.93 254	72.69 248	70.05 341

FMVSNet1		79.50 243	76.54 254	88.39 196	88.47 243	81.95 99	94.30 215	93.38 250	73.14 284	72.04 255	85.66 259	43.86 312	93.84 281	65.48 264	72.53 249	89.38 232

test_0402		72.68 293	69.54 297	82.09 295	88.67 241	71.81 276	92.72 253	86.77 322	61.52 322	62.21 305	83.91 279	43.22 316	93.76 284	34.60 341	72.23 250	80.72 331

testgi		74.88 283	73.40 281	79.32 306	80.13 318	61.75 319	93.21 242	86.64 323	79.49 213	66.56 281	91.06 183	35.51 331	88.67 324	56.79 299	71.25 251	87.56 275

nrg030		86.79 147	85.43 144	90.87 142	88.76 238	85.34 39	97.06 98	94.33 201	84.31 120	80.45 172	91.98 170	72.36 149	96.36 188	88.48 86	71.13 252	90.93 208

ACMH+	76.62 16	77.47 264	74.94 267	85.05 248	91.07 207	71.58 280	93.26 240	90.01 299	71.80 294	64.76 289	88.55 214	41.62 321	96.48 184	62.35 282	71.00 253	87.09 282

VPA-MVSNet		85.32 173	83.83 174	89.77 175	90.25 217	82.63 90	96.36 144	97.07 35	83.03 147	81.21 165	89.02 209	61.58 237	96.31 190	85.02 111	70.95 254	90.36 212

IterMVS		80.67 237	79.16 235	85.20 247	89.79 224	76.08 244	92.97 247	91.86 273	80.28 196	71.20 258	85.14 271	57.93 263	91.34 311	72.52 211	70.74 255	88.18 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-LS		83.93 190	82.80 190	87.31 225	91.46 203	77.39 230	95.66 178	93.43 247	80.44 190	75.51 232	87.26 230	73.72 142	95.16 254	76.99 179	70.72 256	89.39 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				84.73 257	89.63 229	74.66 253		91.81 275	80.05 202	71.06 260	85.18 269	57.98 262	91.40 310	72.48 212	70.70 257	88.12 264

v7		82.99 210	81.41 210	87.73 214	86.41 269	78.86 189	96.10 160	93.98 222	79.88 206	77.49 202	87.11 235	65.44 208	95.97 206	75.69 193	70.59 258	88.36 258

v1240		81.70 227	79.83 231	87.30 226	85.50 290	77.70 225	95.48 184	93.44 245	78.46 228	76.53 218	86.44 250	60.85 239	95.84 216	71.59 219	70.17 259	88.35 259

V42		83.04 207	81.53 208	87.57 220	86.27 277	79.09 179	95.87 171	94.11 217	80.35 194	77.22 210	86.79 243	65.32 212	96.02 204	77.74 165	70.14 260	87.61 273

v1192		82.31 220	80.55 220	87.60 217	85.94 285	78.47 204	95.85 173	93.80 232	79.33 215	76.97 213	86.51 247	63.33 224	95.87 214	73.11 207	70.13 261	88.46 255

v1144		82.90 211	81.27 213	87.78 213	86.29 275	79.07 180	96.14 157	93.93 224	80.05 202	77.38 206	86.80 242	65.50 206	95.93 212	75.21 196	70.13 261	88.33 260

Anonymous20231206		75.29 281	73.64 280	80.22 302	80.75 314	63.38 315	93.36 234	90.71 293	73.09 285	67.12 277	83.70 285	50.33 297	90.85 315	53.63 314	70.10 263	86.44 287

WR-MVS		84.32 186	82.96 186	88.41 195	89.38 234	80.32 139	96.59 127	96.25 108	83.97 129	76.63 216	90.36 195	67.53 179	94.86 263	75.82 191	70.09 264	90.06 223

v1neww		83.45 197	81.95 198	87.95 208	86.66 258	79.04 181	96.32 147	94.17 208	80.76 181	77.56 200	87.25 231	67.02 189	96.08 197	77.73 166	70.07 265	88.74 250

v7new		83.45 197	81.95 198	87.95 208	86.66 258	79.04 181	96.32 147	94.17 208	80.76 181	77.56 200	87.25 231	67.02 189	96.08 197	77.73 166	70.07 265	88.74 250

v6		83.45 197	81.94 200	87.95 208	86.62 262	79.03 184	96.32 147	94.17 208	80.76 181	77.57 199	87.23 233	67.03 188	96.09 196	77.73 166	70.06 267	88.75 248

EU-MVSNet		76.92 274	76.95 248	76.83 311	84.10 303	54.73 332	91.77 270	92.71 265	72.74 288	69.57 269	88.69 212	58.03 261	87.43 328	64.91 267	70.00 268	88.33 260

IB-MVS	85.34 4	88.67 109	87.14 123	93.26 62	93.12 165	84.32 59	98.76 10	97.27 20	87.19 65	79.36 188	90.45 193	83.92 28	98.53 102	84.41 114	69.79 269	96.93 121
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
v1921920		82.02 224	80.23 224	87.41 223	85.62 289	77.92 220	95.79 175	93.69 238	78.86 224	76.67 215	86.44 250	62.50 227	95.83 217	72.69 209	69.77 270	88.47 254

v2v482		83.46 196	81.86 201	88.25 201	86.19 279	79.65 162	96.34 146	94.02 221	81.56 171	77.32 208	88.23 219	65.62 205	96.03 201	77.77 163	69.72 271	89.09 236

v1141		83.36 201	81.81 204	88.01 205	86.61 264	79.26 169	96.44 137	94.12 215	80.88 177	77.48 204	86.87 238	67.08 186	96.03 201	77.14 176	69.69 272	88.75 248

divwei89l23v2f112		83.36 201	81.81 204	88.01 205	86.60 265	79.23 172	96.44 137	94.12 215	80.88 177	77.49 202	86.87 238	67.08 186	96.03 201	77.14 176	69.67 273	88.76 246

v1		83.37 200	81.82 202	88.01 205	86.58 266	79.24 170	96.45 135	94.13 212	80.88 177	77.48 204	86.88 237	67.15 184	96.04 200	77.15 175	69.67 273	88.76 246

v144192		82.43 216	80.73 218	87.54 221	85.81 288	78.22 209	95.98 162	93.78 234	79.09 220	77.11 211	86.49 248	64.66 217	95.91 213	74.20 203	69.42 275	88.49 253

FMVSNet5		76.46 276	74.16 278	83.35 285	90.05 222	76.17 243	89.58 284	89.85 300	71.39 297	65.29 288	80.42 300	50.61 295	87.70 327	61.05 285	69.24 276	86.18 291

TinyColmap		72.41 294	68.99 299	82.68 289	88.11 246	69.59 296	88.41 294	85.20 328	65.55 311	57.91 322	84.82 274	30.80 339	95.94 211	51.38 317	68.70 277	82.49 326

LF4IMVS		72.36 295	70.82 290	76.95 310	79.18 320	56.33 328	86.12 312	86.11 325	69.30 303	63.06 298	86.66 244	33.03 335	92.25 294	65.33 265	68.64 278	82.28 327

OurMVSNet-221017-0		77.18 270	76.06 256	80.55 301	83.78 306	60.00 323	90.35 279	91.05 284	77.01 244	66.62 280	87.92 224	47.73 306	94.03 278	71.63 218	68.44 279	87.62 272

CP-MVSNet		81.01 235	80.08 226	83.79 277	87.91 249	70.51 288	94.29 218	95.65 139	80.83 180	72.54 253	88.84 211	63.71 219	92.32 293	68.58 246	68.36 280	88.55 252

UniMVSNet_NR-MVSNet		85.49 171	84.59 158	88.21 202	89.44 233	79.36 166	96.71 119	96.41 93	85.22 93	78.11 197	90.98 186	76.97 95	95.14 255	79.14 156	68.30 281	90.12 219

DU-MVS		84.57 184	83.33 184	88.28 200	88.76 238	79.36 166	96.43 141	95.41 154	85.42 89	78.11 197	90.82 187	67.61 177	95.14 255	79.14 156	68.30 281	90.33 214

PS-CasMVS		80.27 239	79.18 234	83.52 283	87.56 252	69.88 293	94.08 220	95.29 160	80.27 197	72.08 254	88.51 217	59.22 250	92.23 295	67.49 250	68.15 283	88.45 256

UniMVSNet (Re)		85.31 174	84.23 171	88.55 193	89.75 225	80.55 134	96.72 117	96.89 48	85.42 89	78.40 194	88.93 210	75.38 126	95.52 240	78.58 160	68.02 284	89.57 228

tfpnnormal		78.14 256	75.42 262	86.31 237	88.33 245	79.24 170	94.41 211	96.22 110	73.51 282	69.81 268	85.52 265	55.43 281	95.75 222	47.65 328	67.86 285	83.95 309

VPNet		84.69 182	82.92 187	90.01 164	89.01 236	83.45 75	96.71 119	95.46 149	85.71 82	79.65 180	92.18 169	56.66 273	96.01 205	83.05 134	67.84 286	90.56 210

v10		81.43 230	79.53 233	87.11 227	86.38 270	78.87 188	94.31 214	93.43 247	77.88 231	73.24 246	85.26 266	65.44 208	95.75 222	72.14 213	67.71 287	86.72 285

v8		81.88 225	80.06 228	87.32 224	86.63 261	79.04 181	94.41 211	93.65 240	78.77 225	73.19 247	85.57 263	66.87 192	95.81 218	73.84 206	67.61 288	87.11 281

v7n		79.32 246	77.34 245	85.28 246	84.05 305	72.89 268	93.38 233	93.87 227	75.02 266	70.68 261	84.37 275	59.58 244	95.62 235	67.60 249	67.50 289	87.32 280

WR-MVS_H		81.02 234	80.09 225	83.79 277	88.08 247	71.26 285	94.46 209	96.54 81	80.08 201	72.81 251	86.82 241	70.36 168	92.65 290	64.18 269	67.50 289	87.46 278

Patchmtry		77.36 266	74.59 273	85.67 243	89.75 225	75.75 247	77.85 332	91.12 281	60.28 327	71.23 257	80.35 301	75.45 116	93.56 287	57.94 294	67.34 291	87.68 271

v148		82.41 219	80.89 215	86.99 229	86.18 280	76.81 238	96.27 152	93.82 229	80.49 189	75.28 235	86.11 257	67.32 182	95.75 222	75.48 194	67.03 292	88.42 257

NR-MVSNet		83.35 203	81.52 209	88.84 187	88.76 238	81.31 119	94.45 210	95.16 163	84.65 110	67.81 275	90.82 187	70.36 168	94.87 262	74.75 199	66.89 293	90.33 214

V4		78.70 250	76.95 248	83.95 273	81.66 313	71.34 283	91.94 266	93.44 245	74.69 273	70.35 266	83.73 283	58.07 258	95.50 241	71.84 214	66.86 294	85.02 299

v52		78.70 250	76.95 248	83.95 273	81.71 312	71.34 283	91.93 267	93.43 247	74.69 273	70.36 264	83.71 284	58.04 259	95.50 241	71.84 214	66.82 295	85.00 300

Baseline_NR-MVSNet		81.22 233	80.07 227	84.68 259	85.32 293	75.12 252	96.48 131	88.80 309	76.24 245	77.28 209	86.40 253	67.61 177	94.39 272	75.73 192	66.73 296	84.54 303

TranMVSNet+NR-MVSNet		83.24 205	81.71 206	87.83 211	87.71 250	78.81 192	96.13 159	94.82 177	84.52 113	76.18 225	90.78 189	64.07 218	94.60 268	74.60 201	66.59 297	90.09 221

PEN-MVS		79.47 244	78.26 240	83.08 286	86.36 272	68.58 300	93.85 223	94.77 181	79.76 208	71.37 256	88.55 214	59.79 241	92.46 291	64.50 268	65.40 298	88.19 262

FPMVS		55.09 314	52.93 316	61.57 331	55.98 344	40.51 348	83.11 321	83.41 338	37.61 343	34.95 342	71.95 330	14.40 350	76.95 344	29.81 345	65.16 299	67.25 343

v748		78.69 252	76.79 252	84.39 270	83.40 308	70.78 286	93.25 241	93.62 241	74.96 267	69.40 270	83.74 282	59.40 246	95.39 245	68.74 243	64.59 300	86.99 284

pm-mvs1		80.05 240	78.02 241	86.15 239	85.42 291	75.81 246	95.11 198	92.69 266	77.13 240	70.36 264	87.43 227	58.44 255	95.27 252	71.36 221	64.25 301	87.36 279

N_pmnet		61.30 311	60.20 312	64.60 328	84.32 300	17.00 358	91.67 273	10.98 358	61.77 321	58.45 320	78.55 307	49.89 298	91.83 304	42.27 333	63.94 302	84.97 301

SixPastTwentyTwo		76.04 277	74.32 277	81.22 298	84.54 298	61.43 321	91.16 275	89.30 305	77.89 230	64.04 291	86.31 254	48.23 302	94.29 274	63.54 278	63.84 303	87.93 267

MIMVSNet1		69.44 301	66.65 303	77.84 308	76.48 327	62.84 317	87.42 301	88.97 307	66.96 310	57.75 324	79.72 305	32.77 336	85.83 333	46.32 329	63.42 304	84.85 302

DTE-MVSNet		78.37 254	77.06 247	82.32 294	85.22 294	67.17 305	93.40 232	93.66 239	78.71 226	70.53 263	88.29 218	59.06 251	92.23 295	61.38 284	63.28 305	87.56 275

new_pmnet		66.18 307	63.18 309	75.18 317	76.27 329	61.74 320	83.79 319	84.66 330	56.64 333	51.57 329	71.85 331	31.29 338	87.93 326	49.98 323	62.55 306	75.86 336

test20.03		72.36 295	71.15 289	75.98 315	77.79 323	59.16 326	92.40 259	89.35 304	74.09 278	61.50 311	84.32 276	48.09 303	85.54 336	50.63 322	62.15 307	83.24 319

LP		68.54 306	63.92 308	82.39 291	87.93 248	71.79 277	72.37 342	86.01 327	55.89 334	58.33 321	71.46 333	49.58 300	90.10 322	32.25 343	61.48 308	85.27 296

pmmvs6		74.65 284	71.67 287	83.60 282	79.13 321	69.94 292	93.31 239	90.88 288	61.05 326	65.83 284	84.15 278	43.43 314	94.83 264	66.62 256	60.63 309	86.02 294

test1235678		64.50 310	62.19 310	71.42 322	66.82 341	48.00 339	89.44 286	87.90 314	62.82 317	49.12 334	71.31 334	30.14 340	82.19 340	41.88 334	60.32 310	84.06 307

MDA-MVSNet_test_wron		73.54 288	70.43 294	82.86 287	84.55 297	71.85 274	91.74 271	91.32 280	67.63 305	46.73 336	81.09 298	55.11 284	90.42 319	55.91 308	59.76 311	86.31 289

YYNet1		73.53 289	70.43 294	82.85 288	84.52 299	71.73 278	91.69 272	91.37 279	67.63 305	46.79 335	81.21 297	55.04 285	90.43 318	55.93 307	59.70 312	86.38 288

Patchmatch-RL test		76.65 275	74.01 279	84.55 264	77.37 326	64.23 311	78.49 331	82.84 339	78.48 227	64.63 290	73.40 327	76.05 109	91.70 309	76.99 179	57.84 313	97.72 83

pmmvs-eth3d		73.59 287	70.66 291	82.38 292	76.40 328	73.38 261	89.39 288	89.43 303	72.69 289	60.34 315	77.79 309	46.43 310	91.26 313	66.42 260	57.06 314	82.51 324

PM-MVS		69.32 302	66.93 302	76.49 312	73.60 334	55.84 330	85.91 313	79.32 346	74.72 272	61.09 312	78.18 308	21.76 343	91.10 314	70.86 227	56.90 315	82.51 324

1111		65.60 309	64.33 307	69.41 323	68.26 337	45.11 343	87.06 304	87.32 318	54.99 335	51.20 331	73.45 325	63.57 220	85.70 334	36.53 338	56.59 316	77.42 335

v18		77.96 259	75.61 260	84.98 250	86.66 258	79.01 185	93.02 246	90.94 286	75.69 254	63.19 295	77.62 310	67.11 185	92.07 298	70.05 231	56.24 317	83.87 310

v16		77.84 260	75.47 261	84.93 252	86.62 262	78.93 187	92.84 250	90.89 287	75.50 257	63.03 299	77.54 311	66.82 194	92.04 299	69.82 232	56.22 318	83.82 312

v17		77.79 261	75.41 263	84.94 251	86.53 267	78.94 186	92.83 251	90.88 288	75.51 256	62.97 300	77.50 312	66.69 195	92.03 300	69.80 233	56.01 319	83.83 311

v11		77.21 268	74.72 270	84.68 259	86.29 275	78.62 197	92.30 262	90.63 296	74.86 269	62.32 304	76.73 319	65.49 207	91.83 304	68.17 248	55.72 320	83.59 317

v15		77.52 263	75.09 264	84.82 254	86.37 271	78.82 190	92.58 254	90.78 290	75.47 258	62.53 302	77.17 313	66.58 198	91.92 301	69.18 236	55.16 321	83.73 313

V14		77.43 265	74.99 265	84.75 255	86.32 274	78.67 194	92.44 258	90.77 291	75.28 262	62.42 303	77.13 314	66.40 199	91.88 302	69.01 241	55.14 322	83.70 314

v13		77.11 271	74.63 272	84.55 264	86.08 284	78.27 208	92.06 265	90.68 295	74.73 271	61.86 310	76.93 318	65.73 204	91.81 307	68.55 247	55.07 323	83.59 317

V9		77.32 267	74.87 268	84.69 258	86.26 278	78.52 200	92.33 261	90.72 292	75.11 265	62.21 305	77.08 316	66.19 201	91.87 303	68.84 242	55.06 324	83.69 315

v12		77.20 269	74.73 269	84.63 262	86.15 281	78.41 205	92.17 263	90.71 293	74.92 268	62.05 307	77.00 317	65.83 203	91.83 304	68.69 244	55.01 325	83.64 316

Gipuma		45.11 321	42.05 321	54.30 334	80.69 315	51.30 337	35.80 350	83.81 336	28.13 346	27.94 346	34.53 348	11.41 354	76.70 346	21.45 348	54.65 326	34.90 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		71.45 297	67.94 300	81.98 296	85.33 292	68.50 301	92.35 260	88.76 310	70.40 299	42.99 337	81.96 293	46.57 309	91.31 312	48.75 327	54.39 327	86.11 292

K. test v3		73.62 286	71.59 288	79.69 304	82.98 309	59.85 324	90.85 278	88.83 308	77.13 240	58.90 317	82.11 292	43.62 313	91.72 308	65.83 263	54.10 328	87.50 277

TDRefinement		69.20 303	65.78 305	79.48 305	66.04 342	62.21 318	88.21 295	86.12 324	62.92 316	61.03 313	85.61 262	33.23 334	94.16 276	55.82 309	53.02 329	82.08 328

ambc				76.02 314	68.11 339	51.43 336	64.97 344	89.59 301		60.49 314	74.49 322	17.17 347	92.46 291	61.50 283	52.85 330	84.17 306

TransMVSNet (Re)		76.94 273	74.38 276	84.62 263	85.92 286	75.25 251	95.28 186	89.18 306	73.88 280	67.22 276	86.46 249	59.64 242	94.10 277	59.24 292	52.57 331	84.50 304

test12356		58.24 313	56.06 315	64.77 326	60.65 343	39.42 349	82.78 322	84.34 333	57.47 332	42.65 338	69.10 335	19.21 345	81.18 341	38.97 337	49.40 332	73.69 337

PMVS	34.80 23	39.19 324	35.53 325	50.18 336	29.72 356	30.30 353	59.60 347	66.20 352	26.06 347	17.91 350	49.53 343	3.12 358	74.09 348	18.19 350	49.40 332	46.14 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lessismore_v0				79.98 303	80.59 316	58.34 327		80.87 342		58.49 319	83.46 287	43.10 317	93.89 280	63.11 280	48.68 334	87.72 269

UnsupCasMVSNet_eth		73.25 290	70.57 292	81.30 297	77.53 324	66.33 307	87.24 303	93.89 226	80.38 193	57.90 323	81.59 295	42.91 318	90.56 317	65.18 266	48.51 335	87.01 283

new-patchmatchnet		68.85 304	65.93 304	77.61 309	73.57 335	63.94 314	90.11 281	88.73 311	71.62 295	55.08 326	73.60 324	40.84 323	87.22 329	51.35 319	48.49 336	81.67 330

pmmvs3		65.75 308	62.18 311	76.45 313	67.12 340	64.54 310	88.68 292	85.05 329	54.77 338	57.54 325	73.79 323	29.40 341	86.21 332	55.49 310	47.77 337	78.62 333

Anonymous20231211		61.03 312	56.76 314	73.82 319	71.24 336	53.47 334	87.60 300	81.65 341	44.19 341	51.08 333	71.82 332	20.79 344	88.46 325	35.45 340	47.07 338	79.52 332

UnsupCasMVSNet_bld		68.60 305	64.50 306	80.92 300	74.63 332	67.80 302	83.97 318	92.94 262	65.12 312	54.63 327	68.23 336	35.97 329	92.17 297	60.13 287	44.83 339	82.78 322

LCM-MVSNet		52.52 317	48.24 318	65.35 325	47.63 352	41.45 346	72.55 341	83.62 337	31.75 344	37.66 340	57.92 340	9.19 356	76.76 345	49.26 325	44.60 340	77.84 334

PVSNet_0	77.72 15	81.70 227	78.95 237	89.94 169	90.77 211	76.72 239	95.96 163	96.95 43	85.01 101	70.24 267	88.53 216	52.32 291	98.20 113	86.68 102	44.08 341	94.89 169

testmv		54.58 316	51.53 317	63.74 330	53.58 348	40.82 347	83.26 320	83.92 335	54.07 339	36.35 341	61.26 337	14.80 349	77.07 343	33.00 342	43.53 342	73.33 338

DeepMVS_CX				64.06 329	78.53 322	43.26 345		68.11 351	69.94 300	38.55 339	76.14 320	18.53 346	79.34 342	43.72 332	41.62 343	69.57 342

PMMVS2		50.90 319	46.31 319	64.67 327	55.53 345	46.67 341	77.30 334	71.02 348	40.89 342	34.16 343	59.32 338	9.83 355	76.14 347	40.09 335	28.63 344	71.21 339

MVE	35.65 22	33.85 327	29.49 330	46.92 337	41.86 353	36.28 351	50.45 348	56.52 355	18.75 351	18.28 349	37.84 347	2.41 359	58.41 352	18.71 349	20.62 345	46.06 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		41.20 323	38.13 324	50.41 335	55.23 346	36.24 352	73.80 340	65.45 353	29.75 345	21.36 348	47.05 346	3.43 357	63.23 351	28.17 347	18.92 346	51.76 346

no-one		51.12 318	45.81 320	67.03 324	53.16 350	52.22 335	75.21 336	80.40 343	54.89 337	28.26 345	48.50 344	15.54 348	82.81 339	39.29 336	17.06 347	66.07 344

wuykxyi23d		37.75 325	31.85 328	55.46 333	40.00 354	38.01 350	59.81 346	69.47 349	25.46 348	12.42 353	30.55 352	2.06 360	67.08 349	31.81 344	15.03 348	61.29 345

E-PMN		32.70 328	32.39 327	33.65 340	53.35 349	25.70 355	74.07 338	53.33 356	21.08 349	17.17 351	33.63 350	11.85 353	54.84 353	12.98 351	14.04 349	20.42 351

ANet_high		46.22 320	41.28 323	61.04 332	39.91 355	46.25 342	70.59 343	76.18 347	58.87 330	23.09 347	48.00 345	12.58 352	66.54 350	28.65 346	13.62 350	70.35 340

tmp_tt		41.54 322	41.93 322	40.38 339	20.10 357	26.84 354	61.93 345	59.09 354	14.81 352	28.51 344	80.58 299	35.53 330	48.33 355	63.70 277	13.11 351	45.96 349

EMVS		31.70 329	31.45 329	32.48 341	50.72 351	23.95 356	74.78 337	52.30 357	20.36 350	16.08 352	31.48 351	12.80 351	53.60 354	11.39 352	13.10 352	19.88 352

wuyk23d		14.10 331	13.89 332	14.72 343	55.23 346	22.91 357	33.83 351	3.56 359	4.94 353	4.11 354	2.28 357	2.06 360	19.66 356	10.23 353	8.74 353	1.59 356

.test1245		54.61 315	58.07 313	44.24 338	68.26 337	45.11 343	87.06 304	87.32 318	54.99 335	51.20 331	73.45 325	63.57 220	85.70 334	36.53 338	0.21 354	1.91 354

testmvs		9.92 332	12.94 333	0.84 345	0.65 358	0.29 360	93.78 224	0.39 360	0.42 354	2.85 355	15.84 355	0.17 363	0.30 358	2.18 354	0.21 354	1.91 354

test123		9.07 333	11.73 334	1.11 344	0.50 359	0.77 359	89.44 286	0.20 361	0.34 355	2.15 356	10.72 356	0.34 362	0.32 357	1.79 355	0.08 356	2.23 353

cdsmvs_eth3d_5k		21.43 330	28.57 331	0.00 346	0.00 360	0.00 361	0.00 352	95.93 128	0.00 356	0.00 357	97.66 51	63.57 220	0.00 359	0.00 356	0.00 357	0.00 357

pcd_1.5k_mvsjas		5.92 335	7.89 336	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	71.04 162	0.00 359	0.00 356	0.00 357	0.00 357

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

ab-mvs-re		8.11 334	10.81 335	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	97.30 71	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																97.54 94

test_part3							98.15 25		84.95 102		98.83 2		99.80 14	97.78 2

test_part2					98.90 7	85.14 44				96.07 8

sam_mvs1												77.59 87				97.54 94

sam_mvs												75.35 129

MTGPA								96.33 103

test_post1							85.88 314				30.24 353	73.77 141	95.07 259	73.89 205

test_post											33.80 349	76.17 107	95.97 206

patchmatchnet-post											77.09 315	77.78 86	95.39 245

MTMP								68.16 350

gm-plane-assit					92.27 180	79.64 163			84.47 116		95.15 124		97.93 118	85.81 104

TEST9					98.64 21	83.71 69	97.82 37	96.65 67	84.29 122	95.16 15	98.09 29	84.39 21	99.36 56

test_8					98.63 23	83.64 72	97.81 39	96.63 73	84.50 114	95.10 17	98.11 28	84.33 22	99.23 62

agg_prior					98.59 26	83.13 79		96.56 78		94.19 29			99.16 75

test_prior4						82.34 94	97.75 46

test_prior				93.09 69	98.68 15	81.91 102		96.40 95					99.06 82			98.29 44

旧先验2							96.97 105		74.06 279	96.10 7			97.76 130	88.38 87

新几何2							96.42 142

无先验							96.87 110	96.78 52	77.39 236				99.52 44	79.95 150		98.43 36

原ACMM2							96.84 111

testdata2													99.48 49	76.45 185

segment_acmp												82.69 36

testdata1							95.57 181		87.44 59

plane_prior7					91.86 197	77.55 227

plane_prior6					91.98 192	77.92 220						64.77 215

plane_prior4											94.15 142

plane_prior3						77.75 224			90.17 28	81.33 163

plane_prior2							97.18 79		89.89 30

plane_prior1					91.95 195

n2								0.00 362

nn								0.00 362

door-mid								79.75 345

test11								96.50 85

door								80.13 344

HQP5-MVS						78.48 201

HQP-NCC					92.08 187		97.63 51		90.52 24	82.30 148

ACMP_Plane					92.08 187		97.63 51		90.52 24	82.30 148

BP-MVS														87.67 94

HQP4-MVS										82.30 148			97.32 149			91.13 204

HQP2-MVS												65.40 210

NP-MVS					92.04 191	78.22 209					94.56 136

MDTV_nov1_ep13_2view						81.74 109	86.80 307		80.65 186	85.65 112		74.26 139		76.52 184		96.98 118

Test By Simon												71.65 155

High-resolution multi-view benchmark