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.

CS-MVS		97.84 76	97.69 73	98.31 124	98.28 153	96.27 136	100.00 1	97.52 232	95.29 73	99.25 70	99.65 99	91.18 153	98.94 162	98.96 57	99.04 121	99.73 108

DELS-MVS		98.54 39	98.22 48	99.50 29	99.15 111	98.65 48	100.00 1	98.58 72	97.70 9	98.21 117	99.24 132	92.58 128	99.94 68	98.63 82	99.94 57	99.92 87
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
PVSNet_Blended		97.94 71	97.64 75	98.83 88	99.59 90	96.99 114	100.00 1	99.10 28	95.38 70	98.27 113	99.08 139	89.00 181	99.95 60	99.12 47	99.25 116	99.57 137

ET-MVSNet_ETH3D		94.37 179	93.28 193	97.64 149	98.30 150	97.99 74	99.99 4	97.61 220	94.35 103	71.57 344	99.45 115	96.23 27	95.34 321	96.91 136	85.14 265	99.59 130

alignmvs		97.81 79	97.33 87	99.25 49	98.77 136	98.66 46	99.99 4	98.44 106	94.40 102	98.41 106	99.47 112	93.65 100	99.42 150	98.57 83	94.26 203	99.67 117

lupinMVS		97.85 75	97.60 77	98.62 100	97.28 211	97.70 85	99.99 4	97.55 226	95.50 69	99.43 52	99.67 95	90.92 158	98.71 175	98.40 88	99.62 98	99.45 154

IB-MVS	92.85 6	94.99 162	93.94 174	98.16 129	97.72 191	95.69 163	99.99 4	98.81 47	94.28 108	92.70 206	96.90 225	95.08 50	99.17 155	96.07 143	73.88 332	99.60 129
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
EIA-MVS		97.53 88	97.46 81	97.76 145	98.04 168	94.84 184	99.98 8	97.61 220	94.41 101	97.90 123	99.59 103	92.40 132	98.87 163	98.04 102	99.13 119	99.59 130

ETV-MVS		97.92 73	97.80 71	98.25 127	98.14 164	96.48 128	99.98 8	97.63 215	95.61 66	99.29 67	99.46 114	92.55 129	98.82 165	99.02 56	98.54 129	99.46 152

CANet		98.27 59	97.82 70	99.63 12	99.72 83	99.10 17	99.98 8	98.51 95	97.00 28	98.52 101	99.71 86	87.80 190	99.95 60	99.75 22	99.38 113	99.83 96

MSLP-MVS++		99.13 7	99.01 9	99.49 31	99.94 14	98.46 59	99.98 8	98.86 44	97.10 25	99.80 16	99.94 4	95.92 33	100.00 1	99.51 34	100.00 1	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 4	99.98 2	99.51 4	99.98 8	98.69 54	98.20 3	99.93 1	99.98 2	96.82 19	100.00 1	99.75 22	100.00 1	99.99 20

SteuartSystems-ACMMP		99.02 11	98.97 11	99.18 54	98.72 137	97.71 83	99.98 8	98.44 106	96.85 29	99.80 16	99.91 13	97.57 6	99.85 94	99.44 38	99.99 20	99.99 20
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.41 49	98.21 49	99.03 75	99.86 54	97.10 111	99.98 8	98.80 49	90.78 225	99.62 37	99.78 66	95.30 46	100.00 1	99.80 18	99.93 63	99.99 20

CLD-MVS		94.06 184	93.90 175	94.55 236	96.02 246	90.69 267	99.98 8	97.72 210	96.62 39	91.05 217	98.85 167	77.21 274	98.47 186	98.11 98	89.51 224	94.48 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		97.41 93	97.02 98	98.59 104	97.71 193	97.52 91	99.97 16	98.54 85	91.83 196	97.45 132	99.04 141	97.50 8	99.10 156	94.75 164	96.37 174	99.16 179

Fast-Effi-MVS+		95.02 161	94.19 168	97.52 153	97.88 175	94.55 190	99.97 16	97.08 275	88.85 255	94.47 185	97.96 199	84.59 220	98.41 193	89.84 247	97.10 161	99.59 130

MCST-MVS		99.32 3	99.14 4	99.86 3	99.97 3	99.59 3	99.97 16	98.64 62	98.47 2	99.13 76	99.92 11	96.38 26	100.00 1	99.74 24	100.00 1	100.00 1

TSAR-MVS + GP.		98.60 33	98.51 28	98.86 87	99.73 81	96.63 123	99.97 16	97.92 197	98.07 5	98.76 91	99.55 106	95.00 57	99.94 68	99.91 11	97.68 148	99.99 20

jason		97.24 98	96.86 100	98.38 122	95.73 257	97.32 103	99.97 16	97.40 248	95.34 72	98.60 100	99.54 108	87.70 191	98.56 182	97.94 108	99.47 109	99.25 174
jason: jason.
NCCC		99.37 2	99.25 2	99.71 10	99.96 8	99.15 16	99.97 16	98.62 66	98.02 6	99.90 2	99.95 3	97.33 13	100.00 1	99.54 33	100.00 1	100.00 1

CP-MVS		98.45 46	98.32 44	98.87 86	99.96 8	96.62 124	99.97 16	98.39 133	94.43 98	98.90 86	99.87 26	94.30 81	100.00 1	99.04 54	99.99 20	99.99 20

SED-MVS		99.28 5	99.11 6	99.77 6	99.93 26	99.30 8	99.96 23	98.43 114	97.27 20	99.80 16	99.94 4	96.71 20	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 45	99.80 2	99.96 23				99.80 58	97.44 11	100.00 1	100.00 1	99.98 33	100.00 1

xxxxxxxxxxxxxcwj		98.98 14	98.79 15	99.54 23	99.82 65	98.79 33	99.96 23	97.52 232	97.66 10	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

save fliter					99.82 65	98.79 33	99.96 23	98.40 130	97.66 10

test0726					99.93 26	99.29 10	99.96 23	98.42 125	97.28 18	99.86 4	99.94 4	97.22 15

DPM-MVS		98.83 21	98.46 30	99.97 1	99.33 106	99.92 1	99.96 23	98.44 106	97.96 7	99.55 42	99.94 4	97.18 17	100.00 1	93.81 187	99.94 57	99.98 51

TEST9					99.92 35	98.92 23	99.96 23	98.43 114	93.90 126	99.71 30	99.86 29	95.88 34	99.85 94

train_agg		98.88 19	98.65 20	99.59 18	99.92 35	98.92 23	99.96 23	98.43 114	94.35 103	99.71 30	99.86 29	95.94 31	99.85 94	99.69 31	99.98 33	99.99 20

test_8					99.92 35	98.88 26	99.96 23	98.43 114	94.35 103	99.69 32	99.85 33	95.94 31	99.85 94

agg_prior1		98.88 19	98.66 19	99.54 23	99.93 26	98.77 36	99.96 23	98.43 114	94.63 92	99.63 35	99.85 33	95.79 37	99.85 94	99.72 28	99.99 20	99.99 20

region2R		98.54 39	98.37 40	99.05 73	99.96 8	97.18 107	99.96 23	98.55 81	94.87 83	99.45 50	99.85 33	94.07 89	100.00 1	98.67 77	100.00 1	99.98 51

test-LLR		96.47 126	96.04 121	97.78 142	97.02 219	95.44 166	99.96 23	98.21 166	94.07 115	95.55 171	96.38 242	93.90 94	98.27 211	90.42 239	98.83 124	99.64 123

TESTMET0.1,1		96.74 117	96.26 117	98.16 129	97.36 205	96.48 128	99.96 23	98.29 155	91.93 193	95.77 169	98.07 195	95.54 40	98.29 207	90.55 236	98.89 122	99.70 112

test-mter		96.39 130	95.93 132	97.78 142	97.02 219	95.44 166	99.96 23	98.21 166	91.81 198	95.55 171	96.38 242	95.17 47	98.27 211	90.42 239	98.83 124	99.64 123

CPTT-MVS		97.64 86	97.32 88	98.58 105	99.97 3	95.77 157	99.96 23	98.35 144	89.90 238	98.36 109	99.79 62	91.18 153	99.99 36	98.37 89	99.99 20	99.99 20

cascas		94.64 171	93.61 179	97.74 147	97.82 181	96.26 138	99.96 23	97.78 209	85.76 296	94.00 191	97.54 205	76.95 277	99.21 153	97.23 125	95.43 192	97.76 212

DeepPCF-MVS	95.94 2	97.71 84	98.98 10	93.92 262	99.63 88	81.76 336	99.96 23	98.56 76	99.47 1	99.19 74	99.99 1	94.16 87	100.00 1	99.92 9	99.93 63	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 8	99.93 26	99.29 10	99.95 40	98.32 149	97.28 18	99.83 10	99.91 13	97.22 15	100.00 1	99.99 5	100.00 1	99.89 90
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.82 5	99.94 14	99.47 5	99.95 40	98.43 114					100.00 1	99.99 5	100.00 1	100.00 1

MSP-MVS		99.09 8	99.12 5	98.98 80	99.93 26	97.24 104	99.95 40	98.42 125	97.50 14	99.52 47	99.88 22	97.43 12	99.71 128	99.50 35	99.98 33	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
mvs-test1		95.53 151	95.97 128	94.20 250	97.77 184	85.44 319	99.95 40	97.06 277	94.92 80	96.58 150	98.72 170	85.81 208	98.98 159	94.80 161	98.11 139	98.18 203

HFP-MVS		98.56 37	98.37 40	99.14 63	99.96 8	97.43 99	99.95 40	98.61 68	94.77 85	99.31 63	99.85 33	94.22 83	100.00 1	98.70 75	99.98 33	99.98 51

#test#		98.59 35	98.41 33	99.14 63	99.96 8	97.43 99	99.95 40	98.61 68	95.00 79	99.31 63	99.85 33	94.22 83	100.00 1	98.78 72	99.98 33	99.98 51

HPM-MVS++		99.07 9	98.88 13	99.63 12	99.90 42	99.02 19	99.95 40	98.56 76	97.56 13	99.44 51	99.85 33	95.38 45	100.00 1	99.31 43	99.99 20	99.87 93

test_prior3		98.99 13	98.84 14	99.43 35	99.94 14	98.49 57	99.95 40	98.65 59	95.78 58	99.73 26	99.76 72	96.00 29	99.80 106	99.78 20	100.00 1	99.99 20

test_prior2							99.95 40		95.78 58	99.73 26	99.76 72	96.00 29		99.78 20	100.00 1

ACMMPR		98.50 42	98.32 44	99.05 73	99.96 8	97.18 107	99.95 40	98.60 70	94.77 85	99.31 63	99.84 46	93.73 98	100.00 1	98.70 75	99.98 33	99.98 51

MP-MVS		98.23 63	97.97 64	99.03 75	99.94 14	97.17 110	99.95 40	98.39 133	94.70 88	98.26 115	99.81 57	91.84 144	100.00 1	98.85 67	99.97 44	99.93 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		98.39 52	98.20 50	98.97 81	99.97 3	96.92 117	99.95 40	98.38 137	95.04 78	98.61 99	99.80 58	93.39 104	100.00 1	98.64 81	100.00 1	99.98 51

PVSNet_BlendedMVS		96.05 139	95.82 137	96.72 177	99.59 90	96.99 114	99.95 40	99.10 28	94.06 117	98.27 113	95.80 256	89.00 181	99.95 60	99.12 47	87.53 249	93.24 310

PAPR		98.52 41	98.16 53	99.58 20	99.97 3	98.77 36	99.95 40	98.43 114	95.35 71	98.03 120	99.75 77	94.03 90	99.98 42	98.11 98	99.83 81	99.99 20

PVSNet	91.05 13	97.13 101	96.69 106	98.45 116	99.52 96	95.81 155	99.95 40	99.65 10	94.73 87	99.04 79	99.21 134	84.48 221	99.95 60	94.92 156	98.74 126	99.58 136

ZNCC-MVS		98.31 56	98.03 60	99.17 57	99.88 49	97.59 87	99.94 55	98.44 106	94.31 106	98.50 103	99.82 53	93.06 117	99.99 36	98.30 92	99.99 20	99.93 81

test_prior4						98.05 71	99.94 55

XVS		98.70 28	98.55 25	99.15 61	99.94 14	97.50 94	99.94 55	98.42 125	96.22 49	99.41 54	99.78 66	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

X-MVStestdata		93.83 186	92.06 215	99.15 61	99.94 14	97.50 94	99.94 55	98.42 125	96.22 49	99.41 54	41.37 361	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

SD-MVS		98.92 16	98.70 17	99.56 21	99.70 85	98.73 41	99.94 55	98.34 146	96.38 44	99.81 12	99.76 72	94.59 67	99.98 42	99.84 13	99.96 48	99.97 63
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
PVSNet_0	88.03 19	91.80 234	90.27 246	96.38 189	98.27 156	90.46 274	99.94 55	99.61 11	93.99 120	86.26 298	97.39 210	71.13 312	99.89 79	98.77 73	67.05 342	98.79 197

ETH3 D test6400		98.81 22	98.54 26	99.59 18	99.93 26	98.93 22	99.93 61	98.46 103	94.56 93	99.84 8	99.92 11	94.32 80	99.86 90	99.96 8	99.98 33	100.00 1

GST-MVS		98.27 59	97.97 64	99.17 57	99.92 35	97.57 88	99.93 61	98.39 133	94.04 119	98.80 89	99.74 81	92.98 118	100.00 1	98.16 95	99.76 89	99.93 81

test0.0.03 1		93.86 185	93.61 179	94.64 231	95.02 273	92.18 239	99.93 61	98.58 72	94.07 115	87.96 272	98.50 182	93.90 94	94.96 326	81.33 311	93.17 213	96.78 217

MVS_111021_HR		98.72 27	98.62 22	99.01 78	99.36 105	97.18 107	99.93 61	99.90 1	96.81 33	98.67 95	99.77 68	93.92 92	99.89 79	99.27 45	99.94 57	99.96 70

thisisatest0530		97.10 102	96.72 105	98.22 128	97.60 196	96.70 121	99.92 65	98.54 85	91.11 217	97.07 140	98.97 151	97.47 9	99.03 157	93.73 192	96.09 177	98.92 189

PVSNet_Blended_VisFu		97.27 97	96.81 102	98.66 97	98.81 133	96.67 122	99.92 65	98.64 62	94.51 95	96.38 158	98.49 183	89.05 180	99.88 85	97.10 129	98.34 133	99.43 157

DP-MVS Recon		98.41 49	98.02 61	99.56 21	99.97 3	98.70 43	99.92 65	98.44 106	92.06 191	98.40 108	99.84 46	95.68 38	100.00 1	98.19 93	99.71 93	99.97 63

PLC	95.54 3	97.93 72	97.89 69	98.05 135	99.82 65	94.77 188	99.92 65	98.46 103	93.93 124	97.20 136	99.27 127	95.44 44	99.97 51	97.41 121	99.51 108	99.41 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
9.14			98.38 38		99.87 52		99.91 69	98.33 147	93.22 146	99.78 22	99.89 19	94.57 68	99.85 94	99.84 13	99.97 44

APDe-MVS		99.06 10	98.91 12	99.51 28	99.94 14	98.76 40	99.91 69	98.39 133	97.20 24	99.46 49	99.85 33	95.53 42	99.79 109	99.86 12	100.00 1	99.99 20

MVSTER		95.53 151	95.22 150	96.45 184	98.56 140	97.72 82	99.91 69	97.67 213	92.38 181	91.39 213	97.14 215	97.24 14	97.30 250	94.80 161	87.85 244	94.34 245

PMMVS		96.76 115	96.76 104	96.76 175	98.28 153	92.10 240	99.91 69	97.98 190	94.12 112	99.53 44	99.39 120	86.93 200	98.73 173	96.95 134	97.73 146	99.45 154

SF-MVS		98.67 30	98.40 35	99.50 29	99.77 73	98.67 44	99.90 73	98.21 166	93.53 138	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

testtj		98.89 18	98.69 18	99.52 26	99.94 14	98.56 53	99.90 73	98.55 81	95.14 77	99.72 29	99.84 46	95.46 43	100.00 1	99.65 32	99.99 20	99.99 20

ETH3D-3000-0.1		98.68 29	98.42 31	99.47 34	99.83 63	98.57 51	99.90 73	98.37 140	93.81 129	99.81 12	99.90 17	94.34 76	99.86 90	99.84 13	99.98 33	99.97 63

原ACMM2							99.90 73

HPM-MVS		97.96 70	97.72 72	98.68 95	99.84 60	96.39 133	99.90 73	98.17 172	92.61 169	98.62 98	99.57 105	91.87 143	99.67 135	98.87 66	99.99 20	99.99 20
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPNet		98.49 43	98.40 35	98.77 90	99.62 89	96.80 120	99.90 73	99.51 15	97.60 12	99.20 71	99.36 123	93.71 99	99.91 74	97.99 105	98.71 127	99.61 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CSCG		97.10 102	97.04 96	97.27 164	99.89 45	91.92 245	99.90 73	99.07 31	88.67 258	95.26 177	99.82 53	93.17 115	99.98 42	98.15 96	99.47 109	99.90 89

PAPM		98.60 33	98.42 31	99.14 63	96.05 245	98.96 20	99.90 73	99.35 23	96.68 37	98.35 110	99.66 97	96.45 25	98.51 185	99.45 37	99.89 74	99.96 70

RRT_test8_iter05		94.58 173	94.11 170	95.98 197	97.88 175	96.11 149	99.89 81	97.45 239	91.66 202	88.28 268	96.71 233	96.53 24	97.40 243	94.73 166	83.85 277	94.45 236

114514_t		97.41 93	96.83 101	99.14 63	99.51 98	97.83 80	99.89 81	98.27 159	88.48 262	99.06 78	99.66 97	90.30 164	99.64 138	96.32 141	99.97 44	99.96 70

WTY-MVS		98.10 67	97.60 77	99.60 17	98.92 125	99.28 12	99.89 81	99.52 13	95.58 67	98.24 116	99.39 120	93.33 106	99.74 124	97.98 107	95.58 190	99.78 103

GA-MVS		93.83 186	92.84 197	96.80 173	95.73 257	93.57 208	99.88 84	97.24 261	92.57 174	92.92 202	96.66 235	78.73 268	97.67 235	87.75 266	94.06 206	99.17 178

UniMVSNet (Re)		93.07 205	92.13 212	95.88 199	94.84 274	96.24 142	99.88 84	98.98 34	92.49 179	89.25 248	95.40 275	87.09 198	97.14 260	93.13 203	78.16 314	94.26 249

HPM-MVS_fast		97.80 80	97.50 80	98.68 95	99.79 70	96.42 130	99.88 84	98.16 175	91.75 200	98.94 85	99.54 108	91.82 145	99.65 137	97.62 118	99.99 20	99.99 20

DPE-MVS		99.26 6	99.10 7	99.74 7	99.89 45	99.24 14	99.87 87	98.44 106	97.48 15	99.64 34	99.94 4	96.68 22	99.99 36	99.99 5	100.00 1	99.99 20

MTMP							99.87 87	96.49 313

Regformer-1		98.79 24	98.60 23	99.36 45	99.85 55	98.34 62	99.87 87	98.52 88	96.05 53	99.41 54	99.79 62	94.93 60	99.76 117	99.07 49	99.90 72	99.99 20

Regformer-2		98.78 25	98.59 24	99.36 45	99.85 55	98.32 63	99.87 87	98.52 88	96.04 54	99.41 54	99.79 62	94.92 61	99.76 117	99.05 50	99.90 72	99.98 51

CDPH-MVS		98.65 31	98.36 42	99.49 31	99.94 14	98.73 41	99.87 87	98.33 147	93.97 121	99.76 24	99.87 26	94.99 58	99.75 120	98.55 84	100.00 1	99.98 51

HQP-NCC					95.78 251		99.87 87		96.82 30	93.37 196

ACMP_Plane					95.78 251		99.87 87		96.82 30	93.37 196

APD-MVS		98.62 32	98.35 43	99.41 39	99.90 42	98.51 56	99.87 87	98.36 142	94.08 114	99.74 25	99.73 83	94.08 88	99.74 124	99.42 39	99.99 20	99.99 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.42 48	98.38 38	98.53 111	99.39 103	95.79 156	99.87 87	99.86 2	96.70 36	98.78 90	99.79 62	92.03 140	99.90 75	99.17 46	99.86 79	99.88 92

HQP-MVS		94.61 172	94.50 164	94.92 222	95.78 251	91.85 246	99.87 87	97.89 199	96.82 30	93.37 196	98.65 173	80.65 252	98.39 197	97.92 109	89.60 219	94.53 226

CNLPA		97.76 82	97.38 83	98.92 85	99.53 95	96.84 118	99.87 87	98.14 178	93.78 131	96.55 152	99.69 91	92.28 135	99.98 42	97.13 127	99.44 111	99.93 81

ETH3D cwj APD-0.16		98.40 51	98.07 59	99.40 41	99.59 90	98.41 60	99.86 98	98.24 162	92.18 186	99.73 26	99.87 26	93.47 103	99.85 94	99.74 24	99.95 51	99.93 81

SMA-MVS		98.76 26	98.48 29	99.62 15	99.87 52	98.87 27	99.86 98	98.38 137	93.19 147	99.77 23	99.94 4	95.54 40	100.00 1	99.74 24	99.99 20	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
plane_prior						91.74 250	99.86 98		96.76 34						89.59 221

tttt0517		96.85 110	96.49 112	97.92 139	97.48 202	95.89 154	99.85 101	98.54 85	90.72 226	96.63 149	98.93 157	97.47 9	99.02 158	93.03 205	95.76 186	98.85 193

ACMMP_NAP		98.49 43	98.14 54	99.54 23	99.66 87	98.62 50	99.85 101	98.37 140	94.68 89	99.53 44	99.83 49	92.87 120	100.00 1	98.66 80	99.84 80	99.99 20

thres200		96.96 106	96.21 118	99.22 50	98.97 119	98.84 30	99.85 101	99.71 5	93.17 148	96.26 160	98.88 160	89.87 169	99.51 142	94.26 178	94.91 197	99.31 169

F-COLMAP		96.93 108	96.95 99	96.87 172	99.71 84	91.74 250	99.85 101	97.95 193	93.11 150	95.72 170	99.16 136	92.35 133	99.94 68	95.32 151	99.35 114	98.92 189

SR-MVS		98.46 45	98.30 46	98.93 84	99.88 49	97.04 112	99.84 105	98.35 144	94.92 80	99.32 62	99.80 58	93.35 105	99.78 111	99.30 44	99.95 51	99.96 70

CANet_DTU		96.76 115	96.15 119	98.60 102	98.78 135	97.53 90	99.84 105	97.63 215	97.25 23	99.20 71	99.64 100	81.36 243	99.98 42	92.77 207	98.89 122	98.28 202

casdiffmvs		96.42 129	95.97 128	97.77 144	97.30 210	94.98 180	99.84 105	97.09 274	93.75 133	96.58 150	99.26 130	85.07 217	98.78 168	97.77 114	97.04 163	99.54 143

HQP_MVS		94.49 177	94.36 166	94.87 223	95.71 260	91.74 250	99.84 105	97.87 201	96.38 44	93.01 200	98.59 177	80.47 256	98.37 202	97.79 112	89.55 222	94.52 228

plane_prior2							99.84 105		96.38 44

BH-w/o		95.71 148	95.38 146	96.68 178	98.49 145	92.28 236	99.84 105	97.50 236	92.12 188	92.06 209	98.79 168	84.69 219	98.67 178	95.29 152	99.66 96	99.09 185

UniMVSNet_NR-MVSNet		92.95 208	92.11 213	95.49 204	94.61 279	95.28 172	99.83 111	99.08 30	91.49 206	89.21 250	96.86 228	87.14 197	96.73 285	93.20 199	77.52 319	94.46 231

APD-MVS_3200maxsize		98.25 62	98.08 58	98.78 89	99.81 68	96.60 125	99.82 112	98.30 154	93.95 123	99.37 60	99.77 68	92.84 121	99.76 117	98.95 58	99.92 67	99.97 63

PAPM_NR		98.12 66	97.93 68	98.70 94	99.94 14	96.13 146	99.82 112	98.43 114	94.56 93	97.52 130	99.70 88	94.40 71	99.98 42	97.00 131	99.98 33	99.99 20

nrg030		93.51 196	92.53 206	96.45 184	94.36 281	97.20 106	99.81 114	97.16 268	91.60 203	89.86 232	97.46 206	86.37 205	97.68 234	95.88 147	80.31 302	94.46 231

RRT_MVS		95.23 156	94.77 160	96.61 181	98.28 153	98.32 63	99.81 114	97.41 246	92.59 171	91.28 215	97.76 202	95.02 54	97.23 256	93.65 194	87.14 251	94.28 248

diffmvs		97.00 105	96.64 107	98.09 133	97.64 194	96.17 145	99.81 114	97.19 263	94.67 91	98.95 84	99.28 124	86.43 204	98.76 171	98.37 89	97.42 154	99.33 167

DU-MVS		92.46 219	91.45 228	95.49 204	94.05 286	95.28 172	99.81 114	98.74 51	92.25 185	89.21 250	96.64 237	81.66 239	96.73 285	93.20 199	77.52 319	94.46 231

ACMP	92.05 9	92.74 212	92.42 209	93.73 266	95.91 250	88.72 295	99.81 114	97.53 230	94.13 111	87.00 285	98.23 191	74.07 300	98.47 186	96.22 142	88.86 231	93.99 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		98.38 53	98.25 47	98.77 90	99.88 49	96.56 127	99.80 119	98.36 142	94.68 89	99.20 71	99.80 58	93.28 110	99.78 111	99.34 42	99.92 67	99.98 51

Fast-Effi-MVS+-dtu		93.72 193	93.86 177	93.29 277	97.06 216	86.16 313	99.80 119	96.83 299	92.66 166	92.58 207	97.83 201	81.39 242	97.67 235	89.75 248	96.87 167	96.05 224

zzz-MVS		98.33 55	98.00 62	99.30 47	99.85 55	97.93 78	99.80 119	98.28 156	95.76 60	97.18 137	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

BH-untuned		95.18 157	94.83 158	96.22 192	98.36 149	91.22 261	99.80 119	97.32 255	90.91 221	91.08 216	98.67 172	83.51 227	98.54 184	94.23 179	99.61 101	98.92 189

tfpn200view9		96.79 113	95.99 123	99.19 53	98.94 121	98.82 31	99.78 123	99.71 5	92.86 153	96.02 163	98.87 162	89.33 175	99.50 144	93.84 184	94.57 198	99.27 172

thres400		96.78 114	95.99 123	99.16 59	98.94 121	98.82 31	99.78 123	99.71 5	92.86 153	96.02 163	98.87 162	89.33 175	99.50 144	93.84 184	94.57 198	99.16 179

TAPA-MVS	92.12 8	94.42 178	93.60 181	96.90 171	99.33 106	91.78 249	99.78 123	98.00 187	89.89 239	94.52 183	99.47 112	91.97 141	99.18 154	69.90 339	99.52 106	99.73 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + MP.		98.93 15	98.77 16	99.41 39	99.74 77	98.67 44	99.77 126	98.38 137	96.73 35	99.88 3	99.74 81	94.89 62	99.59 139	99.80 18	99.98 33	99.97 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		93.21 201	92.80 198	94.44 243	93.12 303	90.85 266	99.77 126	97.61 220	96.19 51	91.56 212	98.65 173	75.16 294	98.47 186	93.78 190	89.39 225	93.99 278

v2v482		91.30 240	90.07 252	95.01 218	93.13 301	93.79 204	99.77 126	97.02 280	88.05 266	89.25 248	95.37 279	80.73 250	97.15 259	87.28 272	80.04 305	94.09 269

Baseline_NR-MVSNet		90.33 263	89.51 261	92.81 287	92.84 309	89.95 283	99.77 126	93.94 347	84.69 311	89.04 254	95.66 262	81.66 239	96.52 293	90.99 228	76.98 324	91.97 327

ACMM	91.95 10	92.88 209	92.52 207	93.98 261	95.75 256	89.08 293	99.77 126	97.52 232	93.00 151	89.95 229	97.99 198	76.17 286	98.46 189	93.63 195	88.87 230	94.39 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		98.31 56	98.17 52	98.71 93	99.79 70	96.37 134	99.76 131	98.31 151	94.43 98	99.40 58	99.75 77	93.28 110	99.78 111	98.90 64	99.92 67	99.97 63

RE-MVS-def			98.13 55		99.79 70	96.37 134	99.76 131	98.31 151	94.43 98	99.40 58	99.75 77	92.95 119		98.90 64	99.92 67	99.97 63

BH-RMVSNet		95.18 157	94.31 167	97.80 141	98.17 162	95.23 175	99.76 131	97.53 230	92.52 176	94.27 188	99.25 131	76.84 278	98.80 166	90.89 232	99.54 105	99.35 165

v148		90.70 253	89.63 256	93.92 262	92.97 307	90.97 263	99.75 134	96.89 295	87.51 271	88.27 269	95.01 292	81.67 238	97.04 269	87.40 270	77.17 323	93.75 295

PGM-MVS		98.34 54	98.13 55	98.99 79	99.92 35	97.00 113	99.75 134	99.50 16	93.90 126	99.37 60	99.76 72	93.24 113	100.00 1	97.75 116	99.96 48	99.98 51

LPG-MVS_test		92.96 207	92.71 200	93.71 268	95.43 266	88.67 296	99.75 134	97.62 217	92.81 156	90.05 225	98.49 183	75.24 292	98.40 195	95.84 148	89.12 226	94.07 270

thres100view900		96.74 117	95.92 133	99.18 54	98.90 128	98.77 36	99.74 137	99.71 5	92.59 171	95.84 166	98.86 164	89.25 177	99.50 144	93.84 184	94.57 198	99.27 172

MP-MVS-pluss		98.07 68	97.64 75	99.38 44	99.74 77	98.41 60	99.74 137	98.18 171	93.35 142	96.45 154	99.85 33	92.64 127	99.97 51	98.91 63	99.89 74	99.77 104
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs5		90.17 269	89.09 268	93.40 275	92.10 318	89.77 286	99.74 137	95.58 330	85.88 295	87.24 284	95.74 258	73.41 303	96.48 295	88.54 257	83.56 278	93.95 281

thres600view7		96.69 120	95.87 136	99.14 63	98.90 128	98.78 35	99.74 137	99.71 5	92.59 171	95.84 166	98.86 164	89.25 177	99.50 144	93.44 197	94.50 201	99.16 179

baseline2		96.71 119	96.49 112	97.37 160	95.63 264	95.96 152	99.74 137	98.88 42	92.94 152	91.61 211	98.97 151	97.72 5	98.62 180	94.83 160	98.08 143	97.53 215

miper_enhance_ethall		94.36 181	93.98 173	95.49 204	98.68 139	95.24 174	99.73 142	97.29 257	93.28 145	89.86 232	95.97 254	94.37 75	97.05 267	92.20 211	84.45 269	94.19 255

testgi		89.01 284	88.04 285	91.90 297	93.49 296	84.89 322	99.73 142	95.66 328	93.89 128	85.14 305	98.17 192	59.68 343	94.66 330	77.73 325	88.88 229	96.16 223

sss		97.57 87	97.03 97	99.18 54	98.37 148	98.04 72	99.73 142	99.38 21	93.46 140	98.76 91	99.06 140	91.21 149	99.89 79	96.33 140	97.01 164	99.62 125

canonicalmvs		97.09 104	96.32 116	99.39 43	98.93 123	98.95 21	99.72 145	97.35 251	94.45 96	97.88 124	99.42 116	86.71 201	99.52 141	98.48 86	93.97 207	99.72 111

3Dnovator+	91.53 11	96.31 133	95.24 149	99.52 26	96.88 227	98.64 49	99.72 145	98.24 162	95.27 75	88.42 267	98.98 149	82.76 232	99.94 68	97.10 129	99.83 81	99.96 70

HyFIR lowres test		96.66 122	96.43 114	97.36 161	99.05 113	93.91 203	99.70 147	99.80 3	90.54 227	96.26 160	98.08 194	92.15 138	98.23 213	96.84 137	95.46 191	99.93 81

D2MVS		92.76 211	92.59 205	93.27 278	95.13 269	89.54 289	99.69 148	99.38 21	92.26 184	87.59 276	94.61 306	85.05 218	97.79 231	91.59 218	88.01 243	92.47 321

TranMVSNet+NR-MVSNet		91.68 238	90.61 239	94.87 223	93.69 293	93.98 201	99.69 148	98.65 59	91.03 219	88.44 263	96.83 232	80.05 259	96.18 306	90.26 243	76.89 326	94.45 236

V42		91.28 242	90.12 251	94.74 227	93.42 298	93.46 211	99.68 150	97.02 280	87.36 274	89.85 234	95.05 290	81.31 244	97.34 247	87.34 271	80.07 304	93.40 305

testmvs		40.60 328	44.45 331	29.05 343	19.49 365	14.11 366	99.68 150	18.47 364	20.74 359	64.59 347	98.48 186	10.95 364	17.09 362	56.66 352	11.01 358	55.94 355

abl_6		97.67 85	97.34 86	98.66 97	99.68 86	96.11 149	99.68 150	98.14 178	93.80 130	99.27 68	99.70 88	88.65 186	99.98 42	97.46 120	99.72 92	99.89 90

Regformer-3		98.58 36	98.41 33	99.10 69	99.84 60	97.57 88	99.66 153	98.52 88	95.79 57	99.01 81	99.77 68	94.40 71	99.75 120	98.82 68	99.83 81	99.98 51

Regformer-4		98.56 37	98.39 37	99.08 71	99.84 60	97.52 91	99.66 153	98.52 88	95.76 60	99.01 81	99.77 68	94.33 79	99.75 120	98.80 71	99.83 81	99.98 51

DeepC-MVS	94.51 4	96.92 109	96.40 115	98.45 116	99.16 110	95.90 153	99.66 153	98.06 184	96.37 47	94.37 186	99.49 111	83.29 230	99.90 75	97.63 117	99.61 101	99.55 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 1792x2688		96.81 112	96.53 111	97.64 149	98.91 127	93.07 217	99.65 156	99.80 3	95.64 65	95.39 174	98.86 164	84.35 223	99.90 75	96.98 132	99.16 118	99.95 78

Test_1112_low_res		95.72 146	94.83 158	98.42 119	97.79 183	96.41 131	99.65 156	96.65 309	92.70 163	92.86 205	96.13 251	92.15 138	99.30 151	91.88 215	93.64 209	99.55 139

1112_ss		96.01 141	95.20 151	98.42 119	97.80 182	96.41 131	99.65 156	96.66 308	92.71 162	92.88 204	99.40 118	92.16 137	99.30 151	91.92 214	93.66 208	99.55 139

OMC-MVS		97.28 96	97.23 89	97.41 157	99.76 74	93.36 215	99.65 156	97.95 193	96.03 55	97.41 133	99.70 88	89.61 171	99.51 142	96.73 138	98.25 138	99.38 161

test_yl		97.83 77	97.37 84	99.21 51	99.18 108	97.98 75	99.64 160	99.27 25	91.43 210	97.88 124	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

DCV-MVSNet		97.83 77	97.37 84	99.21 51	99.18 108	97.98 75	99.64 160	99.27 25	91.43 210	97.88 124	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

MG-MVS		98.91 17	98.65 20	99.68 11	99.94 14	99.07 18	99.64 160	99.44 18	97.33 17	99.00 83	99.72 84	94.03 90	99.98 42	98.73 74	100.00 1	100.00 1

v1144		91.09 245	89.83 253	94.87 223	93.25 300	93.69 207	99.62 163	96.98 285	86.83 284	89.64 240	94.99 295	80.94 247	97.05 267	85.08 290	81.16 292	93.87 288

cl-mvsnet2		93.77 190	93.25 194	95.33 210	99.49 99	94.43 192	99.61 164	98.09 181	90.38 229	89.16 253	95.61 263	90.56 162	97.34 247	91.93 213	84.45 269	94.21 254

WR-MVS		92.31 222	91.25 230	95.48 207	94.45 280	95.29 171	99.60 165	98.68 55	90.10 234	88.07 271	96.89 226	80.68 251	96.80 283	93.14 202	79.67 306	94.36 241

Effi-MVS+-dtu		94.53 176	95.30 148	92.22 292	97.77 184	82.54 329	99.59 166	97.06 277	94.92 80	95.29 176	95.37 279	85.81 208	97.89 229	94.80 161	97.07 162	96.23 222

cl-mvsnet1		92.32 221	91.60 222	94.47 241	97.31 209	92.74 224	99.58 167	96.75 305	86.99 281	87.64 275	95.54 267	89.55 172	96.50 294	88.58 256	82.44 282	94.17 256

FIs		94.10 183	93.43 186	96.11 194	94.70 277	96.82 119	99.58 167	98.93 39	92.54 175	89.34 246	97.31 211	87.62 192	97.10 264	94.22 180	86.58 254	94.40 238

cl-mvsnet_		92.31 222	91.58 223	94.52 237	97.33 208	92.77 222	99.57 169	96.78 304	86.97 282	87.56 277	95.51 270	89.43 173	96.62 290	88.60 255	82.44 282	94.16 261

EPNet_dtu		95.71 148	95.39 145	96.66 179	98.92 125	93.41 213	99.57 169	98.90 40	96.19 51	97.52 130	98.56 181	92.65 126	97.36 245	77.89 324	98.33 134	99.20 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v144192		90.79 252	89.52 260	94.59 233	93.11 304	92.77 222	99.56 171	96.99 283	86.38 288	89.82 235	94.95 297	80.50 255	97.10 264	83.98 296	80.41 300	93.90 285

OpenMVS	90.15 15	94.77 166	93.59 182	98.33 123	96.07 244	97.48 96	99.56 171	98.57 74	90.46 228	86.51 291	98.95 155	78.57 269	99.94 68	93.86 183	99.74 90	97.57 214

MVSFormer		96.94 107	96.60 108	97.95 137	97.28 211	97.70 85	99.55 173	97.27 259	91.17 214	99.43 52	99.54 108	90.92 158	96.89 277	94.67 168	99.62 98	99.25 174

test_djsdf		92.83 210	92.29 211	94.47 241	91.90 320	92.46 233	99.55 173	97.27 259	91.17 214	89.96 228	96.07 253	81.10 245	96.89 277	94.67 168	88.91 228	94.05 272

PS-MVSNAJ		98.44 47	98.20 50	99.16 59	98.80 134	98.92 23	99.54 175	98.17 172	97.34 16	99.85 6	99.85 33	91.20 150	99.89 79	99.41 40	99.67 95	98.69 199

CDS-MVSNet		96.34 131	96.07 120	97.13 166	97.37 204	94.96 181	99.53 176	97.91 198	91.55 205	95.37 175	98.32 190	95.05 53	97.13 261	93.80 188	95.75 187	99.30 170
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		98.23 63	97.97 64	99.02 77	98.69 138	98.66 46	99.52 177	98.08 183	97.05 26	99.86 4	99.86 29	90.65 160	99.71 128	99.39 41	98.63 128	98.69 199

PatchMatch-RL		96.04 140	95.40 144	97.95 137	99.59 90	95.22 176	99.52 177	99.07 31	93.96 122	96.49 153	98.35 189	82.28 234	99.82 105	90.15 244	99.22 117	98.81 196

baseline		96.43 128	95.98 125	97.76 145	97.34 206	95.17 177	99.51 179	97.17 266	93.92 125	96.90 143	99.28 124	85.37 214	98.64 179	97.50 119	96.86 168	99.46 152

miper_ehance_all_eth		93.16 202	92.60 202	94.82 226	97.57 197	93.56 209	99.50 180	97.07 276	88.75 256	88.85 257	95.52 269	90.97 157	96.74 284	90.77 234	84.45 269	94.17 256

v1192		90.62 257	89.25 265	94.72 229	93.13 301	93.07 217	99.50 180	97.02 280	86.33 289	89.56 242	95.01 292	79.22 263	97.09 266	82.34 306	81.16 292	94.01 275

v1921920		90.46 259	89.12 267	94.50 239	92.96 308	92.46 233	99.49 182	96.98 285	86.10 291	89.61 241	95.30 282	78.55 270	97.03 271	82.17 307	80.89 298	94.01 275

无先验							99.49 182	98.71 52	93.46 140				100.00 1	94.36 174		99.99 20

pmmvs4		92.10 227	91.07 233	95.18 214	92.82 310	94.96 181	99.48 184	96.83 299	87.45 273	88.66 261	96.56 240	83.78 226	96.83 281	89.29 250	84.77 267	93.75 295

Vis-MVSNet (Re-imp)		96.32 132	95.98 125	97.35 162	97.93 173	94.82 185	99.47 185	98.15 177	91.83 196	95.09 178	99.11 137	91.37 148	97.47 242	93.47 196	97.43 152	99.74 107

API-MVS		97.86 74	97.66 74	98.47 114	99.52 96	95.41 168	99.47 185	98.87 43	91.68 201	98.84 87	99.85 33	92.34 134	99.99 36	98.44 87	99.96 48	100.00 1

旧先验2							99.46 187		94.21 110	99.85 6			99.95 60	96.96 133

IterMVS-LS		92.69 214	92.11 213	94.43 245	96.80 231	92.74 224	99.45 188	96.89 295	88.98 249	89.65 239	95.38 278	88.77 183	96.34 300	90.98 229	82.04 285	94.22 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator	91.47 12	96.28 136	95.34 147	99.08 71	96.82 230	97.47 97	99.45 188	98.81 47	95.52 68	89.39 244	99.00 146	81.97 235	99.95 60	97.27 124	99.83 81	99.84 95

bset_n11_16_dypcd		93.05 206	92.30 210	95.31 211	90.23 335	95.05 179	99.44 190	97.28 258	92.51 177	90.65 221	96.68 234	85.30 215	96.71 287	94.49 172	84.14 272	94.16 261

FC-MVSNet-test		93.81 188	93.15 195	95.80 202	94.30 283	96.20 143	99.42 191	98.89 41	92.33 183	89.03 255	97.27 213	87.39 195	96.83 281	93.20 199	86.48 255	94.36 241

cl_fuxian		92.53 217	91.87 219	94.52 237	97.40 203	92.99 220	99.40 192	96.93 292	87.86 268	88.69 260	95.44 273	89.95 168	96.44 296	90.45 238	80.69 299	94.14 266

EI-MVSNet-Vis-set		98.27 59	98.11 57	98.75 92	99.83 63	96.59 126	99.40 192	98.51 95	95.29 73	98.51 102	99.76 72	93.60 102	99.71 128	98.53 85	99.52 106	99.95 78

新几何2							99.40 192

QAPM		95.40 154	94.17 169	99.10 69	96.92 222	97.71 83	99.40 192	98.68 55	89.31 243	88.94 256	98.89 158	82.48 233	99.96 53	93.12 204	99.83 81	99.62 125

MTAPA		98.29 58	97.96 67	99.30 47	99.85 55	97.93 78	99.39 196	98.28 156	95.76 60	97.18 137	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

miper_lstm_enhance		91.81 231	91.39 229	93.06 284	97.34 206	89.18 292	99.38 197	96.79 303	86.70 285	87.47 279	95.22 287	90.00 167	95.86 316	88.26 260	81.37 290	94.15 263

v1240		90.20 267	88.79 274	94.44 243	93.05 306	92.27 237	99.38 197	96.92 293	85.89 293	89.36 245	94.87 299	77.89 273	97.03 271	80.66 314	81.08 294	94.01 275

EPP-MVSNet		96.69 120	96.60 108	96.96 169	97.74 187	93.05 219	99.37 199	98.56 76	88.75 256	95.83 168	99.01 144	96.01 28	98.56 182	96.92 135	97.20 160	99.25 174

MSDG		94.37 179	93.36 191	97.40 158	98.88 130	93.95 202	99.37 199	97.38 249	85.75 298	90.80 219	99.17 135	84.11 225	99.88 85	86.35 281	98.43 132	98.36 201

EI-MVSNet-UG-set		98.14 65	97.99 63	98.60 102	99.80 69	96.27 136	99.36 201	98.50 99	95.21 76	98.30 112	99.75 77	93.29 109	99.73 127	98.37 89	99.30 115	99.81 98

test222					99.55 94	97.41 102	99.34 202	98.55 81	91.86 195	99.27 68	99.83 49	93.84 96			99.95 51	99.99 20

our_test_3		90.39 260	89.48 263	93.12 281	92.40 314	89.57 288	99.33 203	96.35 316	87.84 269	85.30 304	94.99 295	84.14 224	96.09 310	80.38 315	84.56 268	93.71 300

ppachtmachnet_test		89.58 278	88.35 280	93.25 279	92.40 314	90.44 275	99.33 203	96.73 306	85.49 302	85.90 302	95.77 257	81.09 246	96.00 314	76.00 332	82.49 281	93.30 308

mvs_anonymous		95.65 150	95.03 155	97.53 152	98.19 160	95.74 159	99.33 203	97.49 237	90.87 222	90.47 223	97.10 217	88.23 188	97.16 258	95.92 146	97.66 149	99.68 115

AUN-MVS		93.28 200	92.60 202	95.34 209	98.29 151	90.09 281	99.31 206	98.56 76	91.80 199	96.35 159	98.00 197	89.38 174	98.28 209	92.46 208	69.22 338	97.64 213

xiu_mvs_v1_base_debu		97.43 89	97.06 93	98.55 106	97.74 187	98.14 67	99.31 206	97.86 203	96.43 41	99.62 37	99.69 91	85.56 211	99.68 132	99.05 50	98.31 135	97.83 208

xiu_mvs_v1_base		97.43 89	97.06 93	98.55 106	97.74 187	98.14 67	99.31 206	97.86 203	96.43 41	99.62 37	99.69 91	85.56 211	99.68 132	99.05 50	98.31 135	97.83 208

xiu_mvs_v1_base_debi		97.43 89	97.06 93	98.55 106	97.74 187	98.14 67	99.31 206	97.86 203	96.43 41	99.62 37	99.69 91	85.56 211	99.68 132	99.05 50	98.31 135	97.83 208

MVS_Test		96.46 127	95.74 138	98.61 101	98.18 161	97.23 105	99.31 206	97.15 269	91.07 218	98.84 87	97.05 221	88.17 189	98.97 160	94.39 173	97.50 151	99.61 127

testdata1							99.28 211		96.35 48

Vis-MVSNet		95.72 146	95.15 153	97.45 155	97.62 195	94.28 195	99.28 211	98.24 162	94.27 109	96.84 144	98.94 156	79.39 262	98.76 171	93.25 198	98.49 130	99.30 170
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet3		92.69 214	91.58 223	95.99 196	98.29 151	97.42 101	99.26 213	97.62 217	89.80 240	89.68 236	95.32 281	81.62 241	96.27 303	87.01 277	85.65 259	94.29 247

DeepC-MVS_fast	96.59 1	98.81 22	98.54 26	99.62 15	99.90 42	98.85 29	99.24 214	98.47 101	98.14 4	99.08 77	99.91 13	93.09 116	100.00 1	99.04 54	99.99 20	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
YYNet1		85.50 301	83.33 305	92.00 295	90.89 330	88.38 303	99.22 215	96.55 311	79.60 332	57.26 351	92.72 324	79.09 266	93.78 337	77.25 327	77.37 322	93.84 290

v8		90.54 258	89.17 266	94.66 230	93.43 297	93.40 214	99.20 216	96.94 291	85.76 296	87.56 277	94.51 307	81.96 236	97.19 257	84.94 291	78.25 313	93.38 307

MDA-MVSNet_test_wron		85.51 300	83.32 306	92.10 294	90.96 329	88.58 299	99.20 216	96.52 312	79.70 331	57.12 352	92.69 326	79.11 265	93.86 336	77.10 328	77.46 321	93.86 289

ACMMP		97.74 83	97.44 82	98.66 97	99.92 35	96.13 146	99.18 218	99.45 17	94.84 84	96.41 157	99.71 86	91.40 147	99.99 36	97.99 105	98.03 144	99.87 93
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
WR-MVS_H		91.30 240	90.35 243	94.15 251	94.17 285	92.62 231	99.17 219	98.94 36	88.87 254	86.48 293	94.46 311	84.36 222	96.61 291	88.19 261	78.51 312	93.21 311

TAMVS		95.85 143	95.58 141	96.65 180	97.07 215	93.50 210	99.17 219	97.82 207	91.39 213	95.02 179	98.01 196	92.20 136	97.30 250	93.75 191	95.83 184	99.14 182

PS-MVSNAJss		93.64 195	93.31 192	94.61 232	92.11 317	92.19 238	99.12 221	97.38 249	92.51 177	88.45 262	96.99 224	91.20 150	97.29 253	94.36 174	87.71 246	94.36 241

DTE-MVSNet		89.40 279	88.24 283	92.88 286	92.66 312	89.95 283	99.10 222	98.22 165	87.29 275	85.12 306	96.22 248	76.27 285	95.30 323	83.56 300	75.74 329	93.41 304

CP-MVSNet		91.23 243	90.22 247	94.26 248	93.96 288	92.39 235	99.09 223	98.57 74	88.95 252	86.42 294	96.57 239	79.19 264	96.37 298	90.29 242	78.95 309	94.02 273

AdaColmap		97.23 99	96.80 103	98.51 112	99.99 1	95.60 164	99.09 223	98.84 46	93.32 143	96.74 147	99.72 84	86.04 207	100.00 1	98.01 103	99.43 112	99.94 80

v10		90.25 266	88.82 273	94.57 235	93.53 295	93.43 212	99.08 225	96.87 297	85.00 306	87.34 283	94.51 307	80.93 248	97.02 273	82.85 303	79.23 307	93.26 309

XVG-OURS-SEG-HR		94.79 164	94.70 162	95.08 216	98.05 167	89.19 290	99.08 225	97.54 228	93.66 135	94.87 180	99.58 104	78.78 267	99.79 109	97.31 123	93.40 211	96.25 220

XVG-OURS		94.82 163	94.74 161	95.06 217	98.00 169	89.19 290	99.08 225	97.55 226	94.10 113	94.71 181	99.62 101	80.51 254	99.74 124	96.04 144	93.06 215	96.25 220

IS-MVSNet		96.29 135	95.90 134	97.45 155	98.13 165	94.80 186	99.08 225	97.61 220	92.02 192	95.54 173	98.96 153	90.64 161	98.08 218	93.73 192	97.41 155	99.47 151

v7n		89.65 277	88.29 282	93.72 267	92.22 316	90.56 272	99.07 229	97.10 273	85.42 304	86.73 287	94.72 300	80.06 258	97.13 261	81.14 312	78.12 315	93.49 303

EI-MVSNet		93.73 192	93.40 190	94.74 227	96.80 231	92.69 227	99.06 230	97.67 213	88.96 251	91.39 213	99.02 142	88.75 184	97.30 250	91.07 224	87.85 244	94.22 252

CVMVSNet		94.68 170	94.94 156	93.89 264	96.80 231	86.92 311	99.06 230	98.98 34	94.45 96	94.23 189	99.02 142	85.60 210	95.31 322	90.91 231	95.39 193	99.43 157

baseline1		95.78 145	94.86 157	98.54 109	98.47 146	98.07 70	99.06 230	97.99 188	92.68 165	94.13 190	98.62 176	93.28 110	98.69 177	93.79 189	85.76 258	98.84 194

PEN-MVS		90.19 268	89.06 269	93.57 273	93.06 305	90.90 265	99.06 230	98.47 101	88.11 265	85.91 301	96.30 246	76.67 279	95.94 315	87.07 274	76.91 325	93.89 286

Anonymous20231206		86.32 295	85.42 297	89.02 317	89.11 340	80.53 343	99.05 234	95.28 334	85.43 303	82.82 315	93.92 315	74.40 298	93.44 340	66.99 344	81.83 287	93.08 313

MAR-MVS		97.43 89	97.19 90	98.15 132	99.47 100	94.79 187	99.05 234	98.76 50	92.65 167	98.66 96	99.82 53	88.52 187	99.98 42	98.12 97	99.63 97	99.67 117
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
VNet		97.21 100	96.57 110	99.13 68	98.97 119	97.82 81	99.03 236	99.21 27	94.31 106	99.18 75	98.88 160	86.26 206	99.89 79	98.93 60	94.32 202	99.69 114

LCM-MVSNet-Re		92.31 222	92.60 202	91.43 300	97.53 198	79.27 345	99.02 237	91.83 351	92.07 189	80.31 325	94.38 312	83.50 228	95.48 318	97.22 126	97.58 150	99.54 143

jajsoiax		91.92 229	91.18 231	94.15 251	91.35 326	90.95 264	99.00 238	97.42 244	92.61 169	87.38 281	97.08 218	72.46 305	97.36 245	94.53 171	88.77 232	94.13 267

test_part1		92.15 226	90.72 236	96.44 186	98.87 131	97.46 98	98.99 239	98.26 160	85.89 293	86.34 296	96.34 245	81.71 237	97.48 241	91.06 225	78.99 308	94.37 240

VPNet		91.81 231	90.46 240	95.85 201	94.74 276	95.54 165	98.98 240	98.59 71	92.14 187	90.77 220	97.44 207	68.73 319	97.54 239	94.89 159	77.89 316	94.46 231

PS-CasMVS		90.63 256	89.51 261	93.99 260	93.83 290	91.70 254	98.98 240	98.52 88	88.48 262	86.15 299	96.53 241	75.46 290	96.31 301	88.83 254	78.86 311	93.95 281

FMVSNet2		91.02 246	89.56 258	95.41 208	97.53 198	95.74 159	98.98 240	97.41 246	87.05 278	88.43 265	95.00 294	71.34 309	96.24 305	85.12 289	85.21 264	94.25 251

K. test v3		88.05 289	87.24 291	90.47 308	91.82 322	82.23 332	98.96 243	97.42 244	89.05 246	76.93 335	95.60 264	68.49 320	95.42 319	85.87 286	81.01 296	93.75 295

tfpnnormal		89.29 281	87.61 288	94.34 247	94.35 282	94.13 197	98.95 244	98.94 36	83.94 313	84.47 308	95.51 270	74.84 295	97.39 244	77.05 329	80.41 300	91.48 331

AllTest		92.48 218	91.64 221	95.00 219	99.01 115	88.43 300	98.94 245	96.82 301	86.50 286	88.71 258	98.47 187	74.73 296	99.88 85	85.39 287	96.18 175	96.71 218

anonymousdsp		91.79 236	90.92 234	94.41 246	90.76 331	92.93 221	98.93 246	97.17 266	89.08 245	87.46 280	95.30 282	78.43 272	96.92 276	92.38 209	88.73 233	93.39 306

DP-MVS		94.54 174	93.42 187	97.91 140	99.46 102	94.04 198	98.93 246	97.48 238	81.15 327	90.04 227	99.55 106	87.02 199	99.95 60	88.97 253	98.11 139	99.73 108

IterMVS-SCA-FT		90.85 251	90.16 250	92.93 285	96.72 236	89.96 282	98.89 248	96.99 283	88.95 252	86.63 289	95.67 261	76.48 282	95.00 325	87.04 275	84.04 276	93.84 290

IterMVS		90.91 248	90.17 249	93.12 281	96.78 234	90.42 276	98.89 248	97.05 279	89.03 247	86.49 292	95.42 274	76.59 281	95.02 324	87.22 273	84.09 273	93.93 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211		93.10 204	91.99 216	96.40 187	99.10 112	89.65 287	98.88 250	97.93 195	83.71 316	94.00 191	98.75 169	68.79 317	99.88 85	95.08 154	91.71 217	99.68 115

VPA-MVSNet		92.70 213	91.55 225	96.16 193	95.09 270	96.20 143	98.88 250	99.00 33	91.02 220	91.82 210	95.29 285	76.05 288	97.96 225	95.62 150	81.19 291	94.30 246

test20.03		84.72 305	83.99 300	86.91 325	88.19 342	80.62 342	98.88 250	95.94 323	88.36 264	78.87 329	94.62 305	68.75 318	89.11 349	66.52 345	75.82 328	91.00 333

XXY-MVS		91.82 230	90.46 240	95.88 199	93.91 289	95.40 169	98.87 253	97.69 212	88.63 260	87.87 273	97.08 218	74.38 299	97.89 229	91.66 217	84.07 274	94.35 244

SCA		94.69 168	93.81 178	97.33 163	97.10 214	94.44 191	98.86 254	98.32 149	93.30 144	96.17 162	95.59 265	76.48 282	97.95 226	91.06 225	97.43 152	99.59 130

DWT-MVSNet_test		97.31 95	97.19 90	97.66 148	98.24 157	94.67 189	98.86 254	98.20 170	93.60 137	98.09 118	98.89 158	97.51 7	98.78 168	94.04 181	97.28 157	99.55 139

eth_miper_zixun_eth		92.41 220	91.93 217	93.84 265	97.28 211	90.68 268	98.83 256	96.97 287	88.57 261	89.19 252	95.73 260	89.24 179	96.69 288	89.97 246	81.55 288	94.15 263

CL-MVSNet_2432*1600		84.50 306	83.15 308	88.53 321	86.00 345	81.79 335	98.82 257	97.35 251	85.12 305	83.62 313	90.91 335	76.66 280	91.40 344	69.53 340	60.36 347	92.40 322

ACMH+	89.98 16	90.35 262	89.54 259	92.78 288	95.99 247	86.12 314	98.81 258	97.18 265	89.38 242	83.14 314	97.76 202	68.42 321	98.43 191	89.11 252	86.05 257	93.78 294

N_pmnet		80.06 316	80.78 315	77.89 331	91.94 319	45.28 360	98.80 259	56.82 363	78.10 335	80.08 327	93.33 319	77.03 275	95.76 317	68.14 343	82.81 280	92.64 317

VDD-MVS		93.77 190	92.94 196	96.27 191	98.55 141	90.22 278	98.77 260	97.79 208	90.85 223	96.82 145	99.42 116	61.18 342	99.77 115	98.95 58	94.13 204	98.82 195

LFMVS		94.75 167	93.56 184	98.30 125	99.03 114	95.70 162	98.74 261	97.98 190	87.81 270	98.47 104	99.39 120	67.43 325	99.53 140	98.01 103	95.20 196	99.67 117

LS3D		95.84 144	95.11 154	98.02 136	99.85 55	95.10 178	98.74 261	98.50 99	87.22 277	93.66 195	99.86 29	87.45 194	99.95 60	90.94 230	99.81 87	99.02 187

Anonymous20240529		92.10 227	90.65 238	96.47 182	98.82 132	90.61 270	98.72 263	98.67 58	75.54 341	93.90 193	98.58 179	66.23 328	99.90 75	94.70 167	90.67 218	98.90 192

TR-MVS		94.54 174	93.56 184	97.49 154	97.96 171	94.34 194	98.71 264	97.51 235	90.30 233	94.51 184	98.69 171	75.56 289	98.77 170	92.82 206	95.99 179	99.35 165

USDC		90.00 272	88.96 271	93.10 283	94.81 275	88.16 304	98.71 264	95.54 331	93.66 135	83.75 312	97.20 214	65.58 330	98.31 206	83.96 297	87.49 250	92.85 316

VDDNet		93.12 203	91.91 218	96.76 175	96.67 238	92.65 230	98.69 266	98.21 166	82.81 321	97.75 127	99.28 124	61.57 340	99.48 148	98.09 100	94.09 205	98.15 204

EU-MVSNet		90.14 270	90.34 244	89.54 315	92.55 313	81.06 339	98.69 266	98.04 186	91.41 212	86.59 290	96.84 231	80.83 249	93.31 341	86.20 282	81.91 286	94.26 249

mvs_tets		91.81 231	91.08 232	94.00 259	91.63 324	90.58 271	98.67 268	97.43 242	92.43 180	87.37 282	97.05 221	71.76 307	97.32 249	94.75 164	88.68 234	94.11 268

MDA-MVSNet-bldmvs		84.09 308	81.52 314	91.81 298	91.32 327	88.00 307	98.67 268	95.92 324	80.22 330	55.60 353	93.32 320	68.29 322	93.60 339	73.76 334	76.61 327	93.82 292

UGNet		95.33 155	94.57 163	97.62 151	98.55 141	94.85 183	98.67 268	99.32 24	95.75 63	96.80 146	96.27 247	72.18 306	99.96 53	94.58 170	99.05 120	98.04 206
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
pm-mvs1		89.36 280	87.81 287	94.01 258	93.40 299	91.93 244	98.62 271	96.48 314	86.25 290	83.86 311	96.14 250	73.68 302	97.04 269	86.16 283	75.73 330	93.04 314

test_0402		85.58 298	83.94 302	90.50 307	93.81 291	85.04 321	98.55 272	95.20 337	76.01 338	79.72 328	95.13 288	64.15 335	96.26 304	66.04 347	86.88 253	90.21 339

ACMH	89.72 17	90.64 255	89.63 256	93.66 272	95.64 263	88.64 298	98.55 272	97.45 239	89.03 247	81.62 321	97.61 204	69.75 315	98.41 193	89.37 249	87.62 248	93.92 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		89.86 273	88.44 279	94.13 253	98.93 123	90.68 268	98.54 274	98.26 160	76.28 337	86.73 287	95.54 267	70.60 313	97.56 238	90.82 233	80.27 303	94.15 263

TransMVSNet (Re)		87.25 293	85.28 298	93.16 280	93.56 294	91.03 262	98.54 274	94.05 346	83.69 317	81.09 323	96.16 249	75.32 291	96.40 297	76.69 330	68.41 339	92.06 325

XVG-ACMP-BASELINE		91.22 244	90.75 235	92.63 289	93.73 292	85.61 316	98.52 276	97.44 241	92.77 160	89.90 231	96.85 229	66.64 327	98.39 197	92.29 210	88.61 235	93.89 286

CHOSEN 280x420		99.01 12	99.03 8	98.95 83	99.38 104	98.87 27	98.46 277	99.42 20	97.03 27	99.02 80	99.09 138	99.35 1	98.21 214	99.73 27	99.78 88	99.77 104

OpenMVS_ROB	79.82 20	83.77 310	81.68 313	90.03 312	88.30 341	82.82 327	98.46 277	95.22 336	73.92 345	76.00 338	91.29 333	55.00 348	96.94 275	68.40 342	88.51 239	90.34 337

GBi-Net		90.88 249	89.82 254	94.08 254	97.53 198	91.97 241	98.43 279	96.95 288	87.05 278	89.68 236	94.72 300	71.34 309	96.11 307	87.01 277	85.65 259	94.17 256

test1		90.88 249	89.82 254	94.08 254	97.53 198	91.97 241	98.43 279	96.95 288	87.05 278	89.68 236	94.72 300	71.34 309	96.11 307	87.01 277	85.65 259	94.17 256

FMVSNet1		88.50 286	86.64 292	94.08 254	95.62 265	91.97 241	98.43 279	96.95 288	83.00 319	86.08 300	94.72 300	59.09 344	96.11 307	81.82 310	84.07 274	94.17 256

COLMAP_ROB	90.47 14	92.18 225	91.49 227	94.25 249	99.00 117	88.04 306	98.42 282	96.70 307	82.30 324	88.43 265	99.01 144	76.97 276	99.85 94	86.11 284	96.50 172	94.86 225
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test123		37.68 329	39.14 332	33.31 342	19.94 364	24.83 365	98.36 283	9.75 365	15.53 360	51.31 355	87.14 342	19.62 362	17.74 361	47.10 354	3.47 360	57.36 354

1314		96.84 111	95.96 130	99.48 33	96.74 235	98.52 55	98.31 284	98.86 44	95.82 56	89.91 230	98.98 149	87.49 193	99.96 53	97.80 111	99.73 91	99.96 70

1121		98.03 69	97.57 79	99.40 41	99.74 77	98.21 66	98.31 284	98.62 66	92.78 159	99.53 44	99.83 49	95.08 50	100.00 1	94.36 174	99.92 67	99.99 20

MVS		96.60 123	95.56 142	99.72 9	96.85 228	99.22 15	98.31 284	98.94 36	91.57 204	90.90 218	99.61 102	86.66 202	99.96 53	97.36 122	99.88 76	99.99 20

NR-MVSNet		91.56 239	90.22 247	95.60 203	94.05 286	95.76 158	98.25 287	98.70 53	91.16 216	80.78 324	96.64 237	83.23 231	96.57 292	91.41 219	77.73 318	94.46 231

MS-PatchMatch		90.65 254	90.30 245	91.71 299	94.22 284	85.50 318	98.24 288	97.70 211	88.67 258	86.42 294	96.37 244	67.82 323	98.03 221	83.62 299	99.62 98	91.60 329

pmmvs3		80.27 315	77.77 319	87.76 324	80.32 351	82.43 330	98.23 289	91.97 350	72.74 346	78.75 330	87.97 340	57.30 347	90.99 346	70.31 338	62.37 345	89.87 340

SixPastTwentyTwo		88.73 285	88.01 286	90.88 303	91.85 321	82.24 331	98.22 290	95.18 338	88.97 250	82.26 317	96.89 226	71.75 308	96.67 289	84.00 295	82.98 279	93.72 299

EG-PatchMatch MVS		85.35 302	83.81 304	89.99 313	90.39 333	81.89 334	98.21 291	96.09 321	81.78 326	74.73 341	93.72 318	51.56 351	97.12 263	79.16 321	88.61 235	90.96 334

OurMVSNet-221017-0		89.81 274	89.48 263	90.83 305	91.64 323	81.21 337	98.17 292	95.38 333	91.48 207	85.65 303	97.31 211	72.66 304	97.29 253	88.15 262	84.83 266	93.97 280

LF4IMVS		89.25 283	88.85 272	90.45 309	92.81 311	81.19 338	98.12 293	94.79 340	91.44 209	86.29 297	97.11 216	65.30 332	98.11 217	88.53 258	85.25 263	92.07 324

RPSCF		91.80 234	92.79 199	88.83 318	98.15 163	69.87 349	98.11 294	96.60 310	83.93 314	94.33 187	99.27 127	79.60 261	99.46 149	91.99 212	93.16 214	97.18 216

pmmvs-eth3d		84.03 309	81.97 312	90.20 310	84.15 348	87.09 310	98.10 295	94.73 342	83.05 318	74.10 342	87.77 341	65.56 331	94.01 333	81.08 313	69.24 337	89.49 343

DSMNet-mixed		88.28 288	88.24 283	88.42 322	89.64 338	75.38 347	98.06 296	89.86 354	85.59 300	88.20 270	92.14 331	76.15 287	91.95 343	78.46 322	96.05 178	97.92 207

MVP-Stereo		90.93 247	90.45 242	92.37 291	91.25 328	88.76 294	98.05 297	96.17 319	87.27 276	84.04 309	95.30 282	78.46 271	97.27 255	83.78 298	99.70 94	91.09 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UA-Net		96.54 124	95.96 130	98.27 126	98.23 158	95.71 161	98.00 298	98.45 105	93.72 134	98.41 106	99.27 127	88.71 185	99.66 136	91.19 222	97.69 147	99.44 156

new-patchmatchnet		81.19 313	79.34 317	86.76 326	82.86 350	80.36 344	97.92 299	95.27 335	82.09 325	72.02 343	86.87 343	62.81 338	90.74 347	71.10 337	63.08 344	89.19 345

PCF-MVS	94.20 5	95.18 157	94.10 171	98.43 118	98.55 141	95.99 151	97.91 300	97.31 256	90.35 231	89.48 243	99.22 133	85.19 216	99.89 79	90.40 241	98.47 131	99.41 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
pmmvs6		85.69 297	83.84 303	91.26 302	90.00 337	84.41 324	97.82 301	96.15 320	75.86 339	81.29 322	95.39 277	61.21 341	96.87 279	83.52 301	73.29 333	92.50 320

UniMVSNet_ETH3D		90.06 271	88.58 277	94.49 240	94.67 278	88.09 305	97.81 302	97.57 225	83.91 315	88.44 263	97.41 208	57.44 346	97.62 237	91.41 219	88.59 237	97.77 211

MVS_0304		89.28 282	88.31 281	92.21 293	97.05 217	86.53 312	97.76 303	99.57 12	85.58 301	93.86 194	92.71 325	51.04 352	96.30 302	84.49 293	92.72 216	93.79 293

TinyColmap		87.87 292	86.51 293	91.94 296	95.05 272	85.57 317	97.65 304	94.08 345	84.40 312	81.82 320	96.85 229	62.14 339	98.33 204	80.25 316	86.37 256	91.91 328

HY-MVS	92.50 7	97.79 81	97.17 92	99.63 12	98.98 118	99.32 6	97.49 305	99.52 13	95.69 64	98.32 111	97.41 208	93.32 107	99.77 115	98.08 101	95.75 187	99.81 98

Effi-MVS+		96.30 134	95.69 139	98.16 129	97.85 179	96.26 138	97.41 306	97.21 262	90.37 230	98.65 97	98.58 179	86.61 203	98.70 176	97.11 128	97.37 156	99.52 146

TDRefinement		84.76 303	82.56 310	91.38 301	74.58 353	84.80 323	97.36 307	94.56 343	84.73 310	80.21 326	96.12 252	63.56 336	98.39 197	87.92 264	63.97 343	90.95 335

FMVSNet5		88.32 287	87.47 289	90.88 303	96.90 226	88.39 302	97.28 308	95.68 327	82.60 323	84.67 307	92.40 330	79.83 260	91.16 345	76.39 331	81.51 289	93.09 312

DIV-MVS_2432*1600		83.59 311	82.06 311	88.20 323	86.93 343	80.70 341	97.21 309	96.38 315	82.87 320	82.49 316	88.97 338	67.63 324	92.32 342	73.75 335	62.30 346	91.58 330

LTVRE_ROB	88.28 18	90.29 265	89.05 270	94.02 257	95.08 271	90.15 280	97.19 310	97.43 242	84.91 309	83.99 310	97.06 220	74.00 301	98.28 209	84.08 294	87.71 246	93.62 301
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
KD-MVS_2432*1600		88.00 290	86.10 294	93.70 270	96.91 223	94.04 198	97.17 311	97.12 271	84.93 307	81.96 318	92.41 328	92.48 130	94.51 331	79.23 318	52.68 350	92.56 318

miper_refine_blended		88.00 290	86.10 294	93.70 270	96.91 223	94.04 198	97.17 311	97.12 271	84.93 307	81.96 318	92.41 328	92.48 130	94.51 331	79.23 318	52.68 350	92.56 318

CostFormer		96.10 138	95.88 135	96.78 174	97.03 218	92.55 232	97.08 313	97.83 206	90.04 237	98.72 93	94.89 298	95.01 56	98.29 207	96.54 139	95.77 185	99.50 149

tpm		93.70 194	93.41 189	94.58 234	95.36 268	87.41 309	97.01 314	96.90 294	90.85 223	96.72 148	94.14 314	90.40 163	96.84 280	90.75 235	88.54 238	99.51 147

CMPMVS	61.59 21	84.75 304	85.14 299	83.57 328	90.32 334	62.54 352	96.98 315	97.59 224	74.33 344	69.95 346	96.66 235	64.17 334	98.32 205	87.88 265	88.41 240	89.84 341
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpm2		95.47 153	95.18 152	96.35 190	96.91 223	91.70 254	96.96 316	97.93 195	88.04 267	98.44 105	95.40 275	93.32 107	97.97 223	94.00 182	95.61 189	99.38 161

new_pmnet		84.49 307	82.92 309	89.21 316	90.03 336	82.60 328	96.89 317	95.62 329	80.59 329	75.77 340	89.17 337	65.04 333	94.79 329	72.12 336	81.02 295	90.23 338

UnsupCasMVSNet_eth		85.52 299	83.99 300	90.10 311	89.36 339	83.51 326	96.65 318	97.99 188	89.14 244	75.89 339	93.83 316	63.25 337	93.92 334	81.92 309	67.90 341	92.88 315

MIMVSNet1		82.58 312	80.51 316	88.78 319	86.68 344	84.20 325	96.65 318	95.41 332	78.75 333	78.59 331	92.44 327	51.88 350	89.76 348	65.26 348	78.95 309	92.38 323

ab-mvs		94.69 168	93.42 187	98.51 112	98.07 166	96.26 138	96.49 320	98.68 55	90.31 232	94.54 182	97.00 223	76.30 284	99.71 128	95.98 145	93.38 212	99.56 138

EPMVS		96.53 125	96.01 122	98.09 133	98.43 147	96.12 148	96.36 321	99.43 19	93.53 138	97.64 128	95.04 291	94.41 70	98.38 201	91.13 223	98.11 139	99.75 106

tpmrst		96.27 137	95.98 125	97.13 166	97.96 171	93.15 216	96.34 322	98.17 172	92.07 189	98.71 94	95.12 289	93.91 93	98.73 173	94.91 158	96.62 169	99.50 149

dp		95.05 160	94.43 165	96.91 170	97.99 170	92.73 226	96.29 323	97.98 190	89.70 241	95.93 165	94.67 304	93.83 97	98.45 190	86.91 280	96.53 171	99.54 143

tpm cat1		93.51 196	92.52 207	96.47 182	97.77 184	91.47 260	96.13 324	98.06 184	80.98 328	92.91 203	93.78 317	89.66 170	98.87 163	87.03 276	96.39 173	99.09 185

MDTV_nov1_ep13_2view						96.26 138	96.11 325		91.89 194	98.06 119		94.40 71		94.30 177		99.67 117

PatchmatchNet		95.94 142	95.45 143	97.39 159	97.83 180	94.41 193	96.05 326	98.40 130	92.86 153	97.09 139	95.28 286	94.21 86	98.07 220	89.26 251	98.11 139	99.70 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			95.69 139		97.90 174	94.15 196	95.98 327	98.44 106	93.12 149	97.98 121	95.74 258	95.10 49	98.58 181	90.02 245	96.92 166

FPMVS		68.72 318	68.72 321	68.71 336	65.95 357	44.27 362	95.97 328	94.74 341	51.13 351	53.26 354	90.50 336	25.11 359	83.00 353	60.80 350	80.97 297	78.87 349

PM-MVS		80.47 314	78.88 318	85.26 327	83.79 349	72.22 348	95.89 329	91.08 352	85.71 299	76.56 337	88.30 339	36.64 354	93.90 335	82.39 305	69.57 336	89.66 342

test_post1							95.78 330				59.23 360	93.20 114	97.74 233	91.06 225

tpmvs		94.28 182	93.57 183	96.40 187	98.55 141	91.50 259	95.70 331	98.55 81	87.47 272	92.15 208	94.26 313	91.42 146	98.95 161	88.15 262	95.85 183	98.76 198

ADS-MVSNet2		93.80 189	93.88 176	93.55 274	97.87 177	85.94 315	94.24 332	96.84 298	90.07 235	96.43 155	94.48 309	90.29 165	95.37 320	87.44 268	97.23 158	99.36 163

ADS-MVSNet		94.79 164	94.02 172	97.11 168	97.87 177	93.79 204	94.24 332	98.16 175	90.07 235	96.43 155	94.48 309	90.29 165	98.19 215	87.44 268	97.23 158	99.36 163

EMVS		51.44 327	51.22 329	52.11 341	70.71 355	44.97 361	94.04 334	75.66 362	35.34 358	42.40 358	61.56 359	28.93 357	65.87 359	27.64 359	24.73 355	45.49 356

PMMVS2		67.15 320	64.15 324	76.14 333	70.56 356	62.07 353	93.89 335	87.52 358	58.09 350	60.02 349	78.32 349	22.38 360	84.54 352	59.56 351	47.03 352	81.80 348

GG-mvs-BLEND				98.54 109	98.21 159	98.01 73	93.87 336	98.52 88		97.92 122	97.92 200	99.02 2	97.94 228	98.17 94	99.58 103	99.67 117

UnsupCasMVSNet_bld		79.97 317	77.03 320	88.78 319	85.62 346	81.98 333	93.66 337	97.35 251	75.51 342	70.79 345	83.05 347	48.70 353	94.91 327	78.31 323	60.29 348	89.46 344

E-PMN		52.30 325	52.18 327	52.67 340	71.51 354	45.40 359	93.62 338	76.60 361	36.01 356	43.50 357	64.13 356	27.11 358	67.31 358	31.06 358	26.06 354	45.30 357

JIA-IIPM		91.76 237	90.70 237	94.94 221	96.11 243	87.51 308	93.16 339	98.13 180	75.79 340	97.58 129	77.68 350	92.84 121	97.97 223	88.47 259	96.54 170	99.33 167

gg-mvs-nofinetune		93.51 196	91.86 220	98.47 114	97.72 191	97.96 77	92.62 340	98.51 95	74.70 343	97.33 134	69.59 353	98.91 3	97.79 231	97.77 114	99.56 104	99.67 117

MIMVSNet		90.30 264	88.67 276	95.17 215	96.45 239	91.64 256	92.39 341	97.15 269	85.99 292	90.50 222	93.19 323	66.95 326	94.86 328	82.01 308	93.43 210	99.01 188

MVS-HIRNet		86.22 296	83.19 307	95.31 211	96.71 237	90.29 277	92.12 342	97.33 254	62.85 349	86.82 286	70.37 352	69.37 316	97.49 240	75.12 333	97.99 145	98.15 204

CR-MVSNet		93.45 199	92.62 201	95.94 198	96.29 240	92.66 228	92.01 343	96.23 317	92.62 168	96.94 141	93.31 321	91.04 155	96.03 312	79.23 318	95.96 180	99.13 183

RPMNet		89.76 275	87.28 290	97.19 165	96.29 240	92.66 228	92.01 343	98.31 151	70.19 348	96.94 141	85.87 346	87.25 196	99.78 111	62.69 349	95.96 180	99.13 183

Patchmatch-test		92.65 216	91.50 226	96.10 195	96.85 228	90.49 273	91.50 345	97.19 263	82.76 322	90.23 224	95.59 265	95.02 54	98.00 222	77.41 326	96.98 165	99.82 97

Patchmtry		89.70 276	88.49 278	93.33 276	96.24 242	89.94 285	91.37 346	96.23 317	78.22 334	87.69 274	93.31 321	91.04 155	96.03 312	80.18 317	82.10 284	94.02 273

PatchT		90.38 261	88.75 275	95.25 213	95.99 247	90.16 279	91.22 347	97.54 228	76.80 336	97.26 135	86.01 345	91.88 142	96.07 311	66.16 346	95.91 182	99.51 147

Patchmatch-RL test		86.90 294	85.98 296	89.67 314	84.45 347	75.59 346	89.71 348	92.43 349	86.89 283	77.83 333	90.94 334	94.22 83	93.63 338	87.75 266	69.61 335	99.79 100

LCM-MVSNet		67.77 319	64.73 323	76.87 332	62.95 359	56.25 356	89.37 349	93.74 348	44.53 353	61.99 348	80.74 348	20.42 361	86.53 351	69.37 341	59.50 349	87.84 346

ambc				83.23 329	77.17 352	62.61 351	87.38 350	94.55 344		76.72 336	86.65 344	30.16 355	96.36 299	84.85 292	69.86 334	90.73 336

ANet_high		56.10 323	52.24 326	67.66 337	49.27 361	56.82 355	83.94 351	82.02 359	70.47 347	33.28 360	64.54 355	17.23 363	69.16 357	45.59 355	23.85 356	77.02 350

tmp_tt		65.23 322	62.94 325	72.13 335	44.90 362	50.03 358	81.05 352	89.42 357	38.45 354	48.51 356	99.90 17	54.09 349	78.70 355	91.84 216	18.26 357	87.64 347

MVE	53.74 22	51.54 326	47.86 330	62.60 338	59.56 360	50.93 357	79.41 353	77.69 360	35.69 357	36.27 359	61.76 358	5.79 367	69.63 356	37.97 357	36.61 353	67.24 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	49.05 23	53.75 324	51.34 328	60.97 339	40.80 363	34.68 363	74.82 354	89.62 356	37.55 355	28.67 361	72.12 351	7.09 365	81.63 354	43.17 356	68.21 340	66.59 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		66.95 321	65.00 322	72.79 334	91.52 325	67.96 350	66.16 355	95.15 339	47.89 352	58.54 350	67.99 354	29.74 356	87.54 350	50.20 353	77.83 317	62.87 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		20.37 331	20.84 334	18.99 344	65.34 358	27.73 364	50.43 356	7.67 366	9.50 361	8.01 362	6.34 362	6.13 366	26.24 360	23.40 360	10.69 359	2.99 358

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		23.43 330	31.24 333	0.00 345	0.00 366	0.00 367	0.00 357	98.09 181	0.00 362	0.00 363	99.67 95	83.37 229	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		7.60 333	10.13 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	91.20 150	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.28 332	11.04 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	99.40 118	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					99.92 35	98.57 51		98.52 88	92.34 182	99.31 63	99.83 49	95.06 52	99.80 106	99.70 30	99.97 44

IU-MVS					99.93 26	99.31 7		98.41 129	97.71 8	99.84 8				100.00 1	100.00 1	100.00 1

test_241102_TWO								98.43 114	97.27 20	99.80 16	99.94 4	97.18 17	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 26	99.30 8		98.43 114	97.26 22	99.80 16	99.88 22	96.71 20	100.00 1

test_0728_THIRD									96.48 40	99.83 10	99.91 13	97.87 4	100.00 1	99.92 9	100.00 1	100.00 1

GSMVS																99.59 130

test_part2					99.89 45	99.25 13				99.49 48

sam_mvs1												94.72 64				99.59 130

sam_mvs												94.25 82

MTGPA								98.28 156

test_post											63.35 357	94.43 69	98.13 216

patchmatchnet-post											91.70 332	95.12 48	97.95 226

gm-plane-assit					96.97 221	93.76 206			91.47 208		98.96 153		98.79 167	94.92 156

test9_res														99.71 29	99.99 20	100.00 1

agg_prior2														99.48 36	100.00 1	100.00 1

agg_prior					99.93 26	98.77 36		98.43 114		99.63 35			99.85 94

TestCases				95.00 219	99.01 115	88.43 300		96.82 301	86.50 286	88.71 258	98.47 187	74.73 296	99.88 85	85.39 287	96.18 175	96.71 218

test_prior				99.43 35	99.94 14	98.49 57		98.65 59					99.80 106			99.99 20

新几何1				99.42 38	99.75 76	98.27 65		98.63 65	92.69 164	99.55 42	99.82 53	94.40 71	100.00 1	91.21 221	99.94 57	99.99 20

旧先验1					99.76 74	97.52 91		98.64 62			99.85 33	95.63 39			99.94 57	99.99 20

原ACMM1				98.96 82	99.73 81	96.99 114		98.51 95	94.06 117	99.62 37	99.85 33	94.97 59	99.96 53	95.11 153	99.95 51	99.92 87

testdata2													99.99 36	90.54 237

segment_acmp												96.68 22

testdata				98.42 119	99.47 100	95.33 170		98.56 76	93.78 131	99.79 21	99.85 33	93.64 101	99.94 68	94.97 155	99.94 57	100.00 1

test12				99.43 35	99.74 77	98.56 53		98.40 130		99.65 33		94.76 63	99.75 120		99.98 33	99.99 20

plane_prior7					95.71 260	91.59 258

plane_prior6					95.76 255	91.72 253						80.47 256

plane_prior5								97.87 201					98.37 202	97.79 112	89.55 222	94.52 228

plane_prior4											98.59 177

plane_prior3						91.64 256			96.63 38	93.01 200

plane_prior1					95.73 257

n2								0.00 367

nn								0.00 367

door-mid								89.69 355

lessismore_v0				90.53 306	90.58 332	80.90 340		95.80 325		77.01 334	95.84 255	66.15 329	96.95 274	83.03 302	75.05 331	93.74 298

LGP-MVS_train				93.71 268	95.43 266	88.67 296		97.62 217	92.81 156	90.05 225	98.49 183	75.24 292	98.40 195	95.84 148	89.12 226	94.07 270

test11								98.44 106

door								90.31 353

HQP5-MVS						91.85 246

BP-MVS														97.92 109

HQP4-MVS										93.37 196			98.39 197			94.53 226

HQP3-MVS								97.89 199							89.60 219

HQP2-MVS												80.65 252

NP-MVS					95.77 254	91.79 248					98.65 173

ACMMP++_ref															87.04 252

ACMMP++															88.23 241

Test By Simon												92.82 123

ITE_SJBPF				92.38 290	95.69 262	85.14 320		95.71 326	92.81 156	89.33 247	98.11 193	70.23 314	98.42 192	85.91 285	88.16 242	93.59 302

DeepMVS_CX				82.92 330	95.98 249	58.66 354		96.01 322	92.72 161	78.34 332	95.51 270	58.29 345	98.08 218	82.57 304	85.29 262	92.03 326

High-resolution multi-view benchmark