High-res multi-view results

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

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

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

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

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

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

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

test_part2					99.63 21	99.18 1				99.27 6

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 28	99.08 2	98.72 119	98.66 107	97.51 8	98.15 56	98.83 81	95.70 34	99.92 13	97.53 52	99.67 40	99.66 49

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 24	98.96 3	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 6	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 22	98.95 4	98.82 89	98.81 61	95.80 72	99.16 13	99.47 4	95.37 41	99.92 13	97.89 32	99.75 30	99.79 4

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 5	98.43 163	98.78 71	94.10 140	97.69 86	99.42 5	95.25 46	99.92 13	98.09 24	99.80 9	99.67 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 23	98.87 6	98.41 165	98.68 97	97.04 38	98.52 45	98.80 84	96.78 5	99.83 43	97.93 28	99.61 49	99.74 26

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 46	98.87 6	98.47 159	98.81 61	97.72 4	98.76 34	99.16 42	97.05 3	99.78 74	98.06 25	99.66 43	99.69 36

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 27	98.72 8	98.80 98	98.82 58	94.52 130	99.23 9	99.25 28	95.54 38	99.80 57	96.52 89	99.77 18	99.74 26
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 9	98.55 148	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	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 9	98.90 70	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

NCCC		98.61 13	98.35 20	99.38 10	99.28 61	98.61 11	98.45 160	98.76 75	97.82 3	98.45 49	98.93 73	96.65 7	99.83 43	97.38 57	99.41 77	99.71 33

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 166	98.52 12	99.37 7	98.71 91	97.09 37	92.99 245	99.13 44	89.36 137	99.89 27	96.97 65	99.57 56	99.71 33

TEST9					99.31 48	98.50 13	97.92 219	98.73 84	92.63 204	97.74 82	98.68 94	96.20 13	99.80 57

train_agg		97.97 44	97.52 54	99.33 15	99.31 48	98.50 13	97.92 219	98.73 84	92.98 195	97.74 82	98.68 94	96.20 13	99.80 57	96.59 85	99.57 56	99.68 42

test_8					99.29 56	98.44 15	97.89 227	98.72 86	92.98 195	97.70 85	98.66 97	96.20 13	99.80 57

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 32	98.44 15	97.91 222	98.67 104	92.57 208	98.77 33	98.85 79	95.93 28	99.72 86	95.56 118	99.69 39	99.68 42

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 72	98.43 17	99.10 48	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 53	98.38 18	97.81 234	98.72 86	93.16 189	97.57 94	98.66 97	96.14 16	99.81 50	96.63 84	99.56 62	99.66 49

agg_prior					99.30 53	98.38 18		98.72 86		97.57 94			99.81 50

canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 115	98.38 18	99.34 11	98.39 154	96.76 45	97.67 87	97.40 197	92.26 90	99.49 125	98.28 22	96.28 178	99.08 119

alignmvs		97.56 65	97.07 74	99.01 46	98.66 119	98.37 21	98.83 87	98.06 215	96.74 46	98.00 69	97.65 182	90.80 122	99.48 129	98.37 19	96.56 161	99.19 105

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 43	98.36 22	98.90 70	98.85 53	97.28 21	99.72 1	99.39 7	96.63 8	97.60 290	98.17 23	99.85 2	99.64 54

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	99.20 35	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

X-MVStestdata		94.06 238	92.30 256	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	43.50 345	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 26	98.35 23	98.33 172	98.89 44	92.62 205	98.05 61	98.94 72	95.34 43	99.65 98	96.04 100	99.42 76	99.19 105

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 26	99.23 22	98.96 31	96.10 65	98.94 22	99.17 39	96.06 21	99.92 13	97.62 45	99.78 14	99.75 21

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 26	98.98 63	98.96 31	95.65 78	98.94 22	99.17 39	96.06 21	99.92 13	97.21 60	99.78 14	99.75 21

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 28	99.14 41	98.66 107	96.84 43	99.56 2	99.31 21	96.34 11	99.70 91	98.32 20	99.73 35	99.73 28

HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 29	99.26 17	98.58 120	97.52 7	99.41 3	98.78 85	96.00 24	99.79 69	97.79 38	99.59 53	99.69 36

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 56	98.23 29	97.92 219	98.72 86	92.38 221	97.59 93	98.64 99	96.09 19	99.79 69	96.59 85	99.57 56	99.68 42

test_prior3		98.22 42	97.90 43	99.19 28	99.31 48	98.22 31	97.80 235	98.84 54	96.12 63	97.89 76	98.69 92	95.96 26	99.70 91	96.89 71	99.60 50	99.65 51

test_prior				99.19 28	99.31 48	98.22 31		98.84 54					99.70 91			99.65 51

test12				99.18 32	99.16 77	98.19 33		98.53 128		98.07 60		95.13 50	99.72 86		99.56 62	99.63 56

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 34	99.22 28	98.79 69	96.13 62	97.92 74	99.23 29	94.54 60	99.94 3	96.74 81	99.78 14	99.73 28

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 35	99.23 22	98.93 36	96.15 60	98.94 22	99.17 39	95.91 29	99.94 3	97.55 50	99.79 10	99.78 7

nrg030		96.28 118	95.72 120	97.96 108	96.90 223	98.15 36	99.39 5	98.31 162	95.47 84	94.42 190	98.35 123	92.09 97	98.69 203	97.50 53	89.05 269	97.04 204

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 36	99.23 22	98.95 33	96.10 65	98.93 26	99.19 38	95.70 34	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 79	98.12 38	99.04 56	99.09 19	93.32 184	98.83 30	99.10 48	96.54 9	99.83 43	97.70 43	99.76 24	99.59 62

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 39	98.99 60	99.49 5	95.43 86	99.03 16	99.32 20	95.56 36	99.94 3	96.80 79	99.77 18	99.78 7

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 40	99.28 16	98.81 61	96.24 58	98.35 53	99.23 29	95.46 39	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 65	98.04 40	98.50 156	98.78 71	97.72 4	98.92 27	99.28 25	95.27 45	99.82 48	97.55 50	99.77 18	99.69 36
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 38	98.01 42	98.37 168	98.81 61	97.48 11	99.21 10	99.21 32	96.13 17	99.80 57	98.40 18	99.73 35	99.75 21

test_prior4						98.01 42	97.86 230

新几何1				99.16 35	99.34 40	98.01 42		98.69 94	90.06 272	98.13 57	98.95 71	94.60 59	99.89 27	91.97 213	99.47 70	99.59 62

1121		97.37 77	96.77 87	99.16 35	99.34 40	97.99 45	98.19 191	98.68 97	90.14 270	98.01 67	98.97 65	94.80 57	99.87 35	93.36 172	99.46 73	99.61 57

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 28	97.92 46	99.15 40	98.81 61	96.24 58	99.20 11	99.37 12	95.30 44	99.80 57	97.73 41	99.67 40	99.72 31

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 47	99.44 4	98.82 58	94.46 134	98.94 22	99.20 35	95.16 49	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 47	99.34 11	98.87 49	95.96 68	98.60 42	99.13 44	96.05 23	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 96	97.83 49	98.20 187	98.74 79	97.51 8	98.03 64	99.06 56	86.12 224	99.93 9	99.02 1	99.64 46	99.44 85

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 50	99.53 1	98.80 68	94.63 127	98.61 41	98.97 65	95.13 50	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 38	97.81 51	98.37 168	98.76 75	97.49 10	99.20 11	99.21 32	96.08 20	99.79 69	98.42 16	99.73 35	99.75 21

abl_6		98.30 40	98.03 38	99.13 38	99.56 25	97.76 52	99.13 44	98.82 58	96.14 61	99.26 7	99.37 12	93.33 77	99.93 9	96.96 67	99.67 40	99.69 36

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 87	97.66 53	97.75 239	98.89 44	97.71 6	98.33 54	98.97 65	94.97 53	99.88 34	98.42 16	99.76 24	99.42 86
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	97.78 168	97.64 54	99.35 10	99.06 21	97.02 39	93.75 224	99.16 42	89.25 140	99.92 13	97.22 59	99.75 30	99.64 54

114514_t		96.93 93	96.27 104	98.92 53	99.50 28	97.63 55	98.85 83	98.90 42	84.80 317	97.77 79	99.11 46	92.84 82	99.66 97	94.85 135	99.77 18	99.47 78

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 56	99.03 57	99.41 6	95.98 67	97.60 92	99.36 16	94.45 65	99.93 9	97.14 61	98.85 98	99.70 35
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	97.85 165	97.60 57	99.23 22	98.93 36	89.76 281	93.11 242	99.02 58	89.11 144	99.93 9	91.99 212	99.62 48	99.34 89

VNet		97.79 54	97.40 61	98.96 51	98.88 102	97.55 58	98.63 135	98.93 36	96.74 46	99.02 17	98.84 80	90.33 128	99.83 43	98.53 10	96.66 157	99.50 72

FIs		96.51 108	96.12 109	97.67 125	97.13 211	97.54 59	99.36 8	99.22 14	95.89 69	94.03 215	98.35 123	91.98 100	98.44 237	96.40 93	92.76 234	97.01 205

旧先验1					99.29 56	97.48 60		98.70 93			99.09 52	95.56 36			99.47 70	99.61 57

UA-Net		97.96 45	97.62 48	98.98 49	98.86 104	97.47 61	98.89 74	99.08 20	96.67 49	98.72 36	99.54 1	93.15 80	99.81 50	94.87 134	98.83 99	99.65 51

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 217	97.47 61	98.79 103	99.18 16	95.60 79	93.92 218	97.04 230	91.68 104	98.48 227	95.80 109	87.66 291	96.79 229

CNLPA		97.45 70	97.03 75	98.73 60	99.05 82	97.44 63	98.07 206	98.53 128	95.32 98	96.80 123	98.53 107	93.32 78	99.72 86	94.31 150	99.31 83	99.02 122

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 36	97.37 64	98.40 166	98.79 69	97.46 12	99.09 14	99.31 21	95.86 32	99.80 57	98.64 4	99.76 24	99.79 4

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 72	97.32 65	97.91 222	99.58 3	97.20 29	98.33 54	99.00 63	95.99 25	99.64 100	98.05 26	99.76 24	99.69 36

OpenMVS	93.04 13	95.83 130	95.00 149	98.32 87	97.18 208	97.32 65	99.21 31	98.97 29	89.96 274	91.14 273	99.05 57	86.64 216	99.92 13	93.38 171	99.47 70	97.73 181

CANet		98.05 43	97.76 45	98.90 55	98.73 112	97.27 67	98.35 170	98.78 71	97.37 19	97.72 84	98.96 69	91.53 111	99.92 13	98.79 3	99.65 44	99.51 70

FC-MVSNet-test		96.42 111	96.05 110	97.53 137	96.95 218	97.27 67	99.36 8	99.23 12	95.83 71	93.93 217	98.37 121	92.00 99	98.32 256	96.02 101	92.72 235	97.00 206

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 201	97.27 67	98.94 67	99.23 12	95.13 106	95.51 160	97.32 204	85.73 234	98.91 185	97.33 58	89.55 263	96.89 219

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 72	97.25 70	98.11 202	98.29 167	97.19 30	98.99 21	99.02 58	96.22 12	99.67 96	98.52 14	98.56 111	99.51 70

NR-MVSNet		94.98 181	94.16 193	97.44 143	96.53 240	97.22 71	98.74 114	98.95 33	94.96 115	89.25 290	97.69 178	89.32 138	98.18 266	94.59 142	87.40 293	96.92 211

LS3D		97.16 85	96.66 92	98.68 63	98.53 129	97.19 72	98.93 68	98.90 42	92.83 202	95.99 157	99.37 12	92.12 96	99.87 35	93.67 166	99.57 56	98.97 127

test222					99.23 71	97.17 73	97.40 259	98.66 107	88.68 296	98.05 61	98.96 69	94.14 70			99.53 66	99.61 57

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 74	99.12 46	98.81 61	92.34 222	98.09 59	99.08 54	93.01 81	99.92 13	96.06 99	99.77 18	99.75 21

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 36	97.05 75	98.40 166	98.68 97	97.43 13	99.06 15	99.31 21	95.80 33	99.77 79	98.62 6	99.76 24	99.78 7

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 130	97.00 76	98.14 197	98.21 178	93.95 149	96.72 125	97.99 153	91.58 106	99.76 81	94.51 145	96.54 162	98.95 131

UniMVSNet_NR-MVSNet		95.71 136	95.15 144	97.40 147	96.84 226	96.97 77	98.74 114	99.24 10	95.16 105	93.88 219	97.72 177	91.68 104	98.31 258	95.81 107	87.25 296	96.92 211

DU-MVS		95.42 156	94.76 167	97.40 147	96.53 240	96.97 77	98.66 133	98.99 28	95.43 86	93.88 219	97.69 178	88.57 171	98.31 258	95.81 107	87.25 296	96.92 211

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 65	96.93 79	98.83 87	98.75 78	96.96 41	96.89 116	99.50 3	90.46 125	99.87 35	97.84 36	99.76 24	99.52 67
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	98.72 114	96.92 80	97.36 265	98.57 121	93.33 183	96.67 126	97.57 189	94.30 68	99.56 115	91.05 234	98.59 109	99.47 78

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 62	96.90 81	97.95 217	99.58 3	97.14 33	98.44 50	99.01 62	95.03 52	99.62 105	97.91 29	99.75 30	99.50 72

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 117	96.90 81	98.66 133	98.68 97	92.40 220	97.07 105	97.96 154	91.54 110	99.75 83	93.68 165	98.92 93	98.69 143
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	98.86 104	96.89 83	98.31 177	98.71 91	95.26 100	97.67 87	98.56 106	92.21 93	99.78 74	95.89 104	96.85 154	99.48 77

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 64	96.80 84	98.71 120	99.05 23	97.28 21	98.84 28	99.28 25	96.47 10	99.40 131	98.52 14	99.70 38	99.47 78

API-MVS		97.41 74	97.25 65	97.91 109	98.70 115	96.80 84	98.82 89	98.69 94	94.53 129	98.11 58	98.28 131	94.50 64	99.57 113	94.12 155	99.49 68	97.37 193

PCF-MVS	93.45 11	94.68 203	93.43 239	98.42 83	98.62 123	96.77 86	95.48 316	98.20 181	84.63 318	93.34 234	98.32 129	88.55 173	99.81 50	84.80 308	98.96 92	98.68 144
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	98.63 122	96.75 87	97.88 228	98.74 79	93.84 154	96.54 135	98.18 140	85.34 242	99.75 83	95.93 103	96.35 171	99.15 111

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 144	96.72 88	97.37 263	98.43 149	93.71 163	97.65 90	98.02 149	92.20 94	99.25 141	96.87 77	97.79 139	99.19 105

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 77	96.69 89	98.01 211	98.89 44	94.44 135	96.83 119	98.68 94	90.69 123	99.76 81	94.36 147	99.29 84	98.98 126

原ACMM1				98.65 65	99.32 46	96.62 90		98.67 104	93.27 187	97.81 78	98.97 65	95.18 48	99.83 43	93.84 161	99.46 73	99.50 72

FMVSNet3		94.97 182	94.26 187	97.11 160	98.18 146	96.62 90	98.56 146	98.26 172	93.67 170	94.09 211	97.10 218	84.25 263	98.01 275	92.08 207	92.14 238	96.70 241

sss		97.39 75	96.98 77	98.61 67	98.60 125	96.61 92	98.22 185	98.93 36	93.97 148	98.01 67	98.48 112	91.98 100	99.85 40	96.45 91	98.15 127	99.39 87

VPNet		94.99 179	94.19 192	97.40 147	97.16 209	96.57 93	98.71 120	98.97 29	95.67 76	94.84 170	98.24 137	80.36 293	98.67 206	96.46 90	87.32 294	96.96 208

MVS		94.67 204	93.54 233	98.08 101	96.88 224	96.56 94	98.19 191	98.50 137	78.05 331	92.69 250	98.02 149	91.07 118	99.63 103	90.09 249	98.36 120	98.04 172

XXY-MVS		95.20 173	94.45 181	97.46 142	96.75 231	96.56 94	98.86 82	98.65 111	93.30 186	93.27 235	98.27 134	84.85 249	98.87 191	94.82 136	91.26 251	96.96 208

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 48	96.51 96	97.91 222	99.06 21	93.72 162	96.92 114	98.06 147	88.50 176	99.65 98	91.77 219	99.00 91	98.66 146

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 33	96.49 97	98.30 179	98.69 94	97.21 28	98.84 28	99.36 16	95.41 40	99.78 74	98.62 6	99.65 44	99.80 3

WR-MVS		95.15 174	94.46 179	97.22 152	96.67 236	96.45 98	98.21 186	98.81 61	94.15 138	93.16 238	97.69 178	87.51 202	98.30 260	95.29 127	88.62 280	96.90 218

FMVSNet2		94.47 215	93.61 229	97.04 163	98.21 141	96.43 99	98.79 103	98.27 168	92.46 209	93.50 231	97.09 220	81.16 283	98.00 276	91.09 230	91.93 242	96.70 241

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 28	96.41 100	98.63 135	98.60 114	95.18 104	97.06 106	98.06 147	94.26 69	99.57 113	93.80 163	98.87 97	99.52 67

1112_ss		96.63 102	96.00 113	98.50 75	98.56 126	96.37 101	98.18 195	98.10 208	92.92 197	94.84 170	98.43 115	92.14 95	99.58 112	94.35 148	96.51 163	99.56 66

TranMVSNet+NR-MVSNet		95.14 175	94.48 177	97.11 160	96.45 245	96.36 102	99.03 57	99.03 24	95.04 111	93.58 226	97.93 157	88.27 179	98.03 274	94.13 154	86.90 301	96.95 210

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 106	96.36 102	99.19 34	97.97 220	95.39 88	97.23 99	98.99 64	91.11 116	98.93 183	94.60 141	98.59 109	99.47 78

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 34	96.32 104	98.28 181	98.68 97	97.17 31	98.74 35	99.37 12	95.25 46	99.79 69	98.57 8	99.54 65	99.73 28

LFMVS		95.86 129	94.98 151	98.47 78	98.87 103	96.32 104	98.84 86	96.02 312	93.40 181	98.62 40	99.20 35	74.99 316	99.63 103	97.72 42	97.20 149	99.46 82

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 56	96.31 106	98.14 197	98.76 75	92.41 219	96.39 148	98.31 130	94.92 54	99.78 74	94.06 156	98.77 102	99.23 103
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	98.66 119	96.23 107	99.22 28	99.00 26	96.63 51	98.04 63	99.21 32	88.05 186	99.35 136	96.01 102	99.21 85	99.45 84
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 40	96.19 108	98.70 123	98.39 154	89.45 289	94.52 179	99.35 18	91.85 102	99.85 40	92.89 191	98.88 95	99.68 42

diffmvs		96.32 115	95.74 118	98.07 103	98.26 138	96.14 109	98.53 150	98.23 176	90.10 271	96.88 117	97.73 174	90.16 131	99.15 151	93.90 160	97.85 137	98.91 133

EPNet		97.28 80	96.87 81	98.51 74	94.98 306	96.14 109	98.90 70	97.02 283	98.28 1	95.99 157	99.11 46	91.36 112	99.89 27	96.98 64	99.19 86	99.50 72
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	98.17 148	96.07 111	97.98 214	98.21 178	97.24 27	97.13 101	98.93 73	86.88 213	99.91 22	95.00 133	99.37 81	98.66 146

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 131	95.98 112	97.86 230	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 131	95.98 112	97.86 230	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 131	95.98 112	97.86 230	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 154	95.98 112	98.20 187	98.33 161	93.67 170	96.95 109	98.49 111	93.54 75	98.42 240	95.24 130	97.74 142	99.31 92
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	98.29 137	95.97 116	98.58 141	98.25 173	91.74 236	95.29 164	97.23 209	91.03 119	99.15 151	92.90 189	97.96 132	98.97 127

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 135	95.97 116	98.74 114	98.07 213	94.27 137	98.44 50	98.07 146	92.48 86	99.26 140	96.43 92	98.19 126	99.16 110

MG-MVS		97.81 53	97.60 49	98.44 80	99.12 81	95.97 116	97.75 239	98.78 71	96.89 42	98.46 46	99.22 31	93.90 74	99.68 95	94.81 137	99.52 67	99.67 47

test_normal		94.72 199	93.59 230	98.11 99	95.30 303	95.95 119	97.91 222	97.39 263	94.64 126	85.70 307	95.88 285	80.52 291	99.36 135	96.69 82	98.30 123	99.01 125

tfpnnormal		93.66 245	92.70 251	96.55 207	96.94 219	95.94 120	98.97 64	99.19 15	91.04 259	91.38 271	97.34 202	84.94 247	98.61 209	85.45 306	89.02 271	95.11 302

pmmvs4		94.69 200	93.99 206	96.81 176	95.74 290	95.94 120	97.40 259	97.67 231	90.42 265	93.37 233	97.59 187	89.08 145	98.20 265	92.97 184	91.67 246	96.30 281

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 126	95.94 120	97.71 241	98.07 213	92.10 228	94.79 174	97.29 206	91.75 103	99.56 115	94.17 153	96.50 164	99.58 64

tfpn_n400		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

tfpnconf		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

tfpnview11		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

MVSTER		96.06 122	95.72 120	97.08 162	98.23 140	95.93 123	98.73 117	98.27 168	94.86 119	95.07 165	98.09 145	88.21 180	98.54 216	96.59 85	93.46 222	96.79 229

DI_MVS_plusplus_test		94.74 198	93.62 228	98.09 100	95.34 302	95.92 127	98.09 205	97.34 265	94.66 125	85.89 304	95.91 284	80.49 292	99.38 134	96.66 83	98.22 124	98.97 127

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 43	95.92 127	98.28 181	98.59 115	95.52 83	97.97 70	99.10 48	93.28 79	99.49 125	95.09 132	98.88 95	99.19 105

PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 62	95.91 129	98.63 135	99.16 17	94.48 133	97.67 87	98.88 77	92.80 83	99.91 22	97.11 62	99.12 88	99.50 72

anonymousdsp		95.42 156	94.91 159	96.94 170	95.10 305	95.90 130	99.14 41	98.41 150	93.75 158	93.16 238	97.46 193	87.50 204	98.41 247	95.63 117	94.03 211	96.50 272

UGNet		96.78 99	96.30 103	98.19 94	98.24 139	95.89 131	98.88 76	98.93 36	97.39 16	96.81 122	97.84 165	82.60 278	99.90 25	96.53 88	99.49 68	98.79 138
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
tfpn_ndepth		95.53 147	94.90 160	97.39 150	98.96 93	95.88 132	99.05 54	95.27 324	93.80 157	96.95 109	96.93 245	85.53 237	99.40 131	91.54 224	96.10 186	96.89 219

Test4		92.21 264	90.34 280	97.82 115	92.83 320	95.87 133	97.94 218	98.05 218	94.50 131	82.12 323	94.48 301	59.54 338	98.54 216	95.39 123	98.22 124	99.06 121

WR-MVS_H		95.05 177	94.46 179	96.81 176	96.86 225	95.82 134	99.24 20	99.24 10	93.87 153	92.53 255	96.84 253	90.37 126	98.24 264	93.24 175	87.93 286	96.38 277

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 151	95.76 135	99.47 2	98.40 152	94.98 113	98.79 31	98.83 81	92.34 87	98.41 247	96.91 69	99.59 53	99.34 89

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 151	95.76 135	97.68 244	97.76 227	94.50 131	98.79 31	98.61 100	92.34 87	99.30 137	97.58 47	99.59 53	99.31 92

tfpn1000		95.72 134	95.11 145	97.58 134	99.00 87	95.73 137	99.24 20	95.49 323	94.08 141	96.87 118	97.45 195	85.81 233	99.30 137	91.78 218	96.22 183	97.71 183

PAPM		94.95 183	94.00 204	97.78 117	97.04 214	95.65 138	96.03 309	98.25 173	91.23 256	94.19 206	97.80 171	91.27 114	98.86 193	82.61 312	97.61 144	98.84 136

jason		97.32 79	97.08 73	98.06 104	97.45 190	95.59 139	97.87 229	97.91 223	94.79 120	98.55 44	98.83 81	91.12 115	99.23 143	97.58 47	99.60 50	99.34 89
jason: jason.
PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 118	95.58 140	97.34 267	98.51 132	97.29 20	98.66 38	97.88 161	94.51 61	99.90 25	97.87 33	99.17 87	97.39 191

testing_2		90.61 288	88.50 295	96.95 169	90.08 328	95.57 141	97.69 243	98.06 215	93.02 193	76.55 330	92.48 325	61.18 337	98.44 237	95.45 122	91.98 241	96.84 225

CP-MVSNet		94.94 185	94.30 186	96.83 175	96.72 233	95.56 142	99.11 47	98.95 33	93.89 151	92.42 260	97.90 159	87.19 207	98.12 268	94.32 149	88.21 283	96.82 228

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 43	95.56 142	97.38 261	99.65 2	92.34 222	97.61 91	98.20 139	89.29 139	99.10 162	96.97 65	97.60 145	99.77 14

1314		96.25 120	95.73 119	97.79 116	97.13 211	95.55 144	98.19 191	98.59 115	93.47 176	92.03 267	97.82 169	91.33 113	99.49 125	94.62 140	98.44 116	98.32 167

test_djsdf		96.00 123	95.69 125	96.93 171	95.72 292	95.49 145	99.47 2	98.40 152	94.98 113	94.58 177	97.86 162	89.16 143	98.41 247	96.91 69	94.12 209	96.88 221

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 136	98.61 124	95.46 146	97.44 256	98.46 142	97.15 32	98.65 39	98.15 141	94.33 67	99.80 57	97.84 36	98.66 107	97.41 189

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 112	95.46 146	99.20 32	98.30 165	94.96 115	96.60 130	98.87 78	90.05 132	98.59 212	93.67 166	98.60 108	99.46 82

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 121	95.38 148	99.33 13	98.31 162	93.61 172	97.19 100	99.07 55	94.05 71	99.23 143	96.89 71	98.43 118	99.37 88

testdata				98.26 89	99.20 75	95.36 149		98.68 97	91.89 232	98.60 42	99.10 48	94.44 66	99.82 48	94.27 151	99.44 75	99.58 64

MSDG		95.93 126	95.30 139	97.83 113	98.90 96	95.36 149	96.83 293	98.37 157	91.32 251	94.43 189	98.73 91	90.27 129	99.60 106	90.05 252	98.82 100	98.52 152

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 159	99.25 65	95.35 151	98.26 183	99.26 8	94.28 136	97.94 72	97.46 193	92.74 84	99.81 50	96.88 74	93.32 227	96.20 282

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 65	95.35 151	97.28 271	99.26 8	93.13 190	97.94 72	98.21 138	92.74 84	99.81 50	96.88 74	99.40 79	99.27 99

TAMVS		97.02 90	96.79 84	97.70 122	98.06 153	95.31 153	98.52 151	98.31 162	93.95 149	97.05 107	98.61 100	93.49 76	98.52 223	95.33 124	97.81 138	99.29 97

PS-CasMVS		94.67 204	93.99 206	96.71 180	96.68 235	95.26 154	99.13 44	99.03 24	93.68 168	92.33 261	97.95 155	85.35 241	98.10 269	93.59 168	88.16 285	96.79 229

V42		94.78 193	94.14 195	96.70 182	96.33 259	95.22 155	98.97 64	98.09 211	92.32 224	94.31 196	97.06 225	88.39 177	98.55 215	92.90 189	88.87 275	96.34 279

pm-mvs1		93.94 241	93.06 244	96.59 199	96.49 243	95.16 156	98.95 66	98.03 219	92.32 224	91.08 274	97.84 165	84.54 257	98.41 247	92.16 205	86.13 307	96.19 283

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 156	99.22 28	99.32 7	93.04 192	97.02 108	98.92 75	95.36 42	99.91 22	97.43 54	99.64 46	99.52 67

VDDNet		95.36 163	94.53 176	97.86 111	98.10 150	95.13 158	98.85 83	97.75 228	90.46 263	98.36 52	99.39 7	73.27 323	99.64 100	97.98 27	96.58 160	98.81 137

gg-mvs-nofinetune		92.21 264	90.58 278	97.13 158	96.75 231	95.09 159	95.85 312	89.40 345	85.43 314	94.50 180	81.98 336	80.80 289	98.40 253	92.16 205	98.33 121	97.88 176

PS-MVSNAJss		96.43 110	96.26 105	96.92 173	95.84 288	95.08 160	99.16 39	98.50 137	95.87 70	93.84 222	98.34 127	94.51 61	98.61 209	96.88 74	93.45 224	97.06 202

thres600view7		95.49 151	94.77 166	97.67 125	98.98 90	95.02 161	98.85 83	96.90 293	95.38 89	96.63 127	96.90 247	84.29 260	99.59 107	88.65 280	96.33 172	98.40 158

GBi-Net		94.49 213	93.80 216	96.56 204	98.21 141	95.00 162	98.82 89	98.18 185	92.46 209	94.09 211	97.07 222	81.16 283	97.95 278	92.08 207	92.14 238	96.72 237

test1		94.49 213	93.80 216	96.56 204	98.21 141	95.00 162	98.82 89	98.18 185	92.46 209	94.09 211	97.07 222	81.16 283	97.95 278	92.08 207	92.14 238	96.72 237

FMVSNet1		93.19 255	92.07 258	96.56 204	97.54 182	95.00 162	98.82 89	98.18 185	90.38 266	92.27 262	97.07 222	73.68 322	97.95 278	89.36 267	91.30 249	96.72 237

tfpn200view9		95.32 167	94.62 172	97.43 144	98.94 94	94.98 165	98.68 128	96.93 291	95.33 96	96.55 133	96.53 264	84.23 264	99.56 115	88.11 286	96.29 174	97.76 178

GG-mvs-BLEND				96.59 199	96.34 255	94.98 165	96.51 304	88.58 346		93.10 243	94.34 304	80.34 294	98.05 273	89.53 263	96.99 152	96.74 234

thres400		95.38 160	94.62 172	97.65 127	98.94 94	94.98 165	98.68 128	96.93 291	95.33 96	96.55 133	96.53 264	84.23 264	99.56 115	88.11 286	96.29 174	98.40 158

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 34	94.95 168	98.55 148	98.62 113	93.02 193	96.17 152	98.58 105	94.01 72	99.81 50	93.95 158	98.90 94	99.14 113

conf200view11		95.40 159	94.70 169	97.50 141	98.98 90	94.92 169	98.87 77	96.90 293	95.38 89	96.61 128	96.88 250	84.29 260	99.56 115	88.11 286	96.29 174	98.02 173

thres100view900		95.38 160	94.70 169	97.41 145	98.98 90	94.92 169	98.87 77	96.90 293	95.38 89	96.61 128	96.88 250	84.29 260	99.56 115	88.11 286	96.29 174	97.76 178

thres200		95.25 169	94.57 174	97.28 151	98.81 108	94.92 169	98.20 187	97.11 277	95.24 103	96.54 135	96.22 277	84.58 252	99.53 122	87.93 290	96.50 164	97.39 191

v1		94.75 196	94.11 199	96.69 183	96.27 267	94.87 172	98.69 124	98.12 198	92.43 217	94.32 195	96.94 241	88.71 168	98.54 216	92.66 195	88.84 278	96.67 247

v1141		94.75 196	94.11 199	96.67 189	96.27 267	94.86 173	98.69 124	98.12 198	92.43 217	94.31 196	96.94 241	88.78 164	98.48 227	92.63 196	88.85 277	96.67 247

view600		95.60 143	94.93 155	97.62 128	99.05 82	94.85 174	99.09 49	97.01 285	95.36 92	96.52 137	97.37 198	84.55 253	99.59 107	89.07 271	96.39 167	98.40 158

view800		95.60 143	94.93 155	97.62 128	99.05 82	94.85 174	99.09 49	97.01 285	95.36 92	96.52 137	97.37 198	84.55 253	99.59 107	89.07 271	96.39 167	98.40 158

conf0.05thres1000		95.60 143	94.93 155	97.62 128	99.05 82	94.85 174	99.09 49	97.01 285	95.36 92	96.52 137	97.37 198	84.55 253	99.59 107	89.07 271	96.39 167	98.40 158

tfpn		95.60 143	94.93 155	97.62 128	99.05 82	94.85 174	99.09 49	97.01 285	95.36 92	96.52 137	97.37 198	84.55 253	99.59 107	89.07 271	96.39 167	98.40 158

v1neww		94.83 188	94.22 188	96.68 186	96.39 248	94.85 174	98.87 77	98.11 203	92.45 214	94.45 182	97.06 225	88.82 159	98.54 216	92.93 186	88.91 273	96.65 252

v7new		94.83 188	94.22 188	96.68 186	96.39 248	94.85 174	98.87 77	98.11 203	92.45 214	94.45 182	97.06 225	88.82 159	98.54 216	92.93 186	88.91 273	96.65 252

v18		92.10 266	90.97 266	95.50 252	96.34 255	94.85 174	98.82 89	97.52 241	89.99 273	85.31 311	93.26 309	88.90 153	96.92 302	88.82 276	79.77 322	94.73 308

divwei89l23v2f112		94.76 194	94.12 198	96.67 189	96.28 265	94.85 174	98.69 124	98.12 198	92.44 216	94.29 199	96.94 241	88.85 156	98.48 227	92.67 194	88.79 279	96.67 247

v6		94.83 188	94.21 190	96.69 183	96.36 252	94.85 174	98.87 77	98.11 203	92.46 209	94.44 188	97.05 229	88.76 165	98.57 214	92.95 185	88.92 272	96.65 252

v16		92.08 267	90.94 267	95.49 253	96.38 251	94.84 183	98.81 95	97.51 244	89.94 276	85.25 312	93.28 308	88.86 154	96.91 303	88.70 278	79.78 321	94.72 309

PEN-MVS		94.42 217	93.73 223	96.49 211	96.28 265	94.84 183	99.17 35	99.00 26	93.51 174	92.23 263	97.83 168	86.10 228	97.90 281	92.55 199	86.92 300	96.74 234

v17		92.08 267	90.94 267	95.48 254	96.34 255	94.83 185	98.81 95	97.52 241	89.95 275	85.32 309	93.24 310	88.91 152	96.91 303	88.76 277	79.63 323	94.71 310

v15		91.94 269	90.77 271	95.43 259	96.31 263	94.83 185	98.77 106	97.50 247	89.92 277	85.13 313	93.08 313	88.76 165	96.86 305	88.40 281	79.10 325	94.61 314

v8		94.47 215	93.77 219	96.57 203	96.36 252	94.83 185	99.05 54	98.19 182	91.92 231	93.16 238	96.97 237	88.82 159	98.48 227	91.69 221	87.79 289	96.39 276

TAPA-MVS	93.98 7	95.35 164	94.56 175	97.74 118	99.13 80	94.83 185	98.33 172	98.64 112	86.62 305	96.29 150	98.61 100	94.00 73	99.29 139	80.00 317	99.41 77	99.09 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
V14		91.93 270	90.76 272	95.42 262	96.33 259	94.81 189	98.77 106	97.51 244	89.86 279	85.09 314	93.13 311	88.80 163	96.83 307	88.32 282	79.06 327	94.60 315

v12		91.89 272	90.70 274	95.43 259	96.31 263	94.80 190	98.76 109	97.50 247	89.76 281	84.95 317	93.00 316	88.82 159	96.82 309	88.23 284	79.00 329	94.68 313

v10		94.29 223	93.55 232	96.51 210	96.39 248	94.80 190	98.99 60	98.19 182	91.35 249	93.02 244	96.99 235	88.09 185	98.41 247	90.50 245	88.41 282	96.33 280

V9		91.91 271	90.73 273	95.45 256	96.32 262	94.80 190	98.77 106	97.50 247	89.81 280	85.03 316	93.08 313	88.76 165	96.86 305	88.24 283	79.03 328	94.69 311

v7		94.69 200	94.04 201	96.62 196	96.41 247	94.79 193	98.78 105	98.13 196	91.89 232	94.30 198	97.16 212	88.13 184	98.45 234	91.96 214	89.65 260	96.61 257

v2v482		94.69 200	94.03 202	96.65 191	96.17 272	94.79 193	98.67 131	98.08 212	92.72 203	94.00 216	97.16 212	87.69 199	98.45 234	92.91 188	88.87 275	96.72 237

v1144		94.59 209	93.92 209	96.60 198	96.21 269	94.78 195	98.59 139	98.14 195	91.86 235	94.21 205	97.02 232	87.97 187	98.41 247	91.72 220	89.57 261	96.61 257

v13		91.88 273	90.69 275	95.43 259	96.33 259	94.78 195	98.75 110	97.50 247	89.68 284	84.93 318	92.98 317	88.84 157	96.83 307	88.14 285	79.09 326	94.69 311

v11		91.85 274	90.68 276	95.36 264	96.34 255	94.74 197	98.80 98	97.43 258	89.60 287	85.09 314	93.03 315	88.53 174	96.75 310	87.37 293	79.96 320	94.58 316

TransMVSNet (Re)		92.67 259	91.51 263	96.15 231	96.58 238	94.65 198	98.90 70	96.73 300	90.86 261	89.46 288	97.86 162	85.62 236	98.09 271	86.45 298	81.12 318	95.71 294

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 136	94.64 199	98.19 191	97.45 256	94.56 128	96.03 155	98.61 100	85.02 245	99.12 155	90.68 238	99.06 89	99.30 95

OPM-MVS		95.69 138	95.33 136	96.76 178	96.16 275	94.63 200	98.43 163	98.39 154	96.64 50	95.02 167	98.78 85	85.15 244	99.05 166	95.21 131	94.20 204	96.60 259

jajsoiax		95.45 154	95.03 148	96.73 179	95.42 301	94.63 200	99.14 41	98.52 130	95.74 73	93.22 236	98.36 122	83.87 272	98.65 207	96.95 68	94.04 210	96.91 216

plane_prior7					97.42 191	94.63 200

plane_prior6					97.35 196	94.61 203						87.09 208

plane_prior3						94.61 203			97.02 39	95.34 161

HQP_MVS		96.14 121	95.90 115	96.85 174	97.42 191	94.60 205	98.80 98	98.56 122	97.28 21	95.34 161	98.28 131	87.09 208	99.03 171	96.07 97	94.27 201	96.92 211

plane_prior						94.60 205	98.44 161		96.74 46						94.22 203

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 53	94.56 207	98.05 207	99.71 1	93.57 173	97.09 102	98.91 76	88.17 181	99.89 27	96.87 77	99.56 62	99.81 2

NP-MVS					97.28 199	94.51 208					97.73 174

v1192		94.32 221	93.58 231	96.53 208	96.10 276	94.45 209	98.50 156	98.17 190	91.54 240	94.19 206	97.06 225	86.95 212	98.43 239	90.14 248	89.57 261	96.70 241

mvs_tets		95.41 158	95.00 149	96.65 191	95.58 296	94.42 210	99.00 59	98.55 124	95.73 74	93.21 237	98.38 120	83.45 275	98.63 208	97.09 63	94.00 212	96.91 216

LTVRE_ROB	92.95 15	94.60 207	93.90 211	96.68 186	97.41 194	94.42 210	98.52 151	98.59 115	91.69 237	91.21 272	98.35 123	84.87 248	99.04 170	91.06 232	93.44 225	96.60 259
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		93.98 240	93.26 243	96.14 232	96.06 278	94.39 212	99.20 32	98.86 52	93.06 191	91.78 268	97.81 170	85.87 232	97.58 291	90.53 241	86.17 305	96.46 275

v7n		94.19 228	93.43 239	96.47 213	95.90 284	94.38 213	99.26 17	98.34 160	91.99 230	92.76 249	97.13 217	88.31 178	98.52 223	89.48 265	87.70 290	96.52 269

v144192		94.39 219	93.70 224	96.48 212	96.06 278	94.35 214	98.58 141	98.16 192	91.45 242	94.33 194	97.02 232	87.50 204	98.45 234	91.08 231	89.11 268	96.63 255

V4		94.18 230	93.52 234	96.13 233	95.89 285	94.31 215	99.23 22	98.22 177	91.42 244	92.82 248	96.89 248	87.93 189	98.52 223	91.51 225	87.81 287	95.58 297

v52		94.18 230	93.52 234	96.13 233	95.95 283	94.29 216	99.23 22	98.21 178	91.42 244	92.84 247	96.89 248	87.85 193	98.53 222	91.51 225	87.81 287	95.57 298

cascas		94.63 206	93.86 213	96.93 171	96.91 222	94.27 217	96.00 310	98.51 132	85.55 313	94.54 178	96.23 275	84.20 266	98.87 191	95.80 109	96.98 153	97.66 185

HQP5-MVS						94.25 218

HQP-MVS		95.72 134	95.40 129	96.69 183	97.20 205	94.25 218	98.05 207	98.46 142	96.43 54	94.45 182	97.73 174	86.75 214	98.96 178	95.30 125	94.18 205	96.86 224

TR-MVS		94.94 185	94.20 191	97.17 156	97.75 169	94.14 220	97.59 250	97.02 283	92.28 226	95.75 159	97.64 184	83.88 271	98.96 178	89.77 256	96.15 184	98.40 158

v1921920		94.20 227	93.47 238	96.40 219	95.98 281	94.08 221	98.52 151	98.15 193	91.33 250	94.25 202	97.20 211	86.41 219	98.42 240	90.04 253	89.39 266	96.69 246

Baseline_NR-MVSNet		94.35 220	93.81 215	95.96 237	96.20 270	94.05 222	98.61 138	96.67 304	91.44 243	93.85 221	97.60 186	88.57 171	98.14 267	94.39 146	86.93 299	95.68 295

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 107	93.98 223	98.68 128	97.40 261	95.02 112	97.95 71	99.34 19	74.37 321	99.78 74	98.64 4	96.80 155	99.08 119

PMMVS		96.60 103	96.33 102	97.41 145	97.90 162	93.93 224	97.35 266	98.41 150	92.84 201	97.76 80	97.45 195	91.10 117	99.20 148	96.26 95	97.91 133	99.11 115

v1240		94.06 238	93.29 242	96.34 224	96.03 280	93.90 225	98.44 161	98.17 190	91.18 258	94.13 210	97.01 234	86.05 229	98.42 240	89.13 270	89.50 264	96.70 241

GA-MVS		94.81 192	94.03 202	97.14 157	97.15 210	93.86 226	96.76 294	97.58 234	94.00 145	94.76 175	97.04 230	80.91 286	98.48 227	91.79 217	96.25 180	99.09 116

ACMM	93.85 9	95.69 138	95.38 133	96.61 197	97.61 176	93.84 227	98.91 69	98.44 146	95.25 101	94.28 200	98.47 113	86.04 231	99.12 155	95.50 120	93.95 214	96.87 222
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_anonymous		96.70 101	96.53 97	97.18 155	98.19 144	93.78 228	98.31 177	98.19 182	94.01 144	94.47 181	98.27 134	92.08 98	98.46 232	97.39 56	97.91 133	99.31 92

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 164	98.77 110	93.76 229	97.79 237	98.50 137	95.45 85	96.94 111	99.09 52	87.87 192	99.55 121	96.76 80	95.83 193	97.74 180

XVG-OURS		96.55 107	96.41 99	96.99 165	98.75 111	93.76 229	97.50 255	98.52 130	95.67 76	96.83 119	99.30 24	88.95 151	99.53 122	95.88 105	96.26 179	97.69 184

CLD-MVS		95.62 141	95.34 134	96.46 216	97.52 184	93.75 231	97.27 272	98.46 142	95.53 82	94.42 190	98.00 152	86.21 222	98.97 175	96.25 96	94.37 199	96.66 250
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IterMVS-LS		95.46 153	95.21 142	96.22 229	98.12 149	93.72 232	98.32 176	98.13 196	93.71 163	94.26 201	97.31 205	92.24 91	98.10 269	94.63 139	90.12 255	96.84 225
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	96.36 221	97.93 160	93.70 233	98.12 200	98.27 168	93.70 165	95.07 165	99.02 58	92.23 92	98.54 216	94.68 138	93.46 222	96.84 225

LPG-MVS_test		95.62 141	95.34 134	96.47 213	97.46 187	93.54 234	98.99 60	98.54 125	94.67 123	94.36 192	98.77 87	85.39 239	99.11 159	95.71 113	94.15 207	96.76 232

LGP-MVS_train				96.47 213	97.46 187	93.54 234		98.54 125	94.67 123	94.36 192	98.77 87	85.39 239	99.11 159	95.71 113	94.15 207	96.76 232

ACMP	93.49 10	95.34 165	94.98 151	96.43 217	97.67 172	93.48 236	98.73 117	98.44 146	94.94 118	92.53 255	98.53 107	84.50 258	99.14 153	95.48 121	94.00 212	96.66 250
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CR-MVSNet		94.76 194	94.15 194	96.59 199	97.00 215	93.43 237	94.96 320	97.56 235	92.46 209	96.93 112	96.24 273	88.15 182	97.88 285	87.38 292	96.65 158	98.46 155

RPMNet		92.52 261	91.17 264	96.59 199	97.00 215	93.43 237	94.96 320	97.26 273	82.27 324	96.93 112	92.12 328	86.98 211	97.88 285	76.32 326	96.65 158	98.46 155

IB-MVS	91.98 17	93.27 252	91.97 259	97.19 154	97.47 186	93.41 239	97.09 279	95.99 313	93.32 184	92.47 258	95.73 288	78.06 302	99.53 122	94.59 142	82.98 313	98.62 149
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	97.20 153	98.81 108	93.27 240	95.78 314	99.15 18	95.25 101	96.79 124	98.11 144	92.29 89	99.07 165	98.56 9	99.85 2	99.25 101

ACMH	92.88 16	94.55 211	93.95 208	96.34 224	97.63 174	93.26 241	98.81 95	98.49 141	93.43 177	89.74 285	98.53 107	81.91 281	99.08 164	93.69 164	93.30 228	96.70 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	93.27 12	95.33 166	94.87 161	96.71 180	99.29 56	93.24 242	98.58 141	98.11 203	89.92 277	93.57 227	99.10 48	86.37 220	99.79 69	90.78 236	98.10 129	97.09 201
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		95.24 170	94.65 171	96.99 165	99.25 65	93.21 243	98.59 139	98.18 185	91.36 247	93.52 229	98.77 87	84.67 250	99.72 86	89.70 260	97.87 135	98.02 173

TestCases				96.99 165	99.25 65	93.21 243		98.18 185	91.36 247	93.52 229	98.77 87	84.67 250	99.72 86	89.70 260	97.87 135	98.02 173

MIMVSNet		93.26 253	92.21 257	96.41 218	97.73 171	93.13 245	95.65 315	97.03 282	91.27 255	94.04 214	96.06 281	75.33 314	97.19 298	86.56 297	96.23 181	98.92 132

Patchmtry		93.22 254	92.35 255	95.84 242	96.77 228	93.09 246	94.66 326	97.56 235	87.37 303	92.90 246	96.24 273	88.15 182	97.90 281	87.37 293	90.10 256	96.53 268

v148		94.29 223	93.76 221	95.91 239	96.10 276	92.93 247	98.58 141	97.97 220	92.59 207	93.47 232	96.95 239	88.53 174	98.32 256	92.56 198	87.06 298	96.49 273

test0.0.03 1		94.08 236	93.51 236	95.80 244	95.53 298	92.89 248	97.38 261	95.97 314	95.11 107	92.51 257	96.66 259	87.71 196	96.94 301	87.03 295	93.67 217	97.57 186

PatchT		93.06 257	91.97 259	96.35 222	96.69 234	92.67 249	94.48 327	97.08 278	86.62 305	97.08 103	92.23 327	87.94 188	97.90 281	78.89 321	96.69 156	98.49 154

v748		93.75 244	93.06 244	95.82 243	95.73 291	92.64 250	99.25 19	98.24 175	91.60 239	92.22 264	96.52 266	87.60 201	98.46 232	90.64 239	85.72 308	96.36 278

MVP-Stereo		94.28 225	93.92 209	95.35 265	94.95 307	92.60 251	97.97 215	97.65 232	91.61 238	90.68 279	97.09 220	86.32 221	98.42 240	89.70 260	99.34 82	95.02 305
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs5		93.65 247	92.97 246	95.68 248	95.49 299	92.37 252	98.20 187	97.28 271	89.66 285	92.58 253	97.26 207	82.14 279	98.09 271	93.18 178	90.95 252	96.58 261

BH-untuned		95.95 125	95.72 120	96.65 191	98.55 128	92.26 253	98.23 184	97.79 226	93.73 161	94.62 176	98.01 151	88.97 150	99.00 174	93.04 182	98.51 112	98.68 144

pmmvs-eth3d		90.36 289	89.05 292	94.32 291	91.10 325	92.12 254	97.63 249	96.95 290	88.86 295	84.91 319	93.13 311	78.32 301	96.74 311	88.70 278	81.81 317	94.09 322

FMVSNet5		91.81 275	90.92 269	94.49 286	97.21 204	92.09 255	98.00 213	97.55 239	89.31 292	90.86 277	95.61 293	74.48 319	95.32 324	85.57 304	89.70 259	96.07 286

PVSNet	91.96 18	96.35 113	96.15 108	96.96 168	99.17 76	92.05 256	96.08 306	98.68 97	93.69 166	97.75 81	97.80 171	88.86 154	99.69 94	94.26 152	99.01 90	99.15 111

ACMH+	92.99 14	94.30 222	93.77 219	95.88 241	97.81 167	92.04 257	98.71 120	98.37 157	93.99 146	90.60 280	98.47 113	80.86 288	99.05 166	92.75 193	92.40 237	96.55 266

ADS-MVSNet		95.00 178	94.45 181	96.63 194	98.00 155	91.91 258	96.04 307	97.74 229	90.15 268	96.47 145	96.64 261	87.89 190	98.96 178	90.08 250	97.06 150	99.02 122

mvs-test1		96.60 103	96.68 91	96.37 220	97.89 163	91.81 259	98.56 146	98.10 208	96.57 52	96.52 137	97.94 156	90.81 120	99.45 130	95.72 111	98.01 130	97.86 177

BH-w/o		95.38 160	95.08 147	96.26 228	98.34 134	91.79 260	97.70 242	97.43 258	92.87 200	94.24 203	97.22 210	88.66 169	98.84 194	91.55 223	97.70 143	98.16 170

Patchmatch-test		94.42 217	93.68 226	96.63 194	97.60 177	91.76 261	94.83 324	97.49 253	89.45 289	94.14 209	97.10 218	88.99 146	98.83 196	85.37 307	98.13 128	99.29 97

EPMVS		94.99 179	94.48 177	96.52 209	97.22 203	91.75 262	97.23 273	91.66 342	94.11 139	97.28 98	96.81 254	85.70 235	98.84 194	93.04 182	97.28 148	98.97 127

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 239	97.74 170	91.74 263	98.69 124	98.15 193	95.56 81	94.92 168	97.68 181	88.98 149	98.79 200	93.19 177	97.78 140	97.20 200

XVG-ACMP-BASELINE		94.54 212	94.14 195	95.75 247	96.55 239	91.65 264	98.11 202	98.44 146	94.96 115	94.22 204	97.90 159	79.18 299	99.11 159	94.05 157	93.85 215	96.48 274

TDRefinement		91.06 283	89.68 286	95.21 267	85.35 336	91.49 265	98.51 155	97.07 279	91.47 241	88.83 293	97.84 165	77.31 308	99.09 163	92.79 192	77.98 330	95.04 304

MDA-MVSNet-bldmvs		89.97 291	88.35 297	94.83 279	95.21 304	91.34 266	97.64 247	97.51 244	88.36 298	71.17 336	96.13 280	79.22 298	96.63 316	83.65 309	86.27 304	96.52 269

ITE_SJBPF				95.44 257	97.42 191	91.32 267		97.50 247	95.09 110	93.59 225	98.35 123	81.70 282	98.88 190	89.71 259	93.39 226	96.12 284

Patchmatch-test1		95.32 167	94.97 153	96.35 222	97.67 172	91.29 268	97.33 268	97.60 233	94.68 122	96.92 114	96.95 239	83.97 269	98.50 226	91.33 229	98.32 122	99.25 101

pmmvs6		91.77 276	90.63 277	95.17 269	94.69 312	91.24 269	98.67 131	97.92 222	86.14 308	89.62 286	97.56 190	75.79 313	98.34 254	90.75 237	84.56 312	95.94 289

test_0402		91.32 279	90.27 281	94.48 287	96.60 237	91.12 270	98.50 156	97.22 275	86.10 309	88.30 295	96.98 236	77.65 306	97.99 277	78.13 323	92.94 233	94.34 318

MIMVSNet1		89.67 293	88.28 298	93.82 295	92.81 321	91.08 271	98.01 211	97.45 256	87.95 299	87.90 297	95.87 286	67.63 332	94.56 327	78.73 322	88.18 284	95.83 291

USDC		93.33 251	92.71 250	95.21 267	96.83 227	90.83 272	96.91 285	97.50 247	93.84 154	90.72 278	98.14 142	77.69 304	98.82 197	89.51 264	93.21 231	95.97 288

DWT-MVSNet_test		94.82 191	94.36 184	96.20 230	97.35 196	90.79 273	98.34 171	96.57 307	92.91 198	95.33 163	96.44 269	82.00 280	99.12 155	94.52 144	95.78 194	98.70 142

MDA-MVSNet_test_wron		90.71 286	89.38 289	94.68 282	94.83 309	90.78 274	97.19 275	97.46 254	87.60 301	72.41 335	95.72 290	86.51 217	96.71 314	85.92 302	86.80 302	96.56 265

PatchmatchNet		95.71 136	95.52 128	96.29 227	97.58 179	90.72 275	96.84 292	97.52 241	94.06 142	97.08 103	96.96 238	89.24 141	98.90 188	92.03 211	98.37 119	99.26 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchFormer-LS_test		95.47 152	95.27 140	96.08 235	97.59 178	90.66 276	98.10 204	97.34 265	93.98 147	96.08 153	96.15 279	87.65 200	99.12 155	95.27 128	95.24 197	98.44 157

YYNet1		90.70 287	89.39 288	94.62 284	94.79 310	90.65 277	97.20 274	97.46 254	87.54 302	72.54 334	95.74 287	86.51 217	96.66 315	86.00 301	86.76 303	96.54 267

JIA-IIPM		93.35 249	92.49 253	95.92 238	96.48 244	90.65 277	95.01 319	96.96 289	85.93 311	96.08 153	87.33 332	87.70 198	98.78 201	91.35 228	95.58 195	98.34 165

semantic-postprocess				94.85 277	97.98 159	90.56 279		98.11 203	93.75 158	92.58 253	97.48 192	83.91 270	97.41 295	92.48 202	91.30 249	96.58 261

EPNet_dtu		95.21 172	94.95 154	95.99 236	96.17 272	90.45 280	98.16 196	97.27 272	96.77 44	93.14 241	98.33 128	90.34 127	98.42 240	85.57 304	98.81 101	99.09 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS		94.09 235	93.85 214	94.80 280	97.99 157	90.35 281	97.18 276	98.12 198	93.68 168	92.46 259	97.34 202	84.05 268	97.41 295	92.51 201	91.33 248	96.62 256
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	95.51 251	97.89 163	90.22 282	98.80 98	98.10 208	96.57 52	96.45 147	96.66 259	90.81 120	98.91 185	95.72 111	97.99 131	97.40 190

testgi		93.06 257	92.45 254	94.88 276	96.43 246	89.90 283	98.75 110	97.54 240	95.60 79	91.63 270	97.91 158	74.46 320	97.02 300	86.10 300	93.67 217	97.72 182

UnsupCasMVSNet_eth		90.99 284	89.92 285	94.19 293	94.08 315	89.83 284	97.13 278	98.67 104	93.69 166	85.83 306	96.19 278	75.15 315	96.74 311	89.14 269	79.41 324	96.00 287

TinyColmap		92.31 263	91.53 262	94.65 283	96.92 220	89.75 285	96.92 283	96.68 303	90.45 264	89.62 286	97.85 164	76.06 312	98.81 198	86.74 296	92.51 236	95.41 299

test-LLR		95.10 176	94.87 161	95.80 244	96.77 228	89.70 286	96.91 285	95.21 325	95.11 107	94.83 172	95.72 290	87.71 196	98.97 175	93.06 180	98.50 113	98.72 140

test-mter		94.08 236	93.51 236	95.80 244	96.77 228	89.70 286	96.91 285	95.21 325	92.89 199	94.83 172	95.72 290	77.69 304	98.97 175	93.06 180	98.50 113	98.72 140

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 226	99.00 87	89.54 288	97.43 258	98.87 49	98.16 2	99.26 7	99.38 11	96.12 18	99.64 100	98.30 21	99.77 18	99.72 31

MS-PatchMatch		93.84 243	93.63 227	94.46 289	96.18 271	89.45 289	97.76 238	98.27 168	92.23 227	92.13 266	97.49 191	79.50 296	98.69 203	89.75 258	99.38 80	95.25 300

OpenMVS_ROB	86.42 20	89.00 295	87.43 301	93.69 296	93.08 319	89.42 290	97.91 222	96.89 296	78.58 330	85.86 305	94.69 300	69.48 328	98.29 262	77.13 324	93.29 229	93.36 327

SixPastTwentyTwo		93.34 250	92.86 247	94.75 281	95.67 293	89.41 291	98.75 110	96.67 304	93.89 151	90.15 283	98.25 136	80.87 287	98.27 263	90.90 235	90.64 253	96.57 263

K. test v3		92.55 260	91.91 261	94.48 287	95.64 294	89.24 292	99.07 53	94.88 329	94.04 143	86.78 300	97.59 187	77.64 307	97.64 289	92.08 207	89.43 265	96.57 263

OurMVSNet-221017-0		94.21 226	94.00 204	94.85 277	95.60 295	89.22 293	98.89 74	97.43 258	95.29 99	92.18 265	98.52 110	82.86 277	98.59 212	93.46 170	91.76 245	96.74 234

TESTMET0.1,1		94.18 230	93.69 225	95.63 249	96.92 220	89.12 294	96.91 285	94.78 330	93.17 188	94.88 169	96.45 268	78.52 300	98.92 184	93.09 179	98.50 113	98.85 134

CostFormer		94.95 183	94.73 168	95.60 250	97.28 199	89.06 295	97.53 253	96.89 296	89.66 285	96.82 121	96.72 257	86.05 229	98.95 182	95.53 119	96.13 185	98.79 138

tpm2		94.19 228	93.76 221	95.46 255	97.23 202	89.04 296	97.31 270	96.85 299	87.08 304	96.21 151	96.79 255	83.75 274	98.74 202	92.43 203	96.23 181	98.59 150

EG-PatchMatch MVS		91.13 281	90.12 282	94.17 294	94.73 311	89.00 297	98.13 199	97.81 225	89.22 293	85.32 309	96.46 267	67.71 331	98.42 240	87.89 291	93.82 216	95.08 303

UnsupCasMVSNet_bld		87.17 301	85.12 304	93.31 300	91.94 322	88.77 298	94.92 322	98.30 165	84.30 319	82.30 322	90.04 329	63.96 336	97.25 297	85.85 303	74.47 336	93.93 325

ADS-MVSNet2		94.58 210	94.40 183	95.11 271	98.00 155	88.74 299	96.04 307	97.30 269	90.15 268	96.47 145	96.64 261	87.89 190	97.56 292	90.08 250	97.06 150	99.02 122

LP		91.12 282	89.99 284	94.53 285	96.35 254	88.70 300	93.86 331	97.35 264	84.88 316	90.98 275	94.77 299	84.40 259	97.43 294	75.41 329	91.89 244	97.47 187

LF4IMVS		93.14 256	92.79 249	94.20 292	95.88 286	88.67 301	97.66 246	97.07 279	93.81 156	91.71 269	97.65 182	77.96 303	98.81 198	91.47 227	91.92 243	95.12 301

tpmvs		94.60 207	94.36 184	95.33 266	97.46 187	88.60 302	96.88 290	97.68 230	91.29 253	93.80 223	96.42 270	88.58 170	99.24 142	91.06 232	96.04 190	98.17 169

tpmp4_e23		93.91 242	93.42 241	95.38 263	97.62 175	88.59 303	97.52 254	97.34 265	87.94 300	94.17 208	96.79 255	82.91 276	99.05 166	90.62 240	95.91 191	98.50 153

tpmrst		95.63 140	95.69 125	95.44 257	97.54 182	88.54 304	96.97 281	97.56 235	93.50 175	97.52 96	96.93 245	89.49 134	99.16 150	95.25 129	96.42 166	98.64 148

lessismore_v0				94.45 290	94.93 308	88.44 305		91.03 343		86.77 301	97.64 184	76.23 311	98.42 240	90.31 247	85.64 309	96.51 271

MDTV_nov1_ep13			95.40 129		97.48 185	88.34 306	96.85 291	97.29 270	93.74 160	97.48 97	97.26 207	89.18 142	99.05 166	91.92 215	97.43 147

new_pmnet		90.06 290	89.00 293	93.22 302	94.18 313	88.32 307	96.42 305	96.89 296	86.19 307	85.67 308	93.62 306	77.18 309	97.10 299	81.61 314	89.29 267	94.23 319

test20.03		90.89 285	90.38 279	92.43 304	93.48 317	88.14 308	98.33 172	97.56 235	93.40 181	87.96 296	96.71 258	80.69 290	94.13 328	79.15 320	86.17 305	95.01 306

tpm cat1		93.36 248	92.80 248	95.07 272	97.58 179	87.97 309	96.76 294	97.86 224	82.17 325	93.53 228	96.04 282	86.13 223	99.13 154	89.24 268	95.87 192	98.10 171

tpm		94.13 234	93.80 216	95.12 270	96.50 242	87.91 310	97.44 256	95.89 317	92.62 205	96.37 149	96.30 272	84.13 267	98.30 260	93.24 175	91.66 247	99.14 113

LCM-MVSNet-Re		95.22 171	95.32 137	94.91 274	98.18 146	87.85 311	98.75 110	95.66 321	95.11 107	88.96 292	96.85 252	90.26 130	97.65 288	95.65 116	98.44 116	99.22 104

gm-plane-assit					95.88 286	87.47 312			89.74 283		96.94 241		99.19 149	93.32 174

Anonymous20231206		91.66 277	91.10 265	93.33 299	94.02 316	87.35 313	98.58 141	97.26 273	90.48 262	90.16 282	96.31 271	83.83 273	96.53 317	79.36 319	89.90 258	96.12 284

PVSNet_0	88.72 19	91.28 280	90.03 283	95.00 273	97.99 157	87.29 314	94.84 323	98.50 137	92.06 229	89.86 284	95.19 294	79.81 295	99.39 133	92.27 204	69.79 337	98.33 166

pmmvs3		86.67 303	84.86 305	92.11 307	88.16 331	87.19 315	96.63 297	94.75 331	79.88 329	87.22 299	92.75 322	66.56 333	95.20 325	81.24 315	76.56 333	93.96 324

dp		94.15 233	93.90 211	94.90 275	97.31 198	86.82 316	96.97 281	97.19 276	91.22 257	96.02 156	96.61 263	85.51 238	99.02 173	90.00 254	94.30 200	98.85 134

new-patchmatchnet		88.50 299	87.45 300	91.67 308	90.31 327	85.89 317	97.16 277	97.33 268	89.47 288	83.63 321	92.77 321	76.38 310	95.06 326	82.70 311	77.29 331	94.06 323

Patchmatch-RL test		91.49 278	90.85 270	93.41 298	91.37 324	84.40 318	92.81 332	95.93 316	91.87 234	87.25 298	94.87 298	88.99 146	96.53 317	92.54 200	82.00 315	99.30 95

MDTV_nov1_ep13_2view						84.26 319	96.89 289		90.97 260	97.90 75		89.89 133		93.91 159		99.18 109

CVMVSNet		95.43 155	96.04 111	93.57 297	97.93 160	83.62 320	98.12 200	98.59 115	95.68 75	96.56 131	99.02 58	87.51 202	97.51 293	93.56 169	97.44 146	99.60 60

EU-MVSNet		93.66 245	94.14 195	92.25 306	95.96 282	83.38 321	98.52 151	98.12 198	94.69 121	92.61 252	98.13 143	87.36 206	96.39 319	91.82 216	90.00 257	96.98 207

PM-MVS		87.77 300	86.55 302	91.40 309	91.03 326	83.36 322	96.92 283	95.18 327	91.28 254	86.48 303	93.42 307	53.27 339	96.74 311	89.43 266	81.97 316	94.11 321

testpf		88.74 297	89.09 290	87.69 315	95.78 289	83.16 323	84.05 342	94.13 338	85.22 315	90.30 281	94.39 303	74.92 317	95.80 321	89.77 256	93.28 230	84.10 337

DSMNet-mixed		92.52 261	92.58 252	92.33 305	94.15 314	82.65 324	98.30 179	94.26 335	89.08 294	92.65 251	95.73 288	85.01 246	95.76 322	86.24 299	97.76 141	98.59 150

MVS-HIRNet		89.46 294	88.40 296	92.64 303	97.58 179	82.15 325	94.16 330	93.05 341	75.73 333	90.90 276	82.52 335	79.42 297	98.33 255	83.53 310	98.68 103	97.43 188

RPSCF		94.87 187	95.40 129	93.26 301	98.89 101	82.06 326	98.33 172	98.06 215	90.30 267	96.56 131	99.26 27	87.09 208	99.49 125	93.82 162	96.32 173	98.24 168

Gipuma		78.40 310	76.75 311	83.38 324	95.54 297	80.43 327	79.42 343	97.40 261	64.67 337	73.46 333	80.82 338	45.65 343	93.14 333	66.32 337	87.43 292	76.56 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		83.69 306	81.50 308	90.26 310	89.23 330	80.10 328	97.97 215	97.06 281	72.79 335	82.05 324	92.57 323	50.28 340	96.32 320	76.15 327	75.38 334	94.37 317

test2356		88.68 298	88.61 294	88.87 313	89.90 329	78.23 329	95.11 318	96.66 306	88.66 297	89.06 291	94.33 305	73.14 324	92.56 335	75.56 328	95.11 198	95.81 292

no-one		74.41 313	70.76 315	85.35 321	79.88 341	76.83 330	94.68 325	94.22 336	80.33 328	63.81 339	79.73 339	35.45 348	93.36 332	71.78 331	36.99 345	85.86 336

CMPMVS	66.06 21	89.70 292	89.67 287	89.78 311	93.19 318	76.56 331	97.00 280	98.35 159	80.97 327	81.57 325	97.75 173	74.75 318	98.61 209	89.85 255	93.63 219	94.17 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testus		88.91 296	89.08 291	88.40 314	91.39 323	76.05 332	96.56 300	96.48 308	89.38 291	89.39 289	95.17 296	70.94 326	93.56 331	77.04 325	95.41 196	95.61 296

ambc				89.49 312	86.66 335	75.78 333	92.66 333	96.72 301		86.55 302	92.50 324	46.01 342	97.90 281	90.32 246	82.09 314	94.80 307

1111		84.94 305	84.30 306	86.86 317	87.59 332	75.10 334	96.63 297	96.43 309	82.53 322	80.75 327	92.91 319	68.94 329	93.79 329	68.24 335	84.66 311	91.70 329

.test1245		73.05 314	76.31 312	63.27 335	87.59 332	75.10 334	96.63 297	96.43 309	82.53 322	80.75 327	92.91 319	68.94 329	93.79 329	68.24 335	12.72 348	20.91 346

test1235678		86.26 304	85.81 303	87.62 316	86.97 334	75.00 336	96.55 302	96.32 311	86.08 310	81.32 326	92.98 317	73.10 325	92.05 336	71.64 332	87.32 294	95.81 292

PMMVS2		77.95 311	75.44 314	85.46 320	82.54 338	74.95 337	94.23 329	93.08 340	72.80 334	74.68 332	87.38 331	36.36 347	91.56 337	73.95 330	63.94 338	89.87 330

DeepMVS_CX				86.78 318	97.09 213	72.30 338		95.17 328	75.92 332	84.34 320	95.19 294	70.58 327	95.35 323	79.98 318	89.04 270	92.68 328

LCM-MVSNet		78.70 309	76.24 313	86.08 319	77.26 346	71.99 339	94.34 328	96.72 301	61.62 339	76.53 331	89.33 330	33.91 349	92.78 334	81.85 313	74.60 335	93.46 326

ANet_high		69.08 315	65.37 317	80.22 326	65.99 349	71.96 340	90.91 336	90.09 344	82.62 321	49.93 345	78.39 340	29.36 350	81.75 344	62.49 341	38.52 344	86.95 335

test12356		83.47 307	83.37 307	83.78 323	84.43 337	70.09 341	95.12 317	95.60 322	82.98 320	78.89 329	92.43 326	64.99 334	91.41 338	70.36 333	85.55 310	89.82 331

testmv		78.74 308	77.35 309	82.89 325	78.16 345	69.30 342	95.87 311	94.65 332	81.11 326	70.98 337	87.11 333	46.31 341	90.42 339	65.28 338	76.72 332	88.95 332

wuykxyi23d		63.73 321	58.86 323	78.35 328	67.62 348	67.90 343	86.56 339	87.81 348	58.26 340	42.49 347	70.28 344	11.55 354	85.05 342	63.66 339	41.50 341	82.11 339

MVE	62.14 22	63.28 322	59.38 322	74.99 330	74.33 347	65.47 344	85.55 340	80.50 352	52.02 343	51.10 344	75.00 343	10.91 356	80.50 345	51.60 343	53.40 339	78.99 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		87.12 302	87.77 299	85.17 322	95.46 300	61.92 345	97.37 263	70.66 353	85.83 312	88.73 294	96.04 282	85.33 243	97.76 287	80.02 316	90.48 254	95.84 290

FPMVS		77.62 312	77.14 310	79.05 327	79.25 342	60.97 346	95.79 313	95.94 315	65.96 336	67.93 338	94.40 302	37.73 346	88.88 341	68.83 334	88.46 281	87.29 333

tmp_tt		68.90 316	66.97 316	74.68 331	50.78 351	59.95 347	87.13 338	83.47 351	38.80 345	62.21 340	96.23 275	64.70 335	76.91 348	88.91 275	30.49 346	87.19 334

PNet_i23d		67.70 317	65.07 318	75.60 329	78.61 343	59.61 348	89.14 337	88.24 347	61.83 338	52.37 343	80.89 337	18.91 351	84.91 343	62.70 340	52.93 340	82.28 338

E-PMN		64.94 319	64.25 319	67.02 333	82.28 339	59.36 349	91.83 335	85.63 349	52.69 342	60.22 341	77.28 341	41.06 345	80.12 346	46.15 344	41.14 342	61.57 344

EMVS		64.07 320	63.26 321	66.53 334	81.73 340	58.81 350	91.85 334	84.75 350	51.93 344	59.09 342	75.13 342	43.32 344	79.09 347	42.03 345	39.47 343	61.69 343

PMVS	61.03 23	65.95 318	63.57 320	73.09 332	57.90 350	51.22 351	85.05 341	93.93 339	54.45 341	44.32 346	83.57 334	13.22 352	89.15 340	58.68 342	81.00 319	78.91 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		30.17 324	30.18 326	30.16 337	78.61 343	43.29 352	66.79 344	14.21 354	17.31 346	14.82 350	11.93 350	11.55 354	41.43 349	37.08 346	19.30 347	5.76 348

test123		20.95 327	23.72 328	12.64 338	13.54 353	8.19 353	96.55 302	6.13 356	7.48 348	16.74 349	37.98 347	12.97 353	6.05 350	16.69 347	5.43 350	23.68 345

testmvs		21.48 326	24.95 327	11.09 339	14.89 352	6.47 354	96.56 300	9.87 355	7.55 347	17.93 348	39.02 346	9.43 357	5.90 351	16.56 348	12.72 348	20.91 346

cdsmvs_eth3d_5k		23.98 325	31.98 325	0.00 340	0.00 354	0.00 355	0.00 345	98.59 115	0.00 349	0.00 351	98.61 100	90.60 124	0.00 352	0.00 349	0.00 351	0.00 349

pcd_1.5k_mvsjas		7.88 329	10.50 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	94.51 61	0.00 352	0.00 349	0.00 351	0.00 349

pcd1.5k->3k		39.42 323	41.78 324	32.35 336	96.17 272	0.00 355	0.00 345	98.54 125	0.00 349	0.00 351	0.00 351	87.78 195	0.00 352	0.00 349	93.56 221	97.06 202

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

ab-mvs-re		8.20 328	10.94 329	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	98.43 115	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

test_part1								98.84 54				97.38 2			99.78 14	99.76 20

test_full								98.84 54

sam_mvs1												89.45 135

sam_mvs												88.99 146

MTGPA								98.74 79

test_post1							96.68 296				30.43 349	87.85 193	98.69 203	92.59 197

test_post											31.83 348	88.83 158	98.91 185

patchmatchnet-post											95.10 297	89.42 136	98.89 189

MTMP								94.14 337

test9_res														96.39 94	99.57 56	99.69 36

agg_prior2														95.87 106	99.57 56	99.68 42

test_prior2							97.80 235		96.12 63	97.89 76	98.69 92	95.96 26		96.89 71	99.60 50

旧先验2							97.57 252		91.30 252	98.67 37			99.80 57	95.70 115

新几何2							97.64 247

无先验							97.58 251	98.72 86	91.38 246				99.87 35	93.36 172		99.60 60

原ACMM2							97.67 245

testdata2													99.89 27	91.65 222

segment_acmp												96.85 4

testdata1							97.32 269		96.34 57

plane_prior5								98.56 122					99.03 171	96.07 97	94.27 201	96.92 211

plane_prior4											98.28 131

plane_prior2							98.80 98		97.28 21

plane_prior1					97.37 195

n2								0.00 357

nn								0.00 357

door-mid								94.37 334

test11								98.66 107

door								94.64 333

HQP-NCC					97.20 205		98.05 207		96.43 54	94.45 182

ACMP_Plane					97.20 205		98.05 207		96.43 54	94.45 182

BP-MVS														95.30 125

HQP4-MVS										94.45 182			98.96 178			96.87 222

HQP3-MVS								98.46 142							94.18 205

HQP2-MVS												86.75 214

ACMMP++_ref															92.97 232

ACMMP++															93.61 220

Test By Simon												94.64 58

High-resolution multi-view benchmark