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 19	85.34 62		96.86 60	92.05 41	98.74 1				98.15 22	98.97 17	99.42 13

fmvsm_s_conf0.5_n_6		94.17 39	94.70 30	92.58 128	93.50 219	81.20 176	99.08 21	96.48 116	92.24 35	98.62 2	98.39 45	78.58 111	99.72 57	98.08 26	97.36 84	96.81 202

fmvsm_l_conf0.5_n_9		94.91 17	95.60 12	92.84 111	95.20 152	80.55 200	99.45 1	96.36 134	95.17 4	98.48 3	98.55 27	80.53 79	99.78 38	98.87 7	97.79 69	98.19 83

PC_three_1452									91.12 50	98.33 4	98.42 43	92.51 2	99.81 27	98.96 6	99.37 1	99.70 3

fmvsm_l_conf0.5_n		94.89 19	95.24 20	93.86 57	94.42 185	84.61 83	99.13 15	96.15 152	92.06 39	97.92 5	98.52 33	84.52 44	99.74 52	98.76 10	95.67 133	97.22 174

SMA-MVS		94.70 25	94.68 31	94.76 30	98.02 63	85.94 45	97.47 120	96.77 71	85.32 180	97.92 5	98.70 22	83.09 62	99.84 17	95.79 59	99.08 10	98.49 62
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
fmvsm_s_conf0.5_n_10		94.36 34	94.73 29	93.23 90	95.19 153	82.87 121	99.18 9	96.39 127	93.97 18	97.91 7	98.53 31	75.88 168	99.82 23	98.58 11	96.95 101	97.00 190

fmvsm_l_conf0.5_n_a		94.91 17	95.30 19	93.72 66	94.50 182	84.30 88	99.14 14	96.00 164	91.94 42	97.91 7	98.60 25	84.78 41	99.77 42	98.84 8	96.03 126	97.08 187

fmvsm_s_conf0.5_n_8		94.52 30	95.04 24	92.96 103	95.15 157	81.14 178	99.09 20	96.66 89	95.53 3	97.84 9	98.71 21	76.33 158	99.81 27	99.24 1	96.85 108	97.92 106

SED-MVS		95.88 5	96.22 4	94.87 26	99.03 19	85.03 75	99.12 16	96.78 65	88.72 84	97.79 10	98.91 2	88.48 19	99.82 23	98.15 22	98.97 17	99.74 1

test_241102_ONE					99.03 19	85.03 75		96.78 65	88.72 84	97.79 10	98.90 5	88.48 19	99.82 23

fmvsm_s_conf0.5_n_9		94.52 30	95.22 21	92.41 138	95.79 131	78.61 266	98.73 38	96.00 164	94.91 8	97.73 12	98.73 20	79.09 101	99.79 35	99.14 4	96.86 106	98.83 41

DVP-MVS++		96.05 4	96.41 3	94.96 25	99.05 13	85.34 62	98.13 68	96.77 71	88.38 92	97.70 13	98.77 15	92.06 3	99.84 17	97.47 40	99.37 1	99.70 3

test_241102_TWO								96.78 65	88.72 84	97.70 13	98.91 2	87.86 24	99.82 23	98.15 22	99.00 15	99.47 9

fmvsm_s_conf0.5_n_11		94.41 33	95.19 22	92.09 161	95.65 135	80.91 189	99.23 7	94.85 240	94.92 7	97.68 15	98.82 11	79.31 95	99.78 38	98.83 9	97.38 83	95.60 246

patch_mono-2		95.14 15	96.08 7	92.33 144	98.44 47	77.84 295	98.43 51	97.21 25	92.58 29	97.68 15	97.65 97	86.88 29	99.83 21	98.25 18	97.60 74	99.33 18

test0726					99.05 13	85.18 67	99.11 19	96.78 65	88.75 82	97.65 17	98.91 2	87.69 25

fmvsm_s_conf0.5_n_3		93.95 45	94.53 33	92.20 155	94.41 186	80.04 219	98.90 33	95.96 169	94.53 12	97.63 18	98.58 26	75.95 165	99.79 35	98.25 18	96.60 114	96.77 205

fmvsm_l_conf0.5_n_3		94.61 26	94.92 27	93.68 70	94.52 177	82.80 123	99.33 2	96.37 132	95.08 6	97.59 19	98.48 37	77.40 132	99.79 35	98.28 16	97.21 89	98.44 66

TSAR-MVS + MP.		94.79 24	95.17 23	93.64 72	97.66 75	84.10 91	95.85 255	96.42 122	91.26 48	97.49 20	96.80 141	86.50 31	98.49 154	95.54 64	99.03 13	98.33 71
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_4		93.59 50	94.32 40	91.41 200	93.89 204	79.24 240	98.89 34	96.53 108	92.82 27	97.37 21	98.47 38	77.21 139	99.78 38	98.11 25	95.59 135	95.21 261

test_fmvsm_n_1920		94.81 23	95.60 12	92.45 133	95.29 148	80.96 186	99.29 4	97.21 25	94.50 13	97.29 22	98.44 40	82.15 67	99.78 38	98.56 12	97.68 72	96.61 212

MSP-MVS		95.62 8	96.54 1	92.86 108	98.31 52	80.10 218	97.42 127	96.78 65	92.20 36	97.11 23	98.29 52	93.46 1	99.10 121	96.01 55	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
MGCNet		95.58 9	95.44 17	96.01 10	97.63 76	89.26 12	99.27 5	96.59 100	94.71 9	97.08 24	97.99 73	78.69 109	99.86 13	99.15 3	97.85 66	98.91 38

fmvsm_s_conf0.5_n_2		92.97 63	93.38 61	91.73 183	94.10 198	80.64 197	98.96 30	95.89 178	94.09 16	97.05 25	98.40 44	68.92 266	99.80 31	98.53 13	94.50 147	94.74 273

MED-MVS test				94.20 47	99.06 10	83.70 100	98.35 55	97.14 30	87.45 119	97.03 26	98.90 5		99.96 3	97.78 35	98.60 34	98.94 34

MED-MVS		95.43 12	95.84 10	94.20 47	99.06 10	83.70 100	98.35 55	97.14 30	85.79 167	97.03 26	98.90 5	89.87 12	99.96 3	97.78 35	98.60 34	98.94 34

TestfortrainingZip a		95.44 11	95.38 18	95.64 13	99.06 10	88.36 15	98.35 55	97.14 30	87.45 119	97.03 26	98.90 5	89.87 12	99.96 3	91.98 121	98.60 34	98.61 57

fmvsm_s_conf0.5_n_a		93.34 56	93.71 50	92.22 153	93.38 222	81.71 165	98.86 35	96.98 46	91.64 43	96.85 29	98.55 27	75.58 174	99.77 42	97.88 32	93.68 162	95.18 262

CNVR-MVS		96.30 1	96.54 1	95.55 16	99.31 5	87.69 25	99.06 23	97.12 35	94.66 10	96.79 30	98.78 14	86.42 32	99.95 6	97.59 39	99.18 7	99.00 31

DVP-MVS		95.58 9	95.91 9	94.57 35	99.05 13	85.18 67	99.06 23	96.46 117	88.75 82	96.69 31	98.76 17	87.69 25	99.76 44	97.90 30	98.85 21	98.77 44
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 92	96.69 31	98.76 17	89.64 14	99.76 44	97.47 40	98.84 23	99.38 14

SD-MVS		94.84 21	95.02 26	94.29 41	97.87 68	84.61 83	97.76 96	96.19 150	89.59 74	96.66 33	98.17 60	84.33 46	99.60 75	96.09 54	98.50 42	98.66 53
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
MM		95.85 6	95.74 11	96.15 8	96.34 108	89.50 9	99.18 9	98.10 8	95.68 1	96.64 34	97.92 79	80.72 75	99.80 31	99.16 2	97.96 62	99.15 27

fmvsm_s_conf0.1_n_a		92.38 92	92.49 81	92.06 164	88.08 376	81.62 169	97.97 80	96.01 163	90.62 58	96.58 35	98.33 51	74.09 205	99.71 60	97.23 44	93.46 167	94.86 269

test_one_0601					98.91 22	84.56 85		96.70 82	88.06 102	96.57 36	98.77 15	88.04 23

DPE-MVS		95.32 13	95.55 14	94.64 34	98.79 27	84.87 80	97.77 94	96.74 76	86.11 158	96.54 37	98.89 10	88.39 21	99.74 52	97.67 38	99.05 12	99.31 20
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 15	98.26 1	96.26 111	95.09 1	99.15 12	96.98 46	93.39 23	96.45 38	98.79 13	90.17 9	99.99 1	89.33 169	99.25 6	99.70 3

fmvsm_s_conf0.5_n		93.69 48	94.13 45	92.34 142	94.56 174	82.01 147	99.07 22	97.13 33	92.09 37	96.25 39	98.53 31	76.47 153	99.80 31	98.39 14	94.71 143	95.22 260

PS-MVSNAJ		94.17 39	93.52 56	96.10 9	95.65 135	92.35 2	98.21 63	95.79 185	92.42 31	96.24 40	98.18 57	71.04 249	99.17 115	96.77 50	97.39 82	96.79 203

fmvsm_s_conf0.1_n_2		92.26 96	92.48 82	91.60 191	92.29 276	80.55 200	98.73 38	94.33 287	93.80 20	96.18 41	98.11 64	66.93 282	99.75 49	98.19 21	93.74 161	94.50 280

旧先验2							96.97 168		74.06 389	96.10 42			97.76 195	88.38 184

test_part2					98.90 23	85.14 73				96.07 43

fmvsm_s_conf0.1_n		92.93 65	93.16 65	92.24 150	90.52 330	81.92 153	98.42 52	96.24 144	91.17 49	96.02 44	98.35 50	75.34 185	99.74 52	97.84 33	94.58 145	95.05 265

xiu_mvs_v2_base		93.92 46	93.26 62	95.91 11	95.07 160	92.02 6	98.19 64	95.68 191	92.06 39	96.01 45	98.14 62	70.83 254	98.96 129	96.74 52	96.57 115	96.76 207

balanced_conf03		94.60 28	94.30 41	95.48 17	96.45 106	88.82 14	96.33 222	95.58 196	91.12 50	95.84 46	93.87 247	83.47 58	98.37 164	97.26 43	98.81 24	99.24 23

HPM-MVS++		95.32 13	95.48 16	94.85 27	98.62 38	86.04 41	97.81 91	96.93 53	92.45 30	95.69 47	98.50 34	85.38 36	99.85 15	94.75 75	99.18 7	98.65 54

fmvsm_s_conf0.5_n_5		93.57 52	93.75 48	93.01 100	92.87 249	82.73 124	98.93 32	95.90 177	90.96 55	95.61 48	98.39 45	76.57 151	99.63 72	98.32 15	96.24 119	96.68 211

NCCC		95.63 7	95.94 8	94.69 33	99.21 6	85.15 72	99.16 11	96.96 50	94.11 15	95.59 49	98.64 24	85.07 38	99.91 7	95.61 62	99.10 9	99.00 31

EPNet		94.06 43	94.15 44	93.76 61	97.27 97	84.35 86	98.29 60	97.64 14	94.57 11	95.36 50	96.88 136	79.96 90	99.12 120	91.30 127	96.11 123	97.82 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ME-MVS		94.82 22	95.04 24	94.17 49	99.17 8	83.70 100	97.66 103	97.22 24	85.79 167	95.34 51	98.90 5	84.89 39	99.86 13	97.78 35	98.60 34	98.94 34

CANet		94.89 19	94.64 32	95.63 14	97.55 82	88.12 19	99.06 23	96.39 127	94.07 17	95.34 51	97.80 88	76.83 147	99.87 11	97.08 47	97.64 73	98.89 39

fmvsm_s_conf0.5_n_7		92.88 67	93.82 47	90.08 247	92.79 253	76.45 326	98.54 48	96.74 76	92.28 34	95.22 53	98.49 35	74.91 192	98.15 175	98.28 16	97.13 93	95.63 244

test_fmvsmconf_n		93.99 44	94.36 39	92.86 108	92.82 250	81.12 179	99.26 6	96.37 132	93.47 22	95.16 54	98.21 55	79.00 102	99.64 70	98.21 20	96.73 112	97.83 115

TEST9					98.64 35	83.71 98	97.82 89	96.65 90	84.29 218	95.16 54	98.09 66	84.39 45	99.36 97

train_agg		94.28 36	94.45 36	93.74 63	98.64 35	83.71 98	97.82 89	96.65 90	84.50 208	95.16 54	98.09 66	84.33 46	99.36 97	95.91 58	98.96 19	98.16 86

test_8					98.63 37	83.64 104	97.81 91	96.63 95	84.50 208	95.10 57	98.11 64	84.33 46	99.23 105

DeepPCF-MVS	89.82 1	94.61 26	96.17 5	89.91 256	97.09 100	70.21 397	98.99 29	96.69 84	95.57 2	95.08 58	99.23 1	86.40 33	99.87 11	97.84 33	98.66 32	99.65 6

SF-MVS		94.17 39	94.05 46	94.55 36	97.56 81	85.95 43	97.73 98	96.43 121	84.02 225	95.07 59	98.74 19	82.93 63	99.38 94	95.42 66	98.51 40	98.32 72

APDe-MVS		94.56 29	94.75 28	93.96 55	98.84 26	83.40 109	98.04 76	96.41 123	85.79 167	95.00 60	98.28 53	84.32 49	99.18 114	97.35 42	98.77 28	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVSFormer		91.36 119	90.57 125	93.73 65	93.00 237	88.08 20	94.80 305	94.48 267	80.74 295	94.90 61	97.13 124	78.84 105	95.10 360	83.77 225	97.46 77	98.02 95

lupinMVS		93.87 47	93.58 54	94.75 31	93.00 237	88.08 20	99.15 12	95.50 203	91.03 53	94.90 61	97.66 93	78.84 105	97.56 209	94.64 78	97.46 77	98.62 56

SPE-MVS-test		92.98 62	93.67 51	90.90 222	96.52 105	76.87 318	98.68 41	94.73 247	90.36 65	94.84 63	97.89 83	77.94 121	97.15 254	94.28 83	97.80 68	98.70 52

9.14			94.26 43		98.10 61		98.14 65	96.52 109	84.74 200	94.83 64	98.80 12	82.80 65	99.37 96	95.95 57	98.42 46

testdata				90.13 246	95.92 125	74.17 356		96.49 115	73.49 394	94.82 65	97.99 73	78.80 107	97.93 184	83.53 233	97.52 76	98.29 76

lecture		93.17 57	93.57 55	91.96 169	97.80 69	78.79 261	98.50 50	96.98 46	86.61 150	94.75 66	98.16 61	78.36 115	99.35 99	93.89 86	97.12 94	97.75 122

APD-MVS		93.61 49	93.59 53	93.69 69	98.76 28	83.26 112	97.21 139	96.09 156	82.41 271	94.65 67	98.21 55	81.96 70	98.81 139	94.65 77	98.36 51	99.01 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior2							98.37 54		86.08 160	94.57 68	98.02 72	83.14 60		95.05 71	98.79 27

CS-MVS		92.73 73	93.48 58	90.48 235	96.27 110	75.93 339	98.55 47	94.93 233	89.32 77	94.54 69	97.67 92	78.91 104	97.02 259	93.80 87	97.32 86	98.49 62

FOURS1					98.51 43	78.01 287	98.13 68	96.21 147	83.04 254	94.39 70

ACMMP_NAP		93.46 54	93.23 63	94.17 49	97.16 98	84.28 89	96.82 181	96.65 90	86.24 155	94.27 71	97.99 73	77.94 121	99.83 21	93.39 92	98.57 38	98.39 69

agg_prior					98.59 39	83.13 115		96.56 105		94.19 72			99.16 116

SteuartSystems-ACMMP		94.13 42	94.44 37	93.20 92	95.41 143	81.35 174	99.02 27	96.59 100	89.50 76	94.18 73	98.36 49	83.68 57	99.45 91	94.77 74	98.45 45	98.81 43
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		93.59 50	93.63 52	93.48 83	98.05 62	81.76 162	98.64 44	97.13 33	82.60 267	94.09 74	98.49 35	80.35 80	99.85 15	94.74 76	98.62 33	98.83 41

test_fmvsmconf0.1_n		93.08 61	93.22 64	92.65 121	88.45 371	80.81 192	99.00 28	95.11 225	93.21 24	94.00 75	97.91 81	76.84 145	99.59 76	97.91 29	96.55 116	97.54 143

MVSMamba_PlusPlus		92.37 93	91.55 105	94.83 28	95.37 145	87.69 25	95.60 267	95.42 212	74.65 384	93.95 76	92.81 266	83.11 61	97.70 198	94.49 79	98.53 39	99.11 28

TSAR-MVS + GP.		94.35 35	94.50 34	93.89 56	97.38 94	83.04 117	98.10 70	95.29 219	91.57 44	93.81 77	97.45 106	86.64 30	99.43 92	96.28 53	94.01 153	99.20 25

CANet_DTU		90.98 130	90.04 144	93.83 58	94.76 170	86.23 39	96.32 223	93.12 363	93.11 25	93.71 78	96.82 140	63.08 313	99.48 89	84.29 218	95.12 139	95.77 241

VNet		92.11 99	91.22 111	94.79 29	96.91 101	86.98 32	97.91 84	97.96 10	86.38 153	93.65 79	95.74 164	70.16 259	98.95 131	93.39 92	88.87 226	98.43 67

test_vis1_n_1920		89.95 157	90.59 124	88.03 300	92.36 266	68.98 406	99.12 16	94.34 284	93.86 19	93.64 80	97.01 132	51.54 392	99.59 76	96.76 51	96.71 113	95.53 250

ZD-MVS					99.09 9	83.22 113		96.60 99	82.88 260	93.61 81	98.06 71	82.93 63	99.14 117	95.51 65	98.49 43

xiu_mvs_v1_base_debu		90.54 142	89.54 155	93.55 78	92.31 268	87.58 27	96.99 163	94.87 237	87.23 129	93.27 82	97.56 102	57.43 360	98.32 166	92.72 107	93.46 167	94.74 273

xiu_mvs_v1_base		90.54 142	89.54 155	93.55 78	92.31 268	87.58 27	96.99 163	94.87 237	87.23 129	93.27 82	97.56 102	57.43 360	98.32 166	92.72 107	93.46 167	94.74 273

xiu_mvs_v1_base_debi		90.54 142	89.54 155	93.55 78	92.31 268	87.58 27	96.99 163	94.87 237	87.23 129	93.27 82	97.56 102	57.43 360	98.32 166	92.72 107	93.46 167	94.74 273

CDPH-MVS		93.12 59	92.91 70	93.74 63	98.65 34	83.88 93	97.67 102	96.26 142	83.00 257	93.22 85	98.24 54	81.31 72	99.21 107	89.12 170	98.74 30	98.14 88

GDP-MVS		92.85 70	92.55 80	93.75 62	92.82 250	85.76 48	97.63 104	95.05 229	88.34 94	93.15 86	97.10 127	86.92 28	98.01 181	87.95 188	94.00 154	97.47 153

ETV-MVS		92.72 75	92.87 71	92.28 148	94.54 176	81.89 156	97.98 78	95.21 223	89.77 72	93.11 87	96.83 138	77.23 138	97.50 221	95.74 60	95.38 137	97.44 159

MSLP-MVS++		94.28 36	94.39 38	93.97 54	98.30 53	84.06 92	98.64 44	96.93 53	90.71 57	93.08 88	98.70 22	79.98 89	99.21 107	94.12 84	99.07 11	98.63 55

alignmvs		92.97 63	92.26 89	95.12 22	95.54 140	87.77 23	98.67 42	96.38 129	88.04 103	93.01 89	97.45 106	79.20 99	98.60 145	93.25 98	88.76 227	98.99 33

sasdasda		92.27 94	91.22 111	95.41 18	95.80 129	88.31 16	97.09 157	94.64 258	88.49 89	92.99 90	97.31 113	72.68 222	98.57 147	93.38 94	88.58 234	99.36 16

canonicalmvs		92.27 94	91.22 111	95.41 18	95.80 129	88.31 16	97.09 157	94.64 258	88.49 89	92.99 90	97.31 113	72.68 222	98.57 147	93.38 94	88.58 234	99.36 16

EC-MVSNet		91.73 107	92.11 94	90.58 231	93.54 213	77.77 299	98.07 73	94.40 279	87.44 121	92.99 90	97.11 126	74.59 199	96.87 273	93.75 88	97.08 96	97.11 184

MGCFI-Net		91.95 101	91.03 117	94.72 32	95.68 134	86.38 37	96.93 173	94.48 267	88.25 97	92.78 93	97.24 119	72.34 227	98.46 157	93.13 103	88.43 241	99.32 19

jason		92.73 73	92.23 90	94.21 45	90.50 331	87.30 31	98.65 43	95.09 226	90.61 59	92.76 94	97.13 124	75.28 186	97.30 240	93.32 96	96.75 111	98.02 95
jason: jason.
reproduce_model		92.53 87	92.87 71	91.50 196	97.41 89	77.14 316	96.02 241	95.91 176	83.65 243	92.45 95	98.39 45	79.75 92	99.21 107	95.27 70	96.98 99	98.14 88

reproduce-ours		92.70 78	93.02 66	91.75 180	97.45 85	77.77 299	96.16 234	95.94 173	84.12 221	92.45 95	98.43 41	80.06 87	99.24 103	95.35 67	97.18 90	98.24 80

our_new_method		92.70 78	93.02 66	91.75 180	97.45 85	77.77 299	96.16 234	95.94 173	84.12 221	92.45 95	98.43 41	80.06 87	99.24 103	95.35 67	97.18 90	98.24 80

test_cas_vis1_n_1920		89.90 158	90.02 145	89.54 266	90.14 342	74.63 351	98.71 40	94.43 276	93.04 26	92.40 98	96.35 152	53.41 388	99.08 123	95.59 63	96.16 121	94.90 267

test12				94.25 42	98.34 50	85.55 58		96.35 135		92.36 99		80.84 74	99.22 106		98.31 53	97.98 102

MG-MVS		94.25 38	93.72 49	95.85 12	99.38 3	89.35 11	97.98 78	98.09 9	89.99 68	92.34 100	96.97 133	81.30 73	98.99 127	88.54 180	98.88 20	99.20 25

test_fmvs1		87.79 217	88.52 177	85.62 350	92.98 241	64.31 427	97.88 86	92.42 375	87.95 105	92.24 101	95.82 162	47.94 410	98.44 161	95.31 69	94.09 150	94.09 287

h-mvs33		89.30 172	88.95 169	90.36 239	95.07 160	76.04 333	96.96 170	97.11 36	90.39 63	92.22 102	95.10 201	74.70 195	98.86 136	93.14 101	65.89 413	96.16 225

hse-mvs2		88.22 205	88.21 183	88.25 294	93.54 213	73.41 359	95.41 275	95.89 178	90.39 63	92.22 102	94.22 233	74.70 195	96.66 285	93.14 101	64.37 418	94.69 278

NormalMVS		92.88 67	92.97 69	92.59 127	97.80 69	82.02 145	97.94 81	94.70 248	92.34 32	92.15 104	96.53 149	77.03 140	98.57 147	91.13 130	97.12 94	97.19 180

SymmetryMVS		92.45 89	92.33 86	92.82 112	95.19 153	82.02 145	97.94 81	97.43 17	92.34 32	92.15 104	96.53 149	77.03 140	98.57 147	91.13 130	91.19 197	97.87 110

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 31	97.10 37	95.17 4	92.11 106	98.46 39	87.33 27	99.97 2	97.21 45	99.31 4	99.63 7

BP-MVS1		93.55 53	93.50 57	93.71 67	92.64 259	85.39 61	97.78 93	96.84 61	89.52 75	92.00 107	97.06 130	88.21 22	98.03 179	91.45 126	96.00 128	97.70 128

test_fmvsmconf0.01_n		91.08 127	90.68 123	92.29 147	82.43 433	80.12 217	97.94 81	93.93 309	92.07 38	91.97 108	97.60 100	67.56 274	99.53 84	97.09 46	95.56 136	97.21 177

SR-MVS		92.16 97	92.27 88	91.83 178	98.37 49	78.41 272	96.67 195	95.76 186	82.19 275	91.97 108	98.07 70	76.44 154	98.64 143	93.71 89	97.27 87	98.45 65

region2R		92.72 75	92.70 75	92.79 113	98.68 30	80.53 205	97.53 115	96.51 110	85.22 183	91.94 110	97.98 76	77.26 134	99.67 68	90.83 139	98.37 50	98.18 84

Effi-MVS+		90.70 138	89.90 150	93.09 97	93.61 210	83.48 107	95.20 286	92.79 369	83.22 249	91.82 111	95.70 166	71.82 239	97.48 223	91.25 128	93.67 163	98.32 72

HFP-MVS		92.89 66	92.86 73	92.98 102	98.71 29	81.12 179	97.58 110	96.70 82	85.20 185	91.75 112	97.97 78	78.47 112	99.71 60	90.95 132	98.41 47	98.12 91

DeepC-MVS_fast	89.06 2	94.48 32	94.30 41	95.02 23	98.86 25	85.68 52	98.06 74	96.64 93	93.64 21	91.74 113	98.54 29	80.17 85	99.90 8	92.28 113	98.75 29	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 80	92.67 76	92.75 115	98.66 32	80.57 199	97.58 110	96.69 84	85.20 185	91.57 114	97.92 79	77.01 142	99.67 68	90.95 132	98.41 47	98.00 100

DELS-MVS		94.98 16	94.49 35	96.44 6	96.42 107	90.59 7	99.21 8	97.02 43	94.40 14	91.46 115	97.08 128	83.32 59	99.69 64	92.83 106	98.70 31	99.04 29
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 80	92.71 74	92.63 124	98.52 41	80.29 208	97.37 131	96.44 119	87.04 136	91.38 116	97.83 87	77.24 136	99.59 76	90.46 147	98.07 58	98.02 95

X-MVStestdata		86.26 246	84.14 268	92.63 124	98.52 41	80.29 208	97.37 131	96.44 119	87.04 136	91.38 116	20.73 478	77.24 136	99.59 76	90.46 147	98.07 58	98.02 95

PMMVS		89.46 169	89.92 149	88.06 298	94.64 171	69.57 403	96.22 229	94.95 232	87.27 128	91.37 118	96.54 148	65.88 290	97.39 232	88.54 180	93.89 158	97.23 173

test_fmvs1_n		86.34 244	86.72 223	85.17 358	87.54 383	63.64 432	96.91 175	92.37 377	87.49 118	91.33 119	95.58 175	40.81 438	98.46 157	95.00 72	93.49 165	93.41 301

dcpmvs_2		93.10 60	93.46 59	92.02 167	97.77 71	79.73 229	94.82 303	93.86 316	86.91 139	91.33 119	96.76 142	85.20 37	98.06 177	96.90 49	97.60 74	98.27 78

原ACMM1				91.22 211	97.77 71	78.10 285		96.61 96	81.05 289	91.28 121	97.42 110	77.92 123	98.98 128	79.85 270	98.51 40	96.59 213

新几何1				93.12 95	97.44 87	81.60 170		96.71 81	74.54 385	91.22 122	97.57 101	79.13 100	99.51 87	77.40 300	98.46 44	98.26 79

UA-Net		88.92 182	88.48 178	90.24 243	94.06 200	77.18 314	93.04 354	94.66 255	87.39 123	91.09 123	93.89 246	74.92 191	98.18 173	75.83 316	91.43 195	95.35 255

ZNCC-MVS		92.75 71	92.60 78	93.23 90	98.24 55	81.82 160	97.63 104	96.50 112	85.00 195	91.05 124	97.74 90	78.38 113	99.80 31	90.48 145	98.34 52	98.07 93

APD-MVS_3200maxsize		91.23 123	91.35 108	90.89 223	97.89 66	76.35 329	96.30 225	95.52 201	79.82 322	91.03 125	97.88 84	74.70 195	98.54 151	92.11 117	96.89 103	97.77 120

test_vis1_n		85.60 260	85.70 236	85.33 355	84.79 414	64.98 425	96.83 179	91.61 392	87.36 124	91.00 126	94.84 213	36.14 445	97.18 249	95.66 61	93.03 172	93.82 292

GST-MVS		92.43 91	92.22 92	93.04 99	98.17 58	81.64 168	97.40 129	96.38 129	84.71 202	90.90 127	97.40 111	77.55 130	99.76 44	89.75 162	97.74 70	97.72 125

PGM-MVS		91.93 102	91.80 100	92.32 146	98.27 54	79.74 228	95.28 278	97.27 22	83.83 235	90.89 128	97.78 89	76.12 162	99.56 82	88.82 175	97.93 65	97.66 131

SR-MVS-dyc-post		91.29 121	91.45 107	90.80 225	97.76 73	76.03 334	96.20 231	95.44 208	80.56 300	90.72 129	97.84 85	75.76 170	98.61 144	91.99 119	96.79 109	97.75 122

RE-MVS-def			91.18 115		97.76 73	76.03 334	96.20 231	95.44 208	80.56 300	90.72 129	97.84 85	73.36 215		91.99 119	96.79 109	97.75 122

MP-MVS		92.61 84	92.67 76	92.42 137	98.13 60	79.73 229	97.33 134	96.20 148	85.63 171	90.53 131	97.66 93	78.14 119	99.70 63	92.12 116	98.30 54	97.85 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HY-MVS	84.06 6	91.63 111	90.37 133	95.39 20	96.12 116	88.25 18	90.22 389	97.58 15	88.33 95	90.50 132	91.96 284	79.26 97	99.06 124	90.29 154	89.07 222	98.88 40

CP-MVS		92.54 86	92.60 78	92.34 142	98.50 44	79.90 222	98.40 53	96.40 125	84.75 199	90.48 133	98.09 66	77.40 132	99.21 107	91.15 129	98.23 56	97.92 106

diffmvs_AUTHOR		90.86 135	90.41 130	92.24 150	92.01 296	82.22 141	96.18 233	93.64 337	87.28 126	90.46 134	95.64 170	72.82 220	97.39 232	93.17 100	92.46 180	97.11 184

diffmvs		91.17 124	90.74 122	92.44 135	93.11 235	82.50 133	96.25 228	93.62 339	87.79 110	90.40 135	95.93 159	73.44 214	97.42 227	93.62 91	92.55 177	97.41 161
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		90.29 151	89.18 162	93.62 74	95.23 149	84.93 78	94.41 311	94.66 255	84.31 214	90.37 136	91.02 298	75.13 188	97.82 193	83.11 238	94.42 148	98.12 91

MTAPA		92.45 89	92.31 87	92.86 108	97.90 65	80.85 191	92.88 358	96.33 136	87.92 106	90.20 137	98.18 57	76.71 150	99.76 44	92.57 110	98.09 57	97.96 105

test_yl		91.46 115	90.53 126	94.24 43	97.41 89	85.18 67	98.08 71	97.72 11	80.94 290	89.85 138	96.14 155	75.61 171	98.81 139	90.42 150	88.56 236	98.74 46

DCV-MVSNet		91.46 115	90.53 126	94.24 43	97.41 89	85.18 67	98.08 71	97.72 11	80.94 290	89.85 138	96.14 155	75.61 171	98.81 139	90.42 150	88.56 236	98.74 46

WTY-MVS		92.65 83	91.68 102	95.56 15	96.00 119	88.90 13	98.23 62	97.65 13	88.57 87	89.82 140	97.22 121	79.29 96	99.06 124	89.57 165	88.73 228	98.73 50

MVS_111021_HR		93.41 55	93.39 60	93.47 85	97.34 95	82.83 122	97.56 112	98.27 6	89.16 80	89.71 141	97.14 123	79.77 91	99.56 82	93.65 90	97.94 63	98.02 95

sss		90.87 134	89.96 147	93.60 75	94.15 194	83.84 96	97.14 150	98.13 7	85.93 165	89.68 142	96.09 157	71.67 240	99.30 100	87.69 193	89.16 221	97.66 131

test222					96.15 115	78.41 272	95.87 253	96.46 117	71.97 405	89.66 143	97.45 106	76.33 158			98.24 55	98.30 75

LFMVS		89.27 173	87.64 195	94.16 52	97.16 98	85.52 59	97.18 143	94.66 255	79.17 336	89.63 144	96.57 147	55.35 377	98.22 170	89.52 167	89.54 216	98.74 46

CostFormer		89.08 176	88.39 179	91.15 212	93.13 233	79.15 245	88.61 405	96.11 155	83.14 251	89.58 145	86.93 360	83.83 56	96.87 273	88.22 186	85.92 270	97.42 160

PVSNet_BlendedMVS		90.05 154	89.96 147	90.33 240	97.47 83	83.86 94	98.02 77	96.73 78	87.98 104	89.53 146	89.61 319	76.42 155	99.57 80	94.29 81	79.59 316	87.57 389

PVSNet_Blended		93.13 58	92.98 68	93.57 77	97.47 83	83.86 94	99.32 3	96.73 78	91.02 54	89.53 146	96.21 154	76.42 155	99.57 80	94.29 81	95.81 132	97.29 172

HPM-MVS		91.62 112	91.53 106	91.89 173	97.88 67	79.22 242	96.99 163	95.73 189	82.07 277	89.50 148	97.19 122	75.59 173	98.93 134	90.91 134	97.94 63	97.54 143
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testing11		92.48 88	92.04 97	93.78 60	95.94 123	86.00 42	97.56 112	97.08 38	87.52 117	89.32 149	95.40 181	84.60 42	98.02 180	91.93 123	89.04 223	97.32 168

UBG		92.68 82	92.35 84	93.70 68	95.61 137	85.65 55	97.25 137	97.06 40	87.92 106	89.28 150	95.03 204	86.06 35	98.07 176	92.24 114	90.69 207	97.37 165

EI-MVSNet-Vis-set		91.84 106	91.77 101	92.04 166	97.60 78	81.17 177	96.61 196	96.87 58	88.20 99	89.19 151	97.55 105	78.69 109	99.14 117	90.29 154	90.94 202	95.80 236

testing222		91.09 126	90.49 128	92.87 107	95.82 127	85.04 74	96.51 206	97.28 21	86.05 161	89.13 152	95.34 183	80.16 86	96.62 286	85.82 206	88.31 243	96.96 192

MP-MVS-pluss		92.58 85	92.35 84	93.29 87	97.30 96	82.53 128	96.44 211	96.04 162	84.68 203	89.12 153	98.37 48	77.48 131	99.74 52	93.31 97	98.38 49	97.59 139
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 203	87.02 215	92.06 164	95.09 158	80.18 215	97.55 114	94.45 273	83.09 252	89.10 154	95.92 161	47.97 409	98.49 154	93.08 105	86.91 258	97.52 149

baseline		90.76 136	90.10 140	92.74 116	92.90 248	82.56 127	94.60 308	94.56 264	87.69 113	89.06 155	95.67 168	73.76 209	97.51 220	90.43 149	92.23 187	98.16 86

viewmanbaseed2359cas		90.74 137	90.07 142	92.76 114	92.98 241	82.93 120	96.53 203	94.28 290	87.08 135	88.96 156	95.64 170	72.03 237	97.58 207	90.85 137	92.26 185	97.76 121

testing99		91.91 103	91.35 108	93.60 75	95.98 121	85.70 50	97.31 135	96.92 55	86.82 142	88.91 157	95.25 186	84.26 50	97.89 191	88.80 176	87.94 247	97.21 177

EIA-MVS		91.73 107	92.05 96	90.78 227	94.52 177	76.40 328	98.06 74	95.34 217	89.19 79	88.90 158	97.28 118	77.56 129	97.73 197	90.77 140	96.86 106	98.20 82

testing91		91.90 104	91.31 110	93.66 71	95.99 120	85.68 52	97.39 130	96.89 56	86.75 146	88.85 159	95.23 190	83.93 54	97.90 190	88.91 173	87.89 248	97.41 161

mvsany_test1		87.58 223	88.22 182	85.67 348	89.78 346	67.18 414	95.25 283	87.93 429	83.96 228	88.79 160	97.06 130	72.52 224	94.53 380	92.21 115	86.45 262	95.30 257

HPM-MVS_fast		90.38 148	90.17 139	91.03 216	97.61 77	77.35 310	97.15 149	95.48 204	79.51 328	88.79 160	96.90 134	71.64 242	98.81 139	87.01 201	97.44 79	96.94 193

ETVMVS		90.99 129	90.26 134	93.19 93	95.81 128	85.64 56	96.97 168	97.18 28	85.43 177	88.77 162	94.86 211	82.00 69	96.37 293	82.70 241	88.60 233	97.57 140

PAPM		92.87 69	92.40 83	94.30 40	92.25 280	87.85 22	96.40 215	96.38 129	91.07 52	88.72 163	96.90 134	82.11 68	97.37 237	90.05 157	97.70 71	97.67 130

MVS_111021_LR		91.60 113	91.64 104	91.47 198	95.74 132	78.79 261	96.15 236	96.77 71	88.49 89	88.64 164	97.07 129	72.33 228	99.19 113	93.13 103	96.48 117	96.43 217

casdiffmvs		90.95 132	90.39 131	92.63 124	92.82 250	82.53 128	96.83 179	94.47 270	87.69 113	88.47 165	95.56 176	74.04 206	97.54 216	90.90 135	92.74 175	97.83 115
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		91.88 105	91.82 99	92.07 163	98.38 48	78.63 265	97.29 136	96.09 156	85.12 191	88.45 166	97.66 93	75.53 175	99.68 66	89.83 158	98.02 61	97.88 108

PAPR		92.74 72	92.17 93	94.45 37	98.89 24	84.87 80	97.20 141	96.20 148	87.73 112	88.40 167	98.12 63	78.71 108	99.76 44	87.99 187	96.28 118	98.74 46

tpmrst		88.36 200	87.38 206	91.31 203	94.36 188	79.92 221	87.32 417	95.26 221	85.32 180	88.34 168	86.13 377	80.60 78	96.70 282	83.78 224	85.34 278	97.30 171

GG-mvs-BLEND				93.49 82	94.94 164	86.26 38	81.62 445	97.00 44		88.32 169	94.30 230	91.23 5	96.21 301	88.49 182	97.43 80	98.00 100

EI-MVSNet-UG-set		91.35 120	91.22 111	91.73 183	97.39 92	80.68 195	96.47 208	96.83 62	87.92 106	88.30 170	97.36 112	77.84 124	99.13 119	89.43 168	89.45 217	95.37 254

viewmambaseed2359dif		89.52 167	89.02 164	91.03 216	92.24 281	78.83 253	95.89 250	93.77 330	83.04 254	88.28 171	95.80 163	72.08 235	97.40 230	89.76 161	90.32 209	96.87 200

viewcassd2359sk11		90.66 139	90.06 143	92.47 131	93.22 227	82.21 142	96.70 193	94.47 270	86.94 138	88.22 172	95.50 178	73.15 217	97.59 205	90.86 136	91.48 194	97.60 138

myMVS_eth3d28		92.72 75	92.23 90	94.21 45	96.16 114	87.46 30	97.37 131	96.99 45	88.13 101	88.18 173	95.47 179	84.12 51	98.04 178	92.46 112	91.17 199	97.14 183

MAR-MVS		90.63 140	90.22 136	91.86 175	98.47 46	78.20 283	97.18 143	96.61 96	83.87 232	88.18 173	98.18 57	68.71 267	99.75 49	83.66 230	97.15 92	97.63 134
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
viewmacassd2359aftdt		89.89 159	89.01 166	92.52 130	91.56 304	82.46 134	96.32 223	94.06 305	86.41 152	88.11 175	95.01 206	69.68 262	97.47 224	88.73 179	91.19 197	97.63 134

KinetiMVS		89.13 175	87.95 188	92.65 121	92.16 286	82.39 137	97.04 161	96.05 160	86.59 151	88.08 176	94.85 212	61.54 329	98.38 163	81.28 256	93.99 156	97.19 180

DP-MVS Recon		91.72 109	90.85 119	94.34 39	99.50 1	85.00 77	98.51 49	95.96 169	80.57 299	88.08 176	97.63 99	76.84 145	99.89 10	85.67 208	94.88 140	98.13 90

E2		90.33 149	89.65 153	92.37 140	92.66 255	81.99 148	96.58 198	94.39 280	86.71 148	87.88 178	95.25 186	72.18 231	97.56 209	90.37 152	90.88 203	97.57 140

E3		90.33 149	89.65 153	92.37 140	92.64 259	81.99 148	96.58 198	94.39 280	86.71 148	87.87 179	95.27 185	72.17 232	97.56 209	90.37 152	90.88 203	97.57 140

VDDNet		86.44 240	84.51 257	92.22 153	91.56 304	81.83 159	97.10 156	94.64 258	69.50 419	87.84 180	95.19 194	48.01 408	97.92 189	89.82 159	86.92 257	96.89 197

UGNet		87.73 218	86.55 227	91.27 206	95.16 156	79.11 246	96.35 220	96.23 145	88.14 100	87.83 181	90.48 306	50.65 397	99.09 122	80.13 267	94.03 151	95.60 246
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		90.96 131	90.39 131	92.65 121	93.54 213	82.46 134	96.37 216	97.35 19	86.78 144	87.55 182	95.25 186	77.83 125	97.50 221	84.07 220	94.80 141	97.98 102

viewdifsd2359ckpt13		90.08 153	89.36 158	92.26 149	93.03 236	81.90 155	96.37 216	94.34 284	86.16 156	87.44 183	95.30 184	70.93 253	97.55 213	89.05 171	91.59 193	97.35 167

tpm2		87.35 227	86.26 229	90.62 230	92.93 247	78.67 264	88.06 412	95.99 166	79.33 331	87.40 184	86.43 371	80.28 82	96.40 291	80.23 265	85.73 274	96.79 203

CPTT-MVS		89.72 163	89.87 151	89.29 269	98.33 51	73.30 362	97.70 100	95.35 216	75.68 375	87.40 184	97.44 109	70.43 256	98.25 169	89.56 166	96.90 102	96.33 222

gg-mvs-nofinetune		85.48 264	82.90 291	93.24 89	94.51 181	85.82 47	79.22 450	96.97 49	61.19 445	87.33 186	53.01 467	90.58 6	96.07 304	86.07 205	97.23 88	97.81 118

CHOSEN 280x420		91.71 110	91.85 98	91.29 205	94.94 164	82.69 125	87.89 413	96.17 151	85.94 164	87.27 187	94.31 229	90.27 8	95.65 331	94.04 85	95.86 130	95.53 250

test_fmvsmvis_n_1920		92.12 98	92.10 95	92.17 157	90.87 322	81.04 182	98.34 59	93.90 313	92.71 28	87.24 188	97.90 82	74.83 193	99.72 57	96.96 48	96.20 120	95.76 242

casdiffmvs_mvg		91.13 125	90.45 129	93.17 94	92.99 240	83.58 105	97.46 122	94.56 264	87.69 113	87.19 189	94.98 209	74.50 200	97.60 204	91.88 124	92.79 174	98.34 70
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		87.65 222	87.89 189	86.93 327	94.57 173	71.37 389	96.72 189	96.50 112	88.56 88	87.12 190	95.02 205	75.91 167	94.01 390	66.62 378	90.00 212	95.42 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 190	87.82 191	91.24 208	92.68 254	78.82 254	96.95 171	93.85 317	87.55 116	87.07 191	95.13 199	63.43 310	97.21 247	77.58 296	96.15 122	97.70 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvsmamba		90.53 145	90.08 141	91.88 174	94.81 168	80.93 187	93.94 329	94.45 273	88.24 98	87.02 192	92.35 273	68.04 269	95.80 319	94.86 73	97.03 98	98.92 37

thisisatest0515		90.95 132	90.26 134	93.01 100	94.03 203	84.27 90	97.91 84	96.67 86	83.18 250	86.87 193	95.51 177	88.66 17	97.85 192	80.46 261	89.01 224	96.92 196

TESTMET0.1,1		89.83 161	89.34 159	91.31 203	92.54 263	80.19 214	97.11 153	96.57 103	86.15 157	86.85 194	91.83 289	79.32 94	96.95 264	81.30 255	92.35 184	96.77 205

testing3-2		91.37 118	91.01 118	92.44 135	95.93 124	83.77 97	98.83 36	97.45 16	86.88 140	86.63 195	94.69 219	84.57 43	97.75 196	89.65 163	84.44 281	95.80 236

viewdifsd2359ckpt09		90.00 156	89.28 161	92.15 159	93.31 224	81.38 172	96.37 216	93.64 337	86.34 154	86.62 196	95.64 170	71.58 243	97.52 219	88.93 172	91.06 200	97.54 143

LuminaMVS		88.02 210	86.89 219	91.43 199	88.65 369	83.16 114	94.84 302	94.41 278	83.67 242	86.56 197	91.95 286	62.04 323	96.88 272	89.78 160	90.06 211	94.24 282

guyue		89.85 160	89.33 160	91.40 201	92.53 264	80.15 216	96.82 181	95.68 191	89.66 73	86.43 198	94.23 232	67.00 280	97.16 250	91.96 122	89.65 215	96.89 197

PVSNet_Blended_VisFu		91.24 122	90.77 121	92.66 120	95.09 158	82.40 136	97.77 94	95.87 182	88.26 96	86.39 199	93.94 245	76.77 148	99.27 101	88.80 176	94.00 154	96.31 223

API-MVS		90.18 152	88.97 167	93.80 59	98.66 32	82.95 119	97.50 119	95.63 195	75.16 379	86.31 200	97.69 91	72.49 225	99.90 8	81.26 257	96.07 124	98.56 59

test-LLR		88.48 196	87.98 187	89.98 252	92.26 278	77.23 312	97.11 153	95.96 169	83.76 238	86.30 201	91.38 292	72.30 229	96.78 280	80.82 258	91.92 189	95.94 232

test-mter		88.95 180	88.60 175	89.98 252	92.26 278	77.23 312	97.11 153	95.96 169	85.32 180	86.30 201	91.38 292	76.37 157	96.78 280	80.82 258	91.92 189	95.94 232

AstraMVS		88.99 179	88.35 180	90.92 220	90.81 326	78.29 275	96.73 188	94.24 292	89.96 69	86.13 203	95.04 203	62.12 322	97.41 228	92.54 111	87.57 254	97.06 189

PAPM_NR		91.46 115	90.82 120	93.37 86	98.50 44	81.81 161	95.03 298	96.13 153	84.65 204	86.10 204	97.65 97	79.24 98	99.75 49	83.20 236	96.88 104	98.56 59

FA-MVS(test-final)		87.71 220	86.23 231	92.17 157	94.19 192	80.55 200	87.16 419	96.07 159	82.12 276	85.98 205	88.35 336	72.04 236	98.49 154	80.26 264	89.87 213	97.48 152

RRT-MVS		89.67 164	88.67 173	92.67 119	94.44 184	81.08 181	94.34 315	94.45 273	86.05 161	85.79 206	92.39 272	63.39 311	98.16 174	93.22 99	93.95 157	98.76 45

MDTV_nov1_ep13_2view						81.74 163	86.80 421		80.65 297	85.65 207		74.26 202		76.52 308		96.98 191

ECVR-MVS		88.35 201	87.25 208	91.65 187	93.54 213	79.40 236	96.56 202	90.78 408	86.78 144	85.57 208	95.25 186	57.25 364	97.56 209	84.73 216	94.80 141	97.98 102

mmtdpeth		78.04 364	76.76 363	81.86 399	89.60 354	66.12 422	92.34 366	87.18 432	76.83 368	85.55 209	76.49 444	46.77 415	97.02 259	90.85 137	45.24 460	82.43 440

AUN-MVS		86.25 247	85.57 238	88.26 293	93.57 212	73.38 360	95.45 273	95.88 180	83.94 229	85.47 210	94.21 234	73.70 212	96.67 284	83.54 232	64.41 417	94.73 277

PVSNet	82.34 9	89.02 178	87.79 192	92.71 118	95.49 141	81.50 171	97.70 100	97.29 20	87.76 111	85.47 210	95.12 200	56.90 366	98.90 135	80.33 262	94.02 152	97.71 127

viewdifsd2359ckpt07		89.04 177	88.30 181	91.27 206	92.32 267	78.90 251	95.89 250	93.77 330	84.48 210	85.18 212	95.16 196	69.83 260	97.70 198	88.75 178	89.29 219	97.22 174

EPP-MVSNet		89.76 162	89.72 152	89.87 257	93.78 206	76.02 336	97.22 138	96.51 110	79.35 330	85.11 213	95.01 206	84.82 40	97.10 257	87.46 196	88.21 245	96.50 215

test1111		88.11 206	87.04 214	91.35 202	93.15 231	78.79 261	96.57 200	90.78 408	86.88 140	85.04 214	95.20 193	57.23 365	97.39 232	83.88 222	94.59 144	97.87 110

FE-MVS		86.06 249	84.15 267	91.78 179	94.33 189	79.81 223	84.58 437	96.61 96	76.69 369	85.00 215	87.38 351	70.71 255	98.37 164	70.39 360	91.70 192	97.17 182

OMC-MVS		88.80 187	88.16 185	90.72 228	95.30 147	77.92 292	94.81 304	94.51 266	86.80 143	84.97 216	96.85 137	67.53 275	98.60 145	85.08 212	87.62 251	95.63 244

CHOSEN 1792x2688		91.07 128	90.21 137	93.64 72	95.18 155	83.53 106	96.26 227	96.13 153	88.92 81	84.90 217	93.10 263	72.86 219	99.62 74	88.86 174	95.67 133	97.79 119

thres200		88.92 182	87.65 194	92.73 117	96.30 109	85.62 57	97.85 87	98.86 1	84.38 213	84.82 218	93.99 244	75.12 189	98.01 181	70.86 357	86.67 259	94.56 279

UWE-MVS		88.56 195	88.91 171	87.50 314	94.17 193	72.19 374	95.82 257	97.05 41	84.96 196	84.78 219	93.51 257	81.33 71	94.75 372	79.43 273	89.17 220	95.57 248

MDTV_nov1_ep13			83.69 271		94.09 199	81.01 183	86.78 422	96.09 156	83.81 236	84.75 220	84.32 401	74.44 201	96.54 287	63.88 393	85.07 279

CDS-MVSNet		89.50 168	88.96 168	91.14 213	91.94 300	80.93 187	97.09 157	95.81 184	84.26 219	84.72 221	94.20 235	80.31 81	95.64 332	83.37 235	88.96 225	96.85 201
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 146	89.97 146	91.64 188	97.58 80	78.21 282	96.78 185	96.72 80	84.73 201	84.72 221	97.23 120	71.22 246	99.63 72	88.37 185	92.41 183	97.08 187
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
SSM_0404		87.69 221	86.26 229	91.95 170	92.94 243	83.02 118	94.69 307	92.33 378	80.11 315	84.65 223	94.18 236	64.68 303	96.90 268	82.34 244	90.44 208	95.94 232

CSCG		92.02 100	91.65 103	93.12 95	98.53 40	80.59 198	97.47 120	97.18 28	77.06 364	84.64 224	97.98 76	83.98 53	99.52 85	90.72 141	97.33 85	99.23 24

ab-mvs		87.08 229	84.94 253	93.48 83	93.34 223	83.67 103	88.82 402	95.70 190	81.18 287	84.55 225	90.14 314	62.72 314	98.94 133	85.49 210	82.54 300	97.85 113

IMVS_0403		88.07 207	87.02 215	91.24 208	92.30 271	78.81 256	93.62 337	93.84 318	85.14 187	84.36 226	94.49 224	69.49 263	97.46 226	81.33 251	88.61 229	97.46 154

viewmsd2359difaftdt		86.38 241	85.29 243	89.67 264	90.42 333	75.65 343	95.27 281	92.45 373	85.54 175	84.28 227	94.73 215	62.16 318	97.39 232	87.78 190	74.97 345	95.96 229

viewdifsd2359ckpt11		86.38 241	85.29 243	89.66 265	90.42 333	75.65 343	95.27 281	92.45 373	85.54 175	84.27 228	94.73 215	62.16 318	97.39 232	87.78 190	74.97 345	95.96 229

EPMVS		87.47 226	85.90 234	92.18 156	95.41 143	82.26 140	87.00 420	96.28 140	85.88 166	84.23 229	85.57 384	75.07 190	96.26 297	71.14 355	92.50 178	98.03 94

Elysia		85.62 258	83.66 274	91.51 194	88.76 362	82.21 142	95.15 290	94.70 248	76.96 366	84.13 230	92.20 276	50.81 395	97.26 244	77.81 287	92.42 181	95.06 263

StellarMVS		85.62 258	83.66 274	91.51 194	88.76 362	82.21 142	95.15 290	94.70 248	76.96 366	84.13 230	92.20 276	50.81 395	97.26 244	77.81 287	92.42 181	95.06 263

Anonymous202405211		84.41 285	81.93 306	91.85 177	96.78 103	78.41 272	97.44 123	91.34 397	70.29 413	84.06 232	94.26 231	41.09 435	98.96 129	79.46 272	82.65 299	98.17 85

HyFIR lowres test		89.36 170	88.60 175	91.63 190	94.91 166	80.76 194	95.60 267	95.53 199	82.56 268	84.03 233	91.24 295	78.03 120	96.81 277	87.07 200	88.41 242	97.32 168

tfpn200view9		88.48 196	87.15 210	92.47 131	96.21 112	85.30 65	97.44 123	98.85 2	83.37 247	83.99 234	93.82 249	75.36 182	97.93 184	69.04 365	86.24 266	94.17 283

thres400		88.42 199	87.15 210	92.23 152	96.21 112	85.30 65	97.44 123	98.85 2	83.37 247	83.99 234	93.82 249	75.36 182	97.93 184	69.04 365	86.24 266	93.45 299

tpm		85.55 261	84.47 260	88.80 280	90.19 339	75.39 346	88.79 403	94.69 251	84.83 198	83.96 236	85.21 390	78.22 117	94.68 376	76.32 312	78.02 333	96.34 220

Fast-Effi-MVS+		87.93 213	86.94 218	90.92 220	94.04 201	79.16 244	98.26 61	93.72 333	81.29 286	83.94 237	92.90 265	69.83 260	96.68 283	76.70 306	91.74 191	96.93 194

XVG-OURS-SEG-HR		85.74 255	85.16 249	87.49 316	90.22 337	71.45 387	91.29 379	94.09 303	81.37 285	83.90 238	95.22 191	60.30 335	97.53 218	85.58 209	84.42 283	93.50 297

thisisatest0530		89.65 165	89.02 164	91.53 193	93.46 220	80.78 193	96.52 204	96.67 86	81.69 283	83.79 239	94.90 210	88.85 16	97.68 200	77.80 289	87.49 255	96.14 226

DeepC-MVS	86.58 3	91.53 114	91.06 116	92.94 105	94.52 177	81.89 156	95.95 245	95.98 167	90.76 56	83.76 240	96.76 142	73.24 216	99.71 60	91.67 125	96.96 100	97.22 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
icg_test_0407_2		87.55 224	86.59 226	90.43 236	92.30 271	78.81 256	92.17 367	93.84 318	85.14 187	83.68 241	94.49 224	67.75 270	95.02 364	81.33 251	88.61 229	97.46 154

IMVS_0407		87.82 215	86.72 223	91.14 213	92.30 271	78.81 256	93.34 345	93.84 318	85.14 187	83.68 241	94.49 224	67.75 270	97.14 255	81.33 251	88.61 229	97.46 154

IS-MVSNet		88.67 190	88.16 185	90.20 245	93.61 210	76.86 319	96.77 187	93.07 364	84.02 225	83.62 243	95.60 174	74.69 198	96.24 300	78.43 286	93.66 164	97.49 151

mamba_0408		85.26 270	83.10 287	91.74 182	92.94 243	82.53 128	72.52 465	91.77 387	80.36 307	83.50 244	94.01 241	64.97 299	96.90 268	79.37 274	88.51 238	95.79 238

SSM_04072		84.64 279	83.10 287	89.25 270	92.94 243	82.53 128	72.52 465	91.77 387	80.36 307	83.50 244	94.01 241	64.97 299	89.41 436	79.37 274	88.51 238	95.79 238

SSM_0407		87.33 228	85.87 235	91.71 186	92.94 243	82.53 128	94.30 318	92.33 378	80.11 315	83.50 244	94.18 236	64.68 303	96.80 279	82.34 244	88.51 238	95.79 238

thres100view900		88.30 202	86.95 217	92.33 144	96.10 117	84.90 79	97.14 150	98.85 2	82.69 265	83.41 247	93.66 253	75.43 179	97.93 184	69.04 365	86.24 266	94.17 283

thres600view7		88.06 208	86.70 225	92.15 159	96.10 117	85.17 71	97.14 150	98.85 2	82.70 264	83.41 247	93.66 253	75.43 179	97.82 193	67.13 374	85.88 271	93.45 299

XVG-OURS		85.18 271	84.38 262	87.59 310	90.42 333	71.73 384	91.06 383	94.07 304	82.00 279	83.29 249	95.08 202	56.42 371	97.55 213	83.70 229	83.42 288	93.49 298

Vis-MVSNet (Re-imp)		88.88 184	88.87 172	88.91 277	93.89 204	74.43 354	96.93 173	94.19 297	84.39 212	83.22 250	95.67 168	78.24 116	94.70 374	78.88 282	94.40 149	97.61 137

TAMVS		88.48 196	87.79 192	90.56 232	91.09 317	79.18 243	96.45 210	95.88 180	83.64 244	83.12 251	93.33 258	75.94 166	95.74 327	82.40 243	88.27 244	96.75 208

baseline1		88.85 185	87.49 202	92.93 106	95.21 151	86.85 33	95.47 272	94.61 261	87.29 125	83.11 252	94.99 208	80.70 76	96.89 270	82.28 246	73.72 351	95.05 265

AdaColmap		88.81 186	87.61 198	92.39 139	99.33 4	79.95 220	96.70 193	95.58 196	77.51 356	83.05 253	96.69 146	61.90 327	99.72 57	84.29 218	93.47 166	97.50 150

PatchmatchNet		86.83 235	85.12 250	91.95 170	94.12 197	82.27 139	86.55 424	95.64 194	84.59 206	82.98 254	84.99 396	77.26 134	95.96 311	68.61 368	91.34 196	97.64 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		85.63 257	83.64 277	91.60 191	92.30 271	81.86 158	92.88 358	95.56 198	84.85 197	82.52 255	85.12 394	58.04 353	95.39 342	73.89 334	87.58 253	97.54 143

114514_t		88.79 188	87.57 200	92.45 133	98.21 57	81.74 163	96.99 163	95.45 207	75.16 379	82.48 256	95.69 167	68.59 268	98.50 153	80.33 262	95.18 138	97.10 186

PatchT		79.75 350	76.85 362	88.42 286	89.55 355	75.49 345	77.37 456	94.61 261	63.07 435	82.46 257	73.32 453	75.52 176	93.41 402	51.36 440	84.43 282	96.36 218

TR-MVS		86.30 245	84.93 254	90.42 237	94.63 172	77.58 305	96.57 200	93.82 322	80.30 310	82.42 258	95.16 196	58.74 346	97.55 213	74.88 324	87.82 249	96.13 227

HQP-NCC					92.08 291		97.63 104		90.52 60	82.30 259

ACMP_Plane					92.08 291		97.63 104		90.52 60	82.30 259

HQP4-MVS										82.30 259			97.32 238			91.13 311

HQP-MVS		87.91 214	87.55 201	88.98 276	92.08 291	78.48 268	97.63 104	94.80 243	90.52 60	82.30 259	94.56 221	65.40 294	97.32 238	87.67 194	83.01 292	91.13 311

CR-MVSNet		83.53 298	81.36 315	90.06 248	90.16 340	79.75 226	79.02 452	91.12 400	84.24 220	82.27 263	80.35 427	75.45 177	93.67 397	63.37 397	86.25 264	96.75 208

RPMNet		79.85 349	75.92 369	91.64 188	90.16 340	79.75 226	79.02 452	95.44 208	58.43 455	82.27 263	72.55 456	73.03 218	98.41 162	46.10 453	86.25 264	96.75 208

CVMVSNet		84.83 276	85.57 238	82.63 392	91.55 306	60.38 445	95.13 292	95.03 230	80.60 298	82.10 265	94.71 217	66.40 288	90.19 433	74.30 331	90.32 209	97.31 170

PLC	83.97 7	88.00 211	87.38 206	89.83 259	98.02 63	76.46 325	97.16 147	94.43 276	79.26 335	81.98 266	96.28 153	69.36 264	99.27 101	77.71 293	92.25 186	93.77 293
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 359	77.20 358	84.40 372	89.74 350	64.06 430	75.30 460	95.44 208	62.15 439	81.90 267	59.08 465	78.92 103	95.59 336	66.51 381	85.78 273	93.54 296

Anonymous20240529		83.15 305	80.60 326	90.80 225	95.74 132	78.27 277	96.81 183	94.92 234	60.10 450	81.89 268	92.54 270	45.82 418	98.82 138	79.25 278	78.32 331	95.31 256

tttt0517		88.57 194	88.19 184	89.71 263	93.00 237	75.99 337	95.67 262	96.67 86	80.78 294	81.82 269	94.40 228	88.97 15	97.58 207	76.05 314	86.31 263	95.57 248

WB-MVSnew		84.08 290	83.51 281	85.80 343	91.34 311	76.69 323	95.62 266	96.27 141	81.77 281	81.81 270	92.81 266	58.23 350	94.70 374	66.66 377	87.06 256	85.99 413

BH-RMVSNet		86.84 234	85.28 245	91.49 197	95.35 146	80.26 211	96.95 171	92.21 380	82.86 261	81.77 271	95.46 180	59.34 342	97.64 202	69.79 363	93.81 160	96.57 214

HQP_MVS		87.50 225	87.09 213	88.74 281	91.86 301	77.96 289	97.18 143	94.69 251	89.89 70	81.33 272	94.15 238	64.77 301	97.30 240	87.08 198	82.82 296	90.96 313

plane_prior3						77.75 302			90.17 67	81.33 272

VPA-MVSNet		85.32 268	83.83 270	89.77 262	90.25 336	82.63 126	96.36 219	97.07 39	83.03 256	81.21 274	89.02 324	61.58 328	96.31 296	85.02 214	70.95 369	90.36 320

GeoE		86.36 243	85.20 246	89.83 259	93.17 230	76.13 331	97.53 115	92.11 381	79.58 327	80.99 275	94.01 241	66.60 286	96.17 303	73.48 338	89.30 218	97.20 179

GA-MVS		85.79 254	84.04 269	91.02 218	89.47 357	80.27 210	96.90 176	94.84 241	85.57 172	80.88 276	89.08 322	56.56 370	96.47 290	77.72 292	85.35 277	96.34 220

1112_ss		88.60 193	87.47 204	92.00 168	93.21 228	80.97 185	96.47 208	92.46 372	83.64 244	80.86 277	97.30 116	80.24 83	97.62 203	77.60 295	85.49 275	97.40 163

dp		84.30 287	82.31 300	90.28 242	94.24 191	77.97 288	86.57 423	95.53 199	79.94 321	80.75 278	85.16 392	71.49 245	96.39 292	63.73 394	83.36 289	96.48 216

Test_1112_low_res		88.03 209	86.73 222	91.94 172	93.15 231	80.88 190	96.44 211	92.41 376	83.59 246	80.74 279	91.16 296	80.18 84	97.59 205	77.48 298	85.40 276	97.36 166

cascas		86.50 239	84.48 259	92.55 129	92.64 259	85.95 43	97.04 161	95.07 228	75.32 377	80.50 280	91.02 298	54.33 385	97.98 183	86.79 203	87.62 251	93.71 294

TAPA-MVS	81.61 12	85.02 273	83.67 273	89.06 273	96.79 102	73.27 365	95.92 247	94.79 245	74.81 382	80.47 281	96.83 138	71.07 248	98.19 172	49.82 446	92.57 176	95.71 243
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 252	85.10 251	88.06 298	88.34 373	77.83 296	95.72 260	94.20 296	87.89 109	80.45 282	94.05 240	58.57 347	97.26 244	83.88 222	82.76 298	89.09 350
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
nrg030		86.79 236	85.43 240	90.87 224	88.76 362	85.34 62	97.06 160	94.33 287	84.31 214	80.45 282	91.98 283	72.36 226	96.36 294	88.48 183	71.13 367	90.93 315

EI-MVSNet		85.80 253	85.20 246	87.59 310	91.55 306	77.41 308	95.13 292	95.36 214	80.43 305	80.33 284	94.71 217	73.72 210	95.97 308	76.96 304	78.64 325	89.39 337

MVSTER		89.25 174	88.92 170	90.24 243	95.98 121	84.66 82	96.79 184	95.36 214	87.19 132	80.33 284	90.61 305	90.02 11	95.97 308	85.38 211	78.64 325	90.09 329

ADS-MVSNet2		79.57 353	77.53 356	85.71 347	93.78 206	72.13 375	79.48 448	86.11 440	73.09 397	80.14 286	79.99 430	62.15 320	90.14 434	59.49 412	83.52 286	94.85 270

ADS-MVSNet		81.26 335	78.36 349	89.96 254	93.78 206	79.78 224	79.48 448	93.60 340	73.09 397	80.14 286	79.99 430	62.15 320	95.24 351	59.49 412	83.52 286	94.85 270

test_fmvs2		79.59 352	79.90 338	78.67 419	82.86 432	55.82 456	95.20 286	89.55 416	81.09 288	80.12 288	89.80 316	34.31 450	93.51 400	87.82 189	78.36 330	86.69 402

baseline2		90.39 146	90.21 137	90.93 219	90.86 323	80.99 184	95.20 286	97.41 18	86.03 163	80.07 289	94.61 220	90.58 6	97.47 224	87.29 197	89.86 214	94.35 281

Effi-MVS+-dtu		84.61 281	84.90 255	83.72 380	91.96 298	63.14 435	94.95 299	93.34 353	85.57 172	79.79 290	87.12 357	61.99 325	95.61 335	83.55 231	85.83 272	92.41 306

VPNet		84.69 278	82.92 290	90.01 250	89.01 361	83.45 108	96.71 191	95.46 206	85.71 170	79.65 291	92.18 279	56.66 369	96.01 307	83.05 239	67.84 400	90.56 318

SDMVSNet		87.02 230	85.61 237	91.24 208	94.14 195	83.30 111	93.88 331	95.98 167	84.30 216	79.63 292	92.01 280	58.23 350	97.68 200	90.28 156	82.02 304	92.75 302

sd_testset		84.62 280	83.11 286	89.17 271	94.14 195	77.78 298	91.54 378	94.38 282	84.30 216	79.63 292	92.01 280	52.28 390	96.98 262	77.67 294	82.02 304	92.75 302

CLD-MVS		87.97 212	87.48 203	89.44 267	92.16 286	80.54 204	98.14 65	94.92 234	91.41 46	79.43 294	95.40 181	62.34 316	97.27 243	90.60 144	82.90 295	90.50 319
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	88.67 190	87.14 212	93.26 88	93.12 234	84.32 87	98.76 37	97.27 22	87.19 132	79.36 295	90.45 307	83.92 55	98.53 152	84.41 217	69.79 380	96.93 194
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		85.00 274	83.66 274	89.02 275	95.86 126	74.55 353	92.49 362	93.60 340	79.30 333	79.29 296	91.47 290	58.53 348	98.45 159	70.22 361	92.17 188	94.07 288

mamv4		85.50 262	86.76 221	81.72 401	93.23 226	54.93 459	89.95 392	92.94 366	69.96 416	79.00 297	92.20 276	80.69 77	94.22 386	92.06 118	90.77 205	96.01 228

CNLPA		86.96 231	85.37 242	91.72 185	97.59 79	79.34 239	97.21 139	91.05 403	74.22 386	78.90 298	96.75 144	67.21 279	98.95 131	74.68 326	90.77 205	96.88 199

MVS		90.60 141	88.64 174	96.50 5	94.25 190	90.53 8	93.33 346	97.21 25	77.59 355	78.88 299	97.31 113	71.52 244	99.69 64	89.60 164	98.03 60	99.27 22

mvs_anonymous		88.68 189	87.62 197	91.86 175	94.80 169	81.69 166	93.53 341	94.92 234	82.03 278	78.87 300	90.43 308	75.77 169	95.34 345	85.04 213	93.16 171	98.55 61

UWE-MVS-28		85.41 266	86.36 228	82.59 393	91.12 316	66.81 419	93.88 331	97.03 42	83.86 234	78.55 301	93.84 248	77.76 127	88.55 440	73.47 339	87.69 250	92.41 306

tpm cat1		83.63 297	81.38 314	90.39 238	93.53 218	78.19 284	85.56 431	95.09 226	70.78 411	78.51 302	83.28 411	74.80 194	97.03 258	66.77 376	84.05 284	95.95 231

UniMVSNet (Re)		85.31 269	84.23 264	88.55 285	89.75 348	80.55 200	96.72 189	96.89 56	85.42 178	78.40 303	88.93 325	75.38 181	95.52 339	78.58 284	68.02 397	89.57 336

FIs		86.73 238	86.10 232	88.61 284	90.05 343	80.21 213	96.14 237	96.95 51	85.56 174	78.37 304	92.30 274	76.73 149	95.28 349	79.51 271	79.27 319	90.35 321

WBMVS		87.73 218	86.79 220	90.56 232	95.61 137	85.68 52	97.63 104	95.52 201	83.77 237	78.30 305	88.44 334	86.14 34	95.78 321	82.54 242	73.15 358	90.21 324

BH-w/o		88.24 204	87.47 204	90.54 234	95.03 163	78.54 267	97.41 128	93.82 322	84.08 223	78.23 306	94.51 223	69.34 265	97.21 247	80.21 266	94.58 145	95.87 235

MonoMVSNet		85.68 256	84.22 265	90.03 249	88.43 372	77.83 296	92.95 357	91.46 393	87.28 126	78.11 307	85.96 379	66.31 289	94.81 370	90.71 142	76.81 336	97.46 154

UniMVSNet_NR-MVSNet		85.49 263	84.59 256	88.21 296	89.44 358	79.36 237	96.71 191	96.41 123	85.22 183	78.11 307	90.98 300	76.97 144	95.14 357	79.14 279	68.30 394	90.12 327

DU-MVS		84.57 282	83.33 284	88.28 292	88.76 362	79.36 237	96.43 213	95.41 213	85.42 178	78.11 307	90.82 301	67.61 272	95.14 357	79.14 279	68.30 394	90.33 322

dmvs_re		84.10 289	82.90 291	87.70 305	91.41 310	73.28 363	90.59 387	93.19 357	85.02 193	77.96 310	93.68 252	57.92 358	96.18 302	75.50 319	80.87 308	93.63 295

miper_enhance_ethall		85.95 251	85.20 246	88.19 297	94.85 167	79.76 225	96.00 242	94.06 305	82.98 258	77.74 311	88.76 327	79.42 93	95.46 341	80.58 260	72.42 360	89.36 343

v1144		82.90 311	81.27 316	87.78 304	86.29 394	79.07 249	96.14 237	93.93 309	80.05 318	77.38 312	86.80 362	65.50 292	95.93 313	75.21 322	70.13 375	88.33 375

FC-MVSNet-test		85.96 250	85.39 241	87.66 307	89.38 359	78.02 286	95.65 264	96.87 58	85.12 191	77.34 313	91.94 287	76.28 160	94.74 373	77.09 301	78.82 323	90.21 324

v2v482		83.46 299	81.86 307	88.25 294	86.19 396	79.65 231	96.34 221	94.02 307	81.56 284	77.32 314	88.23 338	65.62 291	96.03 305	77.77 290	69.72 382	89.09 350

Baseline_NR-MVSNet		81.22 336	80.07 334	84.68 364	85.32 410	75.12 348	96.48 207	88.80 424	76.24 373	77.28 315	86.40 372	67.61 272	94.39 383	75.73 318	66.73 411	84.54 425

V42		83.04 308	81.53 312	87.57 312	86.27 395	79.09 248	95.87 253	94.11 302	80.35 309	77.22 316	86.79 363	65.32 296	96.02 306	77.74 291	70.14 374	87.61 388

v144192		82.43 317	80.73 323	87.54 313	85.81 403	78.22 279	95.98 243	93.78 327	79.09 338	77.11 317	86.49 367	64.66 305	95.91 314	74.20 332	69.42 383	88.49 369

ACMM	80.70 13	83.72 296	82.85 293	86.31 337	91.19 313	72.12 376	95.88 252	94.29 289	80.44 303	77.02 318	91.96 284	55.24 378	97.14 255	79.30 277	80.38 311	89.67 335
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 321	80.55 327	87.60 309	85.94 400	78.47 271	95.85 255	93.80 325	79.33 331	76.97 319	86.51 366	63.33 312	95.87 315	73.11 340	70.13 375	88.46 371

PCF-MVS	84.09 5	86.77 237	85.00 252	92.08 162	92.06 294	83.07 116	92.14 368	94.47 270	79.63 326	76.90 320	94.78 214	71.15 247	99.20 112	72.87 341	91.05 201	93.98 289
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		85.11 272	84.17 266	87.92 301	95.06 162	78.82 254	95.51 270	94.22 295	79.74 324	76.77 321	87.92 343	75.96 164	95.68 328	79.93 269	72.42 360	89.27 345

v1921920		82.02 324	80.23 331	87.41 317	85.62 404	77.92 292	95.79 259	93.69 334	78.86 342	76.67 322	86.44 369	62.50 315	95.83 317	72.69 342	69.77 381	88.47 370

WR-MVS		84.32 286	82.96 289	88.41 287	89.38 359	80.32 207	96.59 197	96.25 143	83.97 227	76.63 323	90.36 309	67.53 275	94.86 368	75.82 317	70.09 378	90.06 331

BH-untuned		86.95 232	85.94 233	89.99 251	94.52 177	77.46 307	96.78 185	93.37 352	81.80 280	76.62 324	93.81 251	66.64 285	97.02 259	76.06 313	93.88 159	95.48 252

SSC-MVS3.2		81.06 338	79.49 342	85.75 346	89.78 346	73.00 368	94.40 314	95.23 222	83.76 238	76.61 325	87.82 345	49.48 404	94.88 366	66.80 375	71.56 365	89.38 339

v1240		81.70 328	79.83 339	87.30 321	85.50 405	77.70 304	95.48 271	93.44 345	78.46 347	76.53 326	86.44 369	60.85 333	95.84 316	71.59 349	70.17 373	88.35 374

PS-MVSNAJss		84.91 275	84.30 263	86.74 328	85.89 402	74.40 355	94.95 299	94.16 299	83.93 230	76.45 327	90.11 315	71.04 249	95.77 322	83.16 237	79.02 322	90.06 331

miper_ehance_all_eth		84.57 282	83.60 279	87.50 314	92.64 259	78.25 278	95.40 276	93.47 344	79.28 334	76.41 328	87.64 348	76.53 152	95.24 351	78.58 284	72.42 360	89.01 356

LPG-MVS_test		84.20 288	83.49 282	86.33 334	90.88 320	73.06 366	95.28 278	94.13 300	82.20 273	76.31 329	93.20 259	54.83 382	96.95 264	83.72 227	80.83 309	88.98 357

LGP-MVS_train				86.33 334	90.88 320	73.06 366		94.13 300	82.20 273	76.31 329	93.20 259	54.83 382	96.95 264	83.72 227	80.83 309	88.98 357

F-COLMAP		84.50 284	83.44 283	87.67 306	95.22 150	72.22 372	95.95 245	93.78 327	75.74 374	76.30 331	95.18 195	59.50 340	98.45 159	72.67 343	86.59 261	92.35 308

tpmvs		83.04 308	80.77 322	89.84 258	95.43 142	77.96 289	85.59 430	95.32 218	75.31 378	76.27 332	83.70 407	73.89 207	97.41 228	59.53 411	81.93 306	94.14 285

tt0805		81.20 337	79.06 346	87.61 308	86.50 390	72.97 369	93.66 335	95.48 204	74.11 387	76.23 333	91.99 282	41.36 434	97.40 230	77.44 299	74.78 347	92.45 305

3Dnovator	82.32 10	89.33 171	87.64 195	94.42 38	93.73 209	85.70 50	97.73 98	96.75 75	86.73 147	76.21 334	95.93 159	62.17 317	99.68 66	81.67 250	97.81 67	97.88 108

TranMVSNet+NR-MVSNet		83.24 304	81.71 309	87.83 302	87.71 380	78.81 256	96.13 239	94.82 242	84.52 207	76.18 335	90.78 303	64.07 306	94.60 378	74.60 329	66.59 412	90.09 329

c3_l		83.80 294	82.65 296	87.25 322	92.10 290	77.74 303	95.25 283	93.04 365	78.58 345	76.01 336	87.21 356	75.25 187	95.11 359	77.54 297	68.89 388	88.91 362

1314		88.94 181	87.20 209	94.17 49	93.21 228	85.73 49	93.33 346	96.64 93	82.89 259	75.98 337	96.36 151	66.83 284	99.39 93	83.52 234	96.02 127	97.39 164

Fast-Effi-MVS+-dtu		83.33 301	82.60 297	85.50 352	89.55 355	69.38 404	96.09 240	91.38 394	82.30 272	75.96 338	91.41 291	56.71 367	95.58 337	75.13 323	84.90 280	91.54 309

XXY-MVS		83.84 293	82.00 305	89.35 268	87.13 385	81.38 172	95.72 260	94.26 291	80.15 314	75.92 339	90.63 304	61.96 326	96.52 288	78.98 281	73.28 356	90.14 326

GBi-Net		82.42 318	80.43 329	88.39 289	92.66 255	81.95 150	94.30 318	93.38 349	79.06 339	75.82 340	85.66 380	56.38 372	93.84 393	71.23 352	75.38 342	89.38 339

test1		82.42 318	80.43 329	88.39 289	92.66 255	81.95 150	94.30 318	93.38 349	79.06 339	75.82 340	85.66 380	56.38 372	93.84 393	71.23 352	75.38 342	89.38 339

FMVSNet3		84.71 277	82.71 295	90.70 229	94.55 175	87.71 24	95.92 247	94.67 254	81.73 282	75.82 340	88.08 341	66.99 281	94.47 381	71.23 352	75.38 342	89.91 333

eth_miper_zixun_eth		83.12 306	82.01 304	86.47 333	91.85 303	74.80 349	94.33 316	93.18 359	79.11 337	75.74 343	87.25 355	72.71 221	95.32 347	76.78 305	67.13 407	89.27 345

IterMVS-LS		83.93 292	82.80 294	87.31 320	91.46 309	77.39 309	95.66 263	93.43 347	80.44 303	75.51 344	87.26 354	73.72 210	95.16 356	76.99 302	70.72 371	89.39 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 166	87.85 190	94.99 24	94.49 183	86.76 35	97.84 88	95.74 188	86.10 159	75.47 345	96.02 158	65.00 298	99.51 87	82.91 240	97.07 97	98.72 51

test_djsdf		83.00 310	82.45 299	84.64 366	84.07 423	69.78 400	94.80 305	94.48 267	80.74 295	75.41 346	87.70 346	61.32 332	95.10 360	83.77 225	79.76 312	89.04 353

v148		82.41 320	80.89 320	86.99 326	86.18 397	76.81 320	96.27 226	93.82 322	80.49 302	75.28 347	86.11 378	67.32 278	95.75 324	75.48 320	67.03 409	88.42 373

QAPM		86.88 233	84.51 257	93.98 53	94.04 201	85.89 46	97.19 142	96.05 160	73.62 391	75.12 348	95.62 173	62.02 324	99.74 52	70.88 356	96.06 125	96.30 224

VortexMVS		85.45 265	84.40 261	88.63 283	93.25 225	81.66 167	95.39 277	94.34 284	87.15 134	75.10 349	87.65 347	66.58 287	95.19 353	86.89 202	73.21 357	89.03 354

UniMVSNet_ETH3D		80.86 342	78.75 348	87.22 323	86.31 393	72.02 377	91.95 369	93.76 332	73.51 392	75.06 350	90.16 313	43.04 427	95.66 329	76.37 311	78.55 328	93.98 289

cl____		83.27 302	82.12 302	86.74 328	92.20 282	75.95 338	95.11 294	93.27 355	78.44 348	74.82 351	87.02 359	74.19 203	95.19 353	74.67 327	69.32 384	89.09 350

DIV-MVS_self_test		83.27 302	82.12 302	86.74 328	92.19 283	75.92 340	95.11 294	93.26 356	78.44 348	74.81 352	87.08 358	74.19 203	95.19 353	74.66 328	69.30 385	89.11 349

FMVSNet2		82.79 312	80.44 328	89.83 259	92.66 255	85.43 60	95.42 274	94.35 283	79.06 339	74.46 353	87.28 352	56.38 372	94.31 384	69.72 364	74.68 348	89.76 334

MIMVSNet		79.18 358	75.99 368	88.72 282	87.37 384	80.66 196	79.96 446	91.82 385	77.38 358	74.33 354	81.87 418	41.78 430	90.74 429	66.36 383	83.10 291	94.76 272

RPSCF		77.73 369	76.63 364	81.06 405	88.66 368	55.76 457	87.77 414	87.88 430	64.82 433	74.14 355	92.79 268	49.22 405	96.81 277	67.47 372	76.88 335	90.62 317

ACMP	81.66 11	84.00 291	83.22 285	86.33 334	91.53 308	72.95 370	95.91 249	93.79 326	83.70 241	73.79 356	92.22 275	54.31 386	96.89 270	83.98 221	79.74 314	89.16 348
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
reproduce_monomvs		87.80 216	87.60 199	88.40 288	96.56 104	80.26 211	95.80 258	96.32 138	91.56 45	73.60 357	88.36 335	88.53 18	96.25 299	90.47 146	67.23 406	88.67 364

pmmvs5		81.34 333	79.54 340	86.73 331	85.02 412	76.91 317	96.22 229	91.65 390	77.65 354	73.55 358	88.61 329	55.70 375	94.43 382	74.12 333	73.35 355	88.86 363

jajsoiax		82.12 323	81.15 318	85.03 360	84.19 421	70.70 392	94.22 323	93.95 308	83.07 253	73.48 359	89.75 317	49.66 403	95.37 344	82.24 247	79.76 312	89.02 355

Syy-MVS		77.97 367	78.05 352	77.74 423	92.13 288	56.85 452	93.97 327	94.23 293	82.43 269	73.39 360	93.57 255	57.95 356	87.86 444	32.40 466	82.34 301	88.51 367

myMVS_eth3d		81.93 325	82.18 301	81.18 404	92.13 288	67.18 414	93.97 327	94.23 293	82.43 269	73.39 360	93.57 255	76.98 143	87.86 444	50.53 444	82.34 301	88.51 367

mvs_tets		81.74 327	80.71 324	84.84 361	84.22 420	70.29 396	93.91 330	93.78 327	82.77 263	73.37 362	89.46 320	47.36 414	95.31 348	81.99 248	79.55 318	88.92 361

pmmvs4		82.54 316	80.79 321	87.79 303	86.11 398	80.49 206	93.55 340	93.18 359	77.29 359	73.35 363	89.40 321	65.26 297	95.05 363	75.32 321	73.61 352	87.83 383

LS3D		82.22 322	79.94 337	89.06 273	97.43 88	74.06 358	93.20 352	92.05 382	61.90 440	73.33 364	95.21 192	59.35 341	99.21 107	54.54 433	92.48 179	93.90 291

v10		81.43 332	79.53 341	87.11 324	86.38 391	78.87 252	94.31 317	93.43 347	77.88 351	73.24 365	85.26 388	65.44 293	95.75 324	72.14 346	67.71 401	86.72 401

v8		81.88 326	80.06 335	87.32 319	86.63 389	79.04 250	94.41 311	93.65 336	78.77 343	73.19 366	85.57 384	66.87 283	95.81 318	73.84 336	67.61 402	87.11 397

test0.0.03 1		82.79 312	82.48 298	83.74 379	86.81 388	72.22 372	96.52 204	95.03 230	83.76 238	73.00 367	93.20 259	72.30 229	88.88 438	64.15 392	77.52 334	90.12 327

anonymousdsp		80.98 341	79.97 336	84.01 374	81.73 435	70.44 395	92.49 362	93.58 342	77.10 363	72.98 368	86.31 373	57.58 359	94.90 365	79.32 276	78.63 327	86.69 402

XVG-ACMP-BASELINE		79.38 356	77.90 354	83.81 376	84.98 413	67.14 418	89.03 401	93.18 359	80.26 313	72.87 369	88.15 340	38.55 440	96.26 297	76.05 314	78.05 332	88.02 380

WR-MVS_H		81.02 339	80.09 332	83.79 377	88.08 376	71.26 390	94.46 309	96.54 106	80.08 317	72.81 370	86.82 361	70.36 257	92.65 406	64.18 391	67.50 403	87.46 394

OpenMVS	79.58 14	86.09 248	83.62 278	93.50 81	90.95 319	86.71 36	97.44 123	95.83 183	75.35 376	72.64 371	95.72 165	57.42 363	99.64 70	71.41 350	95.85 131	94.13 286

Anonymous20231211		79.72 351	77.19 359	87.33 318	95.59 139	77.16 315	95.18 289	94.18 298	59.31 453	72.57 372	86.20 376	47.89 411	95.66 329	74.53 330	69.24 386	89.18 347

CP-MVSNet		81.01 340	80.08 333	83.79 377	87.91 378	70.51 393	94.29 322	95.65 193	80.83 292	72.54 373	88.84 326	63.71 308	92.32 411	68.58 369	68.36 393	88.55 366

IMVS_0404		85.34 267	83.69 271	90.29 241	92.30 271	78.81 256	90.62 386	93.84 318	85.14 187	72.51 374	94.49 224	54.36 384	94.61 377	81.33 251	88.61 229	97.46 154

miper_lstm_enhance		81.66 330	80.66 325	84.67 365	91.19 313	71.97 379	91.94 370	93.19 357	77.86 352	72.27 375	85.26 388	73.46 213	93.42 401	73.71 337	67.05 408	88.61 365

PS-CasMVS		80.27 347	79.18 343	83.52 383	87.56 382	69.88 399	94.08 325	95.29 219	80.27 312	72.08 376	88.51 333	59.22 344	92.23 413	67.49 371	68.15 396	88.45 372

FMVSNet1		79.50 354	76.54 365	88.39 289	88.47 370	81.95 150	94.30 318	93.38 349	73.14 396	72.04 377	85.66 380	43.86 421	93.84 393	65.48 385	72.53 359	89.38 339

SD_0403		81.29 334	81.13 319	81.78 400	90.20 338	60.43 444	89.97 391	91.31 399	83.87 232	71.78 378	93.08 264	63.86 307	89.61 435	60.00 410	86.07 269	95.30 257

mvs5depth		71.40 406	68.36 410	80.54 409	75.31 458	65.56 424	79.94 447	85.14 443	69.11 421	71.75 379	81.59 419	41.02 436	93.94 391	60.90 407	50.46 450	82.10 442

PEN-MVS		79.47 355	78.26 351	83.08 386	86.36 392	68.58 407	93.85 333	94.77 246	79.76 323	71.37 380	88.55 330	59.79 336	92.46 407	64.50 389	65.40 414	88.19 377

testing3		80.74 343	81.17 317	79.44 414	91.15 315	63.48 433	97.16 147	95.76 186	80.83 292	71.36 381	93.15 262	78.22 117	87.30 449	43.19 458	79.67 315	87.55 392

Patchmtry		77.36 373	74.59 378	85.67 348	89.75 348	75.75 342	77.85 455	91.12 400	60.28 448	71.23 382	80.35 427	75.45 177	93.56 399	57.94 418	67.34 405	87.68 386

IterMVS		80.67 344	79.16 344	85.20 357	89.79 345	76.08 332	92.97 356	91.86 384	80.28 311	71.20 383	85.14 393	57.93 357	91.34 423	72.52 344	70.74 370	88.18 378
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 331	78.28 350	91.04 215	98.14 59	78.48 268	95.09 297	86.97 433	61.14 446	71.12 384	92.78 269	59.59 338	99.38 94	53.11 437	86.61 260	95.27 259

IterMVS-SCA-FT		80.51 346	79.10 345	84.73 363	89.63 353	74.66 350	92.98 355	91.81 386	80.05 318	71.06 385	85.18 391	58.04 353	91.40 422	72.48 345	70.70 372	88.12 379

v7n		79.32 357	77.34 357	85.28 356	84.05 424	72.89 371	93.38 343	93.87 315	75.02 381	70.68 386	84.37 400	59.58 339	95.62 334	67.60 370	67.50 403	87.32 396

MS-PatchMatch		83.05 307	81.82 308	86.72 332	89.64 352	79.10 247	94.88 301	94.59 263	79.70 325	70.67 387	89.65 318	50.43 399	96.82 276	70.82 359	95.99 129	84.25 428

DTE-MVSNet		78.37 361	77.06 360	82.32 396	85.22 411	67.17 417	93.40 342	93.66 335	78.71 344	70.53 388	88.29 337	59.06 345	92.23 413	61.38 404	63.28 423	87.56 390

pm-mvs1		80.05 348	78.02 353	86.15 339	85.42 406	75.81 341	95.11 294	92.69 371	77.13 361	70.36 389	87.43 350	58.44 349	95.27 350	71.36 351	64.25 419	87.36 395

D2MVS		82.67 314	81.55 311	86.04 341	87.77 379	76.47 324	95.21 285	96.58 102	82.66 266	70.26 390	85.46 387	60.39 334	95.80 319	76.40 310	79.18 320	85.83 416

PVSNet_0	77.72 15	81.70 328	78.95 347	89.94 255	90.77 327	76.72 322	95.96 244	96.95 51	85.01 194	70.24 391	88.53 332	52.32 389	98.20 171	86.68 204	44.08 463	94.89 268

CL-MVSNet_self_test		75.81 382	74.14 384	80.83 407	78.33 446	67.79 411	94.22 323	93.52 343	77.28 360	69.82 392	81.54 421	61.47 331	89.22 437	57.59 421	53.51 443	85.48 418

tfpnnormal		78.14 363	75.42 371	86.31 337	88.33 374	79.24 240	94.41 311	96.22 146	73.51 392	69.81 393	85.52 386	55.43 376	95.75 324	47.65 451	67.86 399	83.95 431

EU-MVSNet		76.92 377	76.95 361	76.83 428	84.10 422	54.73 460	91.77 373	92.71 370	72.74 400	69.57 394	88.69 328	58.03 355	87.43 448	64.91 388	70.00 379	88.33 375

ITE_SJBPF				82.38 394	87.00 386	65.59 423		89.55 416	79.99 320	69.37 395	91.30 294	41.60 432	95.33 346	62.86 399	74.63 349	86.24 408

DSMNet-mixed		73.13 396	72.45 391	75.19 434	77.51 449	46.82 465	85.09 435	82.01 458	67.61 428	69.27 396	81.33 422	50.89 394	86.28 452	54.54 433	83.80 285	92.46 304

MVP-Stereo		82.65 315	81.67 310	85.59 351	86.10 399	78.29 275	93.33 346	92.82 368	77.75 353	69.17 397	87.98 342	59.28 343	95.76 323	71.77 347	96.88 104	82.73 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sc_t1		72.37 400	68.03 411	85.39 354	83.78 427	70.51 393	91.27 380	83.70 453	52.46 460	68.29 398	82.02 416	30.58 458	94.81 370	64.50 389	55.69 435	90.85 316

MSDG		80.62 345	77.77 355	89.14 272	93.43 221	77.24 311	91.89 371	90.18 412	69.86 418	68.02 399	91.94 287	52.21 391	98.84 137	59.32 414	83.12 290	91.35 310

NR-MVSNet		83.35 300	81.52 313	88.84 278	88.76 362	81.31 175	94.45 310	95.16 224	84.65 204	67.81 400	90.82 301	70.36 257	94.87 367	74.75 325	66.89 410	90.33 322

TransMVSNet (Re)		76.94 376	74.38 380	84.62 367	85.92 401	75.25 347	95.28 278	89.18 421	73.88 390	67.22 401	86.46 368	59.64 337	94.10 388	59.24 415	52.57 447	84.50 426

Anonymous20231206		75.29 385	73.64 386	80.22 410	80.75 436	63.38 434	93.36 344	90.71 410	73.09 397	67.12 402	83.70 407	50.33 400	90.85 428	53.63 436	70.10 377	86.44 405

ppachtmachnet_test		77.19 374	74.22 382	86.13 340	85.39 407	78.22 279	93.98 326	91.36 396	71.74 407	67.11 403	84.87 397	56.67 368	93.37 403	52.21 438	64.59 416	86.80 400

KD-MVS_2432*1600		77.63 370	74.92 375	85.77 344	90.86 323	79.44 234	88.08 410	93.92 311	76.26 371	67.05 404	82.78 413	72.15 233	91.92 416	61.53 401	41.62 466	85.94 414

miper_refine_blended		77.63 370	74.92 375	85.77 344	90.86 323	79.44 234	88.08 410	93.92 311	76.26 371	67.05 404	82.78 413	72.15 233	91.92 416	61.53 401	41.62 466	85.94 414

Patchmatch-test		78.25 362	74.72 377	88.83 279	91.20 312	74.10 357	73.91 463	88.70 427	59.89 451	66.82 406	85.12 394	78.38 113	94.54 379	48.84 449	79.58 317	97.86 112

test_fmvs3		69.56 412	69.19 407	70.67 438	69.01 464	47.05 464	90.87 384	86.81 435	71.31 410	66.79 407	77.15 440	16.40 468	83.17 460	81.84 249	62.51 425	81.79 446

LTVRE_ROB	73.68 18	77.99 365	75.74 370	84.74 362	90.45 332	72.02 377	86.41 425	91.12 400	72.57 402	66.63 408	87.27 353	54.95 381	96.98 262	56.29 427	75.98 337	85.21 420
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		77.18 375	76.06 367	80.55 408	83.78 427	60.00 447	90.35 388	91.05 403	77.01 365	66.62 409	87.92 343	47.73 412	94.03 389	71.63 348	68.44 392	87.62 387

testgi		74.88 387	73.40 387	79.32 415	80.13 440	61.75 439	93.21 351	86.64 438	79.49 329	66.56 410	91.06 297	35.51 448	88.67 439	56.79 426	71.25 366	87.56 390

LCM-MVSNet-Re		83.75 295	83.54 280	84.39 373	93.54 213	64.14 429	92.51 361	84.03 451	83.90 231	66.14 411	86.59 365	67.36 277	92.68 405	84.89 215	92.87 173	96.35 219

pmmvs6		74.65 388	71.67 395	83.60 382	79.13 443	69.94 398	93.31 349	90.88 407	61.05 447	65.83 412	84.15 403	43.43 423	94.83 369	66.62 378	60.63 428	86.02 412

our_test_3		77.90 368	75.37 372	85.48 353	85.39 407	76.74 321	93.63 336	91.67 389	73.39 395	65.72 413	84.65 399	58.20 352	93.13 404	57.82 419	67.87 398	86.57 404

ttmdpeth		69.58 411	66.92 415	77.54 425	75.95 457	62.40 437	88.09 409	84.32 448	62.87 437	65.70 414	86.25 375	36.53 443	88.53 441	55.65 431	46.96 459	81.70 447

COLMAP_ROB	73.24 19	75.74 383	73.00 390	83.94 375	92.38 265	69.08 405	91.85 372	86.93 434	61.48 443	65.32 415	90.27 310	42.27 429	96.93 267	50.91 442	75.63 341	85.80 417
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 379	74.16 383	83.35 385	90.05 343	76.17 330	89.58 396	89.85 414	71.39 409	65.29 416	80.42 426	50.61 398	87.70 447	61.05 406	69.24 386	86.18 409

ACMH+	76.62 16	77.47 372	74.94 374	85.05 359	91.07 318	71.58 386	93.26 350	90.01 413	71.80 406	64.76 417	88.55 330	41.62 431	96.48 289	62.35 400	71.00 368	87.09 398

Patchmatch-RL test		76.65 378	74.01 385	84.55 368	77.37 450	64.23 428	78.49 454	82.84 456	78.48 346	64.63 418	73.40 452	76.05 163	91.70 421	76.99 302	57.84 432	97.72 125

SixPastTwentyTwo		76.04 380	74.32 381	81.22 403	84.54 416	61.43 442	91.16 381	89.30 420	77.89 350	64.04 419	86.31 373	48.23 406	94.29 385	63.54 396	63.84 421	87.93 382

AllTest		75.92 381	73.06 389	84.47 369	92.18 284	67.29 412	91.07 382	84.43 446	67.63 424	63.48 420	90.18 311	38.20 441	97.16 250	57.04 423	73.37 353	88.97 359

TestCases				84.47 369	92.18 284	67.29 412		84.43 446	67.63 424	63.48 420	90.18 311	38.20 441	97.16 250	57.04 423	73.37 353	88.97 359

ACMH	75.40 17	77.99 365	74.96 373	87.10 325	90.67 328	76.41 327	93.19 353	91.64 391	72.47 403	63.44 422	87.61 349	43.34 424	97.16 250	58.34 417	73.94 350	87.72 384
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		90.01 155	89.03 163	92.95 104	94.38 187	86.77 34	98.14 65	96.31 139	89.30 78	63.33 423	96.72 145	90.09 10	93.63 398	90.70 143	82.29 303	98.46 64

USDC		78.65 360	76.25 366	85.85 342	87.58 381	74.60 352	89.58 396	90.58 411	84.05 224	63.13 424	88.23 338	40.69 439	96.86 275	66.57 380	75.81 340	86.09 411

LF4IMVS		72.36 401	70.82 398	76.95 427	79.18 442	56.33 453	86.12 427	86.11 440	69.30 420	63.06 425	86.66 364	33.03 453	92.25 412	65.33 386	68.64 390	82.28 441

dmvs_testset		72.00 404	73.36 388	67.91 440	83.83 426	31.90 480	85.30 433	77.12 465	82.80 262	63.05 426	92.46 271	61.54 329	82.55 462	42.22 461	71.89 364	89.29 344

KD-MVS_self_test		70.97 408	69.31 406	75.95 433	76.24 456	55.39 458	87.45 415	90.94 406	70.20 415	62.96 427	77.48 438	44.01 420	88.09 442	61.25 405	53.26 444	84.37 427

tt0320		70.21 409	66.07 417	82.64 391	83.42 430	70.82 391	89.63 394	84.10 449	49.75 463	62.71 428	77.28 439	33.35 451	92.45 409	58.78 416	55.62 436	84.64 424

Anonymous20240521		72.06 403	69.91 403	78.50 421	77.11 451	61.67 441	91.62 377	90.97 405	65.52 431	62.37 429	79.05 433	36.32 444	90.96 427	57.75 420	68.52 391	82.87 433

test_0402		72.68 398	69.54 405	82.09 397	88.67 367	71.81 383	92.72 360	86.77 437	61.52 442	62.21 430	83.91 405	43.22 425	93.76 396	34.60 464	72.23 363	80.72 451

OpenMVS_ROB	68.52 20	73.02 397	69.57 404	83.37 384	80.54 439	71.82 382	93.60 339	88.22 428	62.37 438	61.98 431	83.15 412	35.31 449	95.47 340	45.08 456	75.88 339	82.82 434

MVS-HIRNet		71.36 407	67.00 413	84.46 371	90.58 329	69.74 401	79.15 451	87.74 431	46.09 464	61.96 432	50.50 468	45.14 419	95.64 332	53.74 435	88.11 246	88.00 381

tt0320-xc		69.70 410	65.27 422	82.99 387	84.33 418	71.92 380	89.56 398	82.08 457	50.11 461	61.87 433	77.50 437	30.48 459	92.34 410	60.30 408	51.20 449	84.71 423

test20.03		72.36 401	71.15 397	75.98 432	77.79 447	59.16 449	92.40 364	89.35 419	74.09 388	61.50 434	84.32 401	48.09 407	85.54 455	50.63 443	62.15 426	83.24 432

mvsany_test3		67.19 420	65.34 421	72.72 436	63.08 470	48.57 463	83.12 442	78.09 464	72.07 404	61.21 435	77.11 441	22.94 463	87.78 446	78.59 283	51.88 448	81.80 445

PM-MVS		69.32 415	66.93 414	76.49 429	73.60 461	55.84 455	85.91 428	79.32 463	74.72 383	61.09 436	78.18 435	21.76 464	91.10 426	70.86 357	56.90 434	82.51 437

TDRefinement		69.20 417	65.78 420	79.48 413	66.04 469	62.21 438	88.21 407	86.12 439	62.92 436	61.03 437	85.61 383	33.23 452	94.16 387	55.82 430	53.02 445	82.08 443

ambc				76.02 431	68.11 466	51.43 461	64.97 470	89.59 415		60.49 438	74.49 449	17.17 467	92.46 407	61.50 403	52.85 446	84.17 429

pmmvs-eth3d		73.59 391	70.66 399	82.38 394	76.40 454	73.38 360	89.39 400	89.43 418	72.69 401	60.34 439	77.79 436	46.43 417	91.26 425	66.42 382	57.06 433	82.51 437

test_vis1_rt		73.96 389	72.40 392	78.64 420	83.91 425	61.16 443	95.63 265	68.18 473	76.32 370	60.09 440	74.77 447	29.01 461	97.54 216	87.74 192	75.94 338	77.22 456

kuosan		73.55 392	72.39 393	77.01 426	89.68 351	66.72 420	85.24 434	93.44 345	67.76 423	60.04 441	83.40 410	71.90 238	84.25 457	45.34 455	54.75 437	80.06 452

K. test v3		73.62 390	71.59 396	79.69 412	82.98 431	59.85 448	90.85 385	88.83 423	77.13 361	58.90 442	82.11 415	43.62 422	91.72 420	65.83 384	54.10 442	87.50 393

EG-PatchMatch MVS		74.92 386	72.02 394	83.62 381	83.76 429	73.28 363	93.62 337	92.04 383	68.57 422	58.88 443	83.80 406	31.87 455	95.57 338	56.97 425	78.67 324	82.00 444

lessismore_v0				79.98 411	80.59 438	58.34 451		80.87 459		58.49 444	83.46 409	43.10 426	93.89 392	63.11 398	48.68 453	87.72 384

N_pmnet		61.30 425	60.20 428	64.60 445	84.32 419	17.00 486	91.67 376	10.98 484	61.77 441	58.45 445	78.55 434	49.89 402	91.83 419	42.27 460	63.94 420	84.97 421

TinyColmap		72.41 399	68.99 408	82.68 390	88.11 375	69.59 402	88.41 406	85.20 442	65.55 430	57.91 446	84.82 398	30.80 457	95.94 312	51.38 439	68.70 389	82.49 439

UnsupCasMVSNet_eth		73.25 395	70.57 400	81.30 402	77.53 448	66.33 421	87.24 418	93.89 314	80.38 306	57.90 447	81.59 419	42.91 428	90.56 430	65.18 387	48.51 454	87.01 399

FE-MVSNET		69.26 416	66.03 418	78.93 417	73.82 460	68.33 409	89.65 393	84.06 450	70.21 414	57.79 448	76.94 443	41.48 433	86.98 451	45.85 454	54.51 440	81.48 449

MIMVSNet1		69.44 414	66.65 416	77.84 422	76.48 453	62.84 436	87.42 416	88.97 422	66.96 429	57.75 449	79.72 432	32.77 454	85.83 454	46.32 452	63.42 422	84.85 422

pmmvs3		65.75 423	62.18 426	76.45 430	67.12 468	64.54 426	88.68 404	85.05 444	54.77 459	57.54 450	73.79 450	29.40 460	86.21 453	55.49 432	47.77 457	78.62 454

dongtai		69.47 413	68.98 409	70.93 437	86.87 387	58.45 450	88.19 408	93.18 359	63.98 434	56.04 451	80.17 429	70.97 252	79.24 464	33.46 465	47.94 456	75.09 458

test_f		64.01 424	62.13 427	69.65 439	63.00 471	45.30 470	83.66 441	80.68 460	61.30 444	55.70 452	72.62 455	14.23 470	84.64 456	69.84 362	58.11 431	79.00 453

new-patchmatchnet		68.85 418	65.93 419	77.61 424	73.57 462	63.94 431	90.11 390	88.73 426	71.62 408	55.08 453	73.60 451	40.84 437	87.22 450	51.35 441	48.49 455	81.67 448

UnsupCasMVSNet_bld		68.60 419	64.50 423	80.92 406	74.63 459	67.80 410	83.97 439	92.94 366	65.12 432	54.63 454	68.23 461	35.97 446	92.17 415	60.13 409	44.83 461	82.78 435

CMPMVS	54.94 21	75.71 384	74.56 379	79.17 416	79.69 441	55.98 454	89.59 395	93.30 354	60.28 448	53.85 455	89.07 323	47.68 413	96.33 295	76.55 307	81.02 307	85.22 419
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 422	63.18 424	75.18 435	76.27 455	61.74 440	83.79 440	84.66 445	56.64 457	51.57 456	71.85 459	31.29 456	87.93 443	49.98 445	62.55 424	75.86 457

test_method		56.77 427	54.53 431	63.49 447	76.49 452	40.70 473	75.68 459	74.24 467	19.47 475	48.73 457	71.89 458	19.31 465	65.80 475	57.46 422	47.51 458	83.97 430

MVStest1		66.93 421	63.01 425	78.69 418	78.56 444	71.43 388	85.51 432	86.81 435	49.79 462	48.57 458	84.15 403	53.46 387	83.31 458	43.14 459	37.15 469	81.34 450

YYNet1		73.53 394	70.43 401	82.85 389	84.52 417	71.73 384	91.69 375	91.37 395	67.63 424	46.79 459	81.21 423	55.04 380	90.43 431	55.93 428	59.70 430	86.38 406

MDA-MVSNet_test_wron		73.54 393	70.43 401	82.86 388	84.55 415	71.85 381	91.74 374	91.32 398	67.63 424	46.73 460	81.09 424	55.11 379	90.42 432	55.91 429	59.76 429	86.31 407

WB-MVS		57.26 426	56.22 429	60.39 451	69.29 463	35.91 478	86.39 426	70.06 471	59.84 452	46.46 461	72.71 454	51.18 393	78.11 465	15.19 475	34.89 470	67.14 464

SSC-MVS		56.01 429	54.96 430	59.17 452	68.42 465	34.13 479	84.98 436	69.23 472	58.08 456	45.36 462	71.67 460	50.30 401	77.46 466	14.28 476	32.33 471	65.91 465

MDA-MVSNet-bldmvs		71.45 405	67.94 412	81.98 398	85.33 409	68.50 408	92.35 365	88.76 425	70.40 412	42.99 463	81.96 417	46.57 416	91.31 424	48.75 450	54.39 441	86.11 410

APD_test1		56.56 428	53.58 432	65.50 442	67.93 467	46.51 467	77.24 458	72.95 468	38.09 466	42.75 464	75.17 446	13.38 471	82.78 461	40.19 462	54.53 439	67.23 463

DeepMVS_CX				64.06 446	78.53 445	43.26 471		68.11 475	69.94 417	38.55 465	76.14 445	18.53 466	79.34 463	43.72 457	41.62 466	69.57 461

LCM-MVSNet		52.52 432	48.24 435	65.35 443	47.63 480	41.45 472	72.55 464	83.62 454	31.75 468	37.66 466	57.92 466	9.19 477	76.76 468	49.26 447	44.60 462	77.84 455

test_vis3_rt		54.10 431	51.04 434	63.27 448	58.16 472	46.08 469	84.17 438	49.32 483	56.48 458	36.56 467	49.48 470	8.03 478	91.91 418	67.29 373	49.87 451	51.82 469

FPMVS		55.09 430	52.93 433	61.57 449	55.98 473	40.51 474	83.11 443	83.41 455	37.61 467	34.95 468	71.95 457	14.40 469	76.95 467	29.81 467	65.16 415	67.25 462

PMMVS2		50.90 434	46.31 437	64.67 444	55.53 474	46.67 466	77.30 457	71.02 470	40.89 465	34.16 469	59.32 464	9.83 476	76.14 470	40.09 463	28.63 472	71.21 459

testf1		45.70 436	42.41 438	55.58 453	53.29 477	40.02 475	68.96 468	62.67 477	27.45 470	29.85 470	61.58 462	5.98 479	73.83 472	28.49 470	43.46 464	52.90 467

APD_test2		45.70 436	42.41 438	55.58 453	53.29 477	40.02 475	68.96 468	62.67 477	27.45 470	29.85 470	61.58 462	5.98 479	73.83 472	28.49 470	43.46 464	52.90 467

tmp_tt		41.54 439	41.93 441	40.38 458	20.10 484	26.84 482	61.93 471	59.09 479	14.81 477	28.51 472	80.58 425	35.53 447	48.33 479	63.70 395	13.11 476	45.96 472

Gipuma		45.11 438	42.05 440	54.30 455	80.69 437	51.30 462	35.80 474	83.81 452	28.13 469	27.94 473	34.53 473	11.41 475	76.70 469	21.45 472	54.65 438	34.90 473
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 435	41.28 442	61.04 450	39.91 482	46.25 468	70.59 467	76.18 466	58.87 454	23.09 474	48.00 471	12.58 473	66.54 474	28.65 469	13.62 475	70.35 460

MVE	35.65 22	33.85 441	29.49 446	46.92 457	41.86 481	36.28 477	50.45 473	56.52 480	18.75 476	18.28 475	37.84 472	2.41 482	58.41 476	18.71 473	20.62 473	46.06 471
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 440	35.53 443	50.18 456	29.72 483	30.30 481	59.60 472	66.20 476	26.06 472	17.91 476	49.53 469	3.12 481	74.09 471	18.19 474	49.40 452	46.14 470
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 442	32.39 444	33.65 459	53.35 476	25.70 483	74.07 462	53.33 481	21.08 473	17.17 477	33.63 475	11.85 474	54.84 477	12.98 477	14.04 474	20.42 474

EMVS		31.70 443	31.45 445	32.48 460	50.72 479	23.95 484	74.78 461	52.30 482	20.36 474	16.08 478	31.48 476	12.80 472	53.60 478	11.39 478	13.10 477	19.88 475

wuyk23d		14.10 445	13.89 448	14.72 461	55.23 475	22.91 485	33.83 475	3.56 485	4.94 478	4.11 479	2.28 481	2.06 483	19.66 480	10.23 479	8.74 478	1.59 478

testmvs		9.92 446	12.94 449	0.84 463	0.65 485	0.29 488	93.78 334	0.39 486	0.42 479	2.85 480	15.84 479	0.17 485	0.30 482	2.18 480	0.21 479	1.91 477

test123		9.07 447	11.73 450	1.11 462	0.50 486	0.77 487	89.44 399	0.20 487	0.34 480	2.15 481	10.72 480	0.34 484	0.32 481	1.79 481	0.08 480	2.23 476

EGC-MVSNET		52.46 433	47.56 436	67.15 441	81.98 434	60.11 446	82.54 444	72.44 469	0.11 481	0.70 482	74.59 448	25.11 462	83.26 459	29.04 468	61.51 427	58.09 466

mmdepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

monomultidepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

test_blank		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet_test		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

DCPMVS		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

cdsmvs_eth3d_5k		21.43 444	28.57 447	0.00 464	0.00 487	0.00 489	0.00 476	95.93 175	0.00 482	0.00 483	97.66 93	63.57 309	0.00 483	0.00 482	0.00 481	0.00 479

pcd_1.5k_mvsjas		5.92 449	7.89 452	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	71.04 249	0.00 483	0.00 482	0.00 481	0.00 479

sosnet-low-res		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uncertanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

Regformer		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

ab-mvs-re		8.11 448	10.81 451	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	97.30 116	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

TestfortrainingZip							98.35 55

WAC-MVS						67.18 414								49.00 448

MSC_two_6792asdad				97.14 3	99.05 13	92.19 4		96.83 62					99.81 27	98.08 26	98.81 24	99.43 11

No_MVS				97.14 3	99.05 13	92.19 4		96.83 62					99.81 27	98.08 26	98.81 24	99.43 11

eth-test2					0.00 487

eth-test					0.00 487

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 16				98.54 29	92.06 3	99.84 17	99.11 5	99.37 1	99.74 1

save fliter					98.24 55	83.34 110	98.61 46	96.57 103	91.32 47

test_0728_SECOND				95.14 21	99.04 18	86.14 40	99.06 23	96.77 71					99.84 17	97.90 30	98.85 21	99.45 10

GSMVS																97.54 143

sam_mvs1												77.59 128				97.54 143

sam_mvs												75.35 184

MTGPA								96.33 136

test_post1							85.88 429				30.24 477	73.77 208	95.07 362	73.89 334

test_post											33.80 474	76.17 161	95.97 308

patchmatchnet-post											77.09 442	77.78 126	95.39 342

MTMP							97.53 115	68.16 474

gm-plane-assit					92.27 277	79.64 232			84.47 211		95.15 198		97.93 184	85.81 207

test9_res														96.00 56	99.03 13	98.31 74

agg_prior2														94.30 80	99.00 15	98.57 58

test_prior4						82.34 138	97.75 97

test_prior				93.09 97	98.68 30	81.91 154		96.40 125					99.06 124			98.29 76

新几何2							96.42 214

旧先验1					97.39 92	79.58 233		96.54 106			98.08 69	84.00 52			97.42 81	97.62 136

无先验							96.87 177	96.78 65	77.39 357				99.52 85	79.95 268		98.43 67

原ACMM2							96.84 178

testdata2													99.48 89	76.45 309

segment_acmp												82.69 66

testdata1							95.57 269		87.44 121

plane_prior7					91.86 301	77.55 306

plane_prior6					91.98 297	77.92 292						64.77 301

plane_prior5								94.69 251					97.30 240	87.08 198	82.82 296	90.96 313

plane_prior4											94.15 238

plane_prior2							97.18 143		89.89 70

plane_prior1					91.95 299

plane_prior						77.96 289	97.52 118		90.36 65						82.96 294

n2								0.00 488

nn								0.00 488

door-mid								79.75 462

test11								96.50 112

door								80.13 461

HQP5-MVS						78.48 268

BP-MVS														87.67 194

HQP3-MVS								94.80 243							83.01 292

HQP2-MVS												65.40 294

NP-MVS					92.04 295	78.22 279					94.56 221

ACMMP++_ref															78.45 329

ACMMP++															79.05 321

Test By Simon												71.65 241

High-resolution multi-view benchmark