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.

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 27	99.08 1	98.72 114	98.66 106	97.51 8	98.15 55	98.83 81	95.70 33	99.92 13	97.53 52	99.67 39	99.66 48

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 23	98.96 2	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 5	99.84 42	98.86 2	99.85 2	99.87 1

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 21	98.95 3	98.82 84	98.81 60	95.80 72	99.16 12	99.47 4	95.37 40	99.92 13	97.89 32	99.75 29	99.79 4

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 4	98.43 158	98.78 70	94.10 140	97.69 85	99.42 5	95.25 45	99.92 13	98.09 24	99.80 9	99.67 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 22	98.87 5	98.41 160	98.68 96	97.04 38	98.52 44	98.80 84	96.78 4	99.83 43	97.93 28	99.61 48	99.74 25

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 45	98.87 5	98.47 154	98.81 60	97.72 4	98.76 33	99.16 42	97.05 2	99.78 74	98.06 25	99.66 42	99.69 35

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 26	98.72 7	98.80 93	98.82 57	94.52 130	99.23 8	99.25 28	95.54 37	99.80 57	96.52 89	99.77 17	99.74 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 8	98.55 143	98.74 78	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 8	98.90 65	98.74 78	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

NCCC		98.61 13	98.35 20	99.38 10	99.28 60	98.61 10	98.45 155	98.76 74	97.82 3	98.45 48	98.93 73	96.65 6	99.83 43	97.38 57	99.41 76	99.71 32

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 160	98.52 11	99.37 7	98.71 90	97.09 37	92.99 239	99.13 44	89.36 136	99.89 27	96.97 65	99.57 55	99.71 32

TEST9					99.31 47	98.50 12	97.92 214	98.73 83	92.63 199	97.74 81	98.68 94	96.20 12	99.80 57

train_agg		97.97 44	97.52 54	99.33 15	99.31 47	98.50 12	97.92 214	98.73 83	92.98 190	97.74 81	98.68 94	96.20 12	99.80 57	96.59 85	99.57 55	99.68 41

test_8					99.29 55	98.44 14	97.89 222	98.72 85	92.98 190	97.70 84	98.66 97	96.20 12	99.80 57

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 31	98.44 14	97.91 217	98.67 103	92.57 203	98.77 32	98.85 79	95.93 27	99.72 86	95.56 118	99.69 38	99.68 41

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 71	98.43 16	99.10 44	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
agg_prior1		97.95 46	97.51 55	99.28 20	99.30 52	98.38 17	97.81 229	98.72 85	93.16 184	97.57 93	98.66 97	96.14 15	99.81 50	96.63 84	99.56 61	99.66 48

agg_prior					99.30 52	98.38 17		98.72 85		97.57 93			99.81 50

canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 109	98.38 17	99.34 11	98.39 153	96.76 45	97.67 86	97.40 196	92.26 89	99.49 125	98.28 22	96.28 177	99.08 118

alignmvs		97.56 65	97.07 74	99.01 46	98.66 113	98.37 20	98.83 82	98.06 214	96.74 46	98.00 68	97.65 182	90.80 121	99.48 129	98.37 19	96.56 160	99.19 104

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 42	98.36 21	98.90 65	98.85 53	97.28 21	99.72 1	99.39 7	96.63 7	97.60 285	98.17 23	99.85 2	99.64 53

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 22	99.29 14	98.88 47	97.40 14	98.46 45	99.20 35	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

X-MVStestdata		94.06 233	92.30 251	99.34 13	99.70 15	98.35 22	99.29 14	98.88 47	97.40 14	98.46 45	43.50 340	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 25	98.35 22	98.33 167	98.89 44	92.62 200	98.05 60	98.94 72	95.34 42	99.65 98	96.04 100	99.42 75	99.19 104

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 25	99.23 21	98.96 31	96.10 65	98.94 21	99.17 39	96.06 20	99.92 13	97.62 45	99.78 14	99.75 20

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 25	98.98 58	98.96 31	95.65 78	98.94 21	99.17 39	96.06 20	99.92 13	97.21 60	99.78 14	99.75 20

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 27	99.14 37	98.66 106	96.84 43	99.56 2	99.31 21	96.34 10	99.70 91	98.32 20	99.73 34	99.73 27

HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 28	99.26 17	98.58 119	97.52 7	99.41 3	98.78 85	96.00 23	99.79 69	97.79 38	99.59 52	99.69 35

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 55	98.23 28	97.92 214	98.72 85	92.38 216	97.59 92	98.64 99	96.09 18	99.79 69	96.59 85	99.57 55	99.68 41

test_prior3		98.22 42	97.90 43	99.19 28	99.31 47	98.22 30	97.80 230	98.84 54	96.12 63	97.89 75	98.69 92	95.96 25	99.70 91	96.89 71	99.60 49	99.65 50

test_prior				99.19 28	99.31 47	98.22 30		98.84 54					99.70 91			99.65 50

test12				99.18 32	99.16 76	98.19 32		98.53 127		98.07 59		95.13 49	99.72 86		99.56 61	99.63 55

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 33	99.22 27	98.79 68	96.13 62	97.92 73	99.23 29	94.54 59	99.94 3	96.74 81	99.78 14	99.73 27

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 34	99.23 21	98.93 36	96.15 60	98.94 21	99.17 39	95.91 28	99.94 3	97.55 50	99.79 10	99.78 7

nrg030		96.28 118	95.72 120	97.96 108	96.90 217	98.15 35	99.39 5	98.31 161	95.47 84	94.42 184	98.35 123	92.09 96	98.69 198	97.50 53	89.05 263	97.04 199

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 35	99.23 21	98.95 33	96.10 65	98.93 25	99.19 38	95.70 33	99.94 3	97.62 45	99.79 10	99.78 7

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 78	98.12 37	99.04 51	99.09 19	93.32 179	98.83 29	99.10 48	96.54 8	99.83 43	97.70 43	99.76 23	99.59 61

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 38	98.99 55	99.49 5	95.43 86	99.03 15	99.32 20	95.56 35	99.94 3	96.80 79	99.77 17	99.78 7

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 39	99.28 16	98.81 60	96.24 58	98.35 52	99.23 29	95.46 38	99.94 3	97.42 55	99.81 8	99.77 14

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 64	98.04 39	98.50 151	98.78 70	97.72 4	98.92 26	99.28 25	95.27 44	99.82 48	97.55 50	99.77 17	99.69 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		98.69 7	98.52 8	99.19 28	99.35 37	98.01 41	98.37 163	98.81 60	97.48 11	99.21 9	99.21 32	96.13 16	99.80 57	98.40 18	99.73 34	99.75 20

test_prior4						98.01 41	97.86 225

新几何1				99.16 35	99.34 39	98.01 41		98.69 93	90.06 267	98.13 56	98.95 71	94.60 58	99.89 27	91.97 213	99.47 69	99.59 61

1121		97.37 77	96.77 87	99.16 35	99.34 39	97.99 44	98.19 186	98.68 96	90.14 265	98.01 66	98.97 65	94.80 56	99.87 35	93.36 172	99.46 72	99.61 56

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 27	97.92 45	99.15 36	98.81 60	96.24 58	99.20 10	99.37 12	95.30 43	99.80 57	97.73 41	99.67 39	99.72 30

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 46	99.44 4	98.82 57	94.46 134	98.94 21	99.20 35	95.16 48	99.74 85	97.58 47	99.85 2	99.77 14

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 46	99.34 11	98.87 49	95.96 68	98.60 41	99.13 44	96.05 22	99.94 3	97.77 39	99.86 1	99.77 14

MVS_0304		97.70 57	97.25 65	99.07 43	98.90 93	97.83 48	98.20 182	98.74 78	97.51 8	98.03 63	99.06 56	86.12 223	99.93 9	99.02 1	99.64 45	99.44 84

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 49	99.53 1	98.80 67	94.63 127	98.61 40	98.97 65	95.13 49	99.77 79	97.65 44	99.83 7	99.79 4

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 37	97.81 50	98.37 163	98.76 74	97.49 10	99.20 10	99.21 32	96.08 19	99.79 69	98.42 16	99.73 34	99.75 20

abl_6		98.30 40	98.03 38	99.13 38	99.56 24	97.76 51	99.13 40	98.82 57	96.14 61	99.26 6	99.37 12	93.33 76	99.93 9	96.96 67	99.67 39	99.69 35

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 86	97.66 52	97.75 234	98.89 44	97.71 6	98.33 53	98.97 65	94.97 52	99.88 34	98.42 16	99.76 23	99.42 85
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
3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 162	97.64 53	99.35 10	99.06 21	97.02 39	93.75 218	99.16 42	89.25 139	99.92 13	97.22 59	99.75 29	99.64 53

114514_t		96.93 93	96.27 104	98.92 53	99.50 27	97.63 54	98.85 78	98.90 42	84.80 312	97.77 78	99.11 46	92.84 81	99.66 97	94.85 135	99.77 17	99.47 77

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 55	99.03 52	99.41 6	95.98 67	97.60 91	99.36 16	94.45 64	99.93 9	97.14 61	98.85 97	99.70 34
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
QAPM		96.29 116	95.40 129	98.96 51	97.85 159	97.60 56	99.23 21	98.93 36	89.76 276	93.11 236	99.02 58	89.11 143	99.93 9	91.99 212	99.62 47	99.34 88

VNet		97.79 54	97.40 61	98.96 51	98.88 96	97.55 57	98.63 130	98.93 36	96.74 46	99.02 16	98.84 80	90.33 127	99.83 43	98.53 10	96.66 156	99.50 71

FIs		96.51 108	96.12 109	97.67 125	97.13 205	97.54 58	99.36 8	99.22 14	95.89 69	94.03 209	98.35 123	91.98 99	98.44 232	96.40 93	92.76 228	97.01 200

旧先验1					99.29 55	97.48 59		98.70 92			99.09 52	95.56 35			99.47 69	99.61 56

UA-Net		97.96 45	97.62 48	98.98 49	98.86 98	97.47 60	98.89 69	99.08 20	96.67 49	98.72 35	99.54 1	93.15 79	99.81 50	94.87 134	98.83 98	99.65 50

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 211	97.47 60	98.79 98	99.18 16	95.60 79	93.92 212	97.04 226	91.68 103	98.48 222	95.80 109	87.66 285	96.79 223

CNLPA		97.45 70	97.03 75	98.73 60	99.05 81	97.44 62	98.07 201	98.53 127	95.32 98	96.80 120	98.53 107	93.32 77	99.72 86	94.31 150	99.31 82	99.02 121

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 35	97.37 63	98.40 161	98.79 68	97.46 12	99.09 13	99.31 21	95.86 31	99.80 57	98.64 4	99.76 23	99.79 4

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 71	97.32 64	97.91 217	99.58 3	97.20 29	98.33 53	99.00 63	95.99 24	99.64 100	98.05 26	99.76 23	99.69 35

OpenMVS	93.04 13	95.83 130	95.00 148	98.32 87	97.18 202	97.32 64	99.21 30	98.97 29	89.96 269	91.14 267	99.05 57	86.64 215	99.92 13	93.38 171	99.47 69	97.73 180

CANet		98.05 43	97.76 45	98.90 55	98.73 106	97.27 66	98.35 165	98.78 70	97.37 19	97.72 83	98.96 69	91.53 110	99.92 13	98.79 3	99.65 43	99.51 69

FC-MVSNet-test		96.42 111	96.05 110	97.53 136	96.95 212	97.27 66	99.36 8	99.23 12	95.83 71	93.93 211	98.37 121	92.00 98	98.32 251	96.02 101	92.72 229	97.00 201

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 195	97.27 66	98.94 62	99.23 12	95.13 106	95.51 154	97.32 203	85.73 229	98.91 180	97.33 58	89.55 257	96.89 214

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 71	97.25 69	98.11 197	98.29 166	97.19 30	98.99 20	99.02 58	96.22 11	99.67 96	98.52 14	98.56 110	99.51 69

NR-MVSNet		94.98 176	94.16 188	97.44 139	96.53 234	97.22 70	98.74 109	98.95 33	94.96 115	89.25 284	97.69 178	89.32 137	98.18 261	94.59 142	87.40 287	96.92 206

LS3D		97.16 85	96.66 92	98.68 63	98.53 123	97.19 71	98.93 63	98.90 42	92.83 197	95.99 151	99.37 12	92.12 95	99.87 35	93.67 166	99.57 55	98.97 126

test222					99.23 70	97.17 72	97.40 254	98.66 106	88.68 291	98.05 60	98.96 69	94.14 69			99.53 65	99.61 56

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 73	99.12 42	98.81 60	92.34 217	98.09 58	99.08 54	93.01 80	99.92 13	96.06 99	99.77 17	99.75 20

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 35	97.05 74	98.40 161	98.68 96	97.43 13	99.06 14	99.31 21	95.80 32	99.77 79	98.62 6	99.76 23	99.78 7

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 124	97.00 75	98.14 192	98.21 177	93.95 148	96.72 122	97.99 153	91.58 105	99.76 81	94.51 145	96.54 161	98.95 130

UniMVSNet_NR-MVSNet		95.71 135	95.15 144	97.40 143	96.84 220	96.97 76	98.74 109	99.24 10	95.16 105	93.88 213	97.72 177	91.68 103	98.31 253	95.81 107	87.25 290	96.92 206

DU-MVS		95.42 151	94.76 162	97.40 143	96.53 234	96.97 76	98.66 128	98.99 28	95.43 86	93.88 213	97.69 178	88.57 170	98.31 253	95.81 107	87.25 290	96.92 206

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 64	96.93 78	98.83 82	98.75 77	96.96 41	96.89 114	99.50 3	90.46 124	99.87 35	97.84 36	99.76 23	99.52 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPR		96.84 97	96.24 106	98.65 65	98.72 108	96.92 79	97.36 260	98.57 120	93.33 178	96.67 123	97.57 189	94.30 67	99.56 115	91.05 232	98.59 108	99.47 77

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 61	96.90 80	97.95 212	99.58 3	97.14 33	98.44 49	99.01 62	95.03 51	99.62 105	97.91 29	99.75 29	99.50 71

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 111	96.90 80	98.66 128	98.68 96	92.40 215	97.07 104	97.96 154	91.54 109	99.75 83	93.68 165	98.92 92	98.69 142
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
WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 98	96.89 82	98.31 172	98.71 90	95.26 100	97.67 86	98.56 106	92.21 92	99.78 74	95.89 104	96.85 153	99.48 76

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 63	96.80 83	98.71 115	99.05 23	97.28 21	98.84 27	99.28 25	96.47 9	99.40 131	98.52 14	99.70 37	99.47 77

API-MVS		97.41 74	97.25 65	97.91 109	98.70 109	96.80 83	98.82 84	98.69 93	94.53 129	98.11 57	98.28 131	94.50 63	99.57 113	94.12 155	99.49 67	97.37 191

PCF-MVS	93.45 11	94.68 198	93.43 234	98.42 83	98.62 117	96.77 85	95.48 311	98.20 180	84.63 313	93.34 228	98.32 129	88.55 172	99.81 50	84.80 303	98.96 91	98.68 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ab-mvs		96.42 111	95.71 123	98.55 71	98.63 116	96.75 86	97.88 223	98.74 78	93.84 153	96.54 132	98.18 140	85.34 236	99.75 83	95.93 103	96.35 170	99.15 110

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 138	96.72 87	97.37 258	98.43 148	93.71 161	97.65 89	98.02 149	92.20 93	99.25 139	96.87 77	97.79 138	99.19 104

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 76	96.69 88	98.01 206	98.89 44	94.44 135	96.83 116	98.68 94	90.69 122	99.76 81	94.36 147	99.29 83	98.98 125

原ACMM1				98.65 65	99.32 45	96.62 89		98.67 103	93.27 182	97.81 77	98.97 65	95.18 47	99.83 43	93.84 161	99.46 72	99.50 71

FMVSNet3		94.97 177	94.26 182	97.11 155	98.18 140	96.62 89	98.56 141	98.26 171	93.67 168	94.09 205	97.10 214	84.25 257	98.01 270	92.08 207	92.14 232	96.70 235

sss		97.39 75	96.98 77	98.61 67	98.60 119	96.61 91	98.22 180	98.93 36	93.97 147	98.01 66	98.48 112	91.98 99	99.85 40	96.45 91	98.15 126	99.39 86

VPNet		94.99 174	94.19 187	97.40 143	97.16 203	96.57 92	98.71 115	98.97 29	95.67 76	94.84 164	98.24 137	80.36 287	98.67 201	96.46 90	87.32 288	96.96 203

MVS		94.67 199	93.54 228	98.08 101	96.88 218	96.56 93	98.19 186	98.50 136	78.05 326	92.69 244	98.02 149	91.07 117	99.63 103	90.09 244	98.36 119	98.04 171

XXY-MVS		95.20 168	94.45 176	97.46 138	96.75 225	96.56 93	98.86 77	98.65 110	93.30 181	93.27 229	98.27 134	84.85 243	98.87 186	94.82 136	91.26 245	96.96 203

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 47	96.51 95	97.91 217	99.06 21	93.72 160	96.92 112	98.06 147	88.50 175	99.65 98	91.77 218	99.00 90	98.66 145

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 32	96.49 96	98.30 174	98.69 93	97.21 28	98.84 27	99.36 16	95.41 39	99.78 74	98.62 6	99.65 43	99.80 3

WR-MVS		95.15 169	94.46 174	97.22 147	96.67 230	96.45 97	98.21 181	98.81 60	94.15 138	93.16 232	97.69 178	87.51 201	98.30 255	95.29 127	88.62 274	96.90 213

FMVSNet2		94.47 210	93.61 224	97.04 158	98.21 135	96.43 98	98.79 98	98.27 167	92.46 204	93.50 225	97.09 216	81.16 277	98.00 271	91.09 228	91.93 236	96.70 235

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 27	96.41 99	98.63 130	98.60 113	95.18 104	97.06 105	98.06 147	94.26 68	99.57 113	93.80 163	98.87 96	99.52 66

1112_ss		96.63 102	96.00 113	98.50 75	98.56 120	96.37 100	98.18 190	98.10 207	92.92 192	94.84 164	98.43 115	92.14 94	99.58 112	94.35 148	96.51 162	99.56 65

TranMVSNet+NR-MVSNet		95.14 170	94.48 172	97.11 155	96.45 239	96.36 101	99.03 52	99.03 24	95.04 111	93.58 220	97.93 157	88.27 178	98.03 269	94.13 154	86.90 295	96.95 205

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 100	96.36 101	99.19 33	97.97 219	95.39 88	97.23 98	98.99 64	91.11 115	98.93 178	94.60 141	98.59 108	99.47 77

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 33	96.32 103	98.28 176	98.68 96	97.17 31	98.74 34	99.37 12	95.25 45	99.79 69	98.57 8	99.54 64	99.73 27

LFMVS		95.86 129	94.98 150	98.47 78	98.87 97	96.32 103	98.84 81	96.02 311	93.40 176	98.62 39	99.20 35	74.99 310	99.63 103	97.72 42	97.20 148	99.46 81

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 55	96.31 105	98.14 192	98.76 74	92.41 214	96.39 142	98.31 130	94.92 53	99.78 74	94.06 156	98.77 101	99.23 102
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 113	96.23 106	99.22 27	99.00 26	96.63 51	98.04 62	99.21 32	88.05 185	99.35 135	96.01 102	99.21 84	99.45 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DP-MVS		96.59 105	95.93 114	98.57 69	99.34 39	96.19 107	98.70 118	98.39 153	89.45 284	94.52 173	99.35 18	91.85 101	99.85 40	92.89 191	98.88 94	99.68 41

diffmvs		96.32 115	95.74 118	98.07 103	98.26 132	96.14 108	98.53 145	98.23 175	90.10 266	96.88 115	97.73 174	90.16 130	99.15 146	93.90 160	97.85 136	98.91 132

EPNet		97.28 80	96.87 81	98.51 74	94.98 300	96.14 108	98.90 65	97.02 282	98.28 1	95.99 151	99.11 46	91.36 111	99.89 27	96.98 64	99.19 85	99.50 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 142	96.07 110	97.98 209	98.21 177	97.24 27	97.13 100	98.93 73	86.88 212	99.91 22	95.00 133	99.37 80	98.66 145

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 125	95.98 111	97.86 225	98.51 131	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 191

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 125	95.98 111	97.86 225	98.51 131	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 191

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 125	95.98 111	97.86 225	98.51 131	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 191

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 148	95.98 111	98.20 182	98.33 160	93.67 168	96.95 108	98.49 111	93.54 74	98.42 235	95.24 130	97.74 141	99.31 91
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 131	95.97 115	98.58 136	98.25 172	91.74 231	95.29 158	97.23 208	91.03 118	99.15 146	92.90 189	97.96 131	98.97 126

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 129	95.97 115	98.74 109	98.07 212	94.27 137	98.44 49	98.07 146	92.48 85	99.26 138	96.43 92	98.19 125	99.16 109

MG-MVS		97.81 53	97.60 49	98.44 80	99.12 80	95.97 115	97.75 234	98.78 70	96.89 42	98.46 45	99.22 31	93.90 73	99.68 95	94.81 137	99.52 66	99.67 46

test_normal		94.72 194	93.59 225	98.11 99	95.30 297	95.95 118	97.91 217	97.39 262	94.64 126	85.70 301	95.88 280	80.52 285	99.36 134	96.69 82	98.30 122	99.01 124

tfpnnormal		93.66 240	92.70 246	96.55 202	96.94 213	95.94 119	98.97 59	99.19 15	91.04 254	91.38 265	97.34 201	84.94 241	98.61 204	85.45 301	89.02 265	95.11 296

pmmvs4		94.69 195	93.99 201	96.81 171	95.74 284	95.94 119	97.40 254	97.67 230	90.42 260	93.37 227	97.59 187	89.08 144	98.20 260	92.97 184	91.67 240	96.30 275

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 120	95.94 119	97.71 236	98.07 212	92.10 223	94.79 168	97.29 205	91.75 102	99.56 115	94.17 153	96.50 163	99.58 63

MVSTER		96.06 122	95.72 120	97.08 157	98.23 134	95.93 122	98.73 112	98.27 167	94.86 119	95.07 159	98.09 145	88.21 179	98.54 211	96.59 85	93.46 216	96.79 223

DI_MVS_plusplus_test		94.74 193	93.62 223	98.09 100	95.34 296	95.92 123	98.09 200	97.34 264	94.66 125	85.89 298	95.91 279	80.49 286	99.38 133	96.66 83	98.22 123	98.97 126

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 42	95.92 123	98.28 176	98.59 114	95.52 83	97.97 69	99.10 48	93.28 78	99.49 125	95.09 132	98.88 94	99.19 104

PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 61	95.91 125	98.63 130	99.16 17	94.48 133	97.67 86	98.88 77	92.80 82	99.91 22	97.11 62	99.12 87	99.50 71

anonymousdsp		95.42 151	94.91 158	96.94 165	95.10 299	95.90 126	99.14 37	98.41 149	93.75 156	93.16 232	97.46 193	87.50 203	98.41 242	95.63 117	94.03 205	96.50 266

UGNet		96.78 99	96.30 103	98.19 94	98.24 133	95.89 127	98.88 71	98.93 36	97.39 16	96.81 119	97.84 165	82.60 272	99.90 25	96.53 88	99.49 67	98.79 137
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Test4		92.21 259	90.34 275	97.82 115	92.83 314	95.87 128	97.94 213	98.05 217	94.50 131	82.12 317	94.48 296	59.54 332	98.54 211	95.39 123	98.22 123	99.06 120

WR-MVS_H		95.05 172	94.46 174	96.81 171	96.86 219	95.82 129	99.24 20	99.24 10	93.87 152	92.53 249	96.84 248	90.37 125	98.24 259	93.24 175	87.93 280	96.38 271

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 145	95.76 130	99.47 2	98.40 151	94.98 113	98.79 30	98.83 81	92.34 86	98.41 242	96.91 69	99.59 52	99.34 88

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 145	95.76 130	97.68 239	97.76 226	94.50 131	98.79 30	98.61 100	92.34 86	99.30 136	97.58 47	99.59 52	99.31 91

PAPM		94.95 178	94.00 199	97.78 117	97.04 208	95.65 132	96.03 304	98.25 172	91.23 251	94.19 200	97.80 171	91.27 113	98.86 188	82.61 307	97.61 143	98.84 135

jason		97.32 79	97.08 73	98.06 104	97.45 184	95.59 133	97.87 224	97.91 222	94.79 120	98.55 43	98.83 81	91.12 114	99.23 141	97.58 47	99.60 49	99.34 88
jason: jason.
PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 112	95.58 134	97.34 262	98.51 131	97.29 20	98.66 37	97.88 161	94.51 60	99.90 25	97.87 33	99.17 86	97.39 189

testing_2		90.61 283	88.50 290	96.95 164	90.08 322	95.57 135	97.69 238	98.06 214	93.02 188	76.55 324	92.48 320	61.18 331	98.44 232	95.45 122	91.98 235	96.84 219

CP-MVSNet		94.94 180	94.30 181	96.83 170	96.72 227	95.56 136	99.11 43	98.95 33	93.89 150	92.42 254	97.90 159	87.19 206	98.12 263	94.32 149	88.21 277	96.82 222

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 42	95.56 136	97.38 256	99.65 2	92.34 217	97.61 90	98.20 139	89.29 138	99.10 157	96.97 65	97.60 144	99.77 14

1314		96.25 120	95.73 119	97.79 116	97.13 205	95.55 138	98.19 186	98.59 114	93.47 174	92.03 261	97.82 169	91.33 112	99.49 125	94.62 140	98.44 115	98.32 166

test_djsdf		96.00 123	95.69 125	96.93 166	95.72 286	95.49 139	99.47 2	98.40 151	94.98 113	94.58 171	97.86 162	89.16 142	98.41 242	96.91 69	94.12 203	96.88 215

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 135	98.61 118	95.46 140	97.44 251	98.46 141	97.15 32	98.65 38	98.15 141	94.33 66	99.80 57	97.84 36	98.66 106	97.41 187

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 106	95.46 140	99.20 31	98.30 164	94.96 115	96.60 127	98.87 78	90.05 131	98.59 207	93.67 166	98.60 107	99.46 81

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 115	95.38 142	99.33 13	98.31 161	93.61 170	97.19 99	99.07 55	94.05 70	99.23 141	96.89 71	98.43 117	99.37 87

testdata				98.26 89	99.20 74	95.36 143		98.68 96	91.89 227	98.60 41	99.10 48	94.44 65	99.82 48	94.27 151	99.44 74	99.58 63

MSDG		95.93 126	95.30 139	97.83 113	98.90 93	95.36 143	96.83 288	98.37 156	91.32 246	94.43 183	98.73 91	90.27 128	99.60 106	90.05 247	98.82 99	98.52 151

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 154	99.25 64	95.35 145	98.26 178	99.26 8	94.28 136	97.94 71	97.46 193	92.74 83	99.81 50	96.88 74	93.32 221	96.20 276

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 64	95.35 145	97.28 266	99.26 8	93.13 185	97.94 71	98.21 138	92.74 83	99.81 50	96.88 74	99.40 78	99.27 98

TAMVS		97.02 90	96.79 84	97.70 122	98.06 147	95.31 147	98.52 146	98.31 161	93.95 148	97.05 106	98.61 100	93.49 75	98.52 218	95.33 124	97.81 137	99.29 96

PS-CasMVS		94.67 199	93.99 201	96.71 175	96.68 229	95.26 148	99.13 40	99.03 24	93.68 166	92.33 255	97.95 155	85.35 235	98.10 264	93.59 168	88.16 279	96.79 223

V42		94.78 188	94.14 190	96.70 177	96.33 253	95.22 149	98.97 59	98.09 210	92.32 219	94.31 190	97.06 221	88.39 176	98.55 210	92.90 189	88.87 269	96.34 273

pm-mvs1		93.94 236	93.06 239	96.59 194	96.49 237	95.16 150	98.95 61	98.03 218	92.32 219	91.08 268	97.84 165	84.54 251	98.41 242	92.16 205	86.13 301	96.19 277

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 150	99.22 27	99.32 7	93.04 187	97.02 107	98.92 75	95.36 41	99.91 22	97.43 54	99.64 45	99.52 66

VDDNet		95.36 158	94.53 171	97.86 111	98.10 144	95.13 152	98.85 78	97.75 227	90.46 258	98.36 51	99.39 7	73.27 317	99.64 100	97.98 27	96.58 159	98.81 136

gg-mvs-nofinetune		92.21 259	90.58 273	97.13 153	96.75 225	95.09 153	95.85 307	89.40 339	85.43 309	94.50 174	81.98 331	80.80 283	98.40 248	92.16 205	98.33 120	97.88 175

PS-MVSNAJss		96.43 110	96.26 105	96.92 168	95.84 282	95.08 154	99.16 35	98.50 136	95.87 70	93.84 216	98.34 127	94.51 60	98.61 204	96.88 74	93.45 218	97.06 197

thres600view7		95.49 146	94.77 161	97.67 125	98.98 88	95.02 155	98.85 78	96.90 292	95.38 89	96.63 124	96.90 242	84.29 254	99.59 107	88.65 275	96.33 171	98.40 157

GBi-Net		94.49 208	93.80 211	96.56 199	98.21 135	95.00 156	98.82 84	98.18 184	92.46 204	94.09 205	97.07 218	81.16 277	97.95 273	92.08 207	92.14 232	96.72 231

test1		94.49 208	93.80 211	96.56 199	98.21 135	95.00 156	98.82 84	98.18 184	92.46 204	94.09 205	97.07 218	81.16 277	97.95 273	92.08 207	92.14 232	96.72 231

FMVSNet1		93.19 250	92.07 253	96.56 199	97.54 176	95.00 156	98.82 84	98.18 184	90.38 261	92.27 256	97.07 218	73.68 316	97.95 273	89.36 262	91.30 243	96.72 231

tfpn200view9		95.32 162	94.62 167	97.43 140	98.94 91	94.98 159	98.68 123	96.93 290	95.33 96	96.55 130	96.53 259	84.23 258	99.56 115	88.11 281	96.29 173	97.76 177

GG-mvs-BLEND				96.59 194	96.34 249	94.98 159	96.51 299	88.58 340		93.10 237	94.34 299	80.34 288	98.05 268	89.53 258	96.99 151	96.74 228

thres400		95.38 155	94.62 167	97.65 127	98.94 91	94.98 159	98.68 123	96.93 290	95.33 96	96.55 130	96.53 259	84.23 258	99.56 115	88.11 281	96.29 173	98.40 157

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 33	94.95 162	98.55 143	98.62 112	93.02 188	96.17 146	98.58 105	94.01 71	99.81 50	93.95 158	98.90 93	99.14 112

conf200view11		95.40 154	94.70 164	97.50 137	98.98 88	94.92 163	98.87 72	96.90 292	95.38 89	96.61 125	96.88 245	84.29 254	99.56 115	88.11 281	96.29 173	98.02 172

thres100view900		95.38 155	94.70 164	97.41 141	98.98 88	94.92 163	98.87 72	96.90 292	95.38 89	96.61 125	96.88 245	84.29 254	99.56 115	88.11 281	96.29 173	97.76 177

thres200		95.25 164	94.57 169	97.28 146	98.81 102	94.92 163	98.20 182	97.11 276	95.24 103	96.54 132	96.22 272	84.58 246	99.53 122	87.93 285	96.50 163	97.39 189

v1		94.75 191	94.11 194	96.69 178	96.27 261	94.87 166	98.69 119	98.12 197	92.43 212	94.32 189	96.94 237	88.71 167	98.54 211	92.66 195	88.84 272	96.67 241

v1141		94.75 191	94.11 194	96.67 184	96.27 261	94.86 167	98.69 119	98.12 197	92.43 212	94.31 190	96.94 237	88.78 163	98.48 222	92.63 196	88.85 271	96.67 241

view600		95.60 142	94.93 154	97.62 128	99.05 81	94.85 168	99.09 45	97.01 284	95.36 92	96.52 134	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

view800		95.60 142	94.93 154	97.62 128	99.05 81	94.85 168	99.09 45	97.01 284	95.36 92	96.52 134	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

conf0.05thres1000		95.60 142	94.93 154	97.62 128	99.05 81	94.85 168	99.09 45	97.01 284	95.36 92	96.52 134	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

tfpn		95.60 142	94.93 154	97.62 128	99.05 81	94.85 168	99.09 45	97.01 284	95.36 92	96.52 134	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

v1neww		94.83 183	94.22 183	96.68 181	96.39 242	94.85 168	98.87 72	98.11 202	92.45 209	94.45 176	97.06 221	88.82 158	98.54 211	92.93 186	88.91 267	96.65 246

v7new		94.83 183	94.22 183	96.68 181	96.39 242	94.85 168	98.87 72	98.11 202	92.45 209	94.45 176	97.06 221	88.82 158	98.54 211	92.93 186	88.91 267	96.65 246

v18		92.10 261	90.97 261	95.50 247	96.34 249	94.85 168	98.82 84	97.52 240	89.99 268	85.31 305	93.26 304	88.90 152	96.92 297	88.82 271	79.77 316	94.73 302

divwei89l23v2f112		94.76 189	94.12 193	96.67 184	96.28 259	94.85 168	98.69 119	98.12 197	92.44 211	94.29 193	96.94 237	88.85 155	98.48 222	92.67 194	88.79 273	96.67 241

v6		94.83 183	94.21 185	96.69 178	96.36 246	94.85 168	98.87 72	98.11 202	92.46 204	94.44 182	97.05 225	88.76 164	98.57 209	92.95 185	88.92 266	96.65 246

v16		92.08 262	90.94 262	95.49 248	96.38 245	94.84 177	98.81 90	97.51 243	89.94 271	85.25 306	93.28 303	88.86 153	96.91 298	88.70 273	79.78 315	94.72 303

PEN-MVS		94.42 212	93.73 218	96.49 206	96.28 259	94.84 177	99.17 34	99.00 26	93.51 172	92.23 257	97.83 168	86.10 224	97.90 276	92.55 199	86.92 294	96.74 228

v17		92.08 262	90.94 262	95.48 249	96.34 249	94.83 179	98.81 90	97.52 240	89.95 270	85.32 303	93.24 305	88.91 151	96.91 298	88.76 272	79.63 317	94.71 304

v15		91.94 264	90.77 266	95.43 254	96.31 257	94.83 179	98.77 101	97.50 246	89.92 272	85.13 307	93.08 308	88.76 164	96.86 300	88.40 276	79.10 319	94.61 308

v8		94.47 210	93.77 214	96.57 198	96.36 246	94.83 179	99.05 50	98.19 181	91.92 226	93.16 232	96.97 233	88.82 158	98.48 222	91.69 220	87.79 283	96.39 270

TAPA-MVS	93.98 7	95.35 159	94.56 170	97.74 118	99.13 79	94.83 179	98.33 167	98.64 111	86.62 300	96.29 144	98.61 100	94.00 72	99.29 137	80.00 312	99.41 76	99.09 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
V14		91.93 265	90.76 267	95.42 257	96.33 253	94.81 183	98.77 101	97.51 243	89.86 274	85.09 308	93.13 306	88.80 162	96.83 302	88.32 277	79.06 321	94.60 309

v12		91.89 267	90.70 269	95.43 254	96.31 257	94.80 184	98.76 104	97.50 246	89.76 276	84.95 311	93.00 311	88.82 158	96.82 304	88.23 279	79.00 323	94.68 307

v10		94.29 218	93.55 227	96.51 205	96.39 242	94.80 184	98.99 55	98.19 181	91.35 244	93.02 238	96.99 231	88.09 184	98.41 242	90.50 240	88.41 276	96.33 274

V9		91.91 266	90.73 268	95.45 251	96.32 256	94.80 184	98.77 101	97.50 246	89.81 275	85.03 310	93.08 308	88.76 164	96.86 300	88.24 278	79.03 322	94.69 305

v7		94.69 195	94.04 196	96.62 191	96.41 241	94.79 187	98.78 100	98.13 195	91.89 227	94.30 192	97.16 211	88.13 183	98.45 229	91.96 214	89.65 254	96.61 251

v2v482		94.69 195	94.03 197	96.65 186	96.17 266	94.79 187	98.67 126	98.08 211	92.72 198	94.00 210	97.16 211	87.69 198	98.45 229	92.91 188	88.87 269	96.72 231

v1144		94.59 204	93.92 204	96.60 193	96.21 263	94.78 189	98.59 134	98.14 194	91.86 230	94.21 199	97.02 228	87.97 186	98.41 242	91.72 219	89.57 255	96.61 251

v13		91.88 268	90.69 270	95.43 254	96.33 253	94.78 189	98.75 105	97.50 246	89.68 279	84.93 312	92.98 312	88.84 156	96.83 302	88.14 280	79.09 320	94.69 305

v11		91.85 269	90.68 271	95.36 259	96.34 249	94.74 191	98.80 93	97.43 257	89.60 282	85.09 308	93.03 310	88.53 173	96.75 305	87.37 288	79.96 314	94.58 310

TransMVSNet (Re)		92.67 254	91.51 258	96.15 226	96.58 232	94.65 192	98.90 65	96.73 299	90.86 256	89.46 282	97.86 162	85.62 231	98.09 266	86.45 293	81.12 312	95.71 288

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 130	94.64 193	98.19 186	97.45 255	94.56 128	96.03 149	98.61 100	85.02 239	99.12 150	90.68 236	99.06 88	99.30 94

OPM-MVS		95.69 137	95.33 136	96.76 173	96.16 269	94.63 194	98.43 158	98.39 153	96.64 50	95.02 161	98.78 85	85.15 238	99.05 161	95.21 131	94.20 198	96.60 253

jajsoiax		95.45 149	95.03 147	96.73 174	95.42 295	94.63 194	99.14 37	98.52 129	95.74 73	93.22 230	98.36 122	83.87 266	98.65 202	96.95 68	94.04 204	96.91 211

plane_prior7					97.42 185	94.63 194

plane_prior6					97.35 190	94.61 197						87.09 207

plane_prior3						94.61 197			97.02 39	95.34 155

HQP_MVS		96.14 121	95.90 115	96.85 169	97.42 185	94.60 199	98.80 93	98.56 121	97.28 21	95.34 155	98.28 131	87.09 207	99.03 166	96.07 97	94.27 195	96.92 206

plane_prior						94.60 199	98.44 156		96.74 46						94.22 197

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 52	94.56 201	98.05 202	99.71 1	93.57 171	97.09 101	98.91 76	88.17 180	99.89 27	96.87 77	99.56 61	99.81 2

NP-MVS					97.28 193	94.51 202					97.73 174

v1192		94.32 216	93.58 226	96.53 203	96.10 270	94.45 203	98.50 151	98.17 189	91.54 235	94.19 200	97.06 221	86.95 211	98.43 234	90.14 243	89.57 255	96.70 235

mvs_tets		95.41 153	95.00 148	96.65 186	95.58 290	94.42 204	99.00 54	98.55 123	95.73 74	93.21 231	98.38 120	83.45 269	98.63 203	97.09 63	94.00 206	96.91 211

LTVRE_ROB	92.95 15	94.60 202	93.90 206	96.68 181	97.41 188	94.42 204	98.52 146	98.59 114	91.69 232	91.21 266	98.35 123	84.87 242	99.04 165	91.06 230	93.44 219	96.60 253
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
DTE-MVSNet		93.98 235	93.26 238	96.14 227	96.06 272	94.39 206	99.20 31	98.86 52	93.06 186	91.78 262	97.81 170	85.87 228	97.58 286	90.53 239	86.17 299	96.46 269

v7n		94.19 223	93.43 234	96.47 208	95.90 278	94.38 207	99.26 17	98.34 159	91.99 225	92.76 243	97.13 213	88.31 177	98.52 218	89.48 260	87.70 284	96.52 263

v144192		94.39 214	93.70 219	96.48 207	96.06 272	94.35 208	98.58 136	98.16 191	91.45 237	94.33 188	97.02 228	87.50 203	98.45 229	91.08 229	89.11 262	96.63 249

V4		94.18 225	93.52 229	96.13 228	95.89 279	94.31 209	99.23 21	98.22 176	91.42 239	92.82 242	96.89 243	87.93 188	98.52 218	91.51 223	87.81 281	95.58 291

v52		94.18 225	93.52 229	96.13 228	95.95 277	94.29 210	99.23 21	98.21 177	91.42 239	92.84 241	96.89 243	87.85 192	98.53 217	91.51 223	87.81 281	95.57 292

cascas		94.63 201	93.86 208	96.93 166	96.91 216	94.27 211	96.00 305	98.51 131	85.55 308	94.54 172	96.23 270	84.20 260	98.87 186	95.80 109	96.98 152	97.66 183

HQP5-MVS						94.25 212

HQP-MVS		95.72 134	95.40 129	96.69 178	97.20 199	94.25 212	98.05 202	98.46 141	96.43 54	94.45 176	97.73 174	86.75 213	98.96 173	95.30 125	94.18 199	96.86 218

TR-MVS		94.94 180	94.20 186	97.17 151	97.75 163	94.14 214	97.59 245	97.02 282	92.28 221	95.75 153	97.64 184	83.88 265	98.96 173	89.77 251	96.15 182	98.40 157

v1921920		94.20 222	93.47 233	96.40 214	95.98 275	94.08 215	98.52 146	98.15 192	91.33 245	94.25 196	97.20 210	86.41 218	98.42 235	90.04 248	89.39 260	96.69 240

Baseline_NR-MVSNet		94.35 215	93.81 210	95.96 232	96.20 264	94.05 216	98.61 133	96.67 303	91.44 238	93.85 215	97.60 186	88.57 170	98.14 262	94.39 146	86.93 293	95.68 289

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 101	93.98 217	98.68 123	97.40 260	95.02 112	97.95 70	99.34 19	74.37 315	99.78 74	98.64 4	96.80 154	99.08 118

PMMVS		96.60 103	96.33 102	97.41 141	97.90 156	93.93 218	97.35 261	98.41 149	92.84 196	97.76 79	97.45 195	91.10 116	99.20 143	96.26 95	97.91 132	99.11 114

v1240		94.06 233	93.29 237	96.34 219	96.03 274	93.90 219	98.44 156	98.17 189	91.18 253	94.13 204	97.01 230	86.05 225	98.42 235	89.13 265	89.50 258	96.70 235

GA-MVS		94.81 187	94.03 197	97.14 152	97.15 204	93.86 220	96.76 289	97.58 233	94.00 144	94.76 169	97.04 226	80.91 280	98.48 222	91.79 217	96.25 179	99.09 115

ACMM	93.85 9	95.69 137	95.38 133	96.61 192	97.61 170	93.84 221	98.91 64	98.44 145	95.25 101	94.28 194	98.47 113	86.04 227	99.12 150	95.50 120	93.95 208	96.87 216
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_anonymous		96.70 101	96.53 97	97.18 150	98.19 138	93.78 222	98.31 172	98.19 181	94.01 143	94.47 175	98.27 134	92.08 97	98.46 227	97.39 56	97.91 132	99.31 91

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 159	98.77 104	93.76 223	97.79 232	98.50 136	95.45 85	96.94 109	99.09 52	87.87 191	99.55 121	96.76 80	95.83 187	97.74 179

XVG-OURS		96.55 107	96.41 99	96.99 160	98.75 105	93.76 223	97.50 250	98.52 129	95.67 76	96.83 116	99.30 24	88.95 150	99.53 122	95.88 105	96.26 178	97.69 182

CLD-MVS		95.62 140	95.34 134	96.46 211	97.52 178	93.75 225	97.27 267	98.46 141	95.53 82	94.42 184	98.00 152	86.21 221	98.97 170	96.25 96	94.37 193	96.66 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IterMVS-LS		95.46 148	95.21 142	96.22 224	98.12 143	93.72 226	98.32 171	98.13 195	93.71 161	94.26 195	97.31 204	92.24 90	98.10 264	94.63 139	90.12 249	96.84 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		95.96 124	95.83 117	96.36 216	97.93 154	93.70 227	98.12 195	98.27 167	93.70 163	95.07 159	99.02 58	92.23 91	98.54 211	94.68 138	93.46 216	96.84 219

LPG-MVS_test		95.62 140	95.34 134	96.47 208	97.46 181	93.54 228	98.99 55	98.54 124	94.67 123	94.36 186	98.77 87	85.39 233	99.11 154	95.71 113	94.15 201	96.76 226

LGP-MVS_train				96.47 208	97.46 181	93.54 228		98.54 124	94.67 123	94.36 186	98.77 87	85.39 233	99.11 154	95.71 113	94.15 201	96.76 226

ACMP	93.49 10	95.34 160	94.98 150	96.43 212	97.67 166	93.48 230	98.73 112	98.44 145	94.94 118	92.53 249	98.53 107	84.50 252	99.14 148	95.48 121	94.00 206	96.66 244
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CR-MVSNet		94.76 189	94.15 189	96.59 194	97.00 209	93.43 231	94.96 315	97.56 234	92.46 204	96.93 110	96.24 268	88.15 181	97.88 280	87.38 287	96.65 157	98.46 154

RPMNet		92.52 256	91.17 259	96.59 194	97.00 209	93.43 231	94.96 315	97.26 272	82.27 319	96.93 110	92.12 323	86.98 210	97.88 280	76.32 321	96.65 157	98.46 154

IB-MVS	91.98 17	93.27 247	91.97 254	97.19 149	97.47 180	93.41 233	97.09 274	95.99 312	93.32 179	92.47 252	95.73 283	78.06 296	99.53 122	94.59 142	82.98 307	98.62 148
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
CHOSEN 280x420		97.18 84	97.18 69	97.20 148	98.81 102	93.27 234	95.78 309	99.15 18	95.25 101	96.79 121	98.11 144	92.29 88	99.07 160	98.56 9	99.85 2	99.25 100

ACMH	92.88 16	94.55 206	93.95 203	96.34 219	97.63 168	93.26 235	98.81 90	98.49 140	93.43 175	89.74 279	98.53 107	81.91 275	99.08 159	93.69 164	93.30 222	96.70 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	93.27 12	95.33 161	94.87 159	96.71 175	99.29 55	93.24 236	98.58 136	98.11 202	89.92 272	93.57 221	99.10 48	86.37 219	99.79 69	90.78 234	98.10 128	97.09 196
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		95.24 165	94.65 166	96.99 160	99.25 64	93.21 237	98.59 134	98.18 184	91.36 242	93.52 223	98.77 87	84.67 244	99.72 86	89.70 255	97.87 134	98.02 172

TestCases				96.99 160	99.25 64	93.21 237		98.18 184	91.36 242	93.52 223	98.77 87	84.67 244	99.72 86	89.70 255	97.87 134	98.02 172

MIMVSNet		93.26 248	92.21 252	96.41 213	97.73 165	93.13 239	95.65 310	97.03 281	91.27 250	94.04 208	96.06 276	75.33 308	97.19 293	86.56 292	96.23 180	98.92 131

Patchmtry		93.22 249	92.35 250	95.84 237	96.77 222	93.09 240	94.66 321	97.56 234	87.37 298	92.90 240	96.24 268	88.15 181	97.90 276	87.37 288	90.10 250	96.53 262

v148		94.29 218	93.76 216	95.91 234	96.10 270	92.93 241	98.58 136	97.97 219	92.59 202	93.47 226	96.95 235	88.53 173	98.32 251	92.56 198	87.06 292	96.49 267

test0.0.03 1		94.08 231	93.51 231	95.80 239	95.53 292	92.89 242	97.38 256	95.97 313	95.11 107	92.51 251	96.66 254	87.71 195	96.94 296	87.03 290	93.67 211	97.57 184

PatchT		93.06 252	91.97 254	96.35 217	96.69 228	92.67 243	94.48 322	97.08 277	86.62 300	97.08 102	92.23 322	87.94 187	97.90 276	78.89 316	96.69 155	98.49 153

v748		93.75 239	93.06 239	95.82 238	95.73 285	92.64 244	99.25 19	98.24 174	91.60 234	92.22 258	96.52 261	87.60 200	98.46 227	90.64 237	85.72 302	96.36 272

MVP-Stereo		94.28 220	93.92 204	95.35 260	94.95 301	92.60 245	97.97 210	97.65 231	91.61 233	90.68 273	97.09 216	86.32 220	98.42 235	89.70 255	99.34 81	95.02 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs5		93.65 242	92.97 241	95.68 243	95.49 293	92.37 246	98.20 182	97.28 270	89.66 280	92.58 247	97.26 206	82.14 273	98.09 266	93.18 178	90.95 246	96.58 255

BH-untuned		95.95 125	95.72 120	96.65 186	98.55 122	92.26 247	98.23 179	97.79 225	93.73 159	94.62 170	98.01 151	88.97 149	99.00 169	93.04 182	98.51 111	98.68 143

pmmvs-eth3d		90.36 284	89.05 287	94.32 286	91.10 319	92.12 248	97.63 244	96.95 289	88.86 290	84.91 313	93.13 306	78.32 295	96.74 306	88.70 273	81.81 311	94.09 316

FMVSNet5		91.81 270	90.92 264	94.49 281	97.21 198	92.09 249	98.00 208	97.55 238	89.31 287	90.86 271	95.61 288	74.48 313	95.32 319	85.57 299	89.70 253	96.07 280

PVSNet	91.96 18	96.35 113	96.15 108	96.96 163	99.17 75	92.05 250	96.08 301	98.68 96	93.69 164	97.75 80	97.80 171	88.86 153	99.69 94	94.26 152	99.01 89	99.15 110

ACMH+	92.99 14	94.30 217	93.77 214	95.88 236	97.81 161	92.04 251	98.71 115	98.37 156	93.99 145	90.60 274	98.47 113	80.86 282	99.05 161	92.75 193	92.40 231	96.55 260

ADS-MVSNet		95.00 173	94.45 176	96.63 189	98.00 149	91.91 252	96.04 302	97.74 228	90.15 263	96.47 139	96.64 256	87.89 189	98.96 173	90.08 245	97.06 149	99.02 121

mvs-test1		96.60 103	96.68 91	96.37 215	97.89 157	91.81 253	98.56 141	98.10 207	96.57 52	96.52 134	97.94 156	90.81 119	99.45 130	95.72 111	98.01 129	97.86 176

BH-w/o		95.38 155	95.08 146	96.26 223	98.34 128	91.79 254	97.70 237	97.43 257	92.87 195	94.24 197	97.22 209	88.66 168	98.84 189	91.55 222	97.70 142	98.16 169

Patchmatch-test		94.42 212	93.68 221	96.63 189	97.60 171	91.76 255	94.83 319	97.49 252	89.45 284	94.14 203	97.10 214	88.99 145	98.83 191	85.37 302	98.13 127	99.29 96

EPMVS		94.99 174	94.48 172	96.52 204	97.22 197	91.75 256	97.23 268	91.66 336	94.11 139	97.28 97	96.81 249	85.70 230	98.84 189	93.04 182	97.28 147	98.97 126

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 234	97.74 164	91.74 257	98.69 119	98.15 192	95.56 81	94.92 162	97.68 181	88.98 148	98.79 195	93.19 177	97.78 139	97.20 195

XVG-ACMP-BASELINE		94.54 207	94.14 190	95.75 242	96.55 233	91.65 258	98.11 197	98.44 145	94.96 115	94.22 198	97.90 159	79.18 293	99.11 154	94.05 157	93.85 209	96.48 268

TDRefinement		91.06 278	89.68 281	95.21 262	85.35 330	91.49 259	98.51 150	97.07 278	91.47 236	88.83 287	97.84 165	77.31 302	99.09 158	92.79 192	77.98 324	95.04 298

MDA-MVSNet-bldmvs		89.97 286	88.35 292	94.83 274	95.21 298	91.34 260	97.64 242	97.51 243	88.36 293	71.17 330	96.13 275	79.22 292	96.63 311	83.65 304	86.27 298	96.52 263

ITE_SJBPF				95.44 252	97.42 185	91.32 261		97.50 246	95.09 110	93.59 219	98.35 123	81.70 276	98.88 185	89.71 254	93.39 220	96.12 278

Patchmatch-test1		95.32 162	94.97 152	96.35 217	97.67 166	91.29 262	97.33 263	97.60 232	94.68 122	96.92 112	96.95 235	83.97 263	98.50 221	91.33 227	98.32 121	99.25 100

pmmvs6		91.77 271	90.63 272	95.17 264	94.69 306	91.24 263	98.67 126	97.92 221	86.14 303	89.62 280	97.56 190	75.79 307	98.34 249	90.75 235	84.56 306	95.94 283

test_0402		91.32 274	90.27 276	94.48 282	96.60 231	91.12 264	98.50 151	97.22 274	86.10 304	88.30 289	96.98 232	77.65 300	97.99 272	78.13 318	92.94 227	94.34 312

MIMVSNet1		89.67 288	88.28 293	93.82 290	92.81 315	91.08 265	98.01 206	97.45 255	87.95 294	87.90 291	95.87 281	67.63 326	94.56 322	78.73 317	88.18 278	95.83 285

USDC		93.33 246	92.71 245	95.21 262	96.83 221	90.83 266	96.91 280	97.50 246	93.84 153	90.72 272	98.14 142	77.69 298	98.82 192	89.51 259	93.21 225	95.97 282

DWT-MVSNet_test		94.82 186	94.36 179	96.20 225	97.35 190	90.79 267	98.34 166	96.57 306	92.91 193	95.33 157	96.44 264	82.00 274	99.12 150	94.52 144	95.78 188	98.70 141

MDA-MVSNet_test_wron		90.71 281	89.38 284	94.68 277	94.83 303	90.78 268	97.19 270	97.46 253	87.60 296	72.41 329	95.72 285	86.51 216	96.71 309	85.92 297	86.80 296	96.56 259

PatchmatchNet		95.71 135	95.52 128	96.29 222	97.58 173	90.72 269	96.84 287	97.52 240	94.06 141	97.08 102	96.96 234	89.24 140	98.90 183	92.03 211	98.37 118	99.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchFormer-LS_test		95.47 147	95.27 140	96.08 230	97.59 172	90.66 270	98.10 199	97.34 264	93.98 146	96.08 147	96.15 274	87.65 199	99.12 150	95.27 128	95.24 191	98.44 156

YYNet1		90.70 282	89.39 283	94.62 279	94.79 304	90.65 271	97.20 269	97.46 253	87.54 297	72.54 328	95.74 282	86.51 216	96.66 310	86.00 296	86.76 297	96.54 261

JIA-IIPM		93.35 244	92.49 248	95.92 233	96.48 238	90.65 271	95.01 314	96.96 288	85.93 306	96.08 147	87.33 327	87.70 197	98.78 196	91.35 226	95.58 189	98.34 164

semantic-postprocess				94.85 272	97.98 153	90.56 273		98.11 202	93.75 156	92.58 247	97.48 192	83.91 264	97.41 290	92.48 202	91.30 243	96.58 255

EPNet_dtu		95.21 167	94.95 153	95.99 231	96.17 266	90.45 274	98.16 191	97.27 271	96.77 44	93.14 235	98.33 128	90.34 126	98.42 235	85.57 299	98.81 100	99.09 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS		94.09 230	93.85 209	94.80 275	97.99 151	90.35 275	97.18 271	98.12 197	93.68 166	92.46 253	97.34 201	84.05 262	97.41 290	92.51 201	91.33 242	96.62 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		96.29 116	96.56 94	95.51 246	97.89 157	90.22 276	98.80 93	98.10 207	96.57 52	96.45 141	96.66 254	90.81 119	98.91 180	95.72 111	97.99 130	97.40 188

testgi		93.06 252	92.45 249	94.88 271	96.43 240	89.90 277	98.75 105	97.54 239	95.60 79	91.63 264	97.91 158	74.46 314	97.02 295	86.10 295	93.67 211	97.72 181

UnsupCasMVSNet_eth		90.99 279	89.92 280	94.19 288	94.08 309	89.83 278	97.13 273	98.67 103	93.69 164	85.83 300	96.19 273	75.15 309	96.74 306	89.14 264	79.41 318	96.00 281

TinyColmap		92.31 258	91.53 257	94.65 278	96.92 214	89.75 279	96.92 278	96.68 302	90.45 259	89.62 280	97.85 164	76.06 306	98.81 193	86.74 291	92.51 230	95.41 293

test-LLR		95.10 171	94.87 159	95.80 239	96.77 222	89.70 280	96.91 280	95.21 319	95.11 107	94.83 166	95.72 285	87.71 195	98.97 170	93.06 180	98.50 112	98.72 139

test-mter		94.08 231	93.51 231	95.80 239	96.77 222	89.70 280	96.91 280	95.21 319	92.89 194	94.83 166	95.72 285	77.69 298	98.97 170	93.06 180	98.50 112	98.72 139

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 221	99.00 86	89.54 282	97.43 253	98.87 49	98.16 2	99.26 6	99.38 11	96.12 17	99.64 100	98.30 21	99.77 17	99.72 30

MS-PatchMatch		93.84 238	93.63 222	94.46 284	96.18 265	89.45 283	97.76 233	98.27 167	92.23 222	92.13 260	97.49 191	79.50 290	98.69 198	89.75 253	99.38 79	95.25 294

OpenMVS_ROB	86.42 20	89.00 290	87.43 296	93.69 291	93.08 313	89.42 284	97.91 217	96.89 295	78.58 325	85.86 299	94.69 295	69.48 322	98.29 257	77.13 319	93.29 223	93.36 321

SixPastTwentyTwo		93.34 245	92.86 242	94.75 276	95.67 287	89.41 285	98.75 105	96.67 303	93.89 150	90.15 277	98.25 136	80.87 281	98.27 258	90.90 233	90.64 247	96.57 257

K. test v3		92.55 255	91.91 256	94.48 282	95.64 288	89.24 286	99.07 49	94.88 323	94.04 142	86.78 294	97.59 187	77.64 301	97.64 284	92.08 207	89.43 259	96.57 257

OurMVSNet-221017-0		94.21 221	94.00 199	94.85 272	95.60 289	89.22 287	98.89 69	97.43 257	95.29 99	92.18 259	98.52 110	82.86 271	98.59 207	93.46 170	91.76 239	96.74 228

TESTMET0.1,1		94.18 225	93.69 220	95.63 244	96.92 214	89.12 288	96.91 280	94.78 324	93.17 183	94.88 163	96.45 263	78.52 294	98.92 179	93.09 179	98.50 112	98.85 133

CostFormer		94.95 178	94.73 163	95.60 245	97.28 193	89.06 289	97.53 248	96.89 295	89.66 280	96.82 118	96.72 252	86.05 225	98.95 177	95.53 119	96.13 183	98.79 137

tpm2		94.19 223	93.76 216	95.46 250	97.23 196	89.04 290	97.31 265	96.85 298	87.08 299	96.21 145	96.79 250	83.75 268	98.74 197	92.43 203	96.23 180	98.59 149

EG-PatchMatch MVS		91.13 276	90.12 277	94.17 289	94.73 305	89.00 291	98.13 194	97.81 224	89.22 288	85.32 303	96.46 262	67.71 325	98.42 235	87.89 286	93.82 210	95.08 297

UnsupCasMVSNet_bld		87.17 296	85.12 299	93.31 295	91.94 316	88.77 292	94.92 317	98.30 164	84.30 314	82.30 316	90.04 324	63.96 330	97.25 292	85.85 298	74.47 330	93.93 319

ADS-MVSNet2		94.58 205	94.40 178	95.11 266	98.00 149	88.74 293	96.04 302	97.30 268	90.15 263	96.47 139	96.64 256	87.89 189	97.56 287	90.08 245	97.06 149	99.02 121

LP		91.12 277	89.99 279	94.53 280	96.35 248	88.70 294	93.86 326	97.35 263	84.88 311	90.98 269	94.77 294	84.40 253	97.43 289	75.41 324	91.89 238	97.47 185

LF4IMVS		93.14 251	92.79 244	94.20 287	95.88 280	88.67 295	97.66 241	97.07 278	93.81 155	91.71 263	97.65 182	77.96 297	98.81 193	91.47 225	91.92 237	95.12 295

tpmvs		94.60 202	94.36 179	95.33 261	97.46 181	88.60 296	96.88 285	97.68 229	91.29 248	93.80 217	96.42 265	88.58 169	99.24 140	91.06 230	96.04 184	98.17 168

tpmp4_e23		93.91 237	93.42 236	95.38 258	97.62 169	88.59 297	97.52 249	97.34 264	87.94 295	94.17 202	96.79 250	82.91 270	99.05 161	90.62 238	95.91 185	98.50 152

tpmrst		95.63 139	95.69 125	95.44 252	97.54 176	88.54 298	96.97 276	97.56 234	93.50 173	97.52 95	96.93 241	89.49 133	99.16 145	95.25 129	96.42 165	98.64 147

lessismore_v0				94.45 285	94.93 302	88.44 299		91.03 337		86.77 295	97.64 184	76.23 305	98.42 235	90.31 242	85.64 303	96.51 265

MDTV_nov1_ep13			95.40 129		97.48 179	88.34 300	96.85 286	97.29 269	93.74 158	97.48 96	97.26 206	89.18 141	99.05 161	91.92 215	97.43 146

new_pmnet		90.06 285	89.00 288	93.22 297	94.18 307	88.32 301	96.42 300	96.89 295	86.19 302	85.67 302	93.62 301	77.18 303	97.10 294	81.61 309	89.29 261	94.23 313

test20.03		90.89 280	90.38 274	92.43 299	93.48 311	88.14 302	98.33 167	97.56 234	93.40 176	87.96 290	96.71 253	80.69 284	94.13 323	79.15 315	86.17 299	95.01 300

tpm cat1		93.36 243	92.80 243	95.07 267	97.58 173	87.97 303	96.76 289	97.86 223	82.17 320	93.53 222	96.04 277	86.13 222	99.13 149	89.24 263	95.87 186	98.10 170

tpm		94.13 229	93.80 211	95.12 265	96.50 236	87.91 304	97.44 251	95.89 316	92.62 200	96.37 143	96.30 267	84.13 261	98.30 255	93.24 175	91.66 241	99.14 112

LCM-MVSNet-Re		95.22 166	95.32 137	94.91 269	98.18 140	87.85 305	98.75 105	95.66 317	95.11 107	88.96 286	96.85 247	90.26 129	97.65 283	95.65 116	98.44 115	99.22 103

gm-plane-assit					95.88 280	87.47 306			89.74 278		96.94 237		99.19 144	93.32 174

Anonymous20231206		91.66 272	91.10 260	93.33 294	94.02 310	87.35 307	98.58 136	97.26 272	90.48 257	90.16 276	96.31 266	83.83 267	96.53 312	79.36 314	89.90 252	96.12 278

PVSNet_0	88.72 19	91.28 275	90.03 278	95.00 268	97.99 151	87.29 308	94.84 318	98.50 136	92.06 224	89.86 278	95.19 289	79.81 289	99.39 132	92.27 204	69.79 331	98.33 165

pmmvs3		86.67 298	84.86 300	92.11 302	88.16 325	87.19 309	96.63 292	94.75 325	79.88 324	87.22 293	92.75 317	66.56 327	95.20 320	81.24 310	76.56 327	93.96 318

dp		94.15 228	93.90 206	94.90 270	97.31 192	86.82 310	96.97 276	97.19 275	91.22 252	96.02 150	96.61 258	85.51 232	99.02 168	90.00 249	94.30 194	98.85 133

new-patchmatchnet		88.50 294	87.45 295	91.67 303	90.31 321	85.89 311	97.16 272	97.33 267	89.47 283	83.63 315	92.77 316	76.38 304	95.06 321	82.70 306	77.29 325	94.06 317

Patchmatch-RL test		91.49 273	90.85 265	93.41 293	91.37 318	84.40 312	92.81 327	95.93 315	91.87 229	87.25 292	94.87 293	88.99 145	96.53 312	92.54 200	82.00 309	99.30 94

MDTV_nov1_ep13_2view						84.26 313	96.89 284		90.97 255	97.90 74		89.89 132		93.91 159		99.18 108

CVMVSNet		95.43 150	96.04 111	93.57 292	97.93 154	83.62 314	98.12 195	98.59 114	95.68 75	96.56 128	99.02 58	87.51 201	97.51 288	93.56 169	97.44 145	99.60 59

EU-MVSNet		93.66 240	94.14 190	92.25 301	95.96 276	83.38 315	98.52 146	98.12 197	94.69 121	92.61 246	98.13 143	87.36 205	96.39 314	91.82 216	90.00 251	96.98 202

PM-MVS		87.77 295	86.55 297	91.40 304	91.03 320	83.36 316	96.92 278	95.18 321	91.28 249	86.48 297	93.42 302	53.27 333	96.74 306	89.43 261	81.97 310	94.11 315

testpf		88.74 292	89.09 285	87.69 310	95.78 283	83.16 317	84.05 337	94.13 332	85.22 310	90.30 275	94.39 298	74.92 311	95.80 316	89.77 251	93.28 224	84.10 331

DSMNet-mixed		92.52 256	92.58 247	92.33 300	94.15 308	82.65 318	98.30 174	94.26 329	89.08 289	92.65 245	95.73 283	85.01 240	95.76 317	86.24 294	97.76 140	98.59 149

MVS-HIRNet		89.46 289	88.40 291	92.64 298	97.58 173	82.15 319	94.16 325	93.05 335	75.73 328	90.90 270	82.52 330	79.42 291	98.33 250	83.53 305	98.68 102	97.43 186

RPSCF		94.87 182	95.40 129	93.26 296	98.89 95	82.06 320	98.33 167	98.06 214	90.30 262	96.56 128	99.26 27	87.09 207	99.49 125	93.82 162	96.32 172	98.24 167

Gipuma		78.40 305	76.75 306	83.38 319	95.54 291	80.43 321	79.42 338	97.40 260	64.67 332	73.46 327	80.82 333	45.65 337	93.14 328	66.32 332	87.43 286	76.56 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		83.69 301	81.50 303	90.26 305	89.23 324	80.10 322	97.97 210	97.06 280	72.79 330	82.05 318	92.57 318	50.28 334	96.32 315	76.15 322	75.38 328	94.37 311

test2356		88.68 293	88.61 289	88.87 308	89.90 323	78.23 323	95.11 313	96.66 305	88.66 292	89.06 285	94.33 300	73.14 318	92.56 330	75.56 323	95.11 192	95.81 286

no-one		74.41 308	70.76 310	85.35 316	79.88 335	76.83 324	94.68 320	94.22 330	80.33 323	63.81 333	79.73 334	35.45 342	93.36 327	71.78 326	36.99 339	85.86 330

CMPMVS	66.06 21	89.70 287	89.67 282	89.78 306	93.19 312	76.56 325	97.00 275	98.35 158	80.97 322	81.57 319	97.75 173	74.75 312	98.61 204	89.85 250	93.63 213	94.17 314
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testus		88.91 291	89.08 286	88.40 309	91.39 317	76.05 326	96.56 295	96.48 307	89.38 286	89.39 283	95.17 291	70.94 320	93.56 326	77.04 320	95.41 190	95.61 290

ambc				89.49 307	86.66 329	75.78 327	92.66 328	96.72 300		86.55 296	92.50 319	46.01 336	97.90 276	90.32 241	82.09 308	94.80 301

1111		84.94 300	84.30 301	86.86 312	87.59 326	75.10 328	96.63 292	96.43 308	82.53 317	80.75 321	92.91 314	68.94 323	93.79 324	68.24 330	84.66 305	91.70 323

.test1245		73.05 309	76.31 307	63.27 330	87.59 326	75.10 328	96.63 292	96.43 308	82.53 317	80.75 321	92.91 314	68.94 323	93.79 324	68.24 330	12.72 342	20.91 340

test1235678		86.26 299	85.81 298	87.62 311	86.97 328	75.00 330	96.55 297	96.32 310	86.08 305	81.32 320	92.98 312	73.10 319	92.05 331	71.64 327	87.32 288	95.81 286

PMMVS2		77.95 306	75.44 309	85.46 315	82.54 332	74.95 331	94.23 324	93.08 334	72.80 329	74.68 326	87.38 326	36.36 341	91.56 332	73.95 325	63.94 332	89.87 324

DeepMVS_CX				86.78 313	97.09 207	72.30 332		95.17 322	75.92 327	84.34 314	95.19 289	70.58 321	95.35 318	79.98 313	89.04 264	92.68 322

LCM-MVSNet		78.70 304	76.24 308	86.08 314	77.26 340	71.99 333	94.34 323	96.72 300	61.62 334	76.53 325	89.33 325	33.91 343	92.78 329	81.85 308	74.60 329	93.46 320

ANet_high		69.08 310	65.37 312	80.22 321	65.99 343	71.96 334	90.91 331	90.09 338	82.62 316	49.93 339	78.39 335	29.36 344	81.75 339	62.49 336	38.52 338	86.95 329

test12356		83.47 302	83.37 302	83.78 318	84.43 331	70.09 335	95.12 312	95.60 318	82.98 315	78.89 323	92.43 321	64.99 328	91.41 333	70.36 328	85.55 304	89.82 325

testmv		78.74 303	77.35 304	82.89 320	78.16 339	69.30 336	95.87 306	94.65 326	81.11 321	70.98 331	87.11 328	46.31 335	90.42 334	65.28 333	76.72 326	88.95 326

wuykxyi23d		63.73 316	58.86 318	78.35 323	67.62 342	67.90 337	86.56 334	87.81 342	58.26 335	42.49 341	70.28 339	11.55 348	85.05 337	63.66 334	41.50 335	82.11 333

MVE	62.14 22	63.28 317	59.38 317	74.99 325	74.33 341	65.47 338	85.55 335	80.50 346	52.02 338	51.10 338	75.00 338	10.91 350	80.50 340	51.60 338	53.40 333	78.99 334
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		87.12 297	87.77 294	85.17 317	95.46 294	61.92 339	97.37 258	70.66 347	85.83 307	88.73 288	96.04 277	85.33 237	97.76 282	80.02 311	90.48 248	95.84 284

FPMVS		77.62 307	77.14 305	79.05 322	79.25 336	60.97 340	95.79 308	95.94 314	65.96 331	67.93 332	94.40 297	37.73 340	88.88 336	68.83 329	88.46 275	87.29 327

tmp_tt		68.90 311	66.97 311	74.68 326	50.78 345	59.95 341	87.13 333	83.47 345	38.80 340	62.21 334	96.23 270	64.70 329	76.91 343	88.91 270	30.49 340	87.19 328

PNet_i23d		67.70 312	65.07 313	75.60 324	78.61 337	59.61 342	89.14 332	88.24 341	61.83 333	52.37 337	80.89 332	18.91 345	84.91 338	62.70 335	52.93 334	82.28 332

E-PMN		64.94 314	64.25 314	67.02 328	82.28 333	59.36 343	91.83 330	85.63 343	52.69 337	60.22 335	77.28 336	41.06 339	80.12 341	46.15 339	41.14 336	61.57 338

EMVS		64.07 315	63.26 316	66.53 329	81.73 334	58.81 344	91.85 329	84.75 344	51.93 339	59.09 336	75.13 337	43.32 338	79.09 342	42.03 340	39.47 337	61.69 337

PMVS	61.03 23	65.95 313	63.57 315	73.09 327	57.90 344	51.22 345	85.05 336	93.93 333	54.45 336	44.32 340	83.57 329	13.22 346	89.15 335	58.68 337	81.00 313	78.91 335
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		30.17 319	30.18 321	30.16 332	78.61 337	43.29 346	66.79 339	14.21 348	17.31 341	14.82 344	11.93 345	11.55 348	41.43 344	37.08 341	19.30 341	5.76 342

test123		20.95 322	23.72 323	12.64 333	13.54 347	8.19 347	96.55 297	6.13 350	7.48 343	16.74 343	37.98 342	12.97 347	6.05 345	16.69 342	5.43 344	23.68 339

testmvs		21.48 321	24.95 322	11.09 334	14.89 346	6.47 348	96.56 295	9.87 349	7.55 342	17.93 342	39.02 341	9.43 351	5.90 346	16.56 343	12.72 342	20.91 340

cdsmvs_eth3d_5k		23.98 320	31.98 320	0.00 335	0.00 348	0.00 349	0.00 340	98.59 114	0.00 344	0.00 345	98.61 100	90.60 123	0.00 347	0.00 344	0.00 345	0.00 343

pcd_1.5k_mvsjas		7.88 324	10.50 325	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	94.51 60	0.00 347	0.00 344	0.00 345	0.00 343

pcd1.5k->3k		39.42 318	41.78 319	32.35 331	96.17 266	0.00 349	0.00 340	98.54 124	0.00 344	0.00 345	0.00 346	87.78 194	0.00 347	0.00 344	93.56 215	97.06 197

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

ab-mvs-re		8.20 323	10.94 324	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	98.43 115	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

ESAPD								98.84 54

sam_mvs1												89.45 134

sam_mvs												88.99 145

MTGPA								98.74 78

test_post1							96.68 291				30.43 344	87.85 192	98.69 198	92.59 197

test_post											31.83 343	88.83 157	98.91 180

patchmatchnet-post											95.10 292	89.42 135	98.89 184

MTMP								94.14 331

test9_res														96.39 94	99.57 55	99.69 35

agg_prior2														95.87 106	99.57 55	99.68 41

test_prior2							97.80 230		96.12 63	97.89 75	98.69 92	95.96 25		96.89 71	99.60 49

旧先验2							97.57 247		91.30 247	98.67 36			99.80 57	95.70 115

新几何2							97.64 242

无先验							97.58 246	98.72 85	91.38 241				99.87 35	93.36 172		99.60 59

原ACMM2							97.67 240

testdata2													99.89 27	91.65 221

segment_acmp												96.85 3

testdata1							97.32 264		96.34 57

plane_prior5								98.56 121					99.03 166	96.07 97	94.27 195	96.92 206

plane_prior4											98.28 131

plane_prior2							98.80 93		97.28 21

plane_prior1					97.37 189

n2								0.00 351

nn								0.00 351

door-mid								94.37 328

test11								98.66 106

door								94.64 327

HQP-NCC					97.20 199		98.05 202		96.43 54	94.45 176

ACMP_Plane					97.20 199		98.05 202		96.43 54	94.45 176

BP-MVS														95.30 125

HQP4-MVS										94.45 176			98.96 173			96.87 216

HQP3-MVS								98.46 141							94.18 199

HQP2-MVS												86.75 213

ACMMP++_ref															92.97 226

ACMMP++															93.61 214

Test By Simon												94.64 57

High-resolution multi-view benchmark