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.

IU-MVS					99.03 15	85.34 48		96.86 42	92.05 21	98.74 1				98.15 6	98.97 17	99.42 13

PC_three_1452									91.12 26	98.33 2	98.42 24	92.51 2	99.81 21	98.96 2	99.37 1	99.70 3

SMA-MVS		94.70 18	94.68 18	94.76 25	98.02 59	85.94 38	97.47 85	96.77 52	85.32 122	97.92 3	98.70 15	83.09 47	99.84 12	95.79 32	99.08 10	98.49 50
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
SED-MVS		95.88 5	96.22 4	94.87 22	99.03 15	85.03 60	99.12 7	96.78 46	88.72 56	97.79 4	98.91 2	88.48 17	99.82 18	98.15 6	98.97 17	99.74 1

test_241102_ONE					99.03 15	85.03 60		96.78 46	88.72 56	97.79 4	98.90 5	88.48 17	99.82 18

DVP-MVS++		96.05 4	96.41 3	94.96 21	99.05 9	85.34 48	98.13 41	96.77 52	88.38 63	97.70 6	98.77 10	92.06 3	99.84 12	97.47 16	99.37 1	99.70 3

test_241102_TWO								96.78 46	88.72 56	97.70 6	98.91 2	87.86 21	99.82 18	98.15 6	99.00 15	99.47 9

patch_mono-2		95.14 12	96.08 7	92.33 104	98.44 43	77.84 225	98.43 29	97.21 21	92.58 16	97.68 8	97.65 67	86.88 26	99.83 16	98.25 5	97.60 66	99.33 17

test0726					99.05 9	85.18 53	99.11 10	96.78 46	88.75 54	97.65 9	98.91 2	87.69 22

TSAR-MVS + MP.		94.79 17	95.17 15	93.64 54	97.66 69	84.10 75	95.85 196	96.42 98	91.26 25	97.49 10	96.80 106	86.50 28	98.49 121	95.54 37	99.03 13	98.33 58
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_fmvsm_n_1920		94.81 16	95.60 10	92.45 98	95.29 123	80.96 136	99.29 2	97.21 21	94.50 6	97.29 11	98.44 23	82.15 52	99.78 26	98.56 4	97.68 64	96.61 155

MSP-MVS		95.62 7	96.54 1	92.86 84	98.31 48	80.10 159	97.42 92	96.78 46	92.20 19	97.11 12	98.29 27	93.46 1	99.10 91	96.01 28	99.30 5	99.38 14
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
CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 11	97.12 26	94.66 4	96.79 13	98.78 9	86.42 29	99.95 3	97.59 15	99.18 7	99.00 26

DVP-MVS		95.58 8	95.91 9	94.57 29	99.05 9	85.18 53	99.06 11	96.46 93	88.75 54	96.69 14	98.76 12	87.69 22	99.76 27	97.90 11	98.85 21	98.77 33
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_THIRD									88.38 63	96.69 14	98.76 12	89.64 13	99.76 27	97.47 16	98.84 23	99.38 14

SD-MVS		94.84 15	95.02 16	94.29 35	97.87 64	84.61 68	97.76 64	96.19 119	89.59 46	96.66 16	98.17 34	84.33 36	99.60 51	96.09 27	98.50 36	98.66 41
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
test_one_0601					98.91 18	84.56 69		96.70 62	88.06 69	96.57 17	98.77 10	88.04 20

DPE-MVS		95.32 10	95.55 11	94.64 28	98.79 23	84.87 65	97.77 62	96.74 57	86.11 107	96.54 18	98.89 6	88.39 19	99.74 34	97.67 14	99.05 12	99.31 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		96.21 2	95.53 12	98.26 1	96.26 98	95.09 1	99.15 5	96.98 32	93.39 12	96.45 19	98.79 8	90.17 10	99.99 1	89.33 113	99.25 6	99.70 3

PS-MVSNAJ		94.17 26	93.52 34	96.10 8	95.65 113	92.35 2	98.21 36	95.79 143	92.42 18	96.24 20	98.18 31	71.04 193	99.17 85	96.77 23	97.39 74	96.79 148

旧先验2							96.97 127		74.06 306	96.10 21			97.76 150	88.38 123

test_part2					98.90 19	85.14 59				96.07 22

xiu_mvs_v2_base		93.92 30	93.26 38	95.91 9	95.07 131	92.02 6	98.19 37	95.68 148	92.06 20	96.01 23	98.14 35	70.83 196	98.96 99	96.74 25	96.57 92	96.76 151

HPM-MVS++		95.32 10	95.48 13	94.85 23	98.62 34	86.04 35	97.81 60	96.93 37	92.45 17	95.69 24	98.50 20	85.38 31	99.85 10	94.75 44	99.18 7	98.65 42

NCCC		95.63 6	95.94 8	94.69 27	99.21 6	85.15 58	99.16 4	96.96 34	94.11 8	95.59 25	98.64 17	85.07 33	99.91 4	95.61 35	99.10 9	99.00 26

EPNet		94.06 29	94.15 27	93.76 49	97.27 87	84.35 71	98.29 33	97.64 14	94.57 5	95.36 26	96.88 101	79.96 68	99.12 90	91.30 82	96.11 98	97.82 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		94.89 14	94.64 19	95.63 12	97.55 75	88.12 15	99.06 11	96.39 103	94.07 9	95.34 27	97.80 58	76.83 110	99.87 8	97.08 20	97.64 65	98.89 29

TEST9					98.64 31	83.71 81	97.82 58	96.65 69	84.29 154	95.16 28	98.09 38	84.39 35	99.36 71

train_agg		94.28 23	94.45 22	93.74 50	98.64 31	83.71 81	97.82 58	96.65 69	84.50 145	95.16 28	98.09 38	84.33 36	99.36 71	95.91 31	98.96 19	98.16 70

MVS_0304		95.36 9	95.20 14	95.85 10	94.89 138	89.22 11	98.83 18	97.88 10	94.68 3	95.14 30	97.99 45	80.80 58	99.81 21	98.60 3	97.95 56	98.50 49

test_8					98.63 33	83.64 84	97.81 60	96.63 74	84.50 145	95.10 31	98.11 37	84.33 36	99.23 76

DeepPCF-MVS	89.82 1	94.61 19	96.17 5	89.91 184	97.09 90	70.21 316	98.99 16	96.69 64	95.57 1	95.08 32	99.23 1	86.40 30	99.87 8	97.84 13	98.66 31	99.65 6

SF-MVS		94.17 26	94.05 28	94.55 30	97.56 74	85.95 36	97.73 66	96.43 97	84.02 159	95.07 33	98.74 14	82.93 48	99.38 68	95.42 39	98.51 34	98.32 59

APDe-MVS		94.56 20	94.75 17	93.96 45	98.84 22	83.40 89	98.04 49	96.41 99	85.79 114	95.00 34	98.28 28	84.32 39	99.18 84	97.35 18	98.77 27	99.28 19

MVSFormer		91.36 76	90.57 81	93.73 52	93.00 192	88.08 16	94.80 237	94.48 212	80.74 220	94.90 35	97.13 91	78.84 79	95.10 287	83.77 161	97.46 69	98.02 78

lupinMVS		93.87 31	93.58 33	94.75 26	93.00 192	88.08 16	99.15 5	95.50 157	91.03 28	94.90 35	97.66 63	78.84 79	97.56 159	94.64 47	97.46 69	98.62 44

CS-MVS-test		92.98 39	93.67 30	90.90 154	96.52 94	76.87 244	98.68 21	94.73 196	90.36 38	94.84 37	97.89 53	77.94 91	97.15 190	94.28 51	97.80 61	98.70 40

9.14			94.26 26		98.10 57		98.14 38	96.52 86	84.74 137	94.83 38	98.80 7	82.80 50	99.37 70	95.95 30	98.42 40

testdata				90.13 176	95.92 107	74.17 280		96.49 92	73.49 311	94.82 39	97.99 45	78.80 81	97.93 141	83.53 169	97.52 68	98.29 63

APD-MVS		93.61 32	93.59 32	93.69 53	98.76 24	83.26 92	97.21 101	96.09 124	82.41 198	94.65 40	98.21 30	81.96 54	98.81 109	94.65 46	98.36 45	99.01 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior2							98.37 31		86.08 109	94.57 41	98.02 44	83.14 46		95.05 41	98.79 26

CS-MVS		92.73 45	93.48 35	90.48 166	96.27 97	75.93 263	98.55 27	94.93 183	89.32 48	94.54 42	97.67 62	78.91 78	97.02 194	93.80 54	97.32 76	98.49 50

FOURS1					98.51 39	78.01 217	98.13 41	96.21 116	83.04 184	94.39 43

ACMMP_NAP		93.46 34	93.23 39	94.17 40	97.16 88	84.28 73	96.82 138	96.65 69	86.24 105	94.27 44	97.99 45	77.94 91	99.83 16	93.39 59	98.57 33	98.39 56

agg_prior					98.59 35	83.13 94		96.56 83		94.19 45			99.16 86

SteuartSystems-ACMMP		94.13 28	94.44 23	93.20 72	95.41 119	81.35 128	99.02 15	96.59 79	89.50 47	94.18 46	98.36 26	83.68 44	99.45 65	94.77 43	98.45 39	98.81 32
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		93.59 33	93.63 31	93.48 64	98.05 58	81.76 120	98.64 24	97.13 25	82.60 196	94.09 47	98.49 21	80.35 61	99.85 10	94.74 45	98.62 32	98.83 31

TSAR-MVS + GP.		94.35 22	94.50 20	93.89 46	97.38 84	83.04 96	98.10 43	95.29 172	91.57 22	93.81 48	97.45 75	86.64 27	99.43 66	96.28 26	94.01 120	99.20 22

CANet_DTU		90.98 84	90.04 94	93.83 47	94.76 141	86.23 33	96.32 170	93.12 283	93.11 13	93.71 49	96.82 105	63.08 238	99.48 63	84.29 153	95.12 109	95.77 176

VNet		92.11 60	91.22 71	94.79 24	96.91 91	86.98 26	97.91 53	97.96 9	86.38 104	93.65 50	95.74 126	70.16 201	98.95 101	93.39 59	88.87 166	98.43 54

test_vis1_n_1920		89.95 104	90.59 80	88.03 223	92.36 208	68.98 325	99.12 7	94.34 222	93.86 10	93.64 51	97.01 97	51.54 310	99.59 52	96.76 24	96.71 91	95.53 182

ZD-MVS					99.09 8	83.22 93		96.60 78	82.88 189	93.61 52	98.06 43	82.93 48	99.14 87	95.51 38	98.49 37

xiu_mvs_v1_base_debu		90.54 93	89.54 104	93.55 59	92.31 209	87.58 22	96.99 122	94.87 187	87.23 90	93.27 53	97.56 71	57.43 279	98.32 130	92.72 70	93.46 129	94.74 197

xiu_mvs_v1_base		90.54 93	89.54 104	93.55 59	92.31 209	87.58 22	96.99 122	94.87 187	87.23 90	93.27 53	97.56 71	57.43 279	98.32 130	92.72 70	93.46 129	94.74 197

xiu_mvs_v1_base_debi		90.54 93	89.54 104	93.55 59	92.31 209	87.58 22	96.99 122	94.87 187	87.23 90	93.27 53	97.56 71	57.43 279	98.32 130	92.72 70	93.46 129	94.74 197

CDPH-MVS		93.12 37	92.91 41	93.74 50	98.65 30	83.88 77	97.67 71	96.26 112	83.00 186	93.22 56	98.24 29	81.31 55	99.21 78	89.12 114	98.74 29	98.14 72

ETV-MVS		92.72 47	92.87 42	92.28 107	94.54 146	81.89 114	97.98 51	95.21 175	89.77 45	93.11 57	96.83 103	77.23 106	97.50 167	95.74 33	95.38 107	97.44 123

MSLP-MVS++		94.28 23	94.39 24	93.97 44	98.30 49	84.06 76	98.64 24	96.93 37	90.71 31	93.08 58	98.70 15	79.98 67	99.21 78	94.12 52	99.07 11	98.63 43

alignmvs		92.97 40	92.26 54	95.12 18	95.54 116	87.77 19	98.67 22	96.38 104	88.04 70	93.01 59	97.45 75	79.20 75	98.60 115	93.25 64	88.76 167	98.99 28

canonicalmvs		92.27 57	91.22 71	95.41 15	95.80 110	88.31 13	97.09 118	94.64 204	88.49 61	92.99 60	97.31 82	72.68 174	98.57 117	93.38 61	88.58 169	99.36 16

EC-MVSNet		91.73 65	92.11 58	90.58 163	93.54 175	77.77 228	98.07 46	94.40 219	87.44 84	92.99 60	97.11 93	74.59 154	96.87 204	93.75 55	97.08 80	97.11 137

jason		92.73 45	92.23 55	94.21 39	90.50 256	87.30 25	98.65 23	95.09 177	90.61 32	92.76 62	97.13 91	75.28 142	97.30 179	93.32 62	96.75 90	98.02 78
jason: jason.
test_cas_vis1_n_1920		89.90 105	90.02 95	89.54 192	90.14 264	74.63 275	98.71 20	94.43 217	93.04 14	92.40 63	96.35 115	53.41 306	99.08 93	95.59 36	96.16 96	94.90 192

test12				94.25 36	98.34 46	85.55 45		96.35 107		92.36 64		80.84 57	99.22 77		98.31 47	97.98 85

MG-MVS		94.25 25	93.72 29	95.85 10	99.38 3	89.35 10	97.98 51	98.09 8	89.99 41	92.34 65	96.97 98	81.30 56	98.99 97	88.54 119	98.88 20	99.20 22

test_fmvs1		87.79 153	88.52 120	85.62 273	92.98 196	64.31 339	97.88 55	92.42 292	87.95 72	92.24 66	95.82 125	47.94 323	98.44 127	95.31 40	94.09 117	94.09 208

h-mvs33		89.30 116	88.95 114	90.36 170	95.07 131	76.04 257	96.96 128	97.11 27	90.39 36	92.22 67	95.10 150	74.70 150	98.86 106	93.14 65	65.89 330	96.16 168

hse-mvs2		88.22 145	88.21 124	88.25 217	93.54 175	73.41 283	95.41 212	95.89 137	90.39 36	92.22 67	94.22 169	74.70 150	96.66 215	93.14 65	64.37 335	94.69 201

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 9	98.94 17	97.10 28	95.17 2	92.11 69	98.46 22	87.33 24	99.97 2	97.21 19	99.31 4	99.63 7

SR-MVS		92.16 58	92.27 53	91.83 126	98.37 45	78.41 203	96.67 149	95.76 144	82.19 202	91.97 70	98.07 42	76.44 115	98.64 113	93.71 56	97.27 77	98.45 53

region2R		92.72 47	92.70 45	92.79 86	98.68 26	80.53 149	97.53 80	96.51 87	85.22 125	91.94 71	97.98 48	77.26 102	99.67 46	90.83 89	98.37 44	98.18 68

Effi-MVS+		90.70 90	89.90 100	93.09 76	93.61 172	83.48 87	95.20 221	92.79 288	83.22 178	91.82 72	95.70 128	71.82 184	97.48 169	91.25 83	93.67 125	98.32 59

HFP-MVS		92.89 41	92.86 43	92.98 80	98.71 25	81.12 131	97.58 76	96.70 62	85.20 127	91.75 73	97.97 50	78.47 84	99.71 39	90.95 85	98.41 41	98.12 74

DeepC-MVS_fast	89.06 2	94.48 21	94.30 25	95.02 19	98.86 21	85.68 43	98.06 47	96.64 72	93.64 11	91.74 74	98.54 18	80.17 66	99.90 5	92.28 74	98.75 28	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		92.69 49	92.67 46	92.75 87	98.66 28	80.57 145	97.58 76	96.69 64	85.20 127	91.57 75	97.92 51	77.01 107	99.67 46	90.95 85	98.41 41	98.00 83

DELS-MVS		94.98 13	94.49 21	96.44 6	96.42 95	90.59 7	99.21 3	97.02 30	94.40 7	91.46 76	97.08 94	83.32 45	99.69 42	92.83 69	98.70 30	99.04 24
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
XVS		92.69 49	92.71 44	92.63 93	98.52 37	80.29 152	97.37 95	96.44 95	87.04 95	91.38 77	97.83 57	77.24 104	99.59 52	90.46 95	98.07 52	98.02 78

X-MVStestdata		86.26 176	84.14 195	92.63 93	98.52 37	80.29 152	97.37 95	96.44 95	87.04 95	91.38 77	20.73 383	77.24 104	99.59 52	90.46 95	98.07 52	98.02 78

PMMVS		89.46 113	89.92 99	88.06 221	94.64 142	69.57 322	96.22 175	94.95 182	87.27 89	91.37 79	96.54 113	65.88 220	97.39 174	88.54 119	93.89 122	97.23 133

test_fmvs1_n		86.34 174	86.72 158	85.17 280	87.54 297	63.64 344	96.91 132	92.37 294	87.49 83	91.33 80	95.58 134	40.81 348	98.46 124	95.00 42	93.49 127	93.41 222

dcpmvs_2		93.10 38	93.46 36	92.02 118	97.77 65	79.73 169	94.82 235	93.86 246	86.91 97	91.33 80	96.76 107	85.20 32	98.06 138	96.90 22	97.60 66	98.27 65

原ACMM1				91.22 145	97.77 65	78.10 215		96.61 75	81.05 215	91.28 82	97.42 79	77.92 93	98.98 98	79.85 198	98.51 34	96.59 156

新几何1				93.12 74	97.44 78	81.60 125		96.71 61	74.54 302	91.22 83	97.57 70	79.13 76	99.51 61	77.40 223	98.46 38	98.26 66

UA-Net		88.92 123	88.48 121	90.24 173	94.06 163	77.18 241	93.04 277	94.66 201	87.39 86	91.09 84	93.89 178	74.92 147	98.18 137	75.83 239	91.43 150	95.35 187

ZNCC-MVS		92.75 43	92.60 48	93.23 71	98.24 51	81.82 118	97.63 72	96.50 89	85.00 133	91.05 85	97.74 60	78.38 85	99.80 25	90.48 94	98.34 46	98.07 76

APD-MVS_3200maxsize		91.23 80	91.35 70	90.89 155	97.89 62	76.35 253	96.30 171	95.52 156	79.82 243	91.03 86	97.88 54	74.70 150	98.54 118	92.11 77	96.89 84	97.77 100

test_vis1_n		85.60 187	85.70 166	85.33 277	84.79 329	64.98 337	96.83 136	91.61 305	87.36 87	91.00 87	94.84 157	36.14 354	97.18 186	95.66 34	93.03 133	93.82 213

GST-MVS		92.43 56	92.22 56	93.04 78	98.17 54	81.64 124	97.40 94	96.38 104	84.71 139	90.90 88	97.40 80	77.55 99	99.76 27	89.75 107	97.74 62	97.72 103

PGM-MVS		91.93 62	91.80 63	92.32 106	98.27 50	79.74 168	95.28 215	97.27 19	83.83 167	90.89 89	97.78 59	76.12 122	99.56 57	88.82 117	97.93 59	97.66 108

SR-MVS-dyc-post		91.29 78	91.45 69	90.80 157	97.76 67	76.03 258	96.20 178	95.44 162	80.56 225	90.72 90	97.84 55	75.76 128	98.61 114	91.99 78	96.79 88	97.75 101

RE-MVS-def			91.18 74		97.76 67	76.03 258	96.20 178	95.44 162	80.56 225	90.72 90	97.84 55	73.36 169		91.99 78	96.79 88	97.75 101

MP-MVS		92.61 52	92.67 46	92.42 101	98.13 56	79.73 169	97.33 97	96.20 117	85.63 116	90.53 92	97.66 63	78.14 89	99.70 41	92.12 76	98.30 48	97.85 94
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HY-MVS	84.06 6	91.63 69	90.37 87	95.39 16	96.12 102	88.25 14	90.22 307	97.58 15	88.33 65	90.50 93	91.96 204	79.26 73	99.06 94	90.29 101	89.07 163	98.88 30

CP-MVS		92.54 54	92.60 48	92.34 103	98.50 40	79.90 162	98.40 30	96.40 101	84.75 136	90.48 94	98.09 38	77.40 101	99.21 78	91.15 84	98.23 50	97.92 89

diffmvs		91.17 81	90.74 79	92.44 100	93.11 191	82.50 104	96.25 174	93.62 261	87.79 76	90.40 95	95.93 122	73.44 168	97.42 171	93.62 58	92.55 138	97.41 125
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		90.29 99	89.18 109	93.62 56	95.23 124	84.93 63	94.41 242	94.66 201	84.31 150	90.37 96	91.02 219	75.13 144	97.82 148	83.11 174	94.42 115	98.12 74

MTAPA		92.45 55	92.31 52	92.86 84	97.90 61	80.85 139	92.88 280	96.33 108	87.92 73	90.20 97	98.18 31	76.71 113	99.76 27	92.57 73	98.09 51	97.96 88

test_yl		91.46 73	90.53 82	94.24 37	97.41 80	85.18 53	98.08 44	97.72 11	80.94 216	89.85 98	96.14 118	75.61 129	98.81 109	90.42 99	88.56 170	98.74 34

DCV-MVSNet		91.46 73	90.53 82	94.24 37	97.41 80	85.18 53	98.08 44	97.72 11	80.94 216	89.85 98	96.14 118	75.61 129	98.81 109	90.42 99	88.56 170	98.74 34

WTY-MVS		92.65 51	91.68 65	95.56 13	96.00 105	88.90 12	98.23 35	97.65 13	88.57 59	89.82 100	97.22 88	79.29 72	99.06 94	89.57 109	88.73 168	98.73 38

MVS_111021_HR		93.41 35	93.39 37	93.47 66	97.34 85	82.83 98	97.56 78	98.27 6	89.16 51	89.71 101	97.14 90	79.77 69	99.56 57	93.65 57	97.94 57	98.02 78

sss		90.87 88	89.96 97	93.60 57	94.15 158	83.84 80	97.14 111	98.13 7	85.93 112	89.68 102	96.09 120	71.67 185	99.30 73	87.69 129	89.16 162	97.66 108

test222					96.15 101	78.41 203	95.87 194	96.46 93	71.97 322	89.66 103	97.45 75	76.33 119			98.24 49	98.30 62

LFMVS		89.27 117	87.64 135	94.16 42	97.16 88	85.52 46	97.18 105	94.66 201	79.17 257	89.63 104	96.57 112	55.35 296	98.22 134	89.52 111	89.54 159	98.74 34

CostFormer		89.08 119	88.39 122	91.15 147	93.13 189	79.15 184	88.61 318	96.11 123	83.14 180	89.58 105	86.93 278	83.83 43	96.87 204	88.22 125	85.92 193	97.42 124

PVSNet_BlendedMVS		90.05 102	89.96 97	90.33 171	97.47 76	83.86 78	98.02 50	96.73 58	87.98 71	89.53 106	89.61 241	76.42 116	99.57 55	94.29 49	79.59 238	87.57 307

PVSNet_Blended		93.13 36	92.98 40	93.57 58	97.47 76	83.86 78	99.32 1	96.73 58	91.02 29	89.53 106	96.21 117	76.42 116	99.57 55	94.29 49	95.81 105	97.29 132

HPM-MVS		91.62 70	91.53 68	91.89 122	97.88 63	79.22 181	96.99 122	95.73 146	82.07 204	89.50 108	97.19 89	75.59 131	98.93 104	90.91 87	97.94 57	97.54 115
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set		91.84 64	91.77 64	92.04 117	97.60 71	81.17 130	96.61 150	96.87 40	88.20 67	89.19 109	97.55 74	78.69 83	99.14 87	90.29 101	90.94 153	95.80 175

MP-MVS-pluss		92.58 53	92.35 51	93.29 68	97.30 86	82.53 102	96.44 161	96.04 129	84.68 140	89.12 110	98.37 25	77.48 100	99.74 34	93.31 63	98.38 43	97.59 114
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 143	87.02 154	92.06 116	95.09 129	80.18 158	97.55 79	94.45 216	83.09 182	89.10 111	95.92 124	47.97 322	98.49 121	93.08 68	86.91 182	97.52 119

baseline		90.76 89	90.10 93	92.74 88	92.90 198	82.56 101	94.60 239	94.56 209	87.69 79	89.06 112	95.67 130	73.76 163	97.51 166	90.43 98	92.23 144	98.16 70

EIA-MVS		91.73 65	92.05 60	90.78 159	94.52 147	76.40 252	98.06 47	95.34 170	89.19 50	88.90 113	97.28 86	77.56 98	97.73 151	90.77 90	96.86 87	98.20 67

mvsany_test1		87.58 157	88.22 123	85.67 271	89.78 268	67.18 332	95.25 218	87.93 342	83.96 162	88.79 114	97.06 96	72.52 175	94.53 302	92.21 75	86.45 186	95.30 189

HPM-MVS_fast		90.38 98	90.17 92	91.03 150	97.61 70	77.35 237	97.15 110	95.48 158	79.51 249	88.79 114	96.90 99	71.64 187	98.81 109	87.01 137	97.44 71	96.94 141

PAPM		92.87 42	92.40 50	94.30 34	92.25 216	87.85 18	96.40 165	96.38 104	91.07 27	88.72 116	96.90 99	82.11 53	97.37 176	90.05 104	97.70 63	97.67 107

MVS_111021_LR		91.60 71	91.64 67	91.47 137	95.74 111	78.79 194	96.15 180	96.77 52	88.49 61	88.64 117	97.07 95	72.33 178	99.19 83	93.13 67	96.48 93	96.43 160

casdiffmvs		90.95 86	90.39 85	92.63 93	92.82 199	82.53 102	96.83 136	94.47 214	87.69 79	88.47 118	95.56 135	74.04 160	97.54 163	90.90 88	92.74 136	97.83 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mPP-MVS		91.88 63	91.82 62	92.07 115	98.38 44	78.63 197	97.29 98	96.09 124	85.12 129	88.45 119	97.66 63	75.53 132	99.68 44	89.83 105	98.02 55	97.88 90

PAPR		92.74 44	92.17 57	94.45 31	98.89 20	84.87 65	97.20 103	96.20 117	87.73 78	88.40 120	98.12 36	78.71 82	99.76 27	87.99 126	96.28 94	98.74 34

tpmrst		88.36 140	87.38 145	91.31 139	94.36 153	79.92 161	87.32 328	95.26 174	85.32 122	88.34 121	86.13 294	80.60 60	96.70 212	83.78 160	85.34 201	97.30 131

GG-mvs-BLEND				93.49 63	94.94 135	86.26 32	81.62 352	97.00 31		88.32 122	94.30 167	91.23 5	96.21 228	88.49 121	97.43 72	98.00 83

EI-MVSNet-UG-set		91.35 77	91.22 71	91.73 128	97.39 82	80.68 142	96.47 158	96.83 43	87.92 73	88.30 123	97.36 81	77.84 94	99.13 89	89.43 112	89.45 160	95.37 186

MAR-MVS		90.63 91	90.22 89	91.86 123	98.47 42	78.20 213	97.18 105	96.61 75	83.87 166	88.18 124	98.18 31	68.71 205	99.75 32	83.66 166	97.15 79	97.63 111
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
DP-MVS Recon		91.72 67	90.85 76	94.34 33	99.50 1	85.00 62	98.51 28	95.96 133	80.57 224	88.08 125	97.63 69	76.84 109	99.89 7	85.67 143	94.88 110	98.13 73

VDDNet		86.44 172	84.51 186	92.22 109	91.56 235	81.83 117	97.10 117	94.64 204	69.50 334	87.84 126	95.19 144	48.01 321	97.92 146	89.82 106	86.92 181	96.89 145

UGNet		87.73 154	86.55 160	91.27 142	95.16 128	79.11 185	96.35 168	96.23 114	88.14 68	87.83 127	90.48 228	50.65 312	99.09 92	80.13 195	94.03 118	95.60 180
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
test2506		90.96 85	90.39 85	92.65 92	93.54 175	82.46 105	96.37 166	97.35 17	86.78 101	87.55 128	95.25 139	77.83 95	97.50 167	84.07 155	94.80 111	97.98 85

tpm2		87.35 160	86.26 161	90.62 162	92.93 197	78.67 196	88.06 323	95.99 130	79.33 252	87.40 129	86.43 289	80.28 63	96.40 220	80.23 193	85.73 197	96.79 148

CPTT-MVS		89.72 108	89.87 101	89.29 195	98.33 47	73.30 286	97.70 68	95.35 169	75.68 293	87.40 129	97.44 78	70.43 198	98.25 133	89.56 110	96.90 83	96.33 165

gg-mvs-nofinetune		85.48 190	82.90 213	93.24 70	94.51 150	85.82 40	79.22 356	96.97 33	61.19 356	87.33 131	53.01 372	90.58 6	96.07 231	86.07 141	97.23 78	97.81 98

CHOSEN 280x420		91.71 68	91.85 61	91.29 141	94.94 135	82.69 99	87.89 324	96.17 120	85.94 111	87.27 132	94.31 166	90.27 9	95.65 259	94.04 53	95.86 103	95.53 182

test_fmvsmvis_n_1920		92.12 59	92.10 59	92.17 111	90.87 249	81.04 132	98.34 32	93.90 243	92.71 15	87.24 133	97.90 52	74.83 148	99.72 37	96.96 21	96.20 95	95.76 177

casdiffmvs_mvg		91.13 82	90.45 84	93.17 73	92.99 195	83.58 85	97.46 87	94.56 209	87.69 79	87.19 134	94.98 155	74.50 155	97.60 156	91.88 80	92.79 135	98.34 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet_dtu		87.65 156	87.89 129	86.93 250	94.57 144	71.37 310	96.72 144	96.50 89	88.56 60	87.12 135	95.02 152	75.91 126	94.01 311	66.62 298	90.00 156	95.42 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 131	87.82 131	91.24 143	92.68 200	78.82 191	96.95 129	93.85 247	87.55 82	87.07 136	95.13 148	63.43 236	97.21 184	77.58 219	96.15 97	97.70 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		90.95 86	90.26 88	93.01 79	94.03 166	84.27 74	97.91 53	96.67 66	83.18 179	86.87 137	95.51 136	88.66 16	97.85 147	80.46 189	89.01 164	96.92 144

TESTMET0.1,1		89.83 106	89.34 107	91.31 139	92.54 206	80.19 157	97.11 114	96.57 81	86.15 106	86.85 138	91.83 208	79.32 71	96.95 198	81.30 183	92.35 142	96.77 150

PVSNet_Blended_VisFu		91.24 79	90.77 78	92.66 91	95.09 129	82.40 106	97.77 62	95.87 140	88.26 66	86.39 139	93.94 177	76.77 111	99.27 74	88.80 118	94.00 121	96.31 166

API-MVS		90.18 100	88.97 112	93.80 48	98.66 28	82.95 97	97.50 84	95.63 151	75.16 297	86.31 140	97.69 61	72.49 176	99.90 5	81.26 184	96.07 99	98.56 46

test-LLR		88.48 136	87.98 128	89.98 180	92.26 214	77.23 239	97.11 114	95.96 133	83.76 170	86.30 141	91.38 212	72.30 179	96.78 210	80.82 186	91.92 146	95.94 172

test-mter		88.95 121	88.60 118	89.98 180	92.26 214	77.23 239	97.11 114	95.96 133	85.32 122	86.30 141	91.38 212	76.37 118	96.78 210	80.82 186	91.92 146	95.94 172

PAPM_NR		91.46 73	90.82 77	93.37 67	98.50 40	81.81 119	95.03 231	96.13 121	84.65 141	86.10 143	97.65 67	79.24 74	99.75 32	83.20 172	96.88 85	98.56 46

FA-MVS(test-final)		87.71 155	86.23 162	92.17 111	94.19 157	80.55 146	87.16 330	96.07 127	82.12 203	85.98 144	88.35 256	72.04 183	98.49 121	80.26 192	89.87 157	97.48 122

MDTV_nov1_ep13_2view						81.74 121	86.80 332		80.65 222	85.65 145		74.26 157		76.52 231		96.98 140

ECVR-MVS		88.35 141	87.25 147	91.65 130	93.54 175	79.40 176	96.56 154	90.78 319	86.78 101	85.57 146	95.25 139	57.25 283	97.56 159	84.73 151	94.80 111	97.98 85

AUN-MVS		86.25 177	85.57 168	88.26 216	93.57 174	73.38 284	95.45 210	95.88 138	83.94 163	85.47 147	94.21 170	73.70 166	96.67 214	83.54 168	64.41 334	94.73 200

PVSNet	82.34 9	89.02 120	87.79 132	92.71 90	95.49 117	81.50 126	97.70 68	97.29 18	87.76 77	85.47 147	95.12 149	56.90 285	98.90 105	80.33 190	94.02 119	97.71 105

EPP-MVSNet		89.76 107	89.72 103	89.87 185	93.78 168	76.02 260	97.22 99	96.51 87	79.35 251	85.11 149	95.01 153	84.82 34	97.10 192	87.46 132	88.21 174	96.50 158

test1111		88.11 146	87.04 153	91.35 138	93.15 187	78.79 194	96.57 152	90.78 319	86.88 99	85.04 150	95.20 143	57.23 284	97.39 174	83.88 158	94.59 113	97.87 92

FE-MVS		86.06 179	84.15 194	91.78 127	94.33 154	79.81 163	84.58 344	96.61 75	76.69 287	85.00 151	87.38 269	70.71 197	98.37 129	70.39 282	91.70 149	97.17 136

OMC-MVS		88.80 128	88.16 126	90.72 160	95.30 122	77.92 222	94.81 236	94.51 211	86.80 100	84.97 152	96.85 102	67.53 209	98.60 115	85.08 147	87.62 177	95.63 179

CHOSEN 1792x2688		91.07 83	90.21 90	93.64 54	95.18 127	83.53 86	96.26 173	96.13 121	88.92 53	84.90 153	93.10 189	72.86 172	99.62 50	88.86 116	95.67 106	97.79 99

thres200		88.92 123	87.65 134	92.73 89	96.30 96	85.62 44	97.85 56	98.86 1	84.38 149	84.82 154	93.99 176	75.12 145	98.01 139	70.86 279	86.67 183	94.56 202

MDTV_nov1_ep13			83.69 198		94.09 162	81.01 133	86.78 333	96.09 124	83.81 168	84.75 155	84.32 317	74.44 156	96.54 216	63.88 312	85.07 202

CDS-MVSNet		89.50 112	88.96 113	91.14 148	91.94 231	80.93 137	97.09 118	95.81 142	84.26 155	84.72 156	94.20 171	80.31 62	95.64 260	83.37 171	88.96 165	96.85 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 96	89.97 96	91.64 131	97.58 73	78.21 212	96.78 141	96.72 60	84.73 138	84.72 156	97.23 87	71.22 190	99.63 49	88.37 124	92.41 141	97.08 139
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
CSCG		92.02 61	91.65 66	93.12 74	98.53 36	80.59 144	97.47 85	97.18 24	77.06 285	84.64 158	97.98 48	83.98 41	99.52 59	90.72 91	97.33 75	99.23 21

ab-mvs		87.08 161	84.94 181	93.48 64	93.34 183	83.67 83	88.82 315	95.70 147	81.18 213	84.55 159	90.14 236	62.72 239	98.94 103	85.49 145	82.54 222	97.85 94

EPMVS		87.47 159	85.90 165	92.18 110	95.41 119	82.26 109	87.00 331	96.28 111	85.88 113	84.23 160	85.57 300	75.07 146	96.26 225	71.14 277	92.50 139	98.03 77

Anonymous202405211		84.41 207	81.93 227	91.85 125	96.78 93	78.41 203	97.44 88	91.34 309	70.29 330	84.06 161	94.26 168	41.09 346	98.96 99	79.46 200	82.65 221	98.17 69

HyFIR lowres test		89.36 114	88.60 118	91.63 133	94.91 137	80.76 141	95.60 205	95.53 154	82.56 197	84.03 162	91.24 216	78.03 90	96.81 208	87.07 136	88.41 172	97.32 129

tfpn200view9		88.48 136	87.15 149	92.47 97	96.21 99	85.30 51	97.44 88	98.85 2	83.37 176	83.99 163	93.82 179	75.36 139	97.93 141	69.04 287	86.24 190	94.17 204

thres400		88.42 139	87.15 149	92.23 108	96.21 99	85.30 51	97.44 88	98.85 2	83.37 176	83.99 163	93.82 179	75.36 139	97.93 141	69.04 287	86.24 190	93.45 220

tpm		85.55 188	84.47 189	88.80 205	90.19 261	75.39 268	88.79 316	94.69 197	84.83 135	83.96 165	85.21 306	78.22 88	94.68 298	76.32 235	78.02 256	96.34 163

Fast-Effi-MVS+		87.93 151	86.94 156	90.92 153	94.04 164	79.16 183	98.26 34	93.72 257	81.29 212	83.94 166	92.90 190	69.83 202	96.68 213	76.70 229	91.74 148	96.93 142

XVG-OURS-SEG-HR		85.74 185	85.16 177	87.49 238	90.22 260	71.45 309	91.29 299	94.09 235	81.37 211	83.90 167	95.22 141	60.30 256	97.53 165	85.58 144	84.42 205	93.50 218

thisisatest0530		89.65 109	89.02 111	91.53 135	93.46 181	80.78 140	96.52 155	96.67 66	81.69 209	83.79 168	94.90 156	88.85 15	97.68 152	77.80 212	87.49 180	96.14 169

DeepC-MVS	86.58 3	91.53 72	91.06 75	92.94 82	94.52 147	81.89 114	95.95 188	95.98 131	90.76 30	83.76 169	96.76 107	73.24 170	99.71 39	91.67 81	96.96 82	97.22 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IS-MVSNet		88.67 131	88.16 126	90.20 175	93.61 172	76.86 245	96.77 143	93.07 284	84.02 159	83.62 170	95.60 133	74.69 153	96.24 227	78.43 211	93.66 126	97.49 121

thres100view900		88.30 142	86.95 155	92.33 104	96.10 103	84.90 64	97.14 111	98.85 2	82.69 194	83.41 171	93.66 183	75.43 136	97.93 141	69.04 287	86.24 190	94.17 204

thres600view7		88.06 147	86.70 159	92.15 113	96.10 103	85.17 57	97.14 111	98.85 2	82.70 193	83.41 171	93.66 183	75.43 136	97.82 148	67.13 296	85.88 194	93.45 220

XVG-OURS		85.18 193	84.38 190	87.59 233	90.42 258	71.73 306	91.06 302	94.07 236	82.00 206	83.29 173	95.08 151	56.42 290	97.55 161	83.70 165	83.42 210	93.49 219

Vis-MVSNet (Re-imp)		88.88 125	88.87 116	88.91 202	93.89 167	74.43 278	96.93 131	94.19 229	84.39 148	83.22 174	95.67 130	78.24 87	94.70 297	78.88 207	94.40 116	97.61 113

TAMVS		88.48 136	87.79 132	90.56 164	91.09 244	79.18 182	96.45 160	95.88 138	83.64 173	83.12 175	93.33 185	75.94 125	95.74 255	82.40 177	88.27 173	96.75 152

baseline1		88.85 126	87.49 141	92.93 83	95.21 126	86.85 28	95.47 209	94.61 206	87.29 88	83.11 176	94.99 154	80.70 59	96.89 202	82.28 178	73.72 272	95.05 191

AdaColmap		88.81 127	87.61 138	92.39 102	99.33 4	79.95 160	96.70 148	95.58 152	77.51 277	83.05 177	96.69 111	61.90 248	99.72 37	84.29 153	93.47 128	97.50 120

PatchmatchNet		86.83 167	85.12 178	91.95 120	94.12 161	82.27 108	86.55 335	95.64 150	84.59 143	82.98 178	84.99 312	77.26 102	95.96 240	68.61 290	91.34 151	97.64 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		85.63 186	83.64 201	91.60 134	92.30 212	81.86 116	92.88 280	95.56 153	84.85 134	82.52 179	85.12 310	58.04 273	95.39 270	73.89 257	87.58 179	97.54 115

114514_t		88.79 129	87.57 139	92.45 98	98.21 53	81.74 121	96.99 122	95.45 161	75.16 297	82.48 180	95.69 129	68.59 206	98.50 120	80.33 190	95.18 108	97.10 138

PatchT		79.75 269	76.85 280	88.42 210	89.55 274	75.49 267	77.37 362	94.61 206	63.07 347	82.46 181	73.32 360	75.52 133	93.41 322	51.36 354	84.43 204	96.36 161

TR-MVS		86.30 175	84.93 182	90.42 167	94.63 143	77.58 232	96.57 152	93.82 248	80.30 233	82.42 182	95.16 146	58.74 267	97.55 161	74.88 247	87.82 176	96.13 170

HQP-NCC					92.08 223		97.63 72		90.52 33	82.30 183

ACMP_Plane					92.08 223		97.63 72		90.52 33	82.30 183

HQP4-MVS										82.30 183			97.32 177			91.13 231

HQP-MVS		87.91 152	87.55 140	88.98 201	92.08 223	78.48 199	97.63 72	94.80 192	90.52 33	82.30 183	94.56 162	65.40 224	97.32 177	87.67 130	83.01 214	91.13 231

CR-MVSNet		83.53 220	81.36 236	90.06 177	90.16 262	79.75 166	79.02 358	91.12 311	84.24 156	82.27 187	80.35 339	75.45 134	93.67 317	63.37 316	86.25 188	96.75 152

RPMNet		79.85 268	75.92 286	91.64 131	90.16 262	79.75 166	79.02 358	95.44 162	58.43 365	82.27 187	72.55 362	73.03 171	98.41 128	46.10 365	86.25 188	96.75 152

CVMVSNet		84.83 199	85.57 168	82.63 312	91.55 236	60.38 354	95.13 225	95.03 180	80.60 223	82.10 189	94.71 159	66.40 219	90.19 351	74.30 254	90.32 155	97.31 130

iter_conf_final		89.51 111	89.21 108	90.39 168	95.60 114	84.44 70	97.22 99	89.09 333	89.11 52	82.07 190	92.80 191	87.03 25	96.03 232	89.10 115	80.89 228	90.70 236

PLC	83.97 7	88.00 149	87.38 145	89.83 187	98.02 59	76.46 250	97.16 109	94.43 217	79.26 256	81.98 191	96.28 116	69.36 203	99.27 74	77.71 216	92.25 143	93.77 214
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 278	77.20 276	84.40 294	89.74 271	64.06 342	75.30 366	95.44 162	62.15 350	81.90 192	59.08 370	78.92 77	95.59 264	66.51 301	85.78 196	93.54 217

Anonymous20240529		83.15 227	80.60 246	90.80 157	95.74 111	78.27 207	96.81 139	94.92 184	60.10 361	81.89 193	92.54 195	45.82 330	98.82 108	79.25 203	78.32 254	95.31 188

tttt0517		88.57 135	88.19 125	89.71 191	93.00 192	75.99 261	95.67 201	96.67 66	80.78 219	81.82 194	94.40 165	88.97 14	97.58 158	76.05 237	86.31 187	95.57 181

BH-RMVSNet		86.84 166	85.28 173	91.49 136	95.35 121	80.26 155	96.95 129	92.21 295	82.86 190	81.77 195	95.46 137	59.34 263	97.64 154	69.79 285	93.81 124	96.57 157

iter_conf05		90.14 101	89.79 102	91.17 146	95.85 109	86.93 27	97.68 70	88.67 340	89.93 42	81.73 196	92.80 191	90.37 8	96.03 232	90.44 97	80.65 232	90.56 238

HQP_MVS		87.50 158	87.09 152	88.74 206	91.86 232	77.96 219	97.18 105	94.69 197	89.89 43	81.33 197	94.15 172	64.77 230	97.30 179	87.08 134	82.82 218	90.96 233

plane_prior3						77.75 229			90.17 40	81.33 197

VPA-MVSNet		85.32 191	83.83 197	89.77 190	90.25 259	82.63 100	96.36 167	97.07 29	83.03 185	81.21 199	89.02 246	61.58 249	96.31 224	85.02 149	70.95 287	90.36 241

GeoE		86.36 173	85.20 174	89.83 187	93.17 186	76.13 255	97.53 80	92.11 296	79.58 248	80.99 200	94.01 175	66.60 218	96.17 230	73.48 261	89.30 161	97.20 135

GA-MVS		85.79 184	84.04 196	91.02 151	89.47 276	80.27 154	96.90 133	94.84 190	85.57 117	80.88 201	89.08 244	56.56 289	96.47 219	77.72 215	85.35 200	96.34 163

1112_ss		88.60 134	87.47 143	92.00 119	93.21 184	80.97 135	96.47 158	92.46 291	83.64 173	80.86 202	97.30 84	80.24 64	97.62 155	77.60 218	85.49 198	97.40 126

dp		84.30 209	82.31 222	90.28 172	94.24 156	77.97 218	86.57 334	95.53 154	79.94 242	80.75 203	85.16 308	71.49 189	96.39 221	63.73 313	83.36 211	96.48 159

Test_1112_low_res		88.03 148	86.73 157	91.94 121	93.15 187	80.88 138	96.44 161	92.41 293	83.59 175	80.74 204	91.16 217	80.18 65	97.59 157	77.48 221	85.40 199	97.36 128

cascas		86.50 171	84.48 188	92.55 96	92.64 204	85.95 36	97.04 121	95.07 179	75.32 295	80.50 205	91.02 219	54.33 303	97.98 140	86.79 139	87.62 177	93.71 215

TAPA-MVS	81.61 12	85.02 196	83.67 199	89.06 198	96.79 92	73.27 289	95.92 190	94.79 194	74.81 300	80.47 206	96.83 103	71.07 192	98.19 136	49.82 359	92.57 137	95.71 178
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 182	85.10 179	88.06 221	88.34 287	77.83 226	95.72 199	94.20 228	87.89 75	80.45 207	94.05 174	58.57 268	97.26 183	83.88 158	82.76 220	89.09 272
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
nrg030		86.79 168	85.43 170	90.87 156	88.76 281	85.34 48	97.06 120	94.33 223	84.31 150	80.45 207	91.98 203	72.36 177	96.36 222	88.48 122	71.13 285	90.93 235

EI-MVSNet		85.80 183	85.20 174	87.59 233	91.55 236	77.41 235	95.13 225	95.36 167	80.43 230	80.33 209	94.71 159	73.72 164	95.97 237	76.96 227	78.64 247	89.39 259

MVSTER		89.25 118	88.92 115	90.24 173	95.98 106	84.66 67	96.79 140	95.36 167	87.19 93	80.33 209	90.61 227	90.02 12	95.97 237	85.38 146	78.64 247	90.09 250

ADS-MVSNet2		79.57 272	77.53 274	85.71 269	93.78 168	72.13 297	79.48 354	86.11 352	73.09 314	80.14 211	79.99 341	62.15 243	90.14 352	59.49 328	83.52 208	94.85 194

ADS-MVSNet		81.26 256	78.36 268	89.96 182	93.78 168	79.78 164	79.48 354	93.60 262	73.09 314	80.14 211	79.99 341	62.15 243	95.24 279	59.49 328	83.52 208	94.85 194

test_fmvs2		79.59 271	79.90 258	78.67 330	82.86 344	55.82 363	95.20 221	89.55 327	81.09 214	80.12 213	89.80 238	34.31 359	93.51 320	87.82 127	78.36 253	86.69 319

baseline2		90.39 96	90.21 90	90.93 152	90.86 250	80.99 134	95.20 221	97.41 16	86.03 110	80.07 214	94.61 161	90.58 6	97.47 170	87.29 133	89.86 158	94.35 203

Effi-MVS+-dtu		84.61 203	84.90 183	83.72 302	91.96 229	63.14 346	94.95 232	93.34 274	85.57 117	79.79 215	87.12 275	61.99 246	95.61 263	83.55 167	85.83 195	92.41 227

VPNet		84.69 201	82.92 212	90.01 178	89.01 280	83.45 88	96.71 146	95.46 160	85.71 115	79.65 216	92.18 199	56.66 288	96.01 236	83.05 175	67.84 318	90.56 238

SDMVSNet		87.02 162	85.61 167	91.24 143	94.14 159	83.30 91	93.88 257	95.98 131	84.30 152	79.63 217	92.01 200	58.23 271	97.68 152	90.28 103	82.02 224	92.75 223

sd_testset		84.62 202	83.11 210	89.17 196	94.14 159	77.78 227	91.54 298	94.38 220	84.30 152	79.63 217	92.01 200	52.28 308	96.98 196	77.67 217	82.02 224	92.75 223

CLD-MVS		87.97 150	87.48 142	89.44 193	92.16 221	80.54 148	98.14 38	94.92 184	91.41 23	79.43 219	95.40 138	62.34 241	97.27 182	90.60 93	82.90 217	90.50 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IB-MVS	85.34 4	88.67 131	87.14 151	93.26 69	93.12 190	84.32 72	98.76 19	97.27 19	87.19 93	79.36 220	90.45 229	83.92 42	98.53 119	84.41 152	69.79 298	96.93 142
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
PatchMatch-RL		85.00 197	83.66 200	89.02 200	95.86 108	74.55 277	92.49 284	93.60 262	79.30 254	79.29 221	91.47 210	58.53 269	98.45 125	70.22 283	92.17 145	94.07 209

CNLPA		86.96 163	85.37 172	91.72 129	97.59 72	79.34 179	97.21 101	91.05 314	74.22 303	78.90 222	96.75 109	67.21 213	98.95 101	74.68 249	90.77 154	96.88 146

MVS		90.60 92	88.64 117	96.50 5	94.25 155	90.53 8	93.33 269	97.21 21	77.59 276	78.88 223	97.31 82	71.52 188	99.69 42	89.60 108	98.03 54	99.27 20

mvs_anonymous		88.68 130	87.62 137	91.86 123	94.80 140	81.69 123	93.53 265	94.92 184	82.03 205	78.87 224	90.43 230	75.77 127	95.34 273	85.04 148	93.16 132	98.55 48

mvsmamba		85.17 194	84.54 185	87.05 248	87.94 291	75.11 271	96.22 175	87.79 344	86.91 97	78.55 225	91.77 209	64.93 229	95.91 243	86.94 138	79.80 234	90.12 247

tpm cat1		83.63 219	81.38 235	90.39 168	93.53 180	78.19 214	85.56 341	95.09 177	70.78 328	78.51 226	83.28 325	74.80 149	97.03 193	66.77 297	84.05 206	95.95 171

UniMVSNet (Re)		85.31 192	84.23 192	88.55 209	89.75 269	80.55 146	96.72 144	96.89 39	85.42 120	78.40 227	88.93 247	75.38 138	95.52 267	78.58 209	68.02 315	89.57 258

FIs		86.73 170	86.10 163	88.61 208	90.05 265	80.21 156	96.14 181	96.95 35	85.56 119	78.37 228	92.30 197	76.73 112	95.28 277	79.51 199	79.27 241	90.35 242

BH-w/o		88.24 144	87.47 143	90.54 165	95.03 134	78.54 198	97.41 93	93.82 248	84.08 157	78.23 229	94.51 164	69.34 204	97.21 184	80.21 194	94.58 114	95.87 174

UniMVSNet_NR-MVSNet		85.49 189	84.59 184	88.21 219	89.44 277	79.36 177	96.71 146	96.41 99	85.22 125	78.11 230	90.98 221	76.97 108	95.14 284	79.14 204	68.30 312	90.12 247

DU-MVS		84.57 204	83.33 207	88.28 215	88.76 281	79.36 177	96.43 163	95.41 166	85.42 120	78.11 230	90.82 223	67.61 207	95.14 284	79.14 204	68.30 312	90.33 243

dmvs_re		84.10 211	82.90 213	87.70 228	91.41 240	73.28 287	90.59 305	93.19 278	85.02 131	77.96 232	93.68 182	57.92 277	96.18 229	75.50 242	80.87 229	93.63 216

miper_enhance_ethall		85.95 181	85.20 174	88.19 220	94.85 139	79.76 165	96.00 185	94.06 237	82.98 187	77.74 233	88.76 249	79.42 70	95.46 269	80.58 188	72.42 279	89.36 264

v1144		82.90 233	81.27 237	87.78 227	86.29 308	79.07 188	96.14 181	93.93 240	80.05 239	77.38 234	86.80 280	65.50 222	95.93 242	75.21 245	70.13 293	88.33 293

FC-MVSNet-test		85.96 180	85.39 171	87.66 230	89.38 278	78.02 216	95.65 203	96.87 40	85.12 129	77.34 235	91.94 206	76.28 120	94.74 296	77.09 224	78.82 245	90.21 245

v2v482		83.46 221	81.86 228	88.25 217	86.19 310	79.65 171	96.34 169	94.02 238	81.56 210	77.32 236	88.23 258	65.62 221	96.03 232	77.77 213	69.72 300	89.09 272

Baseline_NR-MVSNet		81.22 257	80.07 254	84.68 286	85.32 325	75.12 270	96.48 157	88.80 336	76.24 291	77.28 237	86.40 290	67.61 207	94.39 305	75.73 241	66.73 328	84.54 339

V42		83.04 230	81.53 233	87.57 235	86.27 309	79.09 187	95.87 194	94.11 234	80.35 232	77.22 238	86.79 281	65.32 226	96.02 235	77.74 214	70.14 292	87.61 306

v144192		82.43 239	80.73 243	87.54 236	85.81 317	78.22 209	95.98 186	93.78 253	79.09 259	77.11 239	86.49 285	64.66 232	95.91 243	74.20 255	69.42 301	88.49 287

ACMM	80.70 13	83.72 218	82.85 215	86.31 260	91.19 242	72.12 298	95.88 193	94.29 225	80.44 228	77.02 240	91.96 204	55.24 297	97.14 191	79.30 202	80.38 233	89.67 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 243	80.55 247	87.60 232	85.94 314	78.47 202	95.85 196	93.80 251	79.33 252	76.97 241	86.51 284	63.33 237	95.87 245	73.11 262	70.13 293	88.46 289

PCF-MVS	84.09 5	86.77 169	85.00 180	92.08 114	92.06 226	83.07 95	92.14 288	94.47 214	79.63 247	76.90 242	94.78 158	71.15 191	99.20 82	72.87 263	91.05 152	93.98 210
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		85.11 195	84.17 193	87.92 224	95.06 133	78.82 191	95.51 207	94.22 227	79.74 245	76.77 243	87.92 263	75.96 124	95.68 256	79.93 197	72.42 279	89.27 266

v1921920		82.02 247	80.23 251	87.41 239	85.62 319	77.92 222	95.79 198	93.69 258	78.86 263	76.67 244	86.44 287	62.50 240	95.83 247	72.69 264	69.77 299	88.47 288

WR-MVS		84.32 208	82.96 211	88.41 211	89.38 278	80.32 151	96.59 151	96.25 113	83.97 161	76.63 245	90.36 231	67.53 209	94.86 294	75.82 240	70.09 296	90.06 252

BH-untuned		86.95 164	85.94 164	89.99 179	94.52 147	77.46 234	96.78 141	93.37 273	81.80 207	76.62 246	93.81 181	66.64 217	97.02 194	76.06 236	93.88 123	95.48 184

v1240		81.70 250	79.83 259	87.30 243	85.50 320	77.70 231	95.48 208	93.44 267	78.46 268	76.53 247	86.44 287	60.85 253	95.84 246	71.59 271	70.17 291	88.35 292

bld_raw_dy_0_64		82.13 245	80.76 242	86.24 262	85.78 318	75.03 272	94.40 245	82.62 364	83.12 181	76.46 248	90.96 222	53.83 305	94.55 300	81.04 185	78.60 250	89.14 270

PS-MVSNAJss		84.91 198	84.30 191	86.74 251	85.89 316	74.40 279	94.95 232	94.16 231	83.93 164	76.45 249	90.11 237	71.04 193	95.77 250	83.16 173	79.02 244	90.06 252

miper_ehance_all_eth		84.57 204	83.60 203	87.50 237	92.64 204	78.25 208	95.40 213	93.47 266	79.28 255	76.41 250	87.64 266	76.53 114	95.24 279	78.58 209	72.42 279	89.01 277

LPG-MVS_test		84.20 210	83.49 205	86.33 257	90.88 247	73.06 290	95.28 215	94.13 232	82.20 200	76.31 251	93.20 186	54.83 301	96.95 198	83.72 163	80.83 230	88.98 278

LGP-MVS_train				86.33 257	90.88 247	73.06 290		94.13 232	82.20 200	76.31 251	93.20 186	54.83 301	96.95 198	83.72 163	80.83 230	88.98 278

F-COLMAP		84.50 206	83.44 206	87.67 229	95.22 125	72.22 295	95.95 188	93.78 253	75.74 292	76.30 253	95.18 145	59.50 261	98.45 125	72.67 265	86.59 185	92.35 228

tpmvs		83.04 230	80.77 241	89.84 186	95.43 118	77.96 219	85.59 340	95.32 171	75.31 296	76.27 254	83.70 322	73.89 161	97.41 172	59.53 327	81.93 226	94.14 206

tt0805		81.20 258	79.06 265	87.61 231	86.50 304	72.97 292	93.66 260	95.48 158	74.11 304	76.23 255	91.99 202	41.36 345	97.40 173	77.44 222	74.78 268	92.45 226

3Dnovator	82.32 10	89.33 115	87.64 135	94.42 32	93.73 171	85.70 42	97.73 66	96.75 56	86.73 103	76.21 256	95.93 122	62.17 242	99.68 44	81.67 182	97.81 60	97.88 90

TranMVSNet+NR-MVSNet		83.24 226	81.71 230	87.83 225	87.71 294	78.81 193	96.13 183	94.82 191	84.52 144	76.18 257	90.78 225	64.07 233	94.60 299	74.60 252	66.59 329	90.09 250

c3_l		83.80 216	82.65 218	87.25 244	92.10 222	77.74 230	95.25 218	93.04 285	78.58 266	76.01 258	87.21 274	75.25 143	95.11 286	77.54 220	68.89 306	88.91 283

1314		88.94 122	87.20 148	94.17 40	93.21 184	85.73 41	93.33 269	96.64 72	82.89 188	75.98 259	96.36 114	66.83 216	99.39 67	83.52 170	96.02 101	97.39 127

Fast-Effi-MVS+-dtu		83.33 223	82.60 219	85.50 275	89.55 274	69.38 323	96.09 184	91.38 306	82.30 199	75.96 260	91.41 211	56.71 286	95.58 265	75.13 246	84.90 203	91.54 229

XXY-MVS		83.84 215	82.00 226	89.35 194	87.13 300	81.38 127	95.72 199	94.26 226	80.15 237	75.92 261	90.63 226	61.96 247	96.52 217	78.98 206	73.28 277	90.14 246

RRT_MVS		83.88 214	83.27 208	85.71 269	87.53 298	72.12 298	95.35 214	94.33 223	83.81 168	75.86 262	91.28 215	60.55 254	95.09 289	83.93 157	76.76 259	89.90 255

GBi-Net		82.42 240	80.43 249	88.39 212	92.66 201	81.95 110	94.30 248	93.38 270	79.06 260	75.82 263	85.66 296	56.38 291	93.84 313	71.23 274	75.38 265	89.38 261

test1		82.42 240	80.43 249	88.39 212	92.66 201	81.95 110	94.30 248	93.38 270	79.06 260	75.82 263	85.66 296	56.38 291	93.84 313	71.23 274	75.38 265	89.38 261

FMVSNet3		84.71 200	82.71 217	90.70 161	94.55 145	87.71 20	95.92 190	94.67 200	81.73 208	75.82 263	88.08 261	66.99 214	94.47 303	71.23 274	75.38 265	89.91 254

eth_miper_zixun_eth		83.12 228	82.01 225	86.47 256	91.85 234	74.80 273	94.33 246	93.18 280	79.11 258	75.74 266	87.25 273	72.71 173	95.32 275	76.78 228	67.13 324	89.27 266

IterMVS-LS		83.93 213	82.80 216	87.31 242	91.46 239	77.39 236	95.66 202	93.43 268	80.44 228	75.51 267	87.26 272	73.72 164	95.16 283	76.99 225	70.72 289	89.39 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 110	87.85 130	94.99 20	94.49 151	86.76 30	97.84 57	95.74 145	86.10 108	75.47 268	96.02 121	65.00 228	99.51 61	82.91 176	97.07 81	98.72 39

test_djsdf		83.00 232	82.45 221	84.64 288	84.07 337	69.78 319	94.80 237	94.48 212	80.74 220	75.41 269	87.70 265	61.32 252	95.10 287	83.77 161	79.76 235	89.04 275

v148		82.41 242	80.89 239	86.99 249	86.18 311	76.81 246	96.27 172	93.82 248	80.49 227	75.28 270	86.11 295	67.32 212	95.75 252	75.48 243	67.03 326	88.42 291

QAPM		86.88 165	84.51 186	93.98 43	94.04 164	85.89 39	97.19 104	96.05 128	73.62 308	75.12 271	95.62 132	62.02 245	99.74 34	70.88 278	96.06 100	96.30 167

UniMVSNet_ETH3D		80.86 262	78.75 267	87.22 245	86.31 307	72.02 300	91.95 289	93.76 256	73.51 309	75.06 272	90.16 235	43.04 339	95.66 257	76.37 234	78.55 251	93.98 210

cl____		83.27 224	82.12 223	86.74 251	92.20 217	75.95 262	95.11 227	93.27 276	78.44 269	74.82 273	87.02 277	74.19 158	95.19 281	74.67 250	69.32 302	89.09 272

DIV-MVS_self_test		83.27 224	82.12 223	86.74 251	92.19 218	75.92 264	95.11 227	93.26 277	78.44 269	74.81 274	87.08 276	74.19 158	95.19 281	74.66 251	69.30 303	89.11 271

FMVSNet2		82.79 234	80.44 248	89.83 187	92.66 201	85.43 47	95.42 211	94.35 221	79.06 260	74.46 275	87.28 270	56.38 291	94.31 306	69.72 286	74.68 269	89.76 256

MIMVSNet		79.18 277	75.99 285	88.72 207	87.37 299	80.66 143	79.96 353	91.82 300	77.38 279	74.33 276	81.87 331	41.78 342	90.74 347	66.36 303	83.10 213	94.76 196

RPSCF		77.73 286	76.63 281	81.06 320	88.66 285	55.76 364	87.77 325	87.88 343	64.82 346	74.14 277	92.79 193	49.22 318	96.81 208	67.47 294	76.88 258	90.62 237

ACMP	81.66 11	84.00 212	83.22 209	86.33 257	91.53 238	72.95 293	95.91 192	93.79 252	83.70 172	73.79 278	92.22 198	54.31 304	96.89 202	83.98 156	79.74 237	89.16 269
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		81.34 255	79.54 260	86.73 254	85.02 327	76.91 243	96.22 175	91.65 303	77.65 275	73.55 279	88.61 251	55.70 294	94.43 304	74.12 256	73.35 276	88.86 284

jajsoiax		82.12 246	81.15 238	85.03 282	84.19 335	70.70 312	94.22 252	93.95 239	83.07 183	73.48 280	89.75 239	49.66 317	95.37 272	82.24 179	79.76 235	89.02 276

mvs_tets		81.74 249	80.71 244	84.84 283	84.22 334	70.29 315	93.91 256	93.78 253	82.77 192	73.37 281	89.46 242	47.36 327	95.31 276	81.99 180	79.55 240	88.92 282

pmmvs4		82.54 238	80.79 240	87.79 226	86.11 312	80.49 150	93.55 264	93.18 280	77.29 280	73.35 282	89.40 243	65.26 227	95.05 291	75.32 244	73.61 273	87.83 301

LS3D		82.22 244	79.94 257	89.06 198	97.43 79	74.06 282	93.20 275	92.05 297	61.90 351	73.33 283	95.21 142	59.35 262	99.21 78	54.54 347	92.48 140	93.90 212

v10		81.43 254	79.53 261	87.11 246	86.38 305	78.87 190	94.31 247	93.43 268	77.88 272	73.24 284	85.26 304	65.44 223	95.75 252	72.14 268	67.71 319	86.72 318

v8		81.88 248	80.06 255	87.32 241	86.63 303	79.04 189	94.41 242	93.65 260	78.77 264	73.19 285	85.57 300	66.87 215	95.81 248	73.84 259	67.61 320	87.11 314

test0.0.03 1		82.79 234	82.48 220	83.74 301	86.81 302	72.22 295	96.52 155	95.03 180	83.76 170	73.00 286	93.20 186	72.30 179	88.88 354	64.15 311	77.52 257	90.12 247

anonymousdsp		80.98 261	79.97 256	84.01 296	81.73 346	70.44 314	92.49 284	93.58 264	77.10 284	72.98 287	86.31 291	57.58 278	94.90 292	79.32 201	78.63 249	86.69 319

XVG-ACMP-BASELINE		79.38 275	77.90 272	83.81 298	84.98 328	67.14 334	89.03 314	93.18 280	80.26 236	72.87 288	88.15 260	38.55 350	96.26 225	76.05 237	78.05 255	88.02 298

WR-MVS_H		81.02 259	80.09 252	83.79 299	88.08 290	71.26 311	94.46 240	96.54 84	80.08 238	72.81 289	86.82 279	70.36 199	92.65 326	64.18 310	67.50 321	87.46 311

OpenMVS	79.58 14	86.09 178	83.62 202	93.50 62	90.95 246	86.71 31	97.44 88	95.83 141	75.35 294	72.64 290	95.72 127	57.42 282	99.64 48	71.41 272	95.85 104	94.13 207

Anonymous20231211		79.72 270	77.19 277	87.33 240	95.59 115	77.16 242	95.18 224	94.18 230	59.31 363	72.57 291	86.20 293	47.89 324	95.66 257	74.53 253	69.24 304	89.18 268

CP-MVSNet		81.01 260	80.08 253	83.79 299	87.91 292	70.51 313	94.29 251	95.65 149	80.83 218	72.54 292	88.84 248	63.71 234	92.32 329	68.58 291	68.36 311	88.55 286

miper_lstm_enhance		81.66 252	80.66 245	84.67 287	91.19 242	71.97 302	91.94 290	93.19 278	77.86 273	72.27 293	85.26 304	73.46 167	93.42 321	73.71 260	67.05 325	88.61 285

PS-CasMVS		80.27 266	79.18 262	83.52 305	87.56 296	69.88 318	94.08 254	95.29 172	80.27 235	72.08 294	88.51 255	59.22 265	92.23 331	67.49 293	68.15 314	88.45 290

FMVSNet1		79.50 273	76.54 282	88.39 212	88.47 286	81.95 110	94.30 248	93.38 270	73.14 313	72.04 295	85.66 296	43.86 333	93.84 313	65.48 305	72.53 278	89.38 261

PEN-MVS		79.47 274	78.26 270	83.08 308	86.36 306	68.58 326	93.85 258	94.77 195	79.76 244	71.37 296	88.55 252	59.79 257	92.46 327	64.50 309	65.40 331	88.19 295

Patchmtry		77.36 290	74.59 295	85.67 271	89.75 269	75.75 266	77.85 361	91.12 311	60.28 359	71.23 297	80.35 339	75.45 134	93.56 319	57.94 333	67.34 323	87.68 304

IterMVS		80.67 263	79.16 263	85.20 279	89.79 267	76.08 256	92.97 279	91.86 299	80.28 234	71.20 298	85.14 309	57.93 276	91.34 341	72.52 266	70.74 288	88.18 296
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 253	78.28 269	91.04 149	98.14 55	78.48 199	95.09 230	86.97 346	61.14 357	71.12 299	92.78 194	59.59 259	99.38 68	53.11 351	86.61 184	95.27 190

IterMVS-SCA-FT		80.51 265	79.10 264	84.73 285	89.63 273	74.66 274	92.98 278	91.81 301	80.05 239	71.06 300	85.18 307	58.04 273	91.40 340	72.48 267	70.70 290	88.12 297

v7n		79.32 276	77.34 275	85.28 278	84.05 338	72.89 294	93.38 267	93.87 245	75.02 299	70.68 301	84.37 316	59.58 260	95.62 262	67.60 292	67.50 321	87.32 313

MS-PatchMatch		83.05 229	81.82 229	86.72 255	89.64 272	79.10 186	94.88 234	94.59 208	79.70 246	70.67 302	89.65 240	50.43 314	96.82 207	70.82 281	95.99 102	84.25 342

DTE-MVSNet		78.37 280	77.06 278	82.32 315	85.22 326	67.17 333	93.40 266	93.66 259	78.71 265	70.53 303	88.29 257	59.06 266	92.23 331	61.38 323	63.28 340	87.56 308

pm-mvs1		80.05 267	78.02 271	86.15 263	85.42 321	75.81 265	95.11 227	92.69 290	77.13 282	70.36 304	87.43 268	58.44 270	95.27 278	71.36 273	64.25 336	87.36 312

D2MVS		82.67 236	81.55 232	86.04 265	87.77 293	76.47 249	95.21 220	96.58 80	82.66 195	70.26 305	85.46 303	60.39 255	95.80 249	76.40 233	79.18 242	85.83 332

PVSNet_0	77.72 15	81.70 250	78.95 266	89.94 183	90.77 253	76.72 248	95.96 187	96.95 35	85.01 132	70.24 306	88.53 254	52.32 307	98.20 135	86.68 140	44.08 371	94.89 193

CL-MVSNet_self_test		75.81 299	74.14 301	80.83 322	78.33 356	67.79 329	94.22 252	93.52 265	77.28 281	69.82 307	81.54 333	61.47 251	89.22 353	57.59 336	53.51 356	85.48 334

tfpnnormal		78.14 282	75.42 288	86.31 260	88.33 288	79.24 180	94.41 242	96.22 115	73.51 309	69.81 308	85.52 302	55.43 295	95.75 252	47.65 363	67.86 317	83.95 345

EU-MVSNet		76.92 294	76.95 279	76.83 336	84.10 336	54.73 366	91.77 293	92.71 289	72.74 317	69.57 309	88.69 250	58.03 275	87.43 360	64.91 308	70.00 297	88.33 293

ITE_SJBPF				82.38 313	87.00 301	65.59 336		89.55 327	79.99 241	69.37 310	91.30 214	41.60 344	95.33 274	62.86 318	74.63 270	86.24 325

DSMNet-mixed		73.13 312	72.45 308	75.19 342	77.51 359	46.82 371	85.09 343	82.01 365	67.61 341	69.27 311	81.33 334	50.89 311	86.28 362	54.54 347	83.80 207	92.46 225

MVP-Stereo		82.65 237	81.67 231	85.59 274	86.10 313	78.29 206	93.33 269	92.82 287	77.75 274	69.17 312	87.98 262	59.28 264	95.76 251	71.77 269	96.88 85	82.73 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		80.62 264	77.77 273	89.14 197	93.43 182	77.24 238	91.89 291	90.18 323	69.86 333	68.02 313	91.94 206	52.21 309	98.84 107	59.32 330	83.12 212	91.35 230

NR-MVSNet		83.35 222	81.52 234	88.84 203	88.76 281	81.31 129	94.45 241	95.16 176	84.65 141	67.81 314	90.82 223	70.36 199	94.87 293	74.75 248	66.89 327	90.33 243

TransMVSNet (Re)		76.94 293	74.38 297	84.62 289	85.92 315	75.25 269	95.28 215	89.18 332	73.88 307	67.22 315	86.46 286	59.64 258	94.10 309	59.24 331	52.57 360	84.50 340

Anonymous20231206		75.29 302	73.64 303	80.22 324	80.75 347	63.38 345	93.36 268	90.71 321	73.09 314	67.12 316	83.70 322	50.33 315	90.85 346	53.63 350	70.10 295	86.44 322

ppachtmachnet_test		77.19 291	74.22 299	86.13 264	85.39 322	78.22 209	93.98 255	91.36 308	71.74 324	67.11 317	84.87 313	56.67 287	93.37 323	52.21 352	64.59 333	86.80 317

KD-MVS_2432*1600		77.63 287	74.92 292	85.77 267	90.86 250	79.44 174	88.08 321	93.92 241	76.26 289	67.05 318	82.78 327	72.15 181	91.92 334	61.53 320	41.62 374	85.94 330

miper_refine_blended		77.63 287	74.92 292	85.77 267	90.86 250	79.44 174	88.08 321	93.92 241	76.26 289	67.05 318	82.78 327	72.15 181	91.92 334	61.53 320	41.62 374	85.94 330

Patchmatch-test		78.25 281	74.72 294	88.83 204	91.20 241	74.10 281	73.91 369	88.70 339	59.89 362	66.82 320	85.12 310	78.38 85	94.54 301	48.84 361	79.58 239	97.86 93

test_fmvs3		69.56 323	69.19 323	70.67 345	69.01 370	47.05 370	90.87 303	86.81 348	71.31 327	66.79 321	77.15 349	16.40 374	83.17 368	81.84 181	62.51 342	81.79 358

LTVRE_ROB	73.68 18	77.99 283	75.74 287	84.74 284	90.45 257	72.02 300	86.41 336	91.12 311	72.57 319	66.63 322	87.27 271	54.95 300	96.98 196	56.29 342	75.98 260	85.21 336
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
OurMVSNet-221017-0		77.18 292	76.06 284	80.55 323	83.78 341	60.00 356	90.35 306	91.05 314	77.01 286	66.62 323	87.92 263	47.73 325	94.03 310	71.63 270	68.44 310	87.62 305

testgi		74.88 304	73.40 304	79.32 328	80.13 351	61.75 349	93.21 274	86.64 350	79.49 250	66.56 324	91.06 218	35.51 357	88.67 355	56.79 341	71.25 284	87.56 308

LCM-MVSNet-Re		83.75 217	83.54 204	84.39 295	93.54 175	64.14 341	92.51 283	84.03 359	83.90 165	66.14 325	86.59 283	67.36 211	92.68 325	84.89 150	92.87 134	96.35 162

pmmvs6		74.65 305	71.67 311	83.60 304	79.13 354	69.94 317	93.31 272	90.88 318	61.05 358	65.83 326	84.15 319	43.43 335	94.83 295	66.62 298	60.63 345	86.02 329

our_test_3		77.90 285	75.37 289	85.48 276	85.39 322	76.74 247	93.63 261	91.67 302	73.39 312	65.72 327	84.65 315	58.20 272	93.13 324	57.82 334	67.87 316	86.57 321

COLMAP_ROB	73.24 19	75.74 300	73.00 307	83.94 297	92.38 207	69.08 324	91.85 292	86.93 347	61.48 354	65.32 328	90.27 232	42.27 341	96.93 201	50.91 356	75.63 264	85.80 333
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 296	74.16 300	83.35 307	90.05 265	76.17 254	89.58 310	89.85 325	71.39 326	65.29 329	80.42 338	50.61 313	87.70 359	61.05 325	69.24 304	86.18 326

ACMH+	76.62 16	77.47 289	74.94 291	85.05 281	91.07 245	71.58 308	93.26 273	90.01 324	71.80 323	64.76 330	88.55 252	41.62 343	96.48 218	62.35 319	71.00 286	87.09 315

Patchmatch-RL test		76.65 295	74.01 302	84.55 290	77.37 360	64.23 340	78.49 360	82.84 363	78.48 267	64.63 331	73.40 359	76.05 123	91.70 339	76.99 225	57.84 349	97.72 103

SixPastTwentyTwo		76.04 297	74.32 298	81.22 319	84.54 331	61.43 352	91.16 300	89.30 331	77.89 271	64.04 332	86.31 291	48.23 319	94.29 307	63.54 315	63.84 338	87.93 300

AllTest		75.92 298	73.06 306	84.47 291	92.18 219	67.29 330	91.07 301	84.43 357	67.63 337	63.48 333	90.18 233	38.20 351	97.16 187	57.04 338	73.37 274	88.97 280

TestCases				84.47 291	92.18 219	67.29 330		84.43 357	67.63 337	63.48 333	90.18 233	38.20 351	97.16 187	57.04 338	73.37 274	88.97 280

ACMH	75.40 17	77.99 283	74.96 290	87.10 247	90.67 254	76.41 251	93.19 276	91.64 304	72.47 320	63.44 335	87.61 267	43.34 336	97.16 187	58.34 332	73.94 271	87.72 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		90.01 103	89.03 110	92.95 81	94.38 152	86.77 29	98.14 38	96.31 110	89.30 49	63.33 336	96.72 110	90.09 11	93.63 318	90.70 92	82.29 223	98.46 52

USDC		78.65 279	76.25 283	85.85 266	87.58 295	74.60 276	89.58 310	90.58 322	84.05 158	63.13 337	88.23 258	40.69 349	96.86 206	66.57 300	75.81 263	86.09 328

LF4IMVS		72.36 316	70.82 314	76.95 335	79.18 353	56.33 360	86.12 337	86.11 352	69.30 335	63.06 338	86.66 282	33.03 361	92.25 330	65.33 306	68.64 308	82.28 354

dmvs_testset		72.00 319	73.36 305	67.91 347	83.83 340	31.90 384	85.30 342	77.12 372	82.80 191	63.05 339	92.46 196	61.54 250	82.55 370	42.22 369	71.89 283	89.29 265

KD-MVS_self_test		70.97 322	69.31 322	75.95 341	76.24 366	55.39 365	87.45 326	90.94 317	70.20 331	62.96 340	77.48 348	44.01 332	88.09 356	61.25 324	53.26 357	84.37 341

Anonymous20240521		72.06 318	69.91 319	78.50 332	77.11 361	61.67 351	91.62 297	90.97 316	65.52 344	62.37 341	79.05 344	36.32 353	90.96 345	57.75 335	68.52 309	82.87 347

test_0402		72.68 314	69.54 321	82.09 316	88.67 284	71.81 305	92.72 282	86.77 349	61.52 353	62.21 342	83.91 320	43.22 337	93.76 316	34.60 372	72.23 282	80.72 360

OpenMVS_ROB	68.52 20	73.02 313	69.57 320	83.37 306	80.54 350	71.82 304	93.60 263	88.22 341	62.37 349	61.98 343	83.15 326	35.31 358	95.47 268	45.08 366	75.88 262	82.82 348

MVS-HIRNet		71.36 321	67.00 326	84.46 293	90.58 255	69.74 320	79.15 357	87.74 345	46.09 369	61.96 344	50.50 373	45.14 331	95.64 260	53.74 349	88.11 175	88.00 299

test20.03		72.36 316	71.15 313	75.98 340	77.79 357	59.16 358	92.40 286	89.35 330	74.09 305	61.50 345	84.32 317	48.09 320	85.54 365	50.63 357	62.15 343	83.24 346

mvsany_test3		67.19 329	65.34 331	72.72 344	63.08 375	48.57 369	83.12 349	78.09 371	72.07 321	61.21 346	77.11 350	22.94 369	87.78 358	78.59 208	51.88 361	81.80 357

PM-MVS		69.32 325	66.93 327	76.49 337	73.60 368	55.84 362	85.91 338	79.32 370	74.72 301	61.09 347	78.18 346	21.76 370	91.10 344	70.86 279	56.90 351	82.51 351

TDRefinement		69.20 326	65.78 330	79.48 327	66.04 374	62.21 348	88.21 320	86.12 351	62.92 348	61.03 348	85.61 299	33.23 360	94.16 308	55.82 345	53.02 358	82.08 355

ambc				76.02 339	68.11 371	51.43 367	64.97 374	89.59 326		60.49 349	74.49 356	17.17 373	92.46 327	61.50 322	52.85 359	84.17 343

pmmvs-eth3d		73.59 308	70.66 315	82.38 313	76.40 364	73.38 284	89.39 313	89.43 329	72.69 318	60.34 350	77.79 347	46.43 329	91.26 343	66.42 302	57.06 350	82.51 351

test_vis1_rt		73.96 306	72.40 309	78.64 331	83.91 339	61.16 353	95.63 204	68.18 378	76.32 288	60.09 351	74.77 354	29.01 367	97.54 163	87.74 128	75.94 261	77.22 364

K. test v3		73.62 307	71.59 312	79.69 326	82.98 343	59.85 357	90.85 304	88.83 335	77.13 282	58.90 352	82.11 329	43.62 334	91.72 338	65.83 304	54.10 355	87.50 310

EG-PatchMatch MVS		74.92 303	72.02 310	83.62 303	83.76 342	73.28 287	93.62 262	92.04 298	68.57 336	58.88 353	83.80 321	31.87 363	95.57 266	56.97 340	78.67 246	82.00 356

lessismore_v0				79.98 325	80.59 349	58.34 359		80.87 366		58.49 354	83.46 324	43.10 338	93.89 312	63.11 317	48.68 364	87.72 302

N_pmnet		61.30 333	60.20 336	64.60 352	84.32 333	17.00 390	91.67 296	10.98 389	61.77 352	58.45 355	78.55 345	49.89 316	91.83 337	42.27 368	63.94 337	84.97 337

TinyColmap		72.41 315	68.99 324	82.68 311	88.11 289	69.59 321	88.41 319	85.20 354	65.55 343	57.91 356	84.82 314	30.80 365	95.94 241	51.38 353	68.70 307	82.49 353

UnsupCasMVSNet_eth		73.25 311	70.57 316	81.30 318	77.53 358	66.33 335	87.24 329	93.89 244	80.38 231	57.90 357	81.59 332	42.91 340	90.56 348	65.18 307	48.51 365	87.01 316

MIMVSNet1		69.44 324	66.65 328	77.84 333	76.48 363	62.84 347	87.42 327	88.97 334	66.96 342	57.75 358	79.72 343	32.77 362	85.83 364	46.32 364	63.42 339	84.85 338

pmmvs3		65.75 331	62.18 334	76.45 338	67.12 373	64.54 338	88.68 317	85.05 355	54.77 368	57.54 359	73.79 357	29.40 366	86.21 363	55.49 346	47.77 367	78.62 362

test_f		64.01 332	62.13 335	69.65 346	63.00 376	45.30 376	83.66 348	80.68 367	61.30 355	55.70 360	72.62 361	14.23 376	84.64 366	69.84 284	58.11 348	79.00 361

new-patchmatchnet		68.85 327	65.93 329	77.61 334	73.57 369	63.94 343	90.11 308	88.73 338	71.62 325	55.08 361	73.60 358	40.84 347	87.22 361	51.35 355	48.49 366	81.67 359

UnsupCasMVSNet_bld		68.60 328	64.50 332	80.92 321	74.63 367	67.80 328	83.97 346	92.94 286	65.12 345	54.63 362	68.23 366	35.97 355	92.17 333	60.13 326	44.83 369	82.78 349

CMPMVS	54.94 21	75.71 301	74.56 296	79.17 329	79.69 352	55.98 361	89.59 309	93.30 275	60.28 359	53.85 363	89.07 245	47.68 326	96.33 223	76.55 230	81.02 227	85.22 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 330	63.18 333	75.18 343	76.27 365	61.74 350	83.79 347	84.66 356	56.64 366	51.57 364	71.85 365	31.29 364	87.93 357	49.98 358	62.55 341	75.86 365

test_method		56.77 334	54.53 337	63.49 354	76.49 362	40.70 379	75.68 365	74.24 374	19.47 380	48.73 365	71.89 364	19.31 371	65.80 380	57.46 337	47.51 368	83.97 344

YYNet1		73.53 310	70.43 317	82.85 310	84.52 332	71.73 306	91.69 295	91.37 307	67.63 337	46.79 366	81.21 335	55.04 299	90.43 349	55.93 343	59.70 347	86.38 323

MDA-MVSNet_test_wron		73.54 309	70.43 317	82.86 309	84.55 330	71.85 303	91.74 294	91.32 310	67.63 337	46.73 367	81.09 336	55.11 298	90.42 350	55.91 344	59.76 346	86.31 324

MDA-MVSNet-bldmvs		71.45 320	67.94 325	81.98 317	85.33 324	68.50 327	92.35 287	88.76 337	70.40 329	42.99 368	81.96 330	46.57 328	91.31 342	48.75 362	54.39 354	86.11 327

APD_test1		56.56 335	53.58 338	65.50 349	67.93 372	46.51 373	77.24 364	72.95 375	38.09 371	42.75 369	75.17 353	13.38 377	82.78 369	40.19 370	54.53 353	67.23 370

DeepMVS_CX				64.06 353	78.53 355	43.26 377		68.11 380	69.94 332	38.55 370	76.14 352	18.53 372	79.34 371	43.72 367	41.62 374	69.57 368

LCM-MVSNet		52.52 338	48.24 341	65.35 350	47.63 385	41.45 378	72.55 370	83.62 361	31.75 373	37.66 371	57.92 371	9.19 383	76.76 373	49.26 360	44.60 370	77.84 363

test_vis3_rt		54.10 337	51.04 340	63.27 355	58.16 377	46.08 375	84.17 345	49.32 388	56.48 367	36.56 372	49.48 375	8.03 384	91.91 336	67.29 295	49.87 362	51.82 374

FPMVS		55.09 336	52.93 339	61.57 356	55.98 378	40.51 380	83.11 350	83.41 362	37.61 372	34.95 373	71.95 363	14.40 375	76.95 372	29.81 373	65.16 332	67.25 369

PMMVS2		50.90 340	46.31 343	64.67 351	55.53 379	46.67 372	77.30 363	71.02 377	40.89 370	34.16 374	59.32 369	9.83 382	76.14 375	40.09 371	28.63 377	71.21 366

testf1		45.70 342	42.41 344	55.58 358	53.29 382	40.02 381	68.96 372	62.67 382	27.45 375	29.85 375	61.58 367	5.98 385	73.83 377	28.49 376	43.46 372	52.90 372

APD_test2		45.70 342	42.41 344	55.58 358	53.29 382	40.02 381	68.96 372	62.67 382	27.45 375	29.85 375	61.58 367	5.98 385	73.83 377	28.49 376	43.46 372	52.90 372

tmp_tt		41.54 345	41.93 347	40.38 363	20.10 389	26.84 386	61.93 375	59.09 384	14.81 382	28.51 377	80.58 337	35.53 356	48.33 384	63.70 314	13.11 381	45.96 377

Gipuma		45.11 344	42.05 346	54.30 360	80.69 348	51.30 368	35.80 378	83.81 360	28.13 374	27.94 378	34.53 378	11.41 381	76.70 374	21.45 378	54.65 352	34.90 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 341	41.28 348	61.04 357	39.91 387	46.25 374	70.59 371	76.18 373	58.87 364	23.09 379	48.00 376	12.58 379	66.54 379	28.65 375	13.62 380	70.35 367

MVE	35.65 22	33.85 347	29.49 352	46.92 362	41.86 386	36.28 383	50.45 377	56.52 385	18.75 381	18.28 380	37.84 377	2.41 388	58.41 381	18.71 379	20.62 378	46.06 376
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 346	35.53 349	50.18 361	29.72 388	30.30 385	59.60 376	66.20 381	26.06 377	17.91 381	49.53 374	3.12 387	74.09 376	18.19 380	49.40 363	46.14 375
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 348	32.39 350	33.65 364	53.35 381	25.70 387	74.07 368	53.33 386	21.08 378	17.17 382	33.63 380	11.85 380	54.84 382	12.98 381	14.04 379	20.42 379

EMVS		31.70 349	31.45 351	32.48 365	50.72 384	23.95 388	74.78 367	52.30 387	20.36 379	16.08 383	31.48 381	12.80 378	53.60 383	11.39 382	13.10 382	19.88 380

wuyk23d		14.10 351	13.89 354	14.72 366	55.23 380	22.91 389	33.83 379	3.56 390	4.94 383	4.11 384	2.28 386	2.06 389	19.66 385	10.23 383	8.74 383	1.59 383

testmvs		9.92 352	12.94 355	0.84 368	0.65 390	0.29 392	93.78 259	0.39 391	0.42 384	2.85 385	15.84 384	0.17 391	0.30 387	2.18 384	0.21 384	1.91 382

test123		9.07 353	11.73 356	1.11 367	0.50 391	0.77 391	89.44 312	0.20 392	0.34 385	2.15 386	10.72 385	0.34 390	0.32 386	1.79 385	0.08 385	2.23 381

EGC-MVSNET		52.46 339	47.56 342	67.15 348	81.98 345	60.11 355	82.54 351	72.44 376	0.11 386	0.70 387	74.59 355	25.11 368	83.26 367	29.04 374	61.51 344	58.09 371

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		21.43 350	28.57 353	0.00 369	0.00 392	0.00 393	0.00 380	95.93 136	0.00 387	0.00 388	97.66 63	63.57 235	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		5.92 355	7.89 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	71.04 193	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		8.11 354	10.81 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	97.30 84	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 43					99.81 21	98.08 9	98.81 24	99.43 11

No_MVS				97.14 3	99.05 9	92.19 4		96.83 43					99.81 21	98.08 9	98.81 24	99.43 11

eth-test2					0.00 392

eth-test					0.00 392

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 7				98.54 18	92.06 3	99.84 12	99.11 1	99.37 1	99.74 1

save fliter					98.24 51	83.34 90	98.61 26	96.57 81	91.32 24

test_0728_SECOND				95.14 17	99.04 14	86.14 34	99.06 11	96.77 52					99.84 12	97.90 11	98.85 21	99.45 10

GSMVS																97.54 115

sam_mvs1												77.59 97				97.54 115

sam_mvs												75.35 141

MTGPA								96.33 108

test_post1							85.88 339				30.24 382	73.77 162	95.07 290	73.89 257

test_post											33.80 379	76.17 121	95.97 237

patchmatchnet-post											77.09 351	77.78 96	95.39 270

MTMP							97.53 80	68.16 379

gm-plane-assit					92.27 213	79.64 172			84.47 147		95.15 147		97.93 141	85.81 142

test9_res														96.00 29	99.03 13	98.31 61

agg_prior2														94.30 48	99.00 15	98.57 45

test_prior4						82.34 107	97.75 65

test_prior				93.09 76	98.68 26	81.91 113		96.40 101					99.06 94			98.29 63

新几何2							96.42 164

旧先验1					97.39 82	79.58 173		96.54 84			98.08 41	84.00 40			97.42 73	97.62 112

无先验							96.87 134	96.78 46	77.39 278				99.52 59	79.95 196		98.43 54

原ACMM2							96.84 135

testdata2													99.48 63	76.45 232

segment_acmp												82.69 51

testdata1							95.57 206		87.44 84

plane_prior7					91.86 232	77.55 233

plane_prior6					91.98 228	77.92 222						64.77 230

plane_prior5								94.69 197					97.30 179	87.08 134	82.82 218	90.96 233

plane_prior4											94.15 172

plane_prior2							97.18 105		89.89 43

plane_prior1					91.95 230

plane_prior						77.96 219	97.52 83		90.36 38						82.96 216

n2								0.00 393

nn								0.00 393

door-mid								79.75 369

test11								96.50 89

door								80.13 368

HQP5-MVS						78.48 199

BP-MVS														87.67 130

HQP3-MVS								94.80 192							83.01 214

HQP2-MVS												65.40 224

NP-MVS					92.04 227	78.22 209					94.56 162

ACMMP++_ref															78.45 252

ACMMP++															79.05 243

Test By Simon												71.65 186

High-resolution multi-view benchmark