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.

OPU-MVS				99.49 4	99.64 17	98.51 4	99.77 17				99.19 30	95.12 8	99.97 21	99.90 1	99.92 3	99.99 1

PC_three_1452									94.60 36	99.41 4	99.12 46	95.50 7	99.96 28	99.84 2	99.92 3	99.97 7

MM		97.76 10	97.39 19	98.86 5	98.30 93	96.83 7	99.81 11	99.13 9	97.66 2	98.29 39	98.96 66	85.84 127	99.90 50	99.72 3	98.80 91	99.85 30

SED-MVS		98.18 2	98.10 4	98.41 18	99.63 18	95.24 25	99.77 17	97.72 81	94.17 43	99.30 8	99.54 3	93.32 19	99.98 9	99.70 4	99.81 23	99.99 1

test_241102_TWO								97.72 81	94.17 43	99.23 10	99.54 3	93.14 24	99.98 9	99.70 4	99.82 19	99.99 1

IU-MVS					99.63 18	95.38 22		97.73 80	95.54 26	99.54 3				99.69 6	99.81 23	99.99 1

DVP-MVS		98.07 7	98.00 6	98.29 19	99.66 12	95.20 30	99.72 23	97.47 141	93.95 48	99.07 15	99.46 10	93.18 22	99.97 21	99.64 7	99.82 19	99.69 55
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				98.77 8	99.66 12	96.37 14	99.72 23	97.68 90					99.98 9	99.64 7	99.82 19	99.96 10

patch_mono-2		97.10 26	97.97 8	94.49 173	99.21 61	83.73 290	99.62 37	98.25 32	95.28 30	99.38 6	98.91 75	92.28 28	99.94 34	99.61 9	99.22 70	99.78 38

DPE-MVS		98.11 6	98.00 6	98.44 16	99.50 42	95.39 21	99.29 81	97.72 81	94.50 37	98.64 28	99.54 3	93.32 19	99.97 21	99.58 10	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_a		97.70 12	97.80 11	97.42 45	97.59 117	92.91 83	99.86 4	98.04 48	96.70 10	99.58 2	99.26 21	90.90 38	99.94 34	99.57 11	98.66 98	99.40 85

fmvsm_l_conf0.5_n		97.65 13	97.72 12	97.41 46	97.51 121	92.78 85	99.85 7	98.05 46	96.78 8	99.60 1	99.23 26	90.42 47	99.92 40	99.55 12	98.50 103	99.55 72

MSC_two_6792asdad				99.51 2	99.61 24	98.60 2		97.69 88					99.98 9	99.55 12	99.83 15	99.96 10

No_MVS				99.51 2	99.61 24	98.60 2		97.69 88					99.98 9	99.55 12	99.83 15	99.96 10

MVS_0304		97.53 14	97.15 22	98.67 11	97.30 128	96.52 12	99.60 38	98.88 14	97.14 4	97.21 66	98.94 72	86.89 102	99.91 45	99.43 15	98.91 86	99.59 71

DeepPCF-MVS	93.56 1	96.55 40	97.84 10	92.68 225	98.71 85	78.11 348	99.70 26	97.71 85	98.18 1	97.36 62	99.76 1	90.37 49	99.94 34	99.27 16	99.54 52	99.99 1

APDe-MVS		97.53 14	97.47 15	97.70 36	99.58 30	93.63 64	99.56 43	97.52 131	93.59 63	98.01 50	99.12 46	90.80 41	99.55 106	99.26 17	99.79 27	99.93 20
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS++		98.18 2	98.09 5	98.44 16	99.61 24	95.38 22	99.55 44	97.68 90	93.01 70	99.23 10	99.45 14	95.12 8	99.98 9	99.25 18	99.92 3	99.97 7

test_0728_THIRD									93.01 70	99.07 15	99.46 10	94.66 14	99.97 21	99.25 18	99.82 19	99.95 15

dcpmvs_2		95.67 70	96.18 45	94.12 190	98.82 81	84.22 283	97.37 259	95.45 293	90.70 118	95.77 101	98.63 101	90.47 45	98.68 164	99.20 20	99.22 70	99.45 81

fmvsm_s_conf0.5_n		96.19 49	96.49 36	95.30 144	97.37 125	89.16 165	99.86 4	98.47 25	95.68 23	98.87 22	99.15 39	82.44 186	99.92 40	99.14 21	97.43 127	96.83 218

test_fmvsmconf_n		96.78 34	96.84 29	96.61 85	95.99 189	90.25 136	99.90 2	98.13 42	96.68 11	98.42 34	98.92 74	85.34 137	99.88 54	99.12 22	99.08 73	99.70 52

test_fmvsm_n_1920		97.08 27	97.55 14	95.67 131	97.94 105	89.61 160	99.93 1	98.48 24	97.08 5	99.08 14	99.13 44	88.17 73	99.93 38	99.11 23	99.06 75	97.47 198

TSAR-MVS + GP.		96.95 29	96.91 26	97.07 57	98.88 79	91.62 103	99.58 41	96.54 214	95.09 32	96.84 76	98.63 101	91.16 31	99.77 85	99.04 24	96.42 144	99.81 33

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 10	99.70 26	97.98 53	97.18 3	95.96 94	99.33 19	92.62 26	100.00 1	98.99 25	99.93 1	99.98 6

CNVR-MVS		98.46 1	98.38 1	98.72 9	99.80 4	96.19 15	99.80 15	97.99 52	97.05 6	99.41 4	99.59 2	92.89 25	100.00 1	98.99 25	99.90 7	99.96 10

test_fmvsmconf0.1_n		95.94 59	95.79 62	96.40 99	92.42 293	89.92 152	99.79 16	96.85 197	96.53 15	97.22 65	98.67 97	82.71 178	99.84 69	98.92 27	98.98 80	99.43 84

fmvsm_s_conf0.5_n_a		95.97 56	96.19 43	95.31 143	96.51 164	89.01 171	99.81 11	98.39 27	95.46 28	99.19 13	99.16 36	81.44 200	99.91 45	98.83 28	96.97 136	97.01 214

CANet		97.00 28	96.49 36	98.55 12	98.86 80	96.10 16	99.83 9	97.52 131	95.90 19	97.21 66	98.90 76	82.66 179	99.93 38	98.71 29	98.80 91	99.63 64

9.14			96.87 27		99.34 50		99.50 51	97.49 138	89.41 161	98.59 30	99.43 16	89.78 56	99.69 91	98.69 30	99.62 44

SD-MVS		97.51 16	97.40 18	97.81 34	99.01 72	93.79 63	99.33 78	97.38 154	93.73 59	98.83 25	99.02 58	90.87 40	99.88 54	98.69 30	99.74 29	99.77 43
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
fmvsm_s_conf0.1_n		95.56 72	95.68 65	95.20 147	94.35 249	89.10 167	99.50 51	97.67 94	94.76 34	98.68 27	99.03 56	81.13 203	99.86 63	98.63 32	97.36 129	96.63 221

test9_res														98.60 33	99.87 9	99.90 22

PS-MVSNAJ		96.87 31	96.40 39	98.29 19	97.35 126	97.29 5	99.03 119	97.11 179	95.83 20	98.97 19	99.14 42	82.48 182	99.60 103	98.60 33	99.08 73	98.00 185

xiu_mvs_v2_base		96.66 36	96.17 48	98.11 27	97.11 143	96.96 6	99.01 122	97.04 186	95.51 27	98.86 23	99.11 50	82.19 190	99.36 130	98.59 35	98.14 111	98.00 185

train_agg		97.20 23	97.08 23	97.57 42	99.57 33	93.17 73	99.38 71	97.66 95	90.18 135	98.39 35	99.18 33	90.94 36	99.66 94	98.58 36	99.85 13	99.88 26

TSAR-MVS + MP.		97.44 18	97.46 16	97.39 48	99.12 65	93.49 69	98.52 172	97.50 136	94.46 38	98.99 17	98.64 99	91.58 30	99.08 148	98.49 37	99.83 15	99.60 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		97.22 22	96.92 25	98.12 26	99.11 66	94.88 35	99.44 62	97.45 144	89.60 153	98.70 26	99.42 17	90.42 47	99.72 89	98.47 38	99.65 38	99.77 43

PHI-MVS		96.65 37	96.46 38	97.21 54	99.34 50	91.77 100	99.70 26	98.05 46	86.48 247	98.05 47	99.20 29	89.33 59	99.96 28	98.38 39	99.62 44	99.90 22

test_fmvsmvis_n_1920		95.47 73	95.40 71	95.70 129	94.33 250	90.22 139	99.70 26	96.98 193	96.80 7	92.75 151	98.89 78	82.46 185	99.92 40	98.36 40	98.33 107	96.97 215

ZD-MVS					99.67 10	93.28 71		97.61 110	87.78 214	97.41 60	99.16 36	90.15 53	99.56 105	98.35 41	99.70 35

test_prior2							99.57 42		91.43 105	98.12 44	98.97 62	90.43 46		98.33 42	99.81 23

SMA-MVS		97.24 20	96.99 24	98.00 29	99.30 54	94.20 55	99.16 96	97.65 102	89.55 157	99.22 12	99.52 8	90.34 50	99.99 5	98.32 43	99.83 15	99.82 32
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
CHOSEN 280x420		96.80 33	96.85 28	96.66 84	97.85 108	94.42 51	94.76 328	98.36 29	92.50 81	95.62 105	97.52 148	97.92 1	97.38 236	98.31 44	98.80 91	98.20 179

test_fmvsmconf0.01_n		94.14 111	93.51 118	96.04 115	86.79 367	89.19 164	99.28 84	95.94 255	95.70 21	95.50 106	98.49 110	73.27 255	99.79 82	98.28 45	98.32 109	99.15 107

NCCC		98.12 5	98.11 3	98.13 24	99.76 6	94.46 48	99.81 11	97.88 57	96.54 13	98.84 24	99.46 10	92.55 27	99.98 9	98.25 46	99.93 1	99.94 18

fmvsm_s_conf0.1_n_a		95.16 82	95.15 77	95.18 148	92.06 299	88.94 175	99.29 81	97.53 127	94.46 38	98.98 18	98.99 60	79.99 208	99.85 67	98.24 47	96.86 138	96.73 219

MSP-MVS		97.77 9	98.18 2	96.53 92	99.54 36	90.14 141	99.41 68	97.70 86	95.46 28	98.60 29	99.19 30	95.71 4	99.49 112	98.15 48	99.85 13	99.95 15
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
ETV-MVS		96.00 53	96.00 53	96.00 118	96.56 160	91.05 119	99.63 36	96.61 206	93.26 68	97.39 61	98.30 119	86.62 109	98.13 185	98.07 49	97.57 121	98.82 140

MSLP-MVS++		97.50 17	97.45 17	97.63 38	99.65 16	93.21 72	99.70 26	98.13 42	94.61 35	97.78 55	99.46 10	89.85 55	99.81 79	97.97 50	99.91 6	99.88 26

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 41	93.58 65	99.16 96	97.44 147	90.08 140	98.59 30	99.07 51	89.06 61	99.42 123	97.92 51	99.66 37	99.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SteuartSystems-ACMMP		97.25 19	97.34 20	97.01 60	97.38 124	91.46 107	99.75 21	97.66 95	94.14 47	98.13 42	99.26 21	92.16 29	99.66 94	97.91 52	99.64 40	99.90 22
Skip Steuart: Steuart Systems R&D Blog.
test_vis1_n_1920		93.08 148	93.42 120	92.04 238	96.31 173	79.36 336	99.83 9	96.06 246	96.72 9	98.53 32	98.10 127	58.57 344	99.91 45	97.86 53	98.79 94	96.85 217

agg_prior2														97.84 54	99.87 9	99.91 21

mvsany_test1		94.57 103	95.09 80	92.98 216	95.84 193	82.07 312	98.76 147	95.24 306	92.87 77	96.45 87	98.71 94	84.81 144	99.15 141	97.68 55	95.49 162	97.73 190

HPM-MVS++		97.72 11	97.59 13	98.14 23	99.53 40	94.76 42	99.19 90	97.75 76	95.66 24	98.21 40	99.29 20	91.10 33	99.99 5	97.68 55	99.87 9	99.68 56

test_vis1_n		90.40 198	90.27 189	90.79 267	91.55 309	76.48 353	99.12 108	94.44 329	94.31 41	97.34 63	96.95 179	43.60 383	99.42 123	97.57 57	97.60 120	96.47 228

SR-MVS		96.13 50	96.16 50	96.07 114	99.42 47	89.04 169	98.59 167	97.33 158	90.44 129	96.84 76	99.12 46	86.75 105	99.41 126	97.47 58	99.44 58	99.76 45

PVSNet_BlendedMVS		93.36 137	93.20 127	93.84 201	98.77 83	91.61 104	99.47 55	98.04 48	91.44 104	94.21 129	92.63 278	83.50 157	99.87 58	97.41 59	83.37 274	90.05 337

PVSNet_Blended		95.94 59	95.66 66	96.75 76	98.77 83	91.61 104	99.88 3	98.04 48	93.64 62	94.21 129	97.76 135	83.50 157	99.87 58	97.41 59	97.75 119	98.79 143

test_fmvs1		92.35 161	92.94 136	90.57 272	97.19 135	75.43 357	99.55 44	94.97 313	95.20 31	96.82 79	97.57 147	59.59 342	99.84 69	97.30 61	98.29 110	96.46 229

EC-MVSNet		95.09 84	95.17 76	94.84 161	95.42 207	88.17 191	99.48 53	95.92 259	91.47 103	97.34 63	98.36 116	82.77 174	97.41 235	97.24 62	98.58 100	98.94 128

MVS_111021_HR		96.69 35	96.69 33	96.72 80	98.58 88	91.00 121	99.14 104	99.45 1	93.86 54	95.15 113	98.73 89	88.48 68	99.76 86	97.23 63	99.56 50	99.40 85

test_fmvs1_n		91.07 186	91.41 167	90.06 286	94.10 256	74.31 361	99.18 92	94.84 317	94.81 33	96.37 89	97.46 151	50.86 372	99.82 76	97.14 64	97.90 113	96.04 236

xiu_mvs_v1_base_debu		94.73 95	93.98 104	96.99 62	95.19 216	95.24 25	98.62 161	96.50 216	92.99 72	97.52 57	98.83 82	72.37 263	99.15 141	97.03 65	96.74 139	96.58 224

xiu_mvs_v1_base		94.73 95	93.98 104	96.99 62	95.19 216	95.24 25	98.62 161	96.50 216	92.99 72	97.52 57	98.83 82	72.37 263	99.15 141	97.03 65	96.74 139	96.58 224

xiu_mvs_v1_base_debi		94.73 95	93.98 104	96.99 62	95.19 216	95.24 25	98.62 161	96.50 216	92.99 72	97.52 57	98.83 82	72.37 263	99.15 141	97.03 65	96.74 139	96.58 224

lupinMVS		96.32 45	95.94 54	97.44 44	95.05 230	94.87 36	99.86 4	96.50 216	93.82 57	98.04 48	98.77 85	85.52 129	98.09 188	96.98 68	98.97 81	99.37 88

CS-MVS-test		95.98 55	96.34 41	94.90 158	98.06 102	87.66 203	99.69 33	96.10 242	93.66 60	98.35 38	99.05 54	86.28 118	97.66 218	96.96 69	98.90 87	99.37 88

MVS_111021_LR		95.78 65	95.94 54	95.28 145	98.19 98	87.69 200	98.80 141	99.26 7	93.39 65	95.04 115	98.69 96	84.09 151	99.76 86	96.96 69	99.06 75	98.38 166

VNet		95.08 85	94.26 93	97.55 43	98.07 101	93.88 61	98.68 153	98.73 18	90.33 132	97.16 70	97.43 153	79.19 216	99.53 109	96.91 71	91.85 204	99.24 100

test_cas_vis1_n_1920		93.86 121	93.74 114	94.22 186	95.39 210	86.08 246	99.73 22	96.07 245	96.38 17	97.19 69	97.78 134	65.46 319	99.86 63	96.71 72	98.92 85	96.73 219

CS-MVS		95.75 68	96.19 43	94.40 177	97.88 107	86.22 240	99.66 34	96.12 241	92.69 78	98.07 46	98.89 78	87.09 96	97.59 224	96.71 72	98.62 99	99.39 87

APD-MVS_3200maxsize		95.64 71	95.65 68	95.62 133	99.24 58	87.80 199	98.42 185	97.22 166	88.93 176	96.64 86	98.98 61	85.49 132	99.36 130	96.68 74	99.27 68	99.70 52

SR-MVS-dyc-post		95.75 68	95.86 57	95.41 139	99.22 59	87.26 219	98.40 190	97.21 167	89.63 151	96.67 84	98.97 62	86.73 107	99.36 130	96.62 75	99.31 65	99.60 67

RE-MVS-def			95.70 64		99.22 59	87.26 219	98.40 190	97.21 167	89.63 151	96.67 84	98.97 62	85.24 138		96.62 75	99.31 65	99.60 67

DeepC-MVS_fast	93.52 2	97.16 24	96.84 29	98.13 24	99.61 24	94.45 49	98.85 135	97.64 103	96.51 16	95.88 97	99.39 18	87.35 92	99.99 5	96.61 77	99.69 36	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		91.24 184	90.18 190	94.45 176	97.08 144	85.84 256	98.40 190	96.10 242	86.99 230	93.36 144	98.16 125	54.27 361	99.20 138	96.59 78	90.63 226	98.31 172

MP-MVS-pluss		95.80 64	95.30 72	97.29 50	98.95 76	92.66 86	98.59 167	97.14 175	88.95 174	93.12 147	99.25 23	85.62 128	99.94 34	96.56 79	99.48 54	99.28 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
diffmvs		94.59 102	94.19 96	95.81 125	95.54 203	90.69 128	98.70 151	95.68 280	91.61 99	95.96 94	97.81 131	80.11 207	98.06 190	96.52 80	95.76 157	98.67 152
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMMP_NAP		96.59 38	96.18 45	97.81 34	98.82 81	93.55 66	98.88 134	97.59 116	90.66 119	97.98 51	99.14 42	86.59 110	100.00 1	96.47 81	99.46 55	99.89 25

PAPM		96.35 43	95.94 54	97.58 40	94.10 256	95.25 24	98.93 129	98.17 37	94.26 42	93.94 134	98.72 91	89.68 57	97.88 200	96.36 82	99.29 67	99.62 66

MTAPA		96.09 51	95.80 61	96.96 67	99.29 55	91.19 111	97.23 267	97.45 144	92.58 79	94.39 127	99.24 25	86.43 116	99.99 5	96.22 83	99.40 62	99.71 51

alignmvs		95.77 66	95.00 82	98.06 28	97.35 126	95.68 19	99.71 25	97.50 136	91.50 102	96.16 92	98.61 103	86.28 118	99.00 150	96.19 84	91.74 206	99.51 77

canonicalmvs		95.02 86	93.96 107	98.20 21	97.53 120	95.92 17	98.71 149	96.19 236	91.78 97	95.86 99	98.49 110	79.53 213	99.03 149	96.12 85	91.42 218	99.66 60

DELS-MVS		97.12 25	96.60 35	98.68 10	98.03 103	96.57 11	99.84 8	97.84 61	96.36 18	95.20 112	98.24 121	88.17 73	99.83 73	96.11 86	99.60 48	99.64 62
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
jason		95.40 77	94.86 84	97.03 59	92.91 288	94.23 54	99.70 26	96.30 227	93.56 64	96.73 82	98.52 106	81.46 199	97.91 197	96.08 87	98.47 105	98.96 123
jason: jason.
CP-MVS		96.22 48	96.15 51	96.42 97	99.67 10	89.62 159	99.70 26	97.61 110	90.07 141	96.00 93	99.16 36	87.43 86	99.92 40	96.03 88	99.72 31	99.70 52

MP-MVS		96.00 53	95.82 58	96.54 91	99.47 46	90.13 143	99.36 75	97.41 151	90.64 122	95.49 107	98.95 69	85.51 131	99.98 9	96.00 89	99.59 49	99.52 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
h-mvs33		92.47 160	91.95 156	94.05 194	97.13 141	85.01 273	98.36 196	98.08 44	93.85 55	96.27 90	96.73 192	83.19 166	99.43 122	95.81 90	68.09 361	97.70 191

hse-mvs2		91.67 175	91.51 165	92.15 235	96.22 177	82.61 308	97.74 244	97.53 127	93.85 55	96.27 90	96.15 208	83.19 166	97.44 233	95.81 90	66.86 368	96.40 231

HFP-MVS		96.42 42	96.26 42	96.90 69	99.69 8	90.96 122	99.47 55	97.81 68	90.54 126	96.88 73	99.05 54	87.57 83	99.96 28	95.65 92	99.72 31	99.78 38

XVS		96.47 41	96.37 40	96.77 74	99.62 22	90.66 130	99.43 65	97.58 118	92.41 85	96.86 74	98.96 66	87.37 88	99.87 58	95.65 92	99.43 59	99.78 38

X-MVStestdata		90.69 195	88.66 218	96.77 74	99.62 22	90.66 130	99.43 65	97.58 118	92.41 85	96.86 74	29.59 407	87.37 88	99.87 58	95.65 92	99.43 59	99.78 38

ACMMPR		96.28 47	96.14 52	96.73 78	99.68 9	90.47 133	99.47 55	97.80 70	90.54 126	96.83 78	99.03 56	86.51 114	99.95 31	95.65 92	99.72 31	99.75 46

HPM-MVS		95.41 76	95.22 75	95.99 119	99.29 55	89.14 166	99.17 95	97.09 183	87.28 227	95.40 108	98.48 112	84.93 141	99.38 128	95.64 96	99.65 38	99.47 80
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_yl		95.27 80	94.60 87	97.28 51	98.53 89	92.98 79	99.05 116	98.70 19	86.76 239	94.65 122	97.74 137	87.78 80	99.44 119	95.57 97	92.61 188	99.44 82

DCV-MVSNet		95.27 80	94.60 87	97.28 51	98.53 89	92.98 79	99.05 116	98.70 19	86.76 239	94.65 122	97.74 137	87.78 80	99.44 119	95.57 97	92.61 188	99.44 82

region2R		96.30 46	96.17 48	96.70 81	99.70 7	90.31 135	99.46 59	97.66 95	90.55 125	97.07 71	99.07 51	86.85 103	99.97 21	95.43 99	99.74 29	99.81 33

EI-MVSNet-Vis-set		95.76 67	95.63 70	96.17 110	99.14 64	90.33 134	98.49 178	97.82 65	91.92 95	94.75 119	98.88 80	87.06 98	99.48 116	95.40 100	97.17 134	98.70 150

EPNet		96.82 32	96.68 34	97.25 53	98.65 86	93.10 75	99.48 53	98.76 15	96.54 13	97.84 54	98.22 122	87.49 85	99.66 94	95.35 101	97.78 118	99.00 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		97.24 20	96.83 31	98.47 15	99.79 5	95.71 18	99.07 112	99.06 10	94.45 40	96.42 88	98.70 95	88.81 65	99.74 88	95.35 101	99.86 12	99.97 7

HY-MVS	88.56 7	95.29 79	94.23 94	98.48 14	97.72 110	96.41 13	94.03 336	98.74 16	92.42 84	95.65 104	94.76 237	86.52 113	99.49 112	95.29 103	92.97 183	99.53 74

testing11		95.33 78	94.98 83	96.37 101	97.20 133	92.31 92	99.29 81	97.68 90	90.59 123	94.43 124	97.20 164	90.79 42	98.60 167	95.25 104	92.38 192	98.18 180

mPP-MVS		95.90 61	95.75 63	96.38 100	99.58 30	89.41 163	99.26 85	97.41 151	90.66 119	94.82 117	98.95 69	86.15 122	99.98 9	95.24 105	99.64 40	99.74 47

ZNCC-MVS		96.09 51	95.81 60	96.95 68	99.42 47	91.19 111	99.55 44	97.53 127	89.72 148	95.86 99	98.94 72	86.59 110	99.97 21	95.13 106	99.56 50	99.68 56

GG-mvs-BLEND				96.98 65	96.53 162	94.81 41	87.20 376	97.74 77		93.91 135	96.40 201	96.56 2	96.94 252	95.08 107	98.95 84	99.20 104

EIA-MVS		95.11 83	95.27 74	94.64 170	96.34 172	86.51 228	99.59 40	96.62 205	92.51 80	94.08 132	98.64 99	86.05 123	98.24 182	95.07 108	98.50 103	99.18 105

DeepC-MVS	91.02 4	94.56 104	93.92 109	96.46 94	97.16 138	90.76 126	98.39 194	97.11 179	93.92 50	88.66 206	98.33 117	78.14 224	99.85 67	95.02 109	98.57 101	98.78 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs_mvg		94.00 114	93.33 123	96.03 116	95.22 214	90.90 124	99.09 110	95.99 248	90.58 124	91.55 169	97.37 155	79.91 209	98.06 190	95.01 110	95.22 164	99.13 110
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS		95.97 56	95.11 79	98.54 13	97.62 114	96.65 9	99.44 62	98.74 16	92.25 89	95.21 111	98.46 115	86.56 112	99.46 118	95.00 111	92.69 187	99.50 78

CSCG		94.87 90	94.71 85	95.36 140	99.54 36	86.49 229	99.34 77	98.15 40	82.71 309	90.15 193	99.25 23	89.48 58	99.86 63	94.97 112	98.82 90	99.72 50

EI-MVSNet-UG-set		95.43 74	95.29 73	95.86 124	99.07 70	89.87 153	98.43 184	97.80 70	91.78 97	94.11 131	98.77 85	86.25 120	99.48 116	94.95 113	96.45 143	98.22 177

CPTT-MVS		94.60 101	94.43 91	95.09 151	99.66 12	86.85 224	99.44 62	97.47 141	83.22 298	94.34 128	98.96 66	82.50 180	99.55 106	94.81 114	99.50 53	98.88 133

PVSNet_0	83.28 16	87.31 259	85.16 274	93.74 205	94.78 240	84.59 278	98.91 132	98.69 21	89.81 147	78.59 324	93.23 268	61.95 333	99.34 134	94.75 115	55.72 388	97.30 202

CLD-MVS		91.06 187	90.71 183	92.10 236	94.05 260	86.10 245	99.55 44	96.29 230	94.16 45	84.70 242	97.17 168	69.62 284	97.82 204	94.74 116	86.08 249	92.39 260
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
casdiffmvs		93.98 116	93.43 119	95.61 134	95.07 229	89.86 154	98.80 141	95.84 272	90.98 113	92.74 152	97.66 142	79.71 210	98.10 187	94.72 117	95.37 163	98.87 135
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
VDDNet		90.08 208	88.54 224	94.69 167	94.41 248	87.68 201	98.21 208	96.40 221	76.21 356	93.33 145	97.75 136	54.93 359	98.77 157	94.71 118	90.96 221	97.61 196

iter_conf05		93.48 131	93.18 128	94.39 180	97.15 139	94.17 57	99.30 80	92.97 353	92.38 88	86.70 228	95.42 224	95.67 5	96.59 265	94.67 119	84.32 263	92.39 260

CDPH-MVS		96.56 39	96.18 45	97.70 36	99.59 28	93.92 60	99.13 107	97.44 147	89.02 171	97.90 53	99.22 27	88.90 64	99.49 112	94.63 120	99.79 27	99.68 56

GST-MVS		95.97 56	95.66 66	96.90 69	99.49 45	91.22 109	99.45 61	97.48 139	89.69 149	95.89 96	98.72 91	86.37 117	99.95 31	94.62 121	99.22 70	99.52 75

Effi-MVS+		93.87 120	93.15 129	96.02 117	95.79 194	90.76 126	96.70 289	95.78 273	86.98 233	95.71 102	97.17 168	79.58 211	98.01 195	94.57 122	96.09 152	99.31 94

LFMVS		92.23 166	90.84 179	96.42 97	98.24 95	91.08 118	98.24 205	96.22 233	83.39 296	94.74 120	98.31 118	61.12 337	98.85 154	94.45 123	92.82 184	99.32 93

ET-MVSNet_ETH3D		92.56 158	91.45 166	95.88 123	96.39 170	94.13 58	99.46 59	96.97 194	92.18 91	66.94 376	98.29 120	94.65 15	94.28 349	94.34 124	83.82 270	99.24 100

baseline		93.91 118	93.30 124	95.72 128	95.10 227	90.07 145	97.48 255	95.91 264	91.03 111	93.54 142	97.68 140	79.58 211	98.02 194	94.27 125	95.14 165	99.08 115

SDMVSNet		91.09 185	89.91 193	94.65 168	96.80 153	90.54 132	97.78 239	97.81 68	88.34 195	85.73 232	95.26 228	66.44 310	98.26 180	94.25 126	86.75 241	95.14 240

PAPR		96.35 43	95.82 58	97.94 31	99.63 18	94.19 56	99.42 67	97.55 123	92.43 82	93.82 138	99.12 46	87.30 93	99.91 45	94.02 127	99.06 75	99.74 47

iter_conf_final		93.22 143	93.04 132	93.76 203	97.03 147	92.22 95	99.05 116	93.31 350	92.11 93	86.93 223	95.42 224	95.01 10	96.59 265	93.98 128	84.48 260	92.46 259

PGM-MVS		95.85 62	95.65 68	96.45 95	99.50 42	89.77 156	98.22 206	98.90 13	89.19 166	96.74 81	98.95 69	85.91 126	99.92 40	93.94 129	99.46 55	99.66 60

gg-mvs-nofinetune		90.00 209	87.71 236	96.89 73	96.15 182	94.69 45	85.15 382	97.74 77	68.32 382	92.97 150	60.16 395	96.10 3	96.84 255	93.89 130	98.87 88	99.14 108

MVS		93.92 117	92.28 147	98.83 7	95.69 198	96.82 8	96.22 304	98.17 37	84.89 273	84.34 247	98.61 103	79.32 215	99.83 73	93.88 131	99.43 59	99.86 29

旧先验2							98.67 155		85.75 258	98.96 20			98.97 152	93.84 132

ACMMP		94.67 99	94.30 92	95.79 126	99.25 57	88.13 193	98.41 187	98.67 22	90.38 131	91.43 171	98.72 91	82.22 189	99.95 31	93.83 133	95.76 157	99.29 96
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
BP-MVS														93.82 134

HQP-MVS		91.50 176	91.23 170	92.29 230	93.95 261	86.39 233	99.16 96	96.37 223	93.92 50	87.57 214	96.67 195	73.34 252	97.77 208	93.82 134	86.29 244	92.72 254

DP-MVS Recon		95.85 62	95.15 77	97.95 30	99.87 2	94.38 52	99.60 38	97.48 139	86.58 242	94.42 125	99.13 44	87.36 91	99.98 9	93.64 136	98.33 107	99.48 79

CHOSEN 1792x2688		94.35 108	93.82 112	95.95 121	97.40 123	88.74 183	98.41 187	98.27 31	92.18 91	91.43 171	96.40 201	78.88 217	99.81 79	93.59 137	97.81 115	99.30 95

testing91		94.88 88	94.44 90	96.21 106	97.19 135	91.90 99	99.23 87	97.66 95	89.91 144	93.66 140	97.05 175	90.21 52	98.50 169	93.52 138	91.53 215	98.25 173

testing99		94.88 88	94.45 89	96.17 110	97.20 133	91.91 98	99.20 89	97.66 95	89.95 143	93.68 139	97.06 173	90.28 51	98.50 169	93.52 138	91.54 212	98.12 182

cascas		90.93 190	89.33 204	95.76 127	95.69 198	93.03 78	98.99 124	96.59 208	80.49 336	86.79 227	94.45 241	65.23 320	98.60 167	93.52 138	92.18 199	95.66 239

HQP_MVS		91.26 181	90.95 176	92.16 234	93.84 268	86.07 248	99.02 120	96.30 227	93.38 66	86.99 221	96.52 197	72.92 258	97.75 214	93.46 141	86.17 247	92.67 256

plane_prior5								96.30 227					97.75 214	93.46 141	86.17 247	92.67 256

PVSNet_Blended_VisFu		94.67 99	94.11 99	96.34 103	97.14 140	91.10 116	99.32 79	97.43 149	92.10 94	91.53 170	96.38 204	83.29 163	99.68 92	93.42 143	96.37 145	98.25 173

AdaColmap		93.82 122	93.06 130	96.10 113	99.88 1	89.07 168	98.33 198	97.55 123	86.81 238	90.39 190	98.65 98	75.09 237	99.98 9	93.32 144	97.53 124	99.26 99

HyFIR lowres test		93.68 127	93.29 125	94.87 159	97.57 119	88.04 195	98.18 210	98.47 25	87.57 222	91.24 176	95.05 231	85.49 132	97.46 231	93.22 145	92.82 184	99.10 113

HPM-MVS_fast		94.89 87	94.62 86	95.70 129	99.11 66	88.44 189	99.14 104	97.11 179	85.82 255	95.69 103	98.47 113	83.46 159	99.32 135	93.16 146	99.63 43	99.35 90

PMMVS		93.62 130	93.90 110	92.79 220	96.79 155	81.40 319	98.85 135	96.81 198	91.25 109	96.82 79	98.15 126	77.02 230	98.13 185	93.15 147	96.30 148	98.83 139

LCM-MVSNet-Re		88.59 239	88.61 219	88.51 319	95.53 204	72.68 369	96.85 281	88.43 388	88.45 188	73.14 353	90.63 317	75.82 233	94.38 348	92.95 148	95.71 159	98.48 161

EPP-MVSNet		93.75 124	93.67 115	94.01 196	95.86 192	85.70 258	98.67 155	97.66 95	84.46 278	91.36 174	97.18 167	91.16 31	97.79 206	92.93 149	93.75 176	98.53 158

CostFormer		92.89 150	92.48 145	94.12 190	94.99 232	85.89 253	92.89 346	97.00 192	86.98 233	95.00 116	90.78 309	90.05 54	97.51 229	92.92 150	91.73 207	98.96 123

XVG-OURS-SEG-HR		90.95 189	90.66 185	91.83 241	95.18 219	81.14 326	95.92 311	95.92 259	88.40 192	90.33 191	97.85 129	70.66 278	99.38 128	92.83 151	88.83 233	94.98 243

mvsmamba		89.99 210	89.42 201	91.69 248	90.64 322	86.34 236	98.40 190	92.27 362	91.01 112	84.80 241	94.93 232	76.12 232	96.51 273	92.81 152	83.84 267	92.21 270

sss		94.85 91	93.94 108	97.58 40	96.43 167	94.09 59	98.93 129	99.16 8	89.50 158	95.27 110	97.85 129	81.50 197	99.65 98	92.79 153	94.02 174	98.99 120

test_vis1_rt		81.31 323	80.05 326	85.11 342	91.29 314	70.66 375	98.98 126	77.39 403	85.76 257	68.80 367	82.40 374	36.56 390	99.44 119	92.67 154	86.55 243	85.24 378

MAR-MVS		94.43 107	94.09 100	95.45 137	99.10 68	87.47 209	98.39 194	97.79 72	88.37 193	94.02 133	99.17 35	78.64 222	99.91 45	92.48 155	98.85 89	98.96 123
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
API-MVS		94.78 93	94.18 98	96.59 87	99.21 61	90.06 148	98.80 141	97.78 73	83.59 293	93.85 136	99.21 28	83.79 154	99.97 21	92.37 156	99.00 79	99.74 47

nrg030		90.23 202	88.87 212	94.32 182	91.53 310	93.54 67	98.79 145	95.89 267	88.12 203	84.55 244	94.61 239	78.80 220	96.88 254	92.35 157	75.21 316	92.53 258

OMC-MVS		93.90 119	93.62 116	94.73 166	98.63 87	87.00 222	98.04 226	96.56 212	92.19 90	92.46 154	98.73 89	79.49 214	99.14 145	92.16 158	94.34 172	98.03 184

testing222		94.48 106	94.00 103	95.95 121	97.30 128	92.27 93	98.82 138	97.92 55	89.20 165	94.82 117	97.26 159	87.13 95	97.32 239	91.95 159	91.56 210	98.25 173

1314		93.44 133	91.98 155	97.84 32	95.24 212	94.38 52	96.22 304	97.92 55	90.18 135	82.28 276	97.71 139	77.63 227	99.80 81	91.94 160	98.67 97	99.34 92

DPM-MVS		97.86 8	97.25 21	99.68 1	98.25 94	99.10 1	99.76 20	97.78 73	96.61 12	98.15 41	99.53 7	93.62 17	100.00 1	91.79 161	99.80 26	99.94 18

mvs_anonymous		92.50 159	91.65 162	95.06 152	96.60 159	89.64 158	97.06 273	96.44 220	86.64 241	84.14 248	93.93 250	82.49 181	96.17 299	91.47 162	96.08 153	99.35 90

baseline2		94.04 113	93.80 113	94.74 165	93.07 287	90.25 136	98.12 216	98.16 39	89.86 145	86.53 229	96.95 179	95.56 6	98.05 192	91.44 163	94.53 169	95.93 237

IB-MVS	89.43 6	92.12 168	90.83 181	95.98 120	95.40 209	90.78 125	99.81 11	98.06 45	91.23 110	85.63 235	93.66 258	90.63 43	98.78 156	91.22 164	71.85 351	98.36 169
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
ab-mvs		91.05 188	89.17 206	96.69 82	95.96 190	91.72 102	92.62 350	97.23 165	85.61 259	89.74 198	93.89 252	68.55 289	99.42 123	91.09 165	87.84 236	98.92 131

XVG-OURS		90.83 191	90.49 187	91.86 240	95.23 213	81.25 323	95.79 319	95.92 259	88.96 173	90.02 195	98.03 128	71.60 272	99.35 133	91.06 166	87.78 237	94.98 243

3Dnovator	87.35 11	93.17 146	91.77 160	97.37 49	95.41 208	93.07 76	98.82 138	97.85 60	91.53 101	82.56 268	97.58 146	71.97 267	99.82 76	91.01 167	99.23 69	99.22 103

VPA-MVSNet		89.10 221	87.66 237	93.45 208	92.56 290	91.02 120	97.97 230	98.32 30	86.92 235	86.03 231	92.01 285	68.84 288	97.10 246	90.92 168	75.34 315	92.23 268

PAPM_NR		95.43 74	95.05 81	96.57 90	99.42 47	90.14 141	98.58 169	97.51 133	90.65 121	92.44 155	98.90 76	87.77 82	99.90 50	90.88 169	99.32 64	99.68 56

3Dnovator+	87.72 8	93.43 134	91.84 158	98.17 22	95.73 197	95.08 32	98.92 131	97.04 186	91.42 106	81.48 293	97.60 144	74.60 240	99.79 82	90.84 170	98.97 81	99.64 62

test_fmvs2		85.10 293	85.45 271	84.02 350	89.85 332	65.63 384	98.49 178	92.59 358	90.45 128	85.43 238	93.32 264	43.94 381	96.59 265	90.81 171	84.19 264	89.85 341

gm-plane-assit					94.69 242	88.14 192			88.22 200		97.20 164		98.29 178	90.79 172

MVSTER		92.71 152	92.32 146	93.86 200	97.29 130	92.95 82	99.01 122	96.59 208	90.09 139	85.51 236	94.00 248	94.61 16	96.56 269	90.77 173	83.03 277	92.08 277

ETVMVS		94.50 105	93.90 110	96.31 104	97.48 122	92.98 79	99.07 112	97.86 59	88.09 204	94.40 126	96.90 182	88.35 70	97.28 240	90.72 174	92.25 198	98.66 155

ACMP	87.39 10	88.71 235	88.24 228	90.12 285	93.91 266	81.06 327	98.50 176	95.67 281	89.43 160	80.37 302	95.55 220	65.67 314	97.83 203	90.55 175	84.51 258	91.47 295
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT_MVS		88.91 225	88.56 222	89.93 291	90.31 326	81.61 316	98.08 223	96.38 222	89.30 162	82.41 273	94.84 235	73.15 256	96.04 305	90.38 176	82.23 284	92.15 273

ECVR-MVS		92.29 163	91.33 168	95.15 149	96.41 168	87.84 198	98.10 219	94.84 317	90.82 116	91.42 173	97.28 157	65.61 316	98.49 171	90.33 177	97.19 132	99.12 111

testdata				95.26 146	98.20 96	87.28 216		97.60 112	85.21 264	98.48 33	99.15 39	88.15 75	98.72 162	90.29 178	99.45 57	99.78 38

LPG-MVS_test		88.86 227	88.47 225	90.06 286	93.35 282	80.95 328	98.22 206	95.94 255	87.73 218	83.17 258	96.11 210	66.28 311	97.77 208	90.19 179	85.19 254	91.46 296

LGP-MVS_train				90.06 286	93.35 282	80.95 328		95.94 255	87.73 218	83.17 258	96.11 210	66.28 311	97.77 208	90.19 179	85.19 254	91.46 296

MVSFormer		94.71 98	94.08 101	96.61 85	95.05 230	94.87 36	97.77 241	96.17 238	86.84 236	98.04 48	98.52 106	85.52 129	95.99 306	89.83 181	98.97 81	98.96 123

test_djsdf		88.26 244	87.73 235	89.84 294	88.05 356	82.21 310	97.77 241	96.17 238	86.84 236	82.41 273	91.95 289	72.07 266	95.99 306	89.83 181	84.50 259	91.32 303

test2506		94.80 92	94.21 95	96.58 88	96.41 168	92.18 96	98.01 227	98.96 11	90.82 116	93.46 143	97.28 157	85.92 124	98.45 172	89.82 183	97.19 132	99.12 111

tpmrst		92.78 151	92.16 150	94.65 168	96.27 175	87.45 210	91.83 355	97.10 182	89.10 170	94.68 121	90.69 313	88.22 72	97.73 216	89.78 184	91.80 205	98.77 146

PLC	91.07 3	94.23 110	94.01 102	94.87 159	99.17 63	87.49 208	99.25 86	96.55 213	88.43 191	91.26 175	98.21 124	85.92 124	99.86 63	89.77 185	97.57 121	97.24 205
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		92.12 168	91.19 171	94.94 157	96.15 182	87.36 213	98.12 216	94.84 317	90.85 115	90.97 178	97.26 159	65.60 317	98.37 174	89.74 186	97.14 135	99.07 117

CDS-MVSNet		93.47 132	93.04 132	94.76 163	94.75 241	89.45 162	98.82 138	97.03 188	87.91 211	90.97 178	96.48 199	89.06 61	96.36 283	89.50 187	92.81 186	98.49 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu		89.97 211	90.68 184	87.81 324	95.15 220	71.98 371	97.87 235	95.40 297	91.92 95	87.57 214	91.44 297	74.27 246	96.84 255	89.45 188	93.10 182	94.60 245

jajsoiax		87.35 258	86.51 255	89.87 292	87.75 361	81.74 314	97.03 274	95.98 249	88.47 185	80.15 305	93.80 254	61.47 334	96.36 283	89.44 189	84.47 261	91.50 294

mvs_tets		87.09 261	86.22 258	89.71 298	87.87 357	81.39 320	96.73 288	95.90 265	88.19 201	79.99 307	93.61 259	59.96 341	96.31 291	89.40 190	84.34 262	91.43 298

PS-MVSNAJss		89.54 217	89.05 209	91.00 260	88.77 347	84.36 281	97.39 256	95.97 250	88.47 185	81.88 286	93.80 254	82.48 182	96.50 274	89.34 191	83.34 276	92.15 273

VPNet		88.30 242	86.57 253	93.49 207	91.95 302	91.35 108	98.18 210	97.20 171	88.61 182	84.52 245	94.89 233	62.21 332	96.76 260	89.34 191	72.26 348	92.36 262

114514_t		94.06 112	93.05 131	97.06 58	99.08 69	92.26 94	98.97 127	97.01 191	82.58 311	92.57 153	98.22 122	80.68 205	99.30 136	89.34 191	99.02 78	99.63 64

OPM-MVS		89.76 213	89.15 207	91.57 250	90.53 323	85.58 261	98.11 218	95.93 258	92.88 76	86.05 230	96.47 200	67.06 305	97.87 201	89.29 194	86.08 249	91.26 306
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_Test		93.67 128	92.67 141	96.69 82	96.72 157	92.66 86	97.22 268	96.03 247	87.69 220	95.12 114	94.03 246	81.55 196	98.28 179	89.17 195	96.46 142	99.14 108

BH-w/o		92.32 162	91.79 159	93.91 199	96.85 150	86.18 242	99.11 109	95.74 276	88.13 202	84.81 240	97.00 177	77.26 229	97.91 197	89.16 196	98.03 112	97.64 192

TAMVS		92.62 155	92.09 153	94.20 187	94.10 256	87.68 201	98.41 187	96.97 194	87.53 224	89.74 198	96.04 213	84.77 146	96.49 276	88.97 197	92.31 195	98.42 162

CNLPA		93.64 129	92.74 139	96.36 102	98.96 75	90.01 151	99.19 90	95.89 267	86.22 250	89.40 201	98.85 81	80.66 206	99.84 69	88.57 198	96.92 137	99.24 100

baseline1		92.61 156	91.28 169	96.58 88	97.05 146	94.63 46	97.72 245	96.20 234	89.82 146	88.56 207	96.85 186	86.85 103	97.82 204	88.42 199	80.10 293	97.30 202

CANet_DTU		94.31 109	93.35 122	97.20 55	97.03 147	94.71 44	98.62 161	95.54 288	95.61 25	97.21 66	98.47 113	71.88 268	99.84 69	88.38 200	97.46 126	97.04 212

thisisatest0515		94.75 94	94.19 96	96.43 96	96.13 187	92.64 89	99.47 55	97.60 112	87.55 223	93.17 146	97.59 145	94.71 13	98.42 173	88.28 201	93.20 180	98.24 176

原ACMM1				96.18 108	99.03 71	90.08 144		97.63 107	88.98 172	97.00 72	98.97 62	88.14 76	99.71 90	88.23 202	99.62 44	98.76 147

UGNet		91.91 172	90.85 178	95.10 150	97.06 145	88.69 184	98.01 227	98.24 34	92.41 85	92.39 156	93.61 259	60.52 339	99.68 92	88.14 203	97.25 130	96.92 216
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
AUN-MVS		90.17 205	89.50 198	92.19 233	96.21 178	82.67 306	97.76 243	97.53 127	88.05 205	91.67 164	96.15 208	83.10 168	97.47 230	88.11 204	66.91 367	96.43 230

Vis-MVSNet (Re-imp)		93.26 142	93.00 135	94.06 193	96.14 184	86.71 227	98.68 153	96.70 201	88.30 197	89.71 200	97.64 143	85.43 135	96.39 281	88.06 205	96.32 146	99.08 115

bld_raw_dy_0_64		87.82 247	86.71 252	91.15 256	89.54 338	85.61 259	97.37 259	89.16 386	89.26 163	83.42 254	94.50 240	65.79 313	96.18 297	88.00 206	83.37 274	91.67 284

PVSNet	87.13 12	93.69 125	92.83 138	96.28 105	97.99 104	90.22 139	99.38 71	98.93 12	91.42 106	93.66 140	97.68 140	71.29 275	99.64 100	87.94 207	97.20 131	98.98 121

FIs		90.70 194	89.87 194	93.18 212	92.29 294	91.12 114	98.17 212	98.25 32	89.11 169	83.44 253	94.82 236	82.26 188	96.17 299	87.76 208	82.76 279	92.25 266

tpm2		91.77 173	91.09 172	93.82 202	94.83 239	85.56 262	92.51 351	97.16 174	84.00 284	93.83 137	90.66 315	87.54 84	97.17 242	87.73 209	91.55 211	98.72 148

无先验							98.52 172	97.82 65	87.20 228				99.90 50	87.64 210		99.85 30

Anonymous202405211		88.84 228	87.03 247	94.27 183	98.14 100	84.18 284	98.44 183	95.58 286	76.79 355	89.34 202	96.88 185	53.42 364	99.54 108	87.53 211	87.12 240	99.09 114

IS-MVSNet		93.00 149	92.51 144	94.49 173	96.14 184	87.36 213	98.31 201	95.70 278	88.58 184	90.17 192	97.50 149	83.02 170	97.22 241	87.06 212	96.07 154	98.90 132

MDTV_nov1_ep13_2view						91.17 113	91.38 362		87.45 225	93.08 148		86.67 108		87.02 213		98.95 127

Anonymous20240529		87.66 255	85.58 268	93.92 198	97.59 117	85.01 273	98.13 214	97.13 177	66.69 387	88.47 208	96.01 214	55.09 358	99.51 110	87.00 214	84.12 265	97.23 206

UniMVSNet_NR-MVSNet		89.60 215	88.55 223	92.75 222	92.17 297	90.07 145	98.74 148	98.15 40	88.37 193	83.21 256	93.98 249	82.86 172	95.93 310	86.95 215	72.47 345	92.25 266

DU-MVS		88.83 230	87.51 238	92.79 220	91.46 311	90.07 145	98.71 149	97.62 109	88.87 178	83.21 256	93.68 256	74.63 238	95.93 310	86.95 215	72.47 345	92.36 262

FA-MVS(test-final)		92.22 167	91.08 173	95.64 132	96.05 188	88.98 172	91.60 359	97.25 161	86.99 230	91.84 160	92.12 281	83.03 169	99.00 150	86.91 217	93.91 175	98.93 129

ACMM	86.95 13	88.77 233	88.22 229	90.43 277	93.61 274	81.34 321	98.50 176	95.92 259	87.88 212	83.85 251	95.20 230	67.20 303	97.89 199	86.90 218	84.90 256	92.06 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		89.50 218	88.32 227	93.03 214	92.21 296	90.96 122	98.90 133	98.39 27	89.13 168	83.22 255	92.03 283	81.69 195	96.34 289	86.79 219	72.53 344	91.81 282

BH-untuned		91.46 178	90.84 179	93.33 210	96.51 164	84.83 276	98.84 137	95.50 290	86.44 249	83.50 252	96.70 193	75.49 236	97.77 208	86.78 220	97.81 115	97.40 199

mvsany_test3		75.85 346	74.52 348	79.83 363	73.53 395	60.64 388	91.73 357	87.87 390	83.91 287	70.55 362	82.52 373	31.12 392	93.66 352	86.66 221	62.83 374	85.19 379

miper_enhance_ethall		90.33 200	89.70 195	92.22 231	97.12 142	88.93 177	98.35 197	95.96 252	88.60 183	83.14 260	92.33 280	87.38 87	96.18 297	86.49 222	77.89 302	91.55 293

thisisatest0530		94.00 114	93.52 117	95.43 138	95.76 196	90.02 150	98.99 124	97.60 112	86.58 242	91.74 162	97.36 156	94.78 12	98.34 175	86.37 223	92.48 191	97.94 187

UWE-MVS		93.18 144	93.40 121	92.50 228	96.56 160	83.55 292	98.09 222	97.84 61	89.50 158	91.72 163	96.23 207	91.08 34	96.70 261	86.28 224	93.33 179	97.26 204

TESTMET0.1,1		93.82 122	93.26 126	95.49 136	95.21 215	90.25 136	99.15 101	97.54 126	89.18 167	91.79 161	94.87 234	89.13 60	97.63 221	86.21 225	96.29 149	98.60 156

anonymousdsp		86.69 267	85.75 266	89.53 303	86.46 369	82.94 299	96.39 295	95.71 277	83.97 285	79.63 312	90.70 312	68.85 287	95.94 309	86.01 226	84.02 266	89.72 343

F-COLMAP		92.07 170	91.75 161	93.02 215	98.16 99	82.89 302	98.79 145	95.97 250	86.54 244	87.92 211	97.80 132	78.69 221	99.65 98	85.97 227	95.93 156	96.53 227

cl22		89.57 216	88.79 215	91.91 239	97.94 105	87.62 204	97.98 229	96.51 215	85.03 269	82.37 275	91.79 290	83.65 155	96.50 274	85.96 228	77.89 302	91.61 290

test-LLR		93.11 147	92.68 140	94.40 177	94.94 235	87.27 217	99.15 101	97.25 161	90.21 133	91.57 166	94.04 244	84.89 142	97.58 225	85.94 229	96.13 150	98.36 169

test-mter		93.27 141	92.89 137	94.40 177	94.94 235	87.27 217	99.15 101	97.25 161	88.95 174	91.57 166	94.04 244	88.03 78	97.58 225	85.94 229	96.13 150	98.36 169

FC-MVSNet-test		90.22 203	89.40 202	92.67 226	91.78 306	89.86 154	97.89 232	98.22 35	88.81 179	82.96 261	94.66 238	81.90 194	95.96 308	85.89 231	82.52 282	92.20 272

Vis-MVSNet		92.64 154	91.85 157	95.03 155	95.12 223	88.23 190	98.48 180	96.81 198	91.61 99	92.16 159	97.22 163	71.58 273	98.00 196	85.85 232	97.81 115	98.88 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sd_testset		89.23 219	88.05 233	92.74 223	96.80 153	85.33 266	95.85 317	97.03 188	88.34 195	85.73 232	95.26 228	61.12 337	97.76 213	85.61 233	86.75 241	95.14 240

test_fmvs3		75.09 347	75.19 344	74.81 368	77.45 391	54.08 394	95.93 310	90.64 378	82.51 314	73.29 351	81.19 379	22.29 397	86.29 390	85.50 234	67.89 363	84.06 381

WR-MVS		88.54 240	87.22 245	92.52 227	91.93 304	89.50 161	98.56 170	97.84 61	86.99 230	81.87 287	93.81 253	74.25 247	95.92 312	85.29 235	74.43 325	92.12 275

XXY-MVS		87.75 251	86.02 261	92.95 218	90.46 324	89.70 157	97.71 247	95.90 265	84.02 283	80.95 296	94.05 243	67.51 301	97.10 246	85.16 236	78.41 299	92.04 279

thres200		93.69 125	92.59 143	96.97 66	97.76 109	94.74 43	99.35 76	99.36 2	89.23 164	91.21 177	96.97 178	83.42 160	98.77 157	85.08 237	90.96 221	97.39 200

tttt0517		93.30 139	93.01 134	94.17 188	95.57 201	86.47 230	98.51 175	97.60 112	85.99 253	90.55 185	97.19 166	94.80 11	98.31 176	85.06 238	91.86 203	97.74 189

XVG-ACMP-BASELINE		85.86 282	84.95 278	88.57 318	89.90 330	77.12 352	94.30 332	95.60 285	87.40 226	82.12 279	92.99 274	53.42 364	97.66 218	85.02 239	83.83 268	90.92 314

dmvs_re		88.69 236	88.06 232	90.59 271	93.83 270	78.68 342	95.75 320	96.18 237	87.99 208	84.48 246	96.32 205	67.52 300	96.94 252	84.98 240	85.49 253	96.14 234

新几何1				97.40 47	98.92 77	92.51 91		97.77 75	85.52 260	96.69 83	99.06 53	88.08 77	99.89 53	84.88 241	99.62 44	99.79 36

1112_ss		92.71 152	91.55 164	96.20 107	95.56 202	91.12 114	98.48 180	94.69 324	88.29 198	86.89 225	98.50 108	87.02 99	98.66 165	84.75 242	89.77 231	98.81 141

miper_ehance_all_eth		88.94 224	88.12 231	91.40 251	95.32 211	86.93 223	97.85 236	95.55 287	84.19 281	81.97 284	91.50 296	84.16 150	95.91 313	84.69 243	77.89 302	91.36 301

Test_1112_low_res		92.27 165	90.97 175	96.18 108	95.53 204	91.10 116	98.47 182	94.66 325	88.28 199	86.83 226	93.50 263	87.00 100	98.65 166	84.69 243	89.74 232	98.80 142

TR-MVS		90.77 192	89.44 200	94.76 163	96.31 173	88.02 196	97.92 231	95.96 252	85.52 260	88.22 210	97.23 162	66.80 306	98.09 188	84.58 245	92.38 192	98.17 181

tt0805		86.50 273	84.79 282	91.63 249	91.97 300	81.49 317	96.49 293	97.38 154	82.24 318	82.44 270	95.82 217	51.22 369	98.25 181	84.55 246	80.96 289	95.13 242

OpenMVS	85.28 14	90.75 193	88.84 213	96.48 93	93.58 275	93.51 68	98.80 141	97.41 151	82.59 310	78.62 322	97.49 150	68.00 296	99.82 76	84.52 247	98.55 102	96.11 235

UniMVSNet_ETH3D		85.65 289	83.79 297	91.21 254	90.41 325	80.75 330	95.36 323	95.78 273	78.76 345	81.83 290	94.33 242	49.86 374	96.66 262	84.30 248	83.52 273	96.22 233

NR-MVSNet		87.74 254	86.00 262	92.96 217	91.46 311	90.68 129	96.65 290	97.42 150	88.02 207	73.42 350	93.68 256	77.31 228	95.83 316	84.26 249	71.82 352	92.36 262

D2MVS		87.96 246	87.39 240	89.70 299	91.84 305	83.40 294	98.31 201	98.49 23	88.04 206	78.23 328	90.26 328	73.57 250	96.79 259	84.21 250	83.53 272	88.90 353

testdata2													99.88 54	84.16 251

Baseline_NR-MVSNet		85.83 283	84.82 281	88.87 317	88.73 348	83.34 295	98.63 160	91.66 371	80.41 339	82.44 270	91.35 299	74.63 238	95.42 328	84.13 252	71.39 354	87.84 359

thres100view900		93.34 138	92.15 151	96.90 69	97.62 114	94.84 38	99.06 115	99.36 2	87.96 209	90.47 188	96.78 190	83.29 163	98.75 159	84.11 253	90.69 223	97.12 207

tfpn200view9		93.43 134	92.27 148	96.90 69	97.68 112	94.84 38	99.18 92	99.36 2	88.45 188	90.79 180	96.90 182	83.31 161	98.75 159	84.11 253	90.69 223	97.12 207

thres400		93.39 136	92.27 148	96.73 78	97.68 112	94.84 38	99.18 92	99.36 2	88.45 188	90.79 180	96.90 182	83.31 161	98.75 159	84.11 253	90.69 223	96.61 222

c3_l		88.19 245	87.23 244	91.06 258	94.97 233	86.17 243	97.72 245	95.38 298	83.43 295	81.68 291	91.37 298	82.81 173	95.72 319	84.04 256	73.70 333	91.29 305

UA-Net		93.30 139	92.62 142	95.34 141	96.27 175	88.53 188	95.88 314	96.97 194	90.90 114	95.37 109	97.07 172	82.38 187	99.10 147	83.91 257	94.86 168	98.38 166

IterMVS-LS		88.34 241	87.44 239	91.04 259	94.10 256	85.85 255	98.10 219	95.48 291	85.12 265	82.03 283	91.21 302	81.35 201	95.63 322	83.86 258	75.73 314	91.63 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 212	89.38 203	91.36 253	94.32 251	85.87 254	97.61 252	96.59 208	85.10 266	85.51 236	97.10 170	81.30 202	96.56 269	83.85 259	83.03 277	91.64 285

tpm		89.67 214	88.95 211	91.82 242	92.54 291	81.43 318	92.95 345	95.92 259	87.81 213	90.50 187	89.44 341	84.99 140	95.65 321	83.67 260	82.71 280	98.38 166

eth_miper_zixun_eth		87.76 250	87.00 248	90.06 286	94.67 243	82.65 307	97.02 276	95.37 299	84.19 281	81.86 289	91.58 295	81.47 198	95.90 314	83.24 261	73.61 334	91.61 290

Fast-Effi-MVS+		91.72 174	90.79 182	94.49 173	95.89 191	87.40 212	99.54 49	95.70 278	85.01 271	89.28 203	95.68 219	77.75 226	97.57 228	83.22 262	95.06 166	98.51 159

test_post1							90.74 370				41.37 406	85.38 136	96.36 283	83.16 263

SCA		90.64 196	89.25 205	94.83 162	94.95 234	88.83 179	96.26 301	97.21 167	90.06 142	90.03 194	90.62 318	66.61 307	96.81 257	83.16 263	94.36 171	98.84 136

TranMVSNet+NR-MVSNet		87.75 251	86.31 257	92.07 237	90.81 319	88.56 185	98.33 198	97.18 172	87.76 215	81.87 287	93.90 251	72.45 262	95.43 327	83.13 265	71.30 355	92.23 268

CMPMVS	58.40 21	80.48 326	80.11 325	81.59 361	85.10 373	59.56 389	94.14 335	95.95 254	68.54 381	60.71 385	93.31 265	55.35 357	97.87 201	83.06 266	84.85 257	87.33 365
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres600view7		93.18 144	92.00 154	96.75 76	97.62 114	94.92 33	99.07 112	99.36 2	87.96 209	90.47 188	96.78 190	83.29 163	98.71 163	82.93 267	90.47 227	96.61 222

pmmvs4		87.58 257	86.17 260	91.80 243	89.58 336	88.92 178	97.25 265	95.28 302	82.54 312	80.49 301	93.17 270	75.62 235	96.05 304	82.75 268	78.90 297	90.42 328

CVMVSNet		90.30 201	90.91 177	88.46 320	94.32 251	73.58 365	97.61 252	97.59 116	90.16 138	88.43 209	97.10 170	76.83 231	92.86 359	82.64 269	93.54 178	98.93 129

Anonymous20231211		84.72 297	82.65 308	90.91 262	97.71 111	84.55 279	97.28 263	96.67 202	66.88 386	79.18 318	90.87 308	58.47 345	96.60 264	82.61 270	74.20 329	91.59 292

GA-MVS		90.10 207	88.69 217	94.33 181	92.44 292	87.97 197	99.08 111	96.26 231	89.65 150	86.92 224	93.11 271	68.09 294	96.96 250	82.54 271	90.15 228	98.05 183

QAPM		91.41 179	89.49 199	97.17 56	95.66 200	93.42 70	98.60 165	97.51 133	80.92 334	81.39 294	97.41 154	72.89 260	99.87 58	82.33 272	98.68 96	98.21 178

Patchmatch-RL test		81.90 321	80.13 324	87.23 330	80.71 385	70.12 378	84.07 388	88.19 389	83.16 300	70.57 361	82.18 376	87.18 94	92.59 364	82.28 273	62.78 375	98.98 121

v2v482		87.27 260	85.76 265	91.78 247	89.59 335	87.58 205	98.56 170	95.54 288	84.53 277	82.51 269	91.78 291	73.11 257	96.47 277	82.07 274	74.14 331	91.30 304

Fast-Effi-MVS+-dtu		88.84 228	88.59 221	89.58 302	93.44 280	78.18 346	98.65 157	94.62 326	88.46 187	84.12 249	95.37 227	68.91 286	96.52 272	82.06 275	91.70 208	94.06 246

pmmvs5		85.87 281	84.40 292	90.30 282	88.53 351	84.23 282	98.60 165	93.71 344	81.53 326	80.29 303	92.02 284	64.51 322	95.52 324	82.04 276	78.34 300	91.15 308

V42		87.00 262	85.68 267	90.98 261	89.91 329	86.08 246	98.32 200	95.61 284	83.67 292	82.72 263	90.67 314	74.00 249	96.53 271	81.94 277	74.28 328	90.32 330

EPMVS		92.59 157	91.59 163	95.59 135	97.22 132	90.03 149	91.78 356	98.04 48	90.42 130	91.66 165	90.65 316	86.49 115	97.46 231	81.78 278	96.31 147	99.28 97

DIV-MVS_self_test		87.82 247	86.81 250	90.87 265	94.87 238	85.39 265	97.81 237	95.22 311	82.92 307	80.76 298	91.31 300	81.99 191	95.81 317	81.36 279	75.04 318	91.42 299

cl____		87.82 247	86.79 251	90.89 264	94.88 237	85.43 263	97.81 237	95.24 306	82.91 308	80.71 299	91.22 301	81.97 193	95.84 315	81.34 280	75.06 317	91.40 300

RPSCF		85.33 291	85.55 269	84.67 347	94.63 245	62.28 386	93.73 338	93.76 342	74.38 364	85.23 239	97.06 173	64.09 323	98.31 176	80.98 281	86.08 249	93.41 251

OurMVSNet-221017-0		84.13 308	83.59 298	85.77 340	87.81 358	70.24 376	94.89 327	93.65 346	86.08 251	76.53 332	93.28 267	61.41 335	96.14 301	80.95 282	77.69 307	90.93 313

v148		86.38 275	85.06 275	90.37 281	89.47 341	84.10 285	98.52 172	95.48 291	83.80 288	80.93 297	90.22 332	74.60 240	96.31 291	80.92 283	71.55 353	90.69 323

PatchMatch-RL		91.47 177	90.54 186	94.26 184	98.20 96	86.36 235	96.94 277	97.14 175	87.75 216	88.98 204	95.75 218	71.80 270	99.40 127	80.92 283	97.39 128	97.02 213

FE-MVS		91.38 180	90.16 191	95.05 154	96.46 166	87.53 207	89.69 373	97.84 61	82.97 303	92.18 158	92.00 287	84.07 152	98.93 153	80.71 285	95.52 161	98.68 151

miper_lstm_enhance		86.90 263	86.20 259	89.00 314	94.53 246	81.19 324	96.74 287	95.24 306	82.33 317	80.15 305	90.51 325	81.99 191	94.68 345	80.71 285	73.58 335	91.12 309

PCF-MVS	89.78 5	91.26 181	89.63 196	96.16 112	95.44 206	91.58 106	95.29 324	96.10 242	85.07 268	82.75 262	97.45 152	78.28 223	99.78 84	80.60 287	95.65 160	97.12 207
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		91.25 183	89.99 192	95.03 155	96.75 156	88.55 186	98.65 157	94.95 314	87.74 217	87.74 213	97.80 132	68.27 292	98.14 184	80.53 288	97.49 125	98.41 163

GeoE		90.60 197	89.56 197	93.72 206	95.10 227	85.43 263	99.41 68	94.94 315	83.96 286	87.21 220	96.83 189	74.37 244	97.05 248	80.50 289	93.73 177	98.67 152

CP-MVSNet		86.54 271	85.45 271	89.79 296	91.02 318	82.78 305	97.38 258	97.56 122	85.37 262	79.53 314	93.03 272	71.86 269	95.25 332	79.92 290	73.43 339	91.34 302

PatchmatchNet		92.05 171	91.04 174	95.06 152	96.17 181	89.04 169	91.26 364	97.26 160	89.56 156	90.64 184	90.56 322	88.35 70	97.11 244	79.53 291	96.07 154	99.03 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1144		86.83 265	85.31 273	91.40 251	89.75 333	87.21 221	98.31 201	95.45 293	83.22 298	82.70 264	90.78 309	73.36 251	96.36 283	79.49 292	74.69 322	90.63 325

IterMVS		85.81 284	84.67 285	89.22 309	93.51 276	83.67 291	96.32 298	94.80 320	85.09 267	78.69 320	90.17 335	66.57 309	93.17 358	79.48 293	77.42 308	90.81 316
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		85.73 287	84.64 286	89.00 314	93.46 279	82.90 301	96.27 299	94.70 323	85.02 270	78.62 322	90.35 327	66.61 307	93.33 355	79.38 294	77.36 309	90.76 320

GBi-Net		86.67 268	84.96 276	91.80 243	95.11 224	88.81 180	96.77 283	95.25 303	82.94 304	82.12 279	90.25 329	62.89 329	94.97 336	79.04 295	80.24 290	91.62 287

test1		86.67 268	84.96 276	91.80 243	95.11 224	88.81 180	96.77 283	95.25 303	82.94 304	82.12 279	90.25 329	62.89 329	94.97 336	79.04 295	80.24 290	91.62 287

FMVSNet3		88.81 232	87.08 246	93.99 197	96.52 163	94.59 47	98.08 223	96.20 234	85.85 254	82.12 279	91.60 294	74.05 248	95.40 329	79.04 295	80.24 290	91.99 280

LF4IMVS		81.94 320	81.17 319	84.25 349	87.23 365	68.87 381	93.35 342	91.93 369	83.35 297	75.40 341	93.00 273	49.25 377	96.65 263	78.88 298	78.11 301	87.22 367

v8		86.11 278	84.45 289	91.10 257	89.99 328	86.85 224	97.24 266	95.36 300	81.99 321	79.89 309	89.86 337	74.53 242	96.39 281	78.83 299	72.32 347	90.05 337

pm-mvs1		84.68 298	82.78 305	90.40 278	89.58 336	85.18 269	97.31 261	94.73 322	81.93 323	76.05 335	92.01 285	65.48 318	96.11 302	78.75 300	69.14 358	89.91 340

test_f		71.94 352	70.82 353	75.30 367	72.77 396	53.28 395	91.62 358	89.66 384	75.44 359	64.47 381	78.31 387	20.48 398	89.56 382	78.63 301	66.02 370	83.05 386

v144192		86.40 274	84.89 279	90.91 262	89.48 340	85.59 260	98.21 208	95.43 296	82.45 315	82.62 267	90.58 321	72.79 261	96.36 283	78.45 302	74.04 332	90.79 318

PS-CasMVS		85.81 284	84.58 287	89.49 306	90.77 320	82.11 311	97.20 269	97.36 156	84.83 274	79.12 319	92.84 275	67.42 302	95.16 334	78.39 303	73.25 340	91.21 307

tmp_tt		53.66 366	52.86 368	56.05 383	32.75 411	41.97 407	73.42 397	76.12 404	21.91 404	39.68 400	96.39 203	42.59 384	65.10 403	78.00 304	14.92 404	61.08 396

JIA-IIPM		85.97 280	84.85 280	89.33 308	93.23 284	73.68 364	85.05 383	97.13 177	69.62 378	91.56 168	68.03 393	88.03 78	96.96 250	77.89 305	93.12 181	97.34 201

MDTV_nov1_ep13			90.47 188		96.14 184	88.55 186	91.34 363	97.51 133	89.58 154	92.24 157	90.50 326	86.99 101	97.61 223	77.64 306	92.34 194

v1192		86.32 276	84.71 284	91.17 255	89.53 339	86.40 232	98.13 214	95.44 295	82.52 313	82.42 272	90.62 318	71.58 273	96.33 290	77.23 307	74.88 319	90.79 318

FMVSNet2		86.90 263	84.79 282	93.24 211	95.11 224	92.54 90	97.67 250	95.86 271	82.94 304	80.55 300	91.17 303	62.89 329	95.29 331	77.23 307	79.71 296	91.90 281

MVP-Stereo		86.61 270	85.83 264	88.93 316	88.70 349	83.85 289	96.07 308	94.41 333	82.15 320	75.64 340	91.96 288	67.65 299	96.45 279	77.20 309	98.72 95	86.51 371
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		88.89 226	87.27 243	93.76 203	95.79 194	85.32 267	90.76 369	97.09 183	76.14 357	85.72 234	88.59 347	82.92 171	98.04 193	76.96 310	91.43 217	97.90 188

v10		85.73 287	84.01 295	90.87 265	90.03 327	86.73 226	97.20 269	95.22 311	81.25 329	79.85 310	89.75 338	73.30 254	96.28 295	76.87 311	72.64 343	89.61 345

v1921920		86.02 279	84.44 290	90.77 268	89.32 342	85.20 268	98.10 219	95.35 301	82.19 319	82.25 277	90.71 311	70.73 276	96.30 294	76.85 312	74.49 324	90.80 317

MS-PatchMatch		86.75 266	85.92 263	89.22 309	91.97 300	82.47 309	96.91 278	96.14 240	83.74 289	77.73 329	93.53 262	58.19 346	97.37 238	76.75 313	98.35 106	87.84 359

K. test v3		81.04 324	79.77 327	84.83 345	87.41 362	70.23 377	95.60 322	93.93 341	83.70 291	67.51 374	89.35 343	55.76 352	93.58 354	76.67 314	68.03 362	90.67 324

PM-MVS		74.88 348	72.85 351	80.98 362	78.98 389	64.75 385	90.81 368	85.77 392	80.95 333	68.23 371	82.81 372	29.08 394	92.84 360	76.54 315	62.46 377	85.36 376

WR-MVS_H		86.53 272	85.49 270	89.66 301	91.04 317	83.31 296	97.53 254	98.20 36	84.95 272	79.64 311	90.90 307	78.01 225	95.33 330	76.29 316	72.81 341	90.35 329

ACMH+	83.78 15	84.21 305	82.56 310	89.15 311	93.73 273	79.16 337	96.43 294	94.28 335	81.09 331	74.00 347	94.03 246	54.58 360	97.67 217	76.10 317	78.81 298	90.63 325

PEN-MVS		85.21 292	83.93 296	89.07 313	89.89 331	81.31 322	97.09 272	97.24 164	84.45 279	78.66 321	92.68 277	68.44 291	94.87 339	75.98 318	70.92 356	91.04 311

USDC		84.74 296	82.93 301	90.16 284	91.73 307	83.54 293	95.00 326	93.30 351	88.77 180	73.19 352	93.30 266	53.62 363	97.65 220	75.88 319	81.54 287	89.30 348

EU-MVSNet		84.19 306	84.42 291	83.52 353	88.64 350	67.37 382	96.04 309	95.76 275	85.29 263	78.44 325	93.18 269	70.67 277	91.48 375	75.79 320	75.98 312	91.70 283

v1240		85.77 286	84.11 293	90.73 269	89.26 343	85.15 271	97.88 234	95.23 310	81.89 324	82.16 278	90.55 323	69.60 285	96.31 291	75.59 321	74.87 320	90.72 322

ITE_SJBPF				87.93 322	92.26 295	76.44 354		93.47 349	87.67 221	79.95 308	95.49 223	56.50 351	97.38 236	75.24 322	82.33 283	89.98 339

dp		90.16 206	88.83 214	94.14 189	96.38 171	86.42 231	91.57 360	97.06 185	84.76 275	88.81 205	90.19 334	84.29 149	97.43 234	75.05 323	91.35 220	98.56 157

LS3D		90.19 204	88.72 216	94.59 172	98.97 73	86.33 237	96.90 279	96.60 207	74.96 361	84.06 250	98.74 88	75.78 234	99.83 73	74.93 324	97.57 121	97.62 195

TDRefinement		78.01 339	75.31 343	86.10 338	70.06 398	73.84 363	93.59 341	91.58 373	74.51 363	73.08 355	91.04 304	49.63 376	97.12 243	74.88 325	59.47 381	87.33 365

tpmvs		89.16 220	87.76 234	93.35 209	97.19 135	84.75 277	90.58 371	97.36 156	81.99 321	84.56 243	89.31 344	83.98 153	98.17 183	74.85 326	90.00 230	97.12 207

pmmvs6		79.90 329	77.31 335	87.67 325	84.17 376	78.13 347	95.86 316	93.68 345	67.94 383	72.67 358	89.62 340	50.98 371	95.75 318	74.80 327	66.04 369	89.14 351

SixPastTwentyTwo		82.63 316	81.58 314	85.79 339	88.12 355	71.01 374	95.17 325	92.54 359	84.33 280	72.93 357	92.08 282	60.41 340	95.61 323	74.47 328	74.15 330	90.75 321

ACMH	83.09 17	84.60 299	82.61 309	90.57 272	93.18 285	82.94 299	96.27 299	94.92 316	81.01 332	72.61 359	93.61 259	56.54 350	97.79 206	74.31 329	81.07 288	90.99 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_vis3_rt		61.29 359	58.75 362	68.92 375	67.41 399	52.84 397	91.18 366	59.23 410	66.96 385	41.96 398	58.44 398	11.37 406	94.72 344	74.25 330	57.97 384	59.20 397

ADS-MVSNet2		87.62 256	86.88 249	89.86 293	96.21 178	79.14 338	87.15 377	92.99 352	83.01 301	89.91 196	87.27 357	78.87 218	92.80 362	74.20 331	92.27 196	97.64 192

ADS-MVSNet		88.99 222	87.30 242	94.07 192	96.21 178	87.56 206	87.15 377	96.78 200	83.01 301	89.91 196	87.27 357	78.87 218	97.01 249	74.20 331	92.27 196	97.64 192

lessismore_v0				85.08 343	85.59 372	69.28 379		90.56 379		67.68 373	90.21 333	54.21 362	95.46 326	73.88 333	62.64 376	90.50 327

MIMVSNet		84.48 302	81.83 312	92.42 229	91.73 307	87.36 213	85.52 380	94.42 332	81.40 327	81.91 285	87.58 351	51.92 367	92.81 361	73.84 334	88.15 235	97.08 211

v7n		84.42 304	82.75 306	89.43 307	88.15 354	81.86 313	96.75 286	95.67 281	80.53 335	78.38 326	89.43 342	69.89 280	96.35 288	73.83 335	72.13 349	90.07 335

ambc				79.60 364	72.76 397	56.61 391	76.20 395	92.01 368		68.25 370	80.23 383	23.34 396	94.73 343	73.78 336	60.81 379	87.48 362

pmmvs-eth3d		78.71 336	76.16 341	86.38 334	80.25 387	81.19 324	94.17 334	92.13 366	77.97 348	66.90 377	82.31 375	55.76 352	92.56 365	73.63 337	62.31 378	85.38 375

FMVSNet1		83.94 310	81.32 318	91.80 243	91.94 303	88.81 180	96.77 283	95.25 303	77.98 347	78.25 327	90.25 329	50.37 373	94.97 336	73.27 338	77.81 306	91.62 287

MSDG		88.29 243	86.37 256	94.04 195	96.90 149	86.15 244	96.52 292	94.36 334	77.89 351	79.22 317	96.95 179	69.72 282	99.59 104	73.20 339	92.58 190	96.37 232

test0.0.03 1		88.96 223	88.61 219	90.03 290	91.09 316	84.43 280	98.97 127	97.02 190	90.21 133	80.29 303	96.31 206	84.89 142	91.93 373	72.98 340	85.70 252	93.73 247

UnsupCasMVSNet_eth		78.90 334	76.67 339	85.58 341	82.81 381	74.94 359	91.98 354	96.31 226	84.64 276	65.84 380	87.71 350	51.33 368	92.23 369	72.89 341	56.50 387	89.56 346

WB-MVSnew		88.69 236	88.34 226	89.77 297	94.30 255	85.99 251	98.14 213	97.31 159	87.15 229	87.85 212	96.07 212	69.91 279	95.52 324	72.83 342	91.47 216	87.80 361

DTE-MVSNet		84.14 307	82.80 303	88.14 321	88.95 346	79.87 333	96.81 282	96.24 232	83.50 294	77.60 330	92.52 279	67.89 298	94.24 350	72.64 343	69.05 359	90.32 330

EPNet_dtu		92.28 164	92.15 151	92.70 224	97.29 130	84.84 275	98.64 159	97.82 65	92.91 75	93.02 149	97.02 176	85.48 134	95.70 320	72.25 344	94.89 167	97.55 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AllTest		84.97 295	83.12 300	90.52 275	96.82 151	78.84 340	95.89 312	92.17 364	77.96 349	75.94 336	95.50 221	55.48 354	99.18 139	71.15 345	87.14 238	93.55 249

TestCases				90.52 275	96.82 151	78.84 340		92.17 364	77.96 349	75.94 336	95.50 221	55.48 354	99.18 139	71.15 345	87.14 238	93.55 249

DP-MVS		88.75 234	86.56 254	95.34 141	98.92 77	87.45 210	97.64 251	93.52 348	70.55 373	81.49 292	97.25 161	74.43 243	99.88 54	71.14 347	94.09 173	98.67 152

CR-MVSNet		88.83 230	87.38 241	93.16 213	93.47 277	86.24 238	84.97 384	94.20 337	88.92 177	90.76 182	86.88 361	84.43 147	94.82 341	70.64 348	92.17 200	98.41 163

KD-MVS_2432*1600		82.98 314	80.52 322	90.38 279	94.32 251	88.98 172	92.87 347	95.87 269	80.46 337	73.79 348	87.49 354	82.76 176	93.29 356	70.56 349	46.53 397	88.87 354

miper_refine_blended		82.98 314	80.52 322	90.38 279	94.32 251	88.98 172	92.87 347	95.87 269	80.46 337	73.79 348	87.49 354	82.76 176	93.29 356	70.56 349	46.53 397	88.87 354

test_method		70.10 354	68.66 357	74.41 370	86.30 371	55.84 392	94.47 329	89.82 382	35.18 399	66.15 379	84.75 369	30.54 393	77.96 400	70.40 351	60.33 380	89.44 347

LTVRE_ROB	81.71 19	84.59 300	82.72 307	90.18 283	92.89 289	83.18 297	93.15 343	94.74 321	78.99 342	75.14 343	92.69 276	65.64 315	97.63 221	69.46 352	81.82 286	89.74 342
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
myMVS_eth3d		88.68 238	89.07 208	87.50 327	95.14 221	79.74 334	97.68 248	96.66 203	86.52 245	82.63 265	96.84 187	85.22 139	89.89 379	69.43 353	91.54 212	92.87 252

FMVSNet5		82.29 317	80.54 321	87.52 326	93.79 272	84.01 286	93.73 338	92.47 360	76.92 354	74.27 345	86.15 365	63.69 327	89.24 384	69.07 354	74.79 321	89.29 349

our_test_3		84.47 303	82.80 303	89.50 304	89.01 344	83.90 288	97.03 274	94.56 327	81.33 328	75.36 342	90.52 324	71.69 271	94.54 347	68.81 355	76.84 310	90.07 335

UnsupCasMVSNet_bld		73.85 350	70.14 354	84.99 344	79.44 388	75.73 355	88.53 374	95.24 306	70.12 376	61.94 384	74.81 390	41.41 386	93.62 353	68.65 356	51.13 394	85.62 374

Patchmtry		83.61 313	81.64 313	89.50 304	93.36 281	82.84 304	84.10 387	94.20 337	69.47 379	79.57 313	86.88 361	84.43 147	94.78 342	68.48 357	74.30 327	90.88 315

KD-MVS_self_test		77.47 342	75.88 342	82.24 356	81.59 382	68.93 380	92.83 349	94.02 340	77.03 353	73.14 353	83.39 371	55.44 356	90.42 376	67.95 358	57.53 385	87.38 363

WAC-MVS						79.74 334								67.75 359

TransMVSNet (Re)		81.97 319	79.61 328	89.08 312	89.70 334	84.01 286	97.26 264	91.85 370	78.84 343	73.07 356	91.62 293	67.17 304	95.21 333	67.50 360	59.46 382	88.02 358

COLMAP_ROB	82.69 18	84.54 301	82.82 302	89.70 299	96.72 157	78.85 339	95.89 312	92.83 356	71.55 370	77.54 331	95.89 216	59.40 343	99.14 145	67.26 361	88.26 234	91.11 310
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		79.92 328	77.59 333	86.90 332	87.06 366	77.90 350	96.20 306	94.06 339	74.61 362	66.53 378	88.76 346	40.40 388	96.20 296	67.02 362	83.66 271	86.61 369

DSMNet-mixed		81.60 322	81.43 316	82.10 358	84.36 375	60.79 387	93.63 340	86.74 391	79.00 341	79.32 316	87.15 359	63.87 325	89.78 381	66.89 363	91.92 202	95.73 238

testgi		82.29 317	81.00 320	86.17 337	87.24 364	74.84 360	97.39 256	91.62 372	88.63 181	75.85 339	95.42 224	46.07 380	91.55 374	66.87 364	79.94 294	92.12 275

MDA-MVSNet_test_wron		79.65 331	77.05 336	87.45 328	87.79 360	80.13 331	96.25 302	94.44 329	73.87 365	51.80 391	87.47 356	68.04 295	92.12 371	66.02 365	67.79 364	90.09 333

YYNet1		79.64 332	77.04 337	87.43 329	87.80 359	79.98 332	96.23 303	94.44 329	73.83 366	51.83 390	87.53 352	67.96 297	92.07 372	66.00 366	67.75 365	90.23 332

DeepMVS_CX				76.08 366	90.74 321	51.65 399		90.84 377	86.47 248	57.89 387	87.98 348	35.88 391	92.60 363	65.77 367	65.06 372	83.97 382

Anonymous20240521		78.63 337	76.90 338	83.82 351	82.82 380	72.86 367	95.72 321	93.57 347	73.55 367	72.17 360	84.79 368	49.69 375	92.51 366	65.29 368	74.50 323	86.09 373

TinyColmap		80.42 327	77.94 332	87.85 323	92.09 298	78.58 343	93.74 337	89.94 381	74.99 360	69.77 364	91.78 291	46.09 379	97.58 225	65.17 369	77.89 302	87.38 363

MVS-HIRNet		79.01 333	75.13 345	90.66 270	93.82 271	81.69 315	85.16 381	93.75 343	54.54 391	74.17 346	59.15 397	57.46 348	96.58 268	63.74 370	94.38 170	93.72 248

ppachtmachnet_test		83.63 312	81.57 315	89.80 295	89.01 344	85.09 272	97.13 271	94.50 328	78.84 343	76.14 334	91.00 305	69.78 281	94.61 346	63.40 371	74.36 326	89.71 344

CL-MVSNet_self_test		79.89 330	78.34 331	84.54 348	81.56 383	75.01 358	96.88 280	95.62 283	81.10 330	75.86 338	85.81 366	68.49 290	90.26 377	63.21 372	56.51 386	88.35 356

Patchmatch-test		86.25 277	84.06 294	92.82 219	94.42 247	82.88 303	82.88 391	94.23 336	71.58 369	79.39 315	90.62 318	89.00 63	96.42 280	63.03 373	91.37 219	99.16 106

pmmvs3		72.86 351	69.76 356	82.17 357	73.86 394	74.19 362	94.20 333	89.01 387	64.23 390	67.72 372	80.91 382	41.48 385	88.65 386	62.40 374	54.02 390	83.68 383

new_pmnet		76.02 344	73.71 349	82.95 354	83.88 377	72.85 368	91.26 364	92.26 363	70.44 374	62.60 383	81.37 378	47.64 378	92.32 368	61.85 375	72.10 350	83.68 383

tfpnnormal		83.65 311	81.35 317	90.56 274	91.37 313	88.06 194	97.29 262	97.87 58	78.51 346	76.20 333	90.91 306	64.78 321	96.47 277	61.71 376	73.50 336	87.13 368

testing3		87.75 251	88.22 229	86.36 335	94.66 244	77.41 351	99.52 50	97.95 54	86.05 252	81.12 295	96.69 194	86.18 121	89.31 383	61.65 377	90.12 229	92.35 265

MDA-MVSNet-bldmvs		77.82 341	74.75 347	87.03 331	88.33 352	78.52 344	96.34 297	92.85 355	75.57 358	48.87 393	87.89 349	57.32 349	92.49 367	60.79 378	64.80 373	90.08 334

Anonymous20231206		80.76 325	79.42 329	84.79 346	84.78 374	72.98 366	96.53 291	92.97 353	79.56 340	74.33 344	88.83 345	61.27 336	92.15 370	60.59 379	75.92 313	89.24 350

new-patchmatchnet		74.80 349	72.40 352	81.99 359	78.36 390	72.20 370	94.44 330	92.36 361	77.06 352	63.47 382	79.98 384	51.04 370	88.85 385	60.53 380	54.35 389	84.92 380

LCM-MVSNet		60.07 361	56.37 363	71.18 372	54.81 407	48.67 400	82.17 392	89.48 385	37.95 397	49.13 392	69.12 391	13.75 405	81.76 392	59.28 381	51.63 393	83.10 385

TAPA-MVS	87.50 9	90.35 199	89.05 209	94.25 185	98.48 91	85.17 270	98.42 185	96.58 211	82.44 316	87.24 219	98.53 105	82.77 174	98.84 155	59.09 382	97.88 114	98.72 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.03		78.51 338	77.48 334	81.62 360	83.07 379	71.03 373	96.11 307	92.83 356	81.66 325	69.31 366	89.68 339	57.53 347	87.29 389	58.65 383	68.47 360	86.53 370

PatchT		85.44 290	83.19 299	92.22 231	93.13 286	83.00 298	83.80 390	96.37 223	70.62 372	90.55 185	79.63 385	84.81 144	94.87 339	58.18 384	91.59 209	98.79 143

APD_test1		68.93 355	66.98 358	74.77 369	80.62 386	53.15 396	87.97 375	85.01 394	53.76 392	59.26 386	87.52 353	25.19 395	89.95 378	56.20 385	67.33 366	81.19 387

MIMVSNet1		75.92 345	73.30 350	83.81 352	81.29 384	75.57 356	92.26 352	92.05 367	73.09 368	67.48 375	86.18 364	40.87 387	87.64 388	55.78 386	70.68 357	88.21 357

OpenMVS_ROB	73.86 20	77.99 340	75.06 346	86.77 333	83.81 378	77.94 349	96.38 296	91.53 374	67.54 384	68.38 369	87.13 360	43.94 381	96.08 303	55.03 387	81.83 285	86.29 372

RPMNet		85.07 294	81.88 311	94.64 170	93.47 277	86.24 238	84.97 384	97.21 167	64.85 389	90.76 182	78.80 386	80.95 204	99.27 137	53.76 388	92.17 200	98.41 163

N_pmnet		70.19 353	69.87 355	71.12 373	88.24 353	30.63 412	95.85 317	28.70 411	70.18 375	68.73 368	86.55 363	64.04 324	93.81 351	53.12 389	73.46 337	88.94 352

dmvs_testset		77.17 343	78.99 330	71.71 371	87.25 363	38.55 408	91.44 361	81.76 399	85.77 256	69.49 365	95.94 215	69.71 283	84.37 391	52.71 390	76.82 311	92.21 270

PMMVS2		58.97 362	55.07 365	70.69 374	62.72 402	55.37 393	85.97 379	80.52 400	49.48 393	45.94 394	68.31 392	15.73 403	80.78 396	49.79 391	37.12 399	75.91 388

test_0402		78.81 335	76.33 340	86.26 336	91.18 315	78.44 345	95.88 314	91.34 375	68.55 380	70.51 363	89.91 336	52.65 366	94.99 335	47.14 392	79.78 295	85.34 377

Syy-MVS		84.10 309	84.53 288	82.83 355	95.14 221	65.71 383	97.68 248	96.66 203	86.52 245	82.63 265	96.84 187	68.15 293	89.89 379	45.62 393	91.54 212	92.87 252

FPMVS		61.57 358	60.32 361	65.34 378	60.14 405	42.44 406	91.02 367	89.72 383	44.15 394	42.63 397	80.93 380	19.02 399	80.59 397	42.50 394	72.76 342	73.00 391

testf1		56.38 363	53.73 366	64.31 380	64.84 400	45.11 401	80.50 393	75.94 405	38.87 395	42.74 395	75.07 388	11.26 407	81.19 394	41.11 395	53.27 391	66.63 394

APD_test2		56.38 363	53.73 366	64.31 380	64.84 400	45.11 401	80.50 393	75.94 405	38.87 395	42.74 395	75.07 388	11.26 407	81.19 394	41.11 395	53.27 391	66.63 394

EGC-MVSNET		60.70 360	55.37 364	76.72 365	86.35 370	71.08 372	89.96 372	84.44 396	0.38 408	1.50 409	84.09 370	37.30 389	88.10 387	40.85 397	73.44 338	70.97 393

ANet_high		50.71 367	46.17 370	64.33 379	44.27 409	52.30 398	76.13 396	78.73 401	64.95 388	27.37 402	55.23 399	14.61 404	67.74 402	36.01 398	18.23 402	72.95 392

Gipuma		54.77 365	52.22 369	62.40 382	86.50 368	59.37 390	50.20 400	90.35 380	36.52 398	41.20 399	49.49 400	18.33 401	81.29 393	32.10 399	65.34 371	46.54 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	41.42 23	45.67 368	42.50 371	55.17 384	34.28 410	32.37 410	66.24 398	78.71 402	30.72 400	22.04 405	59.59 396	4.59 409	77.85 401	27.49 400	58.84 383	55.29 398
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 369	37.64 374	53.90 385	49.46 408	43.37 405	65.09 399	66.66 407	26.19 403	25.77 404	48.53 401	3.58 411	63.35 404	26.15 401	27.28 400	54.97 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WB-MVS		66.44 356	66.29 359	66.89 376	74.84 392	44.93 403	93.00 344	84.09 397	71.15 371	55.82 388	81.63 377	63.79 326	80.31 398	21.85 402	50.47 395	75.43 389

SSC-MVS		65.42 357	65.20 360	66.06 377	73.96 393	43.83 404	92.08 353	83.54 398	69.77 377	54.73 389	80.92 381	63.30 328	79.92 399	20.48 403	48.02 396	74.44 390

E-PMN		41.02 370	40.93 372	41.29 386	61.97 403	33.83 409	84.00 389	65.17 408	27.17 401	27.56 401	46.72 402	17.63 402	60.41 405	19.32 404	18.82 401	29.61 401

EMVS		39.96 371	39.88 373	40.18 387	59.57 406	32.12 411	84.79 386	64.57 409	26.27 402	26.14 403	44.18 405	18.73 400	59.29 406	17.03 405	17.67 403	29.12 402

wuyk23d		16.71 374	16.73 378	16.65 388	60.15 404	25.22 413	41.24 401	5.17 412	6.56 405	5.48 408	3.61 408	3.64 410	22.72 407	15.20 406	9.52 405	1.99 405

testmvs		18.81 373	23.05 376	6.10 390	4.48 412	2.29 415	97.78 239	3.00 413	3.27 406	18.60 406	62.71 394	1.53 413	2.49 409	14.26 407	1.80 406	13.50 404

test123		16.58 375	19.47 377	7.91 389	3.59 413	5.37 414	94.32 331	1.39 414	2.49 407	13.98 407	44.60 404	2.91 412	2.65 408	11.35 408	0.57 407	15.70 403

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		22.52 372	30.03 375	0.00 391	0.00 414	0.00 416	0.00 402	97.17 173	0.00 409	0.00 410	98.77 85	74.35 245	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		6.87 377	9.16 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	82.48 182	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.21 376	10.94 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	98.50 108	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

FOURS1					99.50 42	88.94 175	99.55 44	97.47 141	91.32 108	98.12 44

test_one_0601					99.59 28	94.89 34		97.64 103	93.14 69	98.93 21	99.45 14	93.45 18

eth-test2					0.00 414

eth-test					0.00 414

test_241102_ONE					99.63 18	95.24 25		97.72 81	94.16 45	99.30 8	99.49 9	93.32 19	99.98 9

save fliter					99.34 50	93.85 62	99.65 35	97.63 107	95.69 22

test0726					99.66 12	95.20 30	99.77 17	97.70 86	93.95 48	99.35 7	99.54 3	93.18 22

GSMVS																98.84 136

test_part2					99.54 36	95.42 20				98.13 42

sam_mvs1												88.39 69				98.84 136

sam_mvs												87.08 97

MTGPA								97.45 144

test_post											46.00 403	87.37 88	97.11 244

patchmatchnet-post											84.86 367	88.73 66	96.81 257

MTMP							99.21 88	91.09 376

TEST9					99.57 33	93.17 73	99.38 71	97.66 95	89.57 155	98.39 35	99.18 33	90.88 39	99.66 94

test_8					99.55 35	93.07 76	99.37 74	97.64 103	90.18 135	98.36 37	99.19 30	90.94 36	99.64 100

agg_prior					99.54 36	92.66 86		97.64 103		97.98 51			99.61 102

test_prior4						92.00 97	99.41 68

test_prior				97.01 60	99.58 30	91.77 100		97.57 121					99.49 112			99.79 36

新几何2							98.26 204

旧先验1					98.97 73	92.90 84		97.74 77			99.15 39	91.05 35			99.33 63	99.60 67

原ACMM2							98.69 152

test222					98.32 92	91.21 110	98.08 223	97.58 118	83.74 289	95.87 98	99.02 58	86.74 106			99.64 40	99.81 33

segment_acmp												90.56 44

testdata1							97.89 232		92.43 82

test12				97.83 33	99.33 53	94.45 49		97.55 123		97.56 56		88.60 67	99.50 111		99.71 34	99.55 72

plane_prior7					93.84 268	85.73 257

plane_prior6					93.92 265	86.02 250						72.92 258

plane_prior4											96.52 197

plane_prior3						85.91 252			93.65 61	86.99 221

plane_prior2							99.02 120		93.38 66

plane_prior1					93.90 267

plane_prior						86.07 248	99.14 104		93.81 58						86.26 246

n2								0.00 415

nn								0.00 415

door-mid								84.90 395

test11								97.68 90

door								85.30 393

HQP5-MVS						86.39 233

HQP-NCC					93.95 261		99.16 96		93.92 50	87.57 214

ACMP_Plane					93.95 261		99.16 96		93.92 50	87.57 214

HQP4-MVS										87.57 214			97.77 208			92.72 254

HQP3-MVS								96.37 223							86.29 244

HQP2-MVS												73.34 252

NP-MVS					93.94 264	86.22 240					96.67 195

ACMMP++_ref															82.64 281

ACMMP++															83.83 268

Test By Simon												83.62 156

High-resolution multi-view benchmark