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.

EPNet		95.20 90	94.56 97	97.14 71	92.80 333	92.68 89	97.85 70	94.87 320	96.64 1	92.46 167	97.80 92	86.23 133	99.65 57	93.72 115	98.62 103	99.10 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
NCCC		97.30 15	97.03 17	98.11 17	98.77 60	95.06 26	97.34 122	98.04 85	95.96 2	97.09 50	97.88 82	93.18 24	99.71 42	95.84 58	99.17 79	99.56 27

CNVR-MVS		97.68 6	97.44 9	98.37 7	98.90 54	95.86 6	97.27 130	98.08 68	95.81 3	97.87 28	98.31 50	94.26 13	99.68 51	97.02 16	99.49 39	99.57 24

HPM-MVS++		97.34 14	96.97 20	98.47 5	99.08 40	96.16 4	97.55 103	97.97 103	95.59 4	96.61 66	97.89 80	92.57 34	99.84 23	95.95 53	99.51 33	99.40 58

MSP-MVS		97.59 8	97.54 6	97.73 41	99.40 12	93.77 62	98.53 13	98.29 26	95.55 5	98.56 14	97.81 90	93.90 15	99.65 57	96.62 27	99.21 74	99.77 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DeepPCF-MVS	93.97 1	96.61 51	97.09 14	95.15 165	98.09 113	86.63 271	96.00 242	98.15 55	95.43 6	97.95 24	98.56 20	93.40 20	99.36 117	96.77 24	99.48 40	99.45 50

CANet		96.39 60	96.02 64	97.50 53	97.62 139	93.38 71	97.02 152	97.96 104	95.42 7	94.86 120	97.81 90	87.38 120	99.82 29	96.88 19	99.20 75	99.29 67

xxxxxxxxxxxxxcwj		97.36 12	97.20 12	97.83 29	98.91 52	94.28 39	97.02 152	97.22 191	95.35 8	98.27 18	98.65 16	93.33 21	99.72 39	96.49 32	99.52 29	99.51 39

save fliter					98.91 52	94.28 39	97.02 152	98.02 92	95.35 8

SteuartSystems-ACMMP		97.62 7	97.53 7	97.87 27	98.39 86	94.25 42	98.43 21	98.27 31	95.34 10	98.11 20	98.56 20	94.53 12	99.71 42	96.57 30	99.62 15	99.65 12
Skip Steuart: Steuart Systems R&D Blog.
CS-MVS-test		96.47 56	96.62 43	96.01 124	98.18 108	90.40 168	98.40 23	97.65 138	95.33 11	97.02 54	96.79 147	89.98 87	98.72 177	97.06 13	99.18 77	98.91 106

CS-MVS		96.79 44	97.05 16	96.00 125	98.17 110	90.38 170	99.09 3	97.89 109	95.31 12	97.02 54	98.02 74	91.74 52	98.71 179	97.06 13	99.18 77	98.90 108

Regformer-2		97.16 19	96.99 19	97.67 46	98.32 92	93.84 57	96.83 173	98.10 65	95.24 13	97.49 31	98.25 58	92.57 34	99.61 66	96.80 21	99.29 62	99.56 27

DELS-MVS		96.61 51	96.38 56	97.30 60	97.79 129	93.19 77	95.96 244	98.18 50	95.23 14	95.87 95	97.65 103	91.45 60	99.70 47	95.87 54	99.44 47	99.00 98
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
Regformer-1		97.10 21	96.96 21	97.54 52	98.32 92	93.48 68	96.83 173	97.99 101	95.20 15	97.46 32	98.25 58	92.48 38	99.58 75	96.79 23	99.29 62	99.55 31

h-mvs33		94.15 114	93.52 122	96.04 121	97.81 127	90.22 172	97.62 98	97.58 146	95.19 16	96.74 57	97.45 118	83.67 168	99.61 66	95.85 56	79.73 341	98.29 157

hse-mvs2		93.45 141	92.99 137	94.81 183	97.02 165	88.59 223	96.69 187	96.47 252	95.19 16	96.74 57	96.16 187	83.67 168	98.48 200	95.85 56	79.13 345	97.35 196

DPE-MVS		97.86 4	97.65 5	98.47 5	99.17 34	95.78 7	97.21 139	98.35 20	95.16 18	98.71 12	98.80 11	95.05 10	99.89 4	96.70 26	99.73 1	99.73 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_one_0601					99.32 24	95.20 21		98.25 36	95.13 19	98.48 16	98.87 6	95.16 7

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 18	98.25 34	98.27 31	95.13 19	99.19 1	98.89 4	95.54 5	99.85 18	97.52 5	99.66 10	99.56 27

test_241102_TWO								98.27 31	95.13 19	98.93 6	98.89 4	94.99 11	99.85 18	97.52 5	99.65 12	99.74 7

Regformer-4		96.97 30	96.87 24	97.25 64	98.34 89	92.66 90	96.96 161	98.01 95	95.12 22	97.14 46	98.42 34	91.82 50	99.61 66	96.90 18	99.13 82	99.50 42

test_241102_ONE					99.42 7	95.30 18		98.27 31	95.09 23	99.19 1	98.81 10	95.54 5	99.65 57

zzz-MVS		97.07 23	96.77 35	97.97 25	99.37 17	94.42 36	97.15 145	98.08 68	95.07 24	96.11 85	98.59 18	90.88 75	99.90 2	96.18 46	99.50 36	99.58 22

MTAPA		97.08 22	96.78 34	97.97 25	99.37 17	94.42 36	97.24 132	98.08 68	95.07 24	96.11 85	98.59 18	90.88 75	99.90 2	96.18 46	99.50 36	99.58 22

Regformer-3		96.85 39	96.80 32	97.01 75	98.34 89	92.02 113	96.96 161	97.76 122	95.01 26	97.08 51	98.42 34	91.71 54	99.54 90	96.80 21	99.13 82	99.48 46

FOURS1					99.55 1	93.34 74	99.29 1	98.35 20	94.98 27	98.49 15

DVP-MVS		97.91 3	97.81 3	98.22 12	99.45 3	95.36 13	98.21 41	97.85 118	94.92 28	98.73 10	98.87 6	95.08 8	99.84 23	97.52 5	99.67 6	99.48 46
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
test0726					99.45 3	95.36 13	98.31 27	98.29 26	94.92 28	98.99 4	98.92 2	95.08 8

XVS		97.18 17	96.96 21	97.81 33	99.38 15	94.03 54	98.59 11	98.20 46	94.85 30	96.59 68	98.29 53	91.70 55	99.80 31	95.66 62	99.40 50	99.62 16

X-MVStestdata		91.71 203	89.67 263	97.81 33	99.38 15	94.03 54	98.59 11	98.20 46	94.85 30	96.59 68	32.69 372	91.70 55	99.80 31	95.66 62	99.40 50	99.62 16

HQP_MVS		93.78 131	93.43 127	94.82 181	96.21 204	89.99 177	97.74 79	97.51 153	94.85 30	91.34 192	96.64 158	81.32 216	98.60 188	93.02 131	92.23 219	95.86 234

plane_prior2							97.74 79		94.85 30

SD-MVS		97.41 10	97.53 7	97.06 74	98.57 78	94.46 34	97.92 64	98.14 57	94.82 34	99.01 3	98.55 22	94.18 14	97.41 310	96.94 17	99.64 13	99.32 65
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
UA-Net		95.95 72	95.53 71	97.20 69	97.67 134	92.98 83	97.65 92	98.13 58	94.81 35	96.61 66	98.35 41	88.87 96	99.51 98	90.36 177	97.35 139	99.11 86

DeepC-MVS_fast	93.89 2	96.93 34	96.64 42	97.78 36	98.64 73	94.30 38	97.41 114	98.04 85	94.81 35	96.59 68	98.37 39	91.24 66	99.64 65	95.16 78	99.52 29	99.42 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 65	95.39 11	99.29 1	98.28 28	94.78 37	98.93 6	98.87 6	96.04 2	99.86 9	97.45 9	99.58 22	99.59 20

test_0728_THIRD									94.78 37	98.73 10	98.87 6	95.87 4	99.84 23	97.45 9	99.72 2	99.77 1

APDe-MVS		97.82 5	97.73 4	98.08 18	99.15 35	94.82 29	98.81 6	98.30 25	94.76 39	98.30 17	98.90 3	93.77 17	99.68 51	97.93 1	99.69 3	99.75 5

EI-MVSNet-Vis-set		96.51 54	96.47 51	96.63 83	98.24 99	91.20 140	96.89 168	97.73 126	94.74 40	96.49 72	98.49 27	90.88 75	99.58 75	96.44 34	98.32 112	99.13 82

patch_mono-2		96.83 41	97.44 9	95.01 171	99.05 43	85.39 291	96.98 159	98.77 5	94.70 41	97.99 23	98.66 14	93.61 19	99.91 1	97.67 4	99.50 36	99.72 10

EI-MVSNet-UG-set		96.34 61	96.30 57	96.47 94	98.20 104	90.93 151	96.86 169	97.72 129	94.67 42	96.16 84	98.46 29	90.43 81	99.58 75	96.23 39	97.96 122	98.90 108

MSLP-MVS++		96.94 33	97.06 15	96.59 86	98.72 62	91.86 117	97.67 89	98.49 13	94.66 43	97.24 41	98.41 37	92.31 41	98.94 157	96.61 28	99.46 43	98.96 100

3Dnovator+	91.43 4	95.40 82	94.48 102	98.16 15	96.90 170	95.34 16	98.48 19	97.87 114	94.65 44	88.53 269	98.02 74	83.69 167	99.71 42	93.18 126	98.96 93	99.44 52

ETV-MVS		96.02 69	95.89 67	96.40 99	97.16 153	92.44 97	97.47 111	97.77 121	94.55 45	96.48 73	94.51 262	91.23 67	98.92 158	95.65 65	98.19 115	97.82 178

canonicalmvs		96.02 69	95.45 75	97.75 40	97.59 142	95.15 25	98.28 30	97.60 143	94.52 46	96.27 81	96.12 188	87.65 113	99.18 130	96.20 45	94.82 187	98.91 106

plane_prior3						90.00 175			94.46 47	91.34 192

DROMVSNet		96.42 58	96.47 51	96.26 111	97.01 166	91.52 127	98.89 4	97.75 123	94.42 48	96.64 64	97.68 99	89.32 91	98.60 188	97.45 9	99.11 87	98.67 128

UGNet		94.04 122	93.28 132	96.31 106	96.85 171	91.19 141	97.88 66	97.68 134	94.40 49	93.00 159	96.18 184	73.39 308	99.61 66	91.72 153	98.46 109	98.13 161
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
alignmvs		95.87 74	95.23 82	97.78 36	97.56 145	95.19 22	97.86 67	97.17 194	94.39 50	96.47 74	96.40 176	85.89 139	99.20 127	96.21 44	95.11 183	98.95 102

CANet_DTU		94.37 109	93.65 117	96.55 87	96.46 194	92.13 109	96.21 231	96.67 242	94.38 51	93.53 147	97.03 138	79.34 249	99.71 42	90.76 171	98.45 110	97.82 178

Vis-MVSNet		95.23 88	94.81 90	96.51 91	97.18 152	91.58 125	98.26 33	98.12 60	94.38 51	94.90 119	98.15 66	82.28 200	98.92 158	91.45 162	98.58 105	99.01 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_111021_HR		96.68 50	96.58 46	96.99 76	98.46 80	92.31 102	96.20 232	98.90 2	94.30 53	95.86 96	97.74 95	92.33 39	99.38 116	96.04 51	99.42 48	99.28 70

TSAR-MVS + GP.		96.69 48	96.49 50	97.27 63	98.31 94	93.39 70	96.79 177	96.72 234	94.17 54	97.44 34	97.66 102	92.76 27	99.33 118	96.86 20	97.76 128	99.08 88

3Dnovator	91.36 5	95.19 91	94.44 104	97.44 55	96.56 187	93.36 73	98.65 10	98.36 17	94.12 55	89.25 253	98.06 71	82.20 202	99.77 33	93.41 122	99.32 58	99.18 77

plane_prior						89.99 177	97.24 132		94.06 56						92.16 223

casdiffmvs		95.64 77	95.49 73	96.08 117	96.76 179	90.45 165	97.29 129	97.44 170	94.00 57	95.46 113	97.98 78	87.52 117	98.73 174	95.64 66	97.33 140	99.08 88

MVS_111021_LR		96.24 64	96.19 62	96.39 101	98.23 103	91.35 133	96.24 230	98.79 4	93.99 58	95.80 98	97.65 103	89.92 89	99.24 125	95.87 54	99.20 75	98.58 130

DeepC-MVS	93.07 3	96.06 67	95.66 70	97.29 61	97.96 117	93.17 78	97.30 128	98.06 77	93.92 59	93.38 151	98.66 14	86.83 126	99.73 36	95.60 71	99.22 73	98.96 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		95.89 73	95.45 75	97.21 68	98.07 115	92.94 84	97.50 106	98.15 55	93.87 60	97.52 30	97.61 109	85.29 146	99.53 93	95.81 59	95.27 179	99.16 78

Effi-MVS+-dtu		93.08 154	93.21 134	92.68 277	96.02 216	83.25 319	97.14 146	96.72 234	93.85 61	91.20 202	93.44 309	83.08 179	98.30 212	91.69 156	95.73 172	96.50 216

mvs-test1		93.63 135	93.69 115	93.46 249	96.02 216	84.61 303	97.24 132	96.72 234	93.85 61	92.30 174	95.76 209	83.08 179	98.89 162	91.69 156	96.54 158	96.87 207

PS-MVSNAJ		95.37 83	95.33 80	95.49 154	97.35 147	90.66 160	95.31 271	97.48 155	93.85 61	96.51 71	95.70 214	88.65 100	99.65 57	94.80 93	98.27 113	96.17 223

test1172		96.93 34	96.86 25	97.15 70	99.10 36	92.34 99	97.96 61	98.04 85	93.79 64	97.35 38	98.53 24	91.40 62	99.56 85	96.30 36	99.30 61	99.55 31

SR-MVS		97.01 29	96.86 25	97.47 54	99.09 38	93.27 76	97.98 56	98.07 74	93.75 65	97.45 33	98.48 28	91.43 61	99.59 72	96.22 40	99.27 66	99.54 34

TSAR-MVS + MP.		97.42 9	97.33 11	97.69 45	99.25 29	94.24 43	98.07 51	97.85 118	93.72 66	98.57 13	98.35 41	93.69 18	99.40 113	97.06 13	99.46 43	99.44 52
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		93.28 146	92.76 144	94.82 181	94.63 286	90.77 157	96.65 191	97.18 192	93.72 66	91.68 186	97.26 126	79.33 250	98.63 185	92.13 144	92.28 218	95.07 280
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
xiu_mvs_v2_base		95.32 85	95.29 81	95.40 159	97.22 149	90.50 163	95.44 265	97.44 170	93.70 68	96.46 75	96.18 184	88.59 103	99.53 93	94.79 95	97.81 125	96.17 223

baseline		95.58 79	95.42 77	96.08 117	96.78 176	90.41 167	97.16 143	97.45 166	93.69 69	95.65 107	97.85 86	87.29 121	98.68 181	95.66 62	97.25 143	99.13 82

EIA-MVS		95.53 81	95.47 74	95.71 140	97.06 161	89.63 186	97.82 72	97.87 114	93.57 70	93.92 139	95.04 239	90.61 79	98.95 156	94.62 97	98.68 101	98.54 132

HQP-NCC					95.86 219		96.65 191		93.55 71	90.14 216

ACMP_Plane					95.86 219		96.65 191		93.55 71	90.14 216

HQP-MVS		93.19 150	92.74 147	94.54 198	95.86 219	89.33 203	96.65 191	97.39 176	93.55 71	90.14 216	95.87 199	80.95 219	98.50 196	92.13 144	92.10 224	95.78 241

MCST-MVS		97.18 17	96.84 28	98.20 13	99.30 26	95.35 15	97.12 147	98.07 74	93.54 74	96.08 87	97.69 98	93.86 16	99.71 42	96.50 31	99.39 52	99.55 31

test1111		93.19 150	92.82 142	94.30 208	97.58 144	84.56 304	98.21 41	89.02 364	93.53 75	94.58 125	98.21 61	72.69 309	99.05 149	93.06 129	98.48 108	99.28 70

SR-MVS-dyc-post		96.88 37	96.80 32	97.11 73	99.02 46	92.34 99	97.98 56	98.03 88	93.52 76	97.43 36	98.51 25	91.40 62	99.56 85	96.05 49	99.26 68	99.43 54

RE-MVS-def			96.72 38		99.02 46	92.34 99	97.98 56	98.03 88	93.52 76	97.43 36	98.51 25	90.71 78		96.05 49	99.26 68	99.43 54

MG-MVS		95.61 78	95.38 78	96.31 106	98.42 83	90.53 162	96.04 238	97.48 155	93.47 78	95.67 106	98.10 67	89.17 93	99.25 124	91.27 165	98.77 98	99.13 82

test2506		91.60 207	90.78 215	94.04 217	97.66 136	83.81 311	98.27 31	75.53 376	93.43 79	95.23 115	98.21 61	67.21 340	99.07 146	93.01 133	98.49 106	99.25 73

ECVR-MVS		93.19 150	92.73 148	94.57 197	97.66 136	85.41 289	98.21 41	88.23 365	93.43 79	94.70 123	98.21 61	72.57 310	99.07 146	93.05 130	98.49 106	99.25 73

FC-MVSNet-test		93.94 125	93.57 118	95.04 169	95.48 235	91.45 131	98.12 47	98.71 6	93.37 81	90.23 214	96.70 153	87.66 112	97.85 269	91.49 160	90.39 251	95.83 238

MP-MVS		96.77 45	96.45 54	97.72 42	99.39 14	93.80 58	98.41 22	98.06 77	93.37 81	95.54 111	98.34 44	90.59 80	99.88 5	94.83 90	99.54 27	99.49 44
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
FIs		94.09 119	93.70 114	95.27 161	95.70 227	92.03 112	98.10 48	98.68 8	93.36 83	90.39 211	96.70 153	87.63 114	97.94 260	92.25 140	90.50 250	95.84 237

abl_6		96.40 59	96.21 60	96.98 77	98.89 57	92.20 107	97.89 65	98.03 88	93.34 84	97.22 42	98.42 34	87.93 109	99.72 39	95.10 81	99.07 88	99.02 91

mPP-MVS		96.86 38	96.60 44	97.64 49	99.40 12	93.44 69	98.50 17	98.09 67	93.27 85	95.95 94	98.33 47	91.04 71	99.88 5	95.20 77	99.57 24	99.60 19

HFP-MVS		97.14 20	96.92 23	97.83 29	99.42 7	94.12 49	98.52 14	98.32 22	93.21 86	97.18 43	98.29 53	92.08 43	99.83 26	95.63 67	99.59 17	99.54 34

ACMMPR		97.07 23	96.84 28	97.79 35	99.44 6	93.88 56	98.52 14	98.31 24	93.21 86	97.15 45	98.33 47	91.35 64	99.86 9	95.63 67	99.59 17	99.62 16

IS-MVSNet		94.90 99	94.52 100	96.05 120	97.67 134	90.56 161	98.44 20	96.22 263	93.21 86	93.99 136	97.74 95	85.55 144	98.45 201	89.98 180	97.86 123	99.14 81

region2R		97.07 23	96.84 28	97.77 38	99.46 2	93.79 59	98.52 14	98.24 38	93.19 89	97.14 46	98.34 44	91.59 59	99.87 8	95.46 74	99.59 17	99.64 13

EPNet_dtu		91.71 203	91.28 197	92.99 266	93.76 312	83.71 314	96.69 187	95.28 298	93.15 90	87.02 301	95.95 196	83.37 174	97.38 312	79.46 324	96.84 149	97.88 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet (Re)		93.31 145	92.55 155	95.61 145	95.39 238	93.34 74	97.39 118	98.71 6	93.14 91	90.10 224	94.83 248	87.71 111	98.03 246	91.67 158	83.99 320	95.46 256

APD-MVS_3200maxsize		96.81 42	96.71 39	97.12 72	99.01 49	92.31 102	97.98 56	98.06 77	93.11 92	97.44 34	98.55 22	90.93 73	99.55 88	96.06 48	99.25 70	99.51 39

testdata1							95.26 276		93.10 93

DU-MVS		92.90 164	92.04 169	95.49 154	94.95 268	92.83 85	97.16 143	98.24 38	93.02 94	90.13 220	95.71 212	83.47 171	97.85 269	91.71 154	83.93 321	95.78 241

xiu_mvs_v1_base_debu		95.01 93	94.76 91	95.75 135	96.58 184	91.71 118	96.25 227	97.35 182	92.99 95	96.70 59	96.63 162	82.67 190	99.44 108	96.22 40	97.46 132	96.11 228

xiu_mvs_v1_base		95.01 93	94.76 91	95.75 135	96.58 184	91.71 118	96.25 227	97.35 182	92.99 95	96.70 59	96.63 162	82.67 190	99.44 108	96.22 40	97.46 132	96.11 228

xiu_mvs_v1_base_debi		95.01 93	94.76 91	95.75 135	96.58 184	91.71 118	96.25 227	97.35 182	92.99 95	96.70 59	96.63 162	82.67 190	99.44 108	96.22 40	97.46 132	96.11 228

CP-MVS		97.02 27	96.81 31	97.64 49	99.33 23	93.54 66	98.80 7	98.28 28	92.99 95	96.45 76	98.30 52	91.90 49	99.85 18	95.61 69	99.68 4	99.54 34

ACMMP		96.27 63	95.93 65	97.28 62	99.24 30	92.62 92	98.25 34	98.81 3	92.99 95	94.56 126	98.39 38	88.96 95	99.85 18	94.57 99	97.63 129	99.36 63
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
UniMVSNet_NR-MVSNet		93.37 143	92.67 150	95.47 157	95.34 244	92.83 85	97.17 142	98.58 11	92.98 100	90.13 220	95.80 204	88.37 105	97.85 269	91.71 154	83.93 321	95.73 247

VPNet		92.23 189	91.31 195	94.99 172	95.56 231	90.96 149	97.22 138	97.86 117	92.96 101	90.96 203	96.62 165	75.06 296	98.20 219	91.90 148	83.65 326	95.80 240

nrg030		94.05 121	93.31 131	96.27 110	95.22 255	94.59 32	98.34 25	97.46 160	92.93 102	91.21 201	96.64 158	87.23 123	98.22 216	94.99 86	85.80 292	95.98 232

TranMVSNet+NR-MVSNet		92.50 174	91.63 183	95.14 166	94.76 279	92.07 110	97.53 104	98.11 63	92.90 103	89.56 241	96.12 188	83.16 176	97.60 293	89.30 198	83.20 330	95.75 245

diffmvs		95.25 87	95.13 85	95.63 143	96.43 196	89.34 202	95.99 243	97.35 182	92.83 104	96.31 79	97.37 122	86.44 131	98.67 182	96.26 37	97.19 145	98.87 113

ACMMP_NAP		97.20 16	96.86 25	98.23 11	99.09 38	95.16 24	97.60 99	98.19 48	92.82 105	97.93 25	98.74 13	91.60 58	99.86 9	96.26 37	99.52 29	99.67 11

test_prior3		96.46 57	96.20 61	97.23 65	98.67 65	92.99 81	96.35 217	98.00 97	92.80 106	96.03 88	97.59 110	92.01 45	99.41 111	95.01 83	99.38 53	99.29 67

test_prior2							96.35 217		92.80 106	96.03 88	97.59 110	92.01 45		95.01 83	99.38 53

testtj		96.93 34	96.56 47	98.05 20	99.10 36	94.66 31	97.78 76	98.22 43	92.74 108	97.59 29	98.20 64	91.96 48	99.86 9	94.21 102	99.25 70	99.63 14

GST-MVS		96.85 39	96.52 49	97.82 32	99.36 20	94.14 48	98.29 29	98.13 58	92.72 109	96.70 59	98.06 71	91.35 64	99.86 9	94.83 90	99.28 64	99.47 49

CLD-MVS		92.98 159	92.53 157	94.32 207	96.12 213	89.20 209	95.28 272	97.47 158	92.66 110	89.90 229	95.62 217	80.58 226	98.40 203	92.73 135	92.40 217	95.38 264
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NR-MVSNet		92.34 181	91.27 198	95.53 150	94.95 268	93.05 80	97.39 118	98.07 74	92.65 111	84.46 323	95.71 212	85.00 150	97.77 279	89.71 187	83.52 327	95.78 241

ZNCC-MVS		96.96 31	96.67 41	97.85 28	99.37 17	94.12 49	98.49 18	98.18 50	92.64 112	96.39 78	98.18 65	91.61 57	99.88 5	95.59 72	99.55 25	99.57 24

#test#		97.02 27	96.75 36	97.83 29	99.42 7	94.12 49	98.15 46	98.32 22	92.57 113	97.18 43	98.29 53	92.08 43	99.83 26	95.12 80	99.59 17	99.54 34

PS-MVSNAJss		93.74 132	93.51 123	94.44 200	93.91 307	89.28 207	97.75 78	97.56 150	92.50 114	89.94 228	96.54 168	88.65 100	98.18 222	93.83 114	90.90 244	95.86 234

VDD-MVS		93.82 129	93.08 135	96.02 122	97.88 124	89.96 181	97.72 84	95.85 275	92.43 115	95.86 96	98.44 31	68.42 334	99.39 114	96.31 35	94.85 185	98.71 125

LCM-MVSNet-Re		92.50 174	92.52 158	92.44 280	96.82 175	81.89 329	96.92 165	93.71 339	92.41 116	84.30 325	94.60 260	85.08 149	97.03 321	91.51 159	97.36 138	98.40 150

SF-MVS		97.39 11	97.13 13	98.17 14	99.02 46	95.28 20	98.23 38	98.27 31	92.37 117	98.27 18	98.65 16	93.33 21	99.72 39	96.49 32	99.52 29	99.51 39

VPA-MVSNet		93.24 147	92.48 160	95.51 151	95.70 227	92.39 98	97.86 67	98.66 10	92.30 118	92.09 181	95.37 228	80.49 228	98.40 203	93.95 108	85.86 291	95.75 245

bset_n11_16_dypcd		91.55 212	90.59 224	94.44 200	91.51 346	90.25 171	92.70 334	93.42 342	92.27 119	90.22 215	94.74 253	78.42 266	97.80 274	94.19 103	87.86 273	95.29 275

PGM-MVS		96.81 42	96.53 48	97.65 47	99.35 22	93.53 67	97.65 92	98.98 1	92.22 120	97.14 46	98.44 31	91.17 69	99.85 18	94.35 100	99.46 43	99.57 24

Vis-MVSNet (Re-imp)		94.15 114	93.88 110	94.95 177	97.61 140	87.92 242	98.10 48	95.80 277	92.22 120	93.02 158	97.45 118	84.53 156	97.91 266	88.24 217	97.97 121	99.02 91

thres100view900		92.43 177	91.58 185	94.98 174	97.92 121	89.37 201	97.71 86	94.66 322	92.20 122	93.31 153	94.90 244	78.06 274	99.08 143	81.40 309	94.08 196	96.48 217

baseline1		92.82 169	91.90 175	95.55 149	97.20 151	90.77 157	97.19 140	94.58 325	92.20 122	92.36 171	96.34 179	84.16 162	98.21 217	89.20 204	83.90 324	97.68 183

tfpn200view9		92.38 180	91.52 188	94.95 177	97.85 125	89.29 205	97.41 114	94.88 317	92.19 124	93.27 155	94.46 267	78.17 270	99.08 143	81.40 309	94.08 196	96.48 217

thres400		92.42 178	91.52 188	95.12 168	97.85 125	89.29 205	97.41 114	94.88 317	92.19 124	93.27 155	94.46 267	78.17 270	99.08 143	81.40 309	94.08 196	96.98 201

thres600view7		92.49 176	91.60 184	95.18 164	97.91 122	89.47 195	97.65 92	94.66 322	92.18 126	93.33 152	94.91 243	78.06 274	99.10 138	81.61 306	94.06 199	96.98 201

Fast-Effi-MVS+-dtu		92.29 185	91.99 172	93.21 260	95.27 251	85.52 287	97.03 149	96.63 246	92.09 127	89.11 255	95.14 236	80.33 232	98.08 236	87.54 238	94.74 190	96.03 231

thres200		92.23 189	91.39 191	94.75 190	97.61 140	89.03 214	96.60 199	95.09 308	92.08 128	93.28 154	94.00 290	78.39 268	99.04 152	81.26 313	94.18 195	96.19 222

mvs_tets		92.31 183	91.76 178	93.94 226	93.41 322	88.29 230	97.63 97	97.53 151	92.04 129	88.76 264	96.45 172	74.62 298	98.09 235	93.91 110	91.48 233	95.45 258

OMC-MVS		95.09 92	94.70 94	96.25 113	98.46 80	91.28 134	96.43 207	97.57 147	92.04 129	94.77 122	97.96 79	87.01 125	99.09 141	91.31 164	96.77 151	98.36 154

jajsoiax		92.42 178	91.89 176	94.03 218	93.33 325	88.50 227	97.73 81	97.53 151	92.00 131	88.85 260	96.50 170	75.62 294	98.11 230	93.88 112	91.56 232	95.48 253

XVG-OURS		93.72 133	93.35 130	94.80 186	97.07 158	88.61 222	94.79 282	97.46 160	91.97 132	93.99 136	97.86 85	81.74 211	98.88 163	92.64 136	92.67 214	96.92 205

WR-MVS		92.34 181	91.53 187	94.77 188	95.13 260	90.83 154	96.40 212	97.98 102	91.88 133	89.29 250	95.54 223	82.50 195	97.80 274	89.79 186	85.27 300	95.69 248

PAPM_NR		95.01 93	94.59 96	96.26 111	98.89 57	90.68 159	97.24 132	97.73 126	91.80 134	92.93 164	96.62 165	89.13 94	99.14 135	89.21 203	97.78 126	98.97 99

testgi		87.97 294	87.21 294	90.24 325	92.86 331	80.76 335	96.67 190	94.97 313	91.74 135	85.52 314	95.83 202	62.66 356	94.47 355	76.25 339	88.36 269	95.48 253

CP-MVSNet		91.89 199	91.24 199	93.82 231	95.05 263	88.57 224	97.82 72	98.19 48	91.70 136	88.21 277	95.76 209	81.96 206	97.52 301	87.86 223	84.65 309	95.37 265

XVG-OURS-SEG-HR		93.86 128	93.55 119	94.81 183	97.06 161	88.53 226	95.28 272	97.45 166	91.68 137	94.08 135	97.68 99	82.41 198	98.90 161	93.84 113	92.47 216	96.98 201

OurMVSNet-221017-0		90.51 259	90.19 244	91.44 306	93.41 322	81.25 333	96.98 159	96.28 259	91.68 137	86.55 307	96.30 180	74.20 301	97.98 250	88.96 208	87.40 279	95.09 279

ETH3D-3000-0.1		97.07 23	96.71 39	98.14 16	98.90 54	95.33 17	97.68 88	98.24 38	91.57 139	97.90 26	98.37 39	92.61 33	99.66 56	95.59 72	99.51 33	99.43 54

ACMP	89.59 10	92.62 173	92.14 167	94.05 216	96.40 197	88.20 235	97.36 121	97.25 190	91.52 140	88.30 273	96.64 158	78.46 265	98.72 177	91.86 151	91.48 233	95.23 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
APD-MVS		96.95 32	96.60 44	98.01 22	99.03 45	94.93 28	97.72 84	98.10 65	91.50 141	98.01 22	98.32 49	92.33 39	99.58 75	94.85 88	99.51 33	99.53 38
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ITE_SJBPF				92.43 281	95.34 244	85.37 292		95.92 271	91.47 142	87.75 287	96.39 177	71.00 319	97.96 257	82.36 303	89.86 256	93.97 326

PS-CasMVS		91.55 212	90.84 213	93.69 238	94.96 267	88.28 231	97.84 71	98.24 38	91.46 143	88.04 281	95.80 204	79.67 244	97.48 303	87.02 248	84.54 314	95.31 268

WR-MVS_H		92.00 196	91.35 192	93.95 224	95.09 262	89.47 195	98.04 53	98.68 8	91.46 143	88.34 271	94.68 256	85.86 140	97.56 295	85.77 268	84.24 317	94.82 297

RRT_MVS		93.21 148	92.32 164	95.91 128	94.92 270	94.15 47	96.92 165	96.86 228	91.42 145	91.28 198	96.43 173	79.66 245	98.10 231	93.29 124	90.06 253	95.46 256

MVSFormer		95.37 83	95.16 84	95.99 126	96.34 200	91.21 138	98.22 39	97.57 147	91.42 145	96.22 82	97.32 123	86.20 136	97.92 263	94.07 105	99.05 89	98.85 114

test_djsdf		93.07 155	92.76 144	94.00 219	93.49 320	88.70 221	98.22 39	97.57 147	91.42 145	90.08 226	95.55 222	82.85 187	97.92 263	94.07 105	91.58 231	95.40 262

ACMM	89.79 8	92.96 160	92.50 159	94.35 205	96.30 202	88.71 220	97.58 100	97.36 181	91.40 148	90.53 207	96.65 157	79.77 242	98.75 173	91.24 166	91.64 229	95.59 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_test8_iter05		91.19 235	90.78 215	92.41 282	95.76 226	83.14 320	97.32 125	97.46 160	91.37 149	89.07 256	95.57 219	70.33 323	98.21 217	93.56 116	86.62 286	95.89 233

PEN-MVS		91.20 232	90.44 229	93.48 247	94.49 290	87.91 244	97.76 77	98.18 50	91.29 150	87.78 286	95.74 211	80.35 231	97.33 314	85.46 272	82.96 331	95.19 278

LPG-MVS_test		92.94 162	92.56 154	94.10 213	96.16 209	88.26 232	97.65 92	97.46 160	91.29 150	90.12 222	97.16 131	79.05 253	98.73 174	92.25 140	91.89 227	95.31 268

LGP-MVS_train				94.10 213	96.16 209	88.26 232		97.46 160	91.29 150	90.12 222	97.16 131	79.05 253	98.73 174	92.25 140	91.89 227	95.31 268

9.14			96.75 36		98.93 50		97.73 81	98.23 42	91.28 153	97.88 27	98.44 31	93.00 25	99.65 57	95.76 60	99.47 41

MVSTER		93.20 149	92.81 143	94.37 204	96.56 187	89.59 189	97.06 148	97.12 198	91.24 154	91.30 195	95.96 195	82.02 205	98.05 242	93.48 119	90.55 248	95.47 255

test_yl		94.78 104	94.23 106	96.43 97	97.74 131	91.22 136	96.85 170	97.10 200	91.23 155	95.71 101	96.93 140	84.30 159	99.31 120	93.10 127	95.12 181	98.75 119

DCV-MVSNet		94.78 104	94.23 106	96.43 97	97.74 131	91.22 136	96.85 170	97.10 200	91.23 155	95.71 101	96.93 140	84.30 159	99.31 120	93.10 127	95.12 181	98.75 119

MVS_Test		94.89 100	94.62 95	95.68 141	96.83 174	89.55 191	96.70 185	97.17 194	91.17 157	95.60 108	96.11 191	87.87 110	98.76 172	93.01 133	97.17 146	98.72 123

HPM-MVS		96.69 48	96.45 54	97.40 56	99.36 20	93.11 79	98.87 5	98.06 77	91.17 157	96.40 77	97.99 77	90.99 72	99.58 75	95.61 69	99.61 16	99.49 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test-LLR		91.42 219	91.19 202	92.12 287	94.59 287	80.66 336	94.29 299	92.98 345	91.11 159	90.76 205	92.37 323	79.02 255	98.07 239	88.81 210	96.74 152	97.63 184

test0.0.03 1		89.37 279	88.70 277	91.41 307	92.47 339	85.63 285	95.22 277	92.70 348	91.11 159	86.91 304	93.65 304	79.02 255	93.19 362	78.00 331	89.18 261	95.41 259

XVG-ACMP-BASELINE		90.93 245	90.21 243	93.09 263	94.31 298	85.89 282	95.33 269	97.26 188	91.06 161	89.38 246	95.44 227	68.61 332	98.60 188	89.46 194	91.05 240	94.79 302

Effi-MVS+		94.93 98	94.45 103	96.36 104	96.61 181	91.47 129	96.41 209	97.41 175	91.02 162	94.50 127	95.92 197	87.53 116	98.78 169	93.89 111	96.81 150	98.84 116

SCA		91.84 200	91.18 203	93.83 230	95.59 229	84.95 299	94.72 283	95.58 287	90.82 163	92.25 175	93.69 300	75.80 291	98.10 231	86.20 258	95.98 165	98.45 144

SixPastTwentyTwo		89.15 280	88.54 280	90.98 313	93.49 320	80.28 343	96.70 185	94.70 321	90.78 164	84.15 328	95.57 219	71.78 314	97.71 283	84.63 282	85.07 304	94.94 286

PC_three_1452									90.77 165	98.89 8	98.28 56	96.24 1	98.35 208	95.76 60	99.58 22	99.59 20

DTE-MVSNet		90.56 257	89.75 261	93.01 265	93.95 305	87.25 254	97.64 96	97.65 138	90.74 166	87.12 297	95.68 215	79.97 239	97.00 325	83.33 293	81.66 336	94.78 304

GA-MVS		91.38 221	90.31 234	94.59 192	94.65 284	87.62 249	94.34 296	96.19 265	90.73 167	90.35 212	93.83 294	71.84 313	97.96 257	87.22 244	93.61 205	98.21 159

EPP-MVSNet		95.22 89	95.04 87	95.76 133	97.49 146	89.56 190	98.67 9	97.00 213	90.69 168	94.24 132	97.62 108	89.79 90	98.81 167	93.39 123	96.49 160	98.92 105

MP-MVS-pluss		96.70 47	96.27 58	97.98 24	99.23 32	94.71 30	96.96 161	98.06 77	90.67 169	95.55 109	98.78 12	91.07 70	99.86 9	96.58 29	99.55 25	99.38 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
IterMVS-LS		92.29 185	91.94 174	93.34 254	96.25 203	86.97 263	96.57 203	97.05 207	90.67 169	89.50 244	94.80 250	86.59 127	97.64 288	89.91 182	86.11 290	95.40 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		93.03 157	92.88 141	93.48 247	95.77 224	86.98 262	96.44 205	97.12 198	90.66 171	91.30 195	97.64 106	86.56 128	98.05 242	89.91 182	90.55 248	95.41 259

K. test v3		87.64 298	86.75 299	90.32 324	93.02 330	79.48 350	96.61 197	92.08 353	90.66 171	80.25 349	94.09 287	67.21 340	96.65 332	85.96 266	80.83 339	94.83 295

tttt0517		92.96 160	92.33 163	94.87 180	97.11 156	87.16 259	97.97 60	92.09 352	90.63 173	93.88 140	97.01 139	76.50 285	99.06 148	90.29 179	95.45 176	98.38 152

BH-RMVSNet		92.72 172	91.97 173	94.97 175	97.16 153	87.99 241	96.15 234	95.60 285	90.62 174	91.87 184	97.15 133	78.41 267	98.57 192	83.16 294	97.60 130	98.36 154

IterMVS-SCA-FT		90.31 262	89.81 257	91.82 295	95.52 233	84.20 308	94.30 298	96.15 266	90.61 175	87.39 293	94.27 278	75.80 291	96.44 333	87.34 241	86.88 285	94.82 297

WTY-MVS		94.71 106	94.02 108	96.79 79	97.71 133	92.05 111	96.59 200	97.35 182	90.61 175	94.64 124	96.93 140	86.41 132	99.39 114	91.20 167	94.71 191	98.94 103

ET-MVSNet_ETH3D		91.49 216	90.11 245	95.63 143	96.40 197	91.57 126	95.34 268	93.48 341	90.60 177	75.58 357	95.49 225	80.08 236	96.79 330	94.25 101	89.76 257	98.52 134

SMA-MVS		97.35 13	97.03 17	98.30 8	99.06 42	95.42 10	97.94 62	98.18 50	90.57 178	98.85 9	98.94 1	93.33 21	99.83 26	96.72 25	99.68 4	99.63 14
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
LFMVS		93.60 136	92.63 151	96.52 88	98.13 112	91.27 135	97.94 62	93.39 343	90.57 178	96.29 80	98.31 50	69.00 330	99.16 132	94.18 104	95.87 168	99.12 85

HPM-MVS_fast		96.51 54	96.27 58	97.22 67	99.32 24	92.74 87	98.74 8	98.06 77	90.57 178	96.77 56	98.35 41	90.21 84	99.53 93	94.80 93	99.63 14	99.38 61

DPM-MVS		95.69 75	94.92 88	98.01 22	98.08 114	95.71 9	95.27 274	97.62 142	90.43 181	95.55 109	97.07 136	91.72 53	99.50 101	89.62 191	98.94 94	98.82 117

IU-MVS					99.42 7	95.39 11		97.94 106	90.40 182	98.94 5				97.41 12	99.66 10	99.74 7

PVSNet_Blended_VisFu		95.27 86	94.91 89	96.38 102	98.20 104	90.86 153	97.27 130	98.25 36	90.21 183	94.18 133	97.27 125	87.48 118	99.73 36	93.53 117	97.77 127	98.55 131

PVSNet_BlendedMVS		94.06 120	93.92 109	94.47 199	98.27 95	89.46 197	96.73 181	98.36 17	90.17 184	94.36 129	95.24 233	88.02 106	99.58 75	93.44 120	90.72 246	94.36 316

thisisatest0530		93.03 157	92.21 166	95.49 154	97.07 158	89.11 213	97.49 110	92.19 351	90.16 185	94.09 134	96.41 175	76.43 288	99.05 149	90.38 176	95.68 174	98.31 156

CNLPA		94.28 111	93.53 121	96.52 88	98.38 87	92.55 94	96.59 200	96.88 225	90.13 186	91.91 183	97.24 127	85.21 147	99.09 141	87.64 235	97.83 124	97.92 170

BH-untuned		92.94 162	92.62 152	93.92 228	97.22 149	86.16 280	96.40 212	96.25 262	90.06 187	89.79 233	96.17 186	83.19 175	98.35 208	87.19 245	97.27 142	97.24 198

IterMVS		90.15 268	89.67 263	91.61 302	95.48 235	83.72 313	94.33 297	96.12 267	89.99 188	87.31 296	94.15 286	75.78 293	96.27 336	86.97 249	86.89 284	94.83 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		94.34 110	93.68 116	96.31 106	98.59 75	91.68 121	96.59 200	97.81 120	89.87 189	92.15 177	97.06 137	83.62 170	99.54 90	89.34 197	98.07 119	97.70 182

UnsupCasMVSNet_eth		85.99 311	84.45 315	90.62 320	89.97 355	82.40 326	93.62 319	97.37 179	89.86 190	78.59 354	92.37 323	65.25 351	95.35 350	82.27 304	70.75 359	94.10 323

PHI-MVS		96.77 45	96.46 53	97.71 44	98.40 84	94.07 52	98.21 41	98.45 16	89.86 190	97.11 49	98.01 76	92.52 36	99.69 48	96.03 52	99.53 28	99.36 63

ETH3D cwj APD-0.16		96.56 53	96.06 63	98.05 20	98.26 98	95.19 22	96.99 157	98.05 84	89.85 192	97.26 40	98.22 60	91.80 51	99.69 48	94.84 89	99.28 64	99.27 72

mvs_anonymous		93.82 129	93.74 113	94.06 215	96.44 195	85.41 289	95.81 251	97.05 207	89.85 192	90.09 225	96.36 178	87.44 119	97.75 280	93.97 107	96.69 155	99.02 91

ab-mvs		93.57 138	92.55 155	96.64 81	97.28 148	91.96 116	95.40 266	97.45 166	89.81 194	93.22 157	96.28 181	79.62 246	99.46 105	90.74 172	93.11 208	98.50 137

test_part1		92.21 191	91.10 205	95.51 151	97.80 128	92.66 90	98.02 54	97.68 134	89.79 195	88.80 263	96.02 193	76.85 283	98.18 222	90.86 169	84.11 319	95.69 248

FMVSNet3		91.78 201	90.69 221	95.03 170	96.53 189	92.27 104	97.02 152	96.93 217	89.79 195	89.35 247	94.65 258	77.01 282	97.47 304	86.12 261	88.82 263	95.35 266

AUN-MVS		91.76 202	90.75 218	94.81 183	97.00 167	88.57 224	96.65 191	96.49 251	89.63 197	92.15 177	96.12 188	78.66 262	98.50 196	90.83 170	79.18 344	97.36 195

v2v482		91.59 208	90.85 212	93.80 232	93.87 309	88.17 237	96.94 164	96.88 225	89.54 198	89.53 242	94.90 244	81.70 212	98.02 247	89.25 201	85.04 306	95.20 277

PatchmatchNet		91.91 198	91.35 192	93.59 242	95.38 239	84.11 309	93.15 327	95.39 291	89.54 198	92.10 180	93.68 302	82.82 188	98.13 226	84.81 279	95.32 178	98.52 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		90.70 254	89.81 257	93.37 253	94.73 281	84.21 307	93.67 317	88.02 366	89.50 200	92.38 170	93.49 307	77.82 278	97.78 277	86.03 264	92.68 213	98.11 165

GeoE		93.89 126	93.28 132	95.72 139	96.96 169	89.75 185	98.24 37	96.92 221	89.47 201	92.12 179	97.21 129	84.42 157	98.39 206	87.71 228	96.50 159	99.01 95

v148		90.99 241	90.38 231	92.81 273	93.83 310	85.80 283	96.78 179	96.68 240	89.45 202	88.75 265	93.93 293	82.96 185	97.82 273	87.83 224	83.25 328	94.80 300

anonymousdsp		92.16 192	91.55 186	93.97 222	92.58 337	89.55 191	97.51 105	97.42 174	89.42 203	88.40 270	94.84 247	80.66 225	97.88 268	91.87 150	91.28 237	94.48 312

baseline2		91.63 206	90.86 210	93.94 226	94.33 296	86.32 274	95.92 246	91.64 356	89.37 204	86.94 302	94.69 255	81.62 213	98.69 180	88.64 214	94.57 192	96.81 209

IB-MVS	87.33 17	89.91 271	88.28 283	94.79 187	95.26 254	87.70 248	95.12 280	93.95 338	89.35 205	87.03 300	92.49 321	70.74 321	99.19 128	89.18 205	81.37 337	97.49 193
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
jason		94.84 102	94.39 105	96.18 115	95.52 233	90.93 151	96.09 236	96.52 250	89.28 206	96.01 92	97.32 123	84.70 153	98.77 171	95.15 79	98.91 96	98.85 114
jason: jason.
TAMVS		94.01 123	93.46 125	95.64 142	96.16 209	90.45 165	96.71 184	96.89 224	89.27 207	93.46 149	96.92 143	87.29 121	97.94 260	88.70 213	95.74 171	98.53 133

ZD-MVS					99.05 43	94.59 32		98.08 68	89.22 208	97.03 52	98.10 67	92.52 36	99.65 57	94.58 98	99.31 60

API-MVS		94.84 102	94.49 101	95.90 129	97.90 123	92.00 114	97.80 74	97.48 155	89.19 209	94.81 121	96.71 151	88.84 97	99.17 131	88.91 209	98.76 99	96.53 214

XXY-MVS		92.16 192	91.23 200	94.95 177	94.75 280	90.94 150	97.47 111	97.43 173	89.14 210	88.90 257	96.43 173	79.71 243	98.24 214	89.56 192	87.68 274	95.67 250

pm-mvs1		90.72 253	89.65 265	93.96 223	94.29 299	89.63 186	97.79 75	96.82 231	89.07 211	86.12 311	95.48 226	78.61 263	97.78 277	86.97 249	81.67 335	94.46 313

HY-MVS	89.66 9	93.87 127	92.95 139	96.63 83	97.10 157	92.49 96	95.64 258	96.64 243	89.05 212	93.00 159	95.79 207	85.77 142	99.45 107	89.16 206	94.35 193	97.96 167

CSCG		96.05 68	95.91 66	96.46 96	99.24 30	90.47 164	98.30 28	98.57 12	89.01 213	93.97 138	97.57 112	92.62 32	99.76 34	94.66 96	99.27 66	99.15 80

v8		91.29 229	90.53 228	93.57 244	94.15 300	88.12 239	97.34 122	97.06 206	88.99 214	88.32 272	94.26 280	83.08 179	98.01 248	87.62 236	83.92 323	94.57 311

PAPR		94.18 113	93.42 129	96.48 93	97.64 138	91.42 132	95.55 260	97.71 133	88.99 214	92.34 173	95.82 203	89.19 92	99.11 137	86.14 260	97.38 137	98.90 108

CDS-MVSNet		94.14 117	93.54 120	95.93 127	96.18 207	91.46 130	96.33 220	97.04 209	88.97 216	93.56 144	96.51 169	87.55 115	97.89 267	89.80 185	95.95 166	98.44 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sss		94.51 108	93.80 112	96.64 81	97.07 158	91.97 115	96.32 221	98.06 77	88.94 217	94.50 127	96.78 148	84.60 154	99.27 123	91.90 148	96.02 164	98.68 127

lupinMVS		94.99 97	94.56 97	96.29 109	96.34 200	91.21 138	95.83 250	96.27 260	88.93 218	96.22 82	96.88 145	86.20 136	98.85 164	95.27 76	99.05 89	98.82 117

D2MVS		91.30 228	90.95 207	92.35 283	94.71 282	85.52 287	96.18 233	98.21 44	88.89 219	86.60 306	93.82 296	79.92 240	97.95 259	89.29 199	90.95 243	93.56 330

v7n		90.76 249	89.86 254	93.45 250	93.54 317	87.60 250	97.70 87	97.37 179	88.85 220	87.65 288	94.08 288	81.08 218	98.10 231	84.68 281	83.79 325	94.66 309

PVSNet_Blended		94.87 101	94.56 97	95.81 132	98.27 95	89.46 197	95.47 264	98.36 17	88.84 221	94.36 129	96.09 192	88.02 106	99.58 75	93.44 120	98.18 116	98.40 150

ACMH+	87.92 14	90.20 266	89.18 272	93.25 257	96.48 193	86.45 273	96.99 157	96.68 240	88.83 222	84.79 322	96.22 183	70.16 326	98.53 194	84.42 286	88.04 270	94.77 305

GBi-Net		91.35 224	90.27 237	94.59 192	96.51 190	91.18 142	97.50 106	96.93 217	88.82 223	89.35 247	94.51 262	73.87 302	97.29 316	86.12 261	88.82 263	95.31 268

test1		91.35 224	90.27 237	94.59 192	96.51 190	91.18 142	97.50 106	96.93 217	88.82 223	89.35 247	94.51 262	73.87 302	97.29 316	86.12 261	88.82 263	95.31 268

FMVSNet2		91.31 227	90.08 246	94.99 172	96.51 190	92.21 105	97.41 114	96.95 215	88.82 223	88.62 266	94.75 252	73.87 302	97.42 309	85.20 276	88.55 268	95.35 266

V42		91.58 210	90.87 209	93.73 234	94.05 304	88.50 227	97.32 125	96.97 214	88.80 226	89.71 234	94.33 273	82.54 194	98.05 242	89.01 207	85.07 304	94.64 310

agg_prior1		96.22 65	95.77 69	97.56 51	98.67 65	93.79 59	96.28 225	98.00 97	88.76 227	95.68 103	97.55 116	92.70 31	99.57 83	95.01 83	99.32 58	99.32 65

BH-w/o		92.14 194	91.75 179	93.31 255	96.99 168	85.73 284	95.67 255	95.69 281	88.73 228	89.26 252	94.82 249	82.97 184	98.07 239	85.26 275	96.32 163	96.13 227

test20.03		86.14 310	85.40 308	88.35 333	90.12 353	80.06 345	95.90 247	95.20 303	88.59 229	81.29 342	93.62 305	71.43 316	92.65 363	71.26 357	81.17 338	92.34 346

train_agg		96.30 62	95.83 68	97.72 42	98.70 63	94.19 44	96.41 209	98.02 92	88.58 230	96.03 88	97.56 114	92.73 29	99.59 72	95.04 82	99.37 57	99.39 59

test_8					98.67 65	94.06 53	96.37 216	98.01 95	88.58 230	95.98 93	97.55 116	92.73 29	99.58 75

eth_miper_zixun_eth		91.02 240	90.59 224	92.34 284	95.33 247	84.35 305	94.10 304	96.90 222	88.56 232	88.84 261	94.33 273	84.08 163	97.60 293	88.77 212	84.37 316	95.06 281

tpmrst		91.44 218	91.32 194	91.79 297	95.15 258	79.20 352	93.42 322	95.37 293	88.55 233	93.49 148	93.67 303	82.49 196	98.27 213	90.41 175	89.34 260	97.90 171

ACMH	87.59 16	90.53 258	89.42 267	93.87 229	96.21 204	87.92 242	97.24 132	96.94 216	88.45 234	83.91 332	96.27 182	71.92 312	98.62 187	84.43 285	89.43 259	95.05 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Baseline_NR-MVSNet		91.20 232	90.62 222	92.95 268	93.83 310	88.03 240	97.01 156	95.12 307	88.42 235	89.70 235	95.13 237	83.47 171	97.44 307	89.66 190	83.24 329	93.37 334

v1144		91.37 223	90.60 223	93.68 239	93.89 308	88.23 234	96.84 172	97.03 211	88.37 236	89.69 236	94.39 269	82.04 204	97.98 250	87.80 225	85.37 297	94.84 294

DP-MVS Recon		95.68 76	95.12 86	97.37 57	99.19 33	94.19 44	97.03 149	98.08 68	88.35 237	95.09 118	97.65 103	89.97 88	99.48 103	92.08 147	98.59 104	98.44 147

tpm		90.25 264	89.74 262	91.76 300	93.92 306	79.73 348	93.98 306	93.54 340	88.28 238	91.99 182	93.25 312	77.51 280	97.44 307	87.30 243	87.94 271	98.12 162

v10		91.04 239	90.23 240	93.49 246	94.12 301	88.16 238	97.32 125	97.08 203	88.26 239	88.29 274	94.22 283	82.17 203	97.97 253	86.45 255	84.12 318	94.33 317

Fast-Effi-MVS+		93.46 140	92.75 146	95.59 146	96.77 177	90.03 174	96.81 176	97.13 197	88.19 240	91.30 195	94.27 278	86.21 135	98.63 185	87.66 234	96.46 162	98.12 162

DWT-MVSNet_test		90.76 249	89.89 253	93.38 252	95.04 264	83.70 315	95.85 249	94.30 333	88.19 240	90.46 209	92.80 316	73.61 306	98.50 196	88.16 218	90.58 247	97.95 169

c3_l		91.38 221	90.89 208	92.88 270	95.58 230	86.30 275	94.68 284	96.84 230	88.17 242	88.83 262	94.23 281	85.65 143	97.47 304	89.36 196	84.63 310	94.89 292

TEST9					98.70 63	94.19 44	96.41 209	98.02 92	88.17 242	96.03 88	97.56 114	92.74 28	99.59 72

ETH3 D test6400		96.16 66	95.52 72	98.07 19	98.90 54	95.06 26	97.03 149	98.21 44	88.16 244	96.64 64	97.70 97	91.18 68	99.67 53	92.44 137	99.47 41	99.48 46

MDTV_nov1_ep13			90.76 217		95.22 255	80.33 341	93.03 330	95.28 298	88.14 245	92.84 165	93.83 294	81.34 215	98.08 236	82.86 297	94.34 194

MAR-MVS		94.22 112	93.46 125	96.51 91	98.00 116	92.19 108	97.67 89	97.47 158	88.13 246	93.00 159	95.84 201	84.86 152	99.51 98	87.99 221	98.17 117	97.83 177
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
UniMVSNet_ETH3D		91.34 226	90.22 242	94.68 191	94.86 275	87.86 245	97.23 137	97.46 160	87.99 247	89.90 229	96.92 143	66.35 345	98.23 215	90.30 178	90.99 242	97.96 167

PatchMatch-RL		92.90 164	92.02 171	95.56 147	98.19 106	90.80 155	95.27 274	97.18 192	87.96 248	91.86 185	95.68 215	80.44 229	98.99 154	84.01 288	97.54 131	96.89 206

thisisatest0515		92.29 185	91.30 196	95.25 162	96.60 182	88.90 217	94.36 295	92.32 350	87.92 249	93.43 150	94.57 261	77.28 281	99.00 153	89.42 195	95.86 169	97.86 174

PVSNet	86.66 18	92.24 188	91.74 181	93.73 234	97.77 130	83.69 316	92.88 331	96.72 234	87.91 250	93.00 159	94.86 246	78.51 264	99.05 149	86.53 252	97.45 136	98.47 142

LTVRE_ROB	88.41 13	90.99 241	89.92 252	94.19 210	96.18 207	89.55 191	96.31 222	97.09 202	87.88 251	85.67 313	95.91 198	78.79 261	98.57 192	81.50 307	89.98 254	94.44 314
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
cl____		90.96 244	90.32 233	92.89 269	95.37 241	86.21 278	94.46 291	96.64 243	87.82 252	88.15 279	94.18 284	82.98 183	97.54 297	87.70 229	85.59 293	94.92 290

DIV-MVS_self_test		90.97 243	90.33 232	92.88 270	95.36 242	86.19 279	94.46 291	96.63 246	87.82 252	88.18 278	94.23 281	82.99 182	97.53 299	87.72 226	85.57 294	94.93 288

cl22		91.21 231	90.56 227	93.14 262	96.09 215	86.80 265	94.41 293	96.58 249	87.80 254	88.58 268	93.99 291	80.85 224	97.62 291	89.87 184	86.93 281	94.99 283

CPTT-MVS		95.57 80	95.19 83	96.70 80	99.27 28	91.48 128	98.33 26	98.11 63	87.79 255	95.17 117	98.03 73	87.09 124	99.61 66	93.51 118	99.42 48	99.02 91

miper_ehance_all_eth		91.59 208	91.13 204	92.97 267	95.55 232	86.57 272	94.47 289	96.88 225	87.77 256	88.88 259	94.01 289	86.22 134	97.54 297	89.49 193	86.93 281	94.79 302

v1192		91.07 237	90.23 240	93.58 243	93.70 313	87.82 246	96.73 181	97.07 204	87.77 256	89.58 239	94.32 275	80.90 223	97.97 253	86.52 253	85.48 295	94.95 284

F-COLMAP		93.58 137	92.98 138	95.37 160	98.40 84	88.98 215	97.18 141	97.29 187	87.75 258	90.49 208	97.10 135	85.21 147	99.50 101	86.70 251	96.72 154	97.63 184

1314		92.81 170	92.03 170	95.14 166	95.33 247	89.52 194	96.04 238	97.44 170	87.72 259	86.25 309	95.33 229	83.84 165	98.79 168	89.26 200	97.05 148	97.11 199

test-mter		90.19 267	89.54 266	92.12 287	94.59 287	80.66 336	94.29 299	92.98 345	87.68 260	90.76 205	92.37 323	67.67 336	98.07 239	88.81 210	96.74 152	97.63 184

TR-MVS		91.48 217	90.59 224	94.16 212	96.40 197	87.33 251	95.67 255	95.34 297	87.68 260	91.46 189	95.52 224	76.77 284	98.35 208	82.85 298	93.61 205	96.79 210

LF4IMVS		87.94 295	87.25 292	89.98 327	92.38 342	80.05 346	94.38 294	95.25 301	87.59 262	84.34 324	94.74 253	64.31 352	97.66 287	84.83 278	87.45 276	92.23 347

miper_lstm_enhance		90.50 260	90.06 249	91.83 294	95.33 247	83.74 312	93.86 311	96.70 239	87.56 263	87.79 285	93.81 297	83.45 173	96.92 327	87.39 240	84.62 311	94.82 297

TransMVSNet (Re)		88.94 282	87.56 289	93.08 264	94.35 295	88.45 229	97.73 81	95.23 302	87.47 264	84.26 326	95.29 230	79.86 241	97.33 314	79.44 325	74.44 354	93.45 333

v144192		91.06 238	90.28 236	93.39 251	93.66 315	87.23 256	96.83 173	97.07 204	87.43 265	89.69 236	94.28 277	81.48 214	98.00 249	87.18 246	84.92 308	94.93 288

原ACMM1				96.38 102	98.59 75	91.09 146		97.89 109	87.41 266	95.22 116	97.68 99	90.25 82	99.54 90	87.95 222	99.12 85	98.49 139

v1921920		90.85 247	90.03 250	93.29 256	93.55 316	86.96 264	96.74 180	97.04 209	87.36 267	89.52 243	94.34 272	80.23 234	97.97 253	86.27 256	85.21 301	94.94 286

USDC		88.94 282	87.83 287	92.27 285	94.66 283	84.96 298	93.86 311	95.90 273	87.34 268	83.40 334	95.56 221	67.43 338	98.19 221	82.64 302	89.67 258	93.66 329

PLC	91.00 6	94.11 118	93.43 127	96.13 116	98.58 77	91.15 145	96.69 187	97.39 176	87.29 269	91.37 191	96.71 151	88.39 104	99.52 97	87.33 242	97.13 147	97.73 180
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		89.70 276	88.40 281	93.60 241	95.15 258	90.10 173	97.56 102	98.16 54	87.28 270	86.16 310	94.63 259	77.57 279	98.05 242	74.48 344	84.59 312	92.65 342

TESTMET0.1,1		90.06 269	89.42 267	91.97 290	94.41 294	80.62 338	94.29 299	91.97 354	87.28 270	90.44 210	92.47 322	68.79 331	97.67 285	88.50 216	96.60 157	97.61 188

v1240		90.70 254	89.85 255	93.23 258	93.51 319	86.80 265	96.61 197	97.02 212	87.16 272	89.58 239	94.31 276	79.55 247	97.98 250	85.52 271	85.44 296	94.90 291

Patchmatch-RL test		87.38 299	86.24 300	90.81 316	88.74 362	78.40 356	88.12 359	93.17 344	87.11 273	82.17 340	89.29 349	81.95 207	95.60 346	88.64 214	77.02 348	98.41 149

CDPH-MVS		95.97 71	95.38 78	97.77 38	98.93 50	94.44 35	96.35 217	97.88 112	86.98 274	96.65 63	97.89 80	91.99 47	99.47 104	92.26 138	99.46 43	99.39 59

PM-MVS		83.48 322	81.86 326	88.31 334	87.83 365	77.59 357	93.43 321	91.75 355	86.91 275	80.63 345	89.91 346	44.42 368	95.84 342	85.17 277	76.73 350	91.50 354

CR-MVSNet		90.82 248	89.77 259	93.95 224	94.45 292	87.19 257	90.23 351	95.68 283	86.89 276	92.40 168	92.36 326	80.91 221	97.05 320	81.09 314	93.95 200	97.60 189

1112_ss		93.37 143	92.42 161	96.21 114	97.05 163	90.99 147	96.31 222	96.72 234	86.87 277	89.83 232	96.69 155	86.51 130	99.14 135	88.12 219	93.67 202	98.50 137

miper_enhance_ethall		91.54 214	91.01 206	93.15 261	95.35 243	87.07 261	93.97 307	96.90 222	86.79 278	89.17 254	93.43 311	86.55 129	97.64 288	89.97 181	86.93 281	94.74 306

CL-MVSNet_self_test		86.31 308	85.15 310	89.80 329	88.83 361	81.74 331	93.93 310	96.22 263	86.67 279	85.03 319	90.80 340	78.09 273	94.50 353	74.92 343	71.86 358	93.15 335

FMVSNet1		89.88 273	88.31 282	94.59 192	95.41 237	91.18 142	97.50 106	96.93 217	86.62 280	87.41 292	94.51 262	65.94 349	97.29 316	83.04 296	87.43 277	95.31 268

CHOSEN 280x420		93.12 153	92.72 149	94.34 206	96.71 180	87.27 253	90.29 350	97.72 129	86.61 281	91.34 192	95.29 230	84.29 161	98.41 202	93.25 125	98.94 94	97.35 196

MVS_0304		88.79 286	87.57 288	92.46 279	94.65 284	86.15 281	96.40 212	97.17 194	86.44 282	88.02 282	91.71 335	56.68 362	97.03 321	84.47 284	92.58 215	94.19 322

MIMVSNet		88.50 290	86.76 298	93.72 236	94.84 276	87.77 247	91.39 341	94.05 335	86.41 283	87.99 283	92.59 320	63.27 354	95.82 343	77.44 332	92.84 211	97.57 191

tpmvs		89.83 275	89.15 273	91.89 292	94.92 270	80.30 342	93.11 328	95.46 290	86.28 284	88.08 280	92.65 318	80.44 229	98.52 195	81.47 308	89.92 255	96.84 208

PAPM		91.52 215	90.30 235	95.20 163	95.30 250	89.83 183	93.38 323	96.85 229	86.26 285	88.59 267	95.80 204	84.88 151	98.15 225	75.67 342	95.93 167	97.63 184

VDDNet		93.05 156	92.07 168	96.02 122	96.84 172	90.39 169	98.08 50	95.85 275	86.22 286	95.79 99	98.46 29	67.59 337	99.19 128	94.92 87	94.85 185	98.47 142

MS-PatchMatch		90.27 263	89.77 259	91.78 298	94.33 296	84.72 302	95.55 260	96.73 233	86.17 287	86.36 308	95.28 232	71.28 317	97.80 274	84.09 287	98.14 118	92.81 339

MVP-Stereo		90.74 252	90.08 246	92.71 275	93.19 327	88.20 235	95.86 248	96.27 260	86.07 288	84.86 321	94.76 251	77.84 277	97.75 280	83.88 291	98.01 120	92.17 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous202405211		92.07 195	90.83 214	95.76 133	98.19 106	88.75 219	97.58 100	95.00 311	86.00 289	93.64 143	97.45 118	66.24 347	99.53 93	90.68 174	92.71 212	99.01 95

KD-MVS_self_test		85.95 312	84.95 311	88.96 332	89.55 359	79.11 353	95.13 279	96.42 254	85.91 290	84.07 330	90.48 341	70.03 327	94.82 352	80.04 318	72.94 357	92.94 337

CVMVSNet		91.23 230	91.75 179	89.67 330	95.77 224	74.69 361	96.44 205	94.88 317	85.81 291	92.18 176	97.64 106	79.07 252	95.58 347	88.06 220	95.86 169	98.74 121

our_test_3		88.78 287	87.98 286	91.20 311	92.45 340	82.53 323	93.61 320	95.69 281	85.77 292	84.88 320	93.71 299	79.99 238	96.78 331	79.47 323	86.24 287	94.28 320

MSDG		91.42 219	90.24 239	94.96 176	97.15 155	88.91 216	93.69 316	96.32 258	85.72 293	86.93 303	96.47 171	80.24 233	98.98 155	80.57 315	95.05 184	96.98 201

CHOSEN 1792x2688		94.15 114	93.51 123	96.06 119	98.27 95	89.38 200	95.18 278	98.48 15	85.60 294	93.76 142	97.11 134	83.15 177	99.61 66	91.33 163	98.72 100	99.19 76

KD-MVS_2432*1600		84.81 319	82.64 322	91.31 308	91.07 349	85.34 293	91.22 343	95.75 278	85.56 295	83.09 336	90.21 343	67.21 340	95.89 339	77.18 336	62.48 365	92.69 340

miper_refine_blended		84.81 319	82.64 322	91.31 308	91.07 349	85.34 293	91.22 343	95.75 278	85.56 295	83.09 336	90.21 343	67.21 340	95.89 339	77.18 336	62.48 365	92.69 340

AllTest		90.23 265	88.98 274	93.98 220	97.94 119	86.64 268	96.51 204	95.54 288	85.38 297	85.49 315	96.77 149	70.28 324	99.15 133	80.02 319	92.87 209	96.15 225

TestCases				93.98 220	97.94 119	86.64 268		95.54 288	85.38 297	85.49 315	96.77 149	70.28 324	99.15 133	80.02 319	92.87 209	96.15 225

Test_1112_low_res		92.84 168	91.84 177	95.85 131	97.04 164	89.97 180	95.53 262	96.64 243	85.38 297	89.65 238	95.18 234	85.86 140	99.10 138	87.70 229	93.58 207	98.49 139

EU-MVSNet		88.72 288	88.90 275	88.20 335	93.15 328	74.21 362	96.63 196	94.22 334	85.18 300	87.32 295	95.97 194	76.16 289	94.98 351	85.27 274	86.17 288	95.41 259

LS3D		93.57 138	92.61 153	96.47 94	97.59 142	91.61 122	97.67 89	97.72 129	85.17 301	90.29 213	98.34 44	84.60 154	99.73 36	83.85 292	98.27 113	98.06 166

dp		88.90 284	88.26 284	90.81 316	94.58 289	76.62 358	92.85 332	94.93 315	85.12 302	90.07 227	93.07 313	75.81 290	98.12 229	80.53 316	87.42 278	97.71 181

HyFIR lowres test		93.66 134	92.92 140	95.87 130	98.24 99	89.88 182	94.58 286	98.49 13	85.06 303	93.78 141	95.78 208	82.86 186	98.67 182	91.77 152	95.71 173	99.07 90

new-patchmatchnet		83.18 323	81.87 325	87.11 339	86.88 366	75.99 360	93.70 315	95.18 304	85.02 304	77.30 355	88.40 351	65.99 348	93.88 358	74.19 348	70.18 360	91.47 355

TDRefinement		86.53 304	84.76 314	91.85 293	82.23 369	84.25 306	96.38 215	95.35 294	84.97 305	84.09 329	94.94 241	65.76 350	98.34 211	84.60 283	74.52 353	92.97 336

OpenMVS	89.19 12	92.86 166	91.68 182	96.40 99	95.34 244	92.73 88	98.27 31	98.12 60	84.86 306	85.78 312	97.75 94	78.89 260	99.74 35	87.50 239	98.65 102	96.73 211

gm-plane-assit					93.22 326	78.89 355			84.82 307		93.52 306		98.64 184	87.72 226

PMMVS		92.86 166	92.34 162	94.42 203	94.92 270	86.73 267	94.53 288	96.38 256	84.78 308	94.27 131	95.12 238	83.13 178	98.40 203	91.47 161	96.49 160	98.12 162

pmmvs4		90.93 245	89.85 255	94.17 211	93.34 324	90.79 156	94.60 285	96.02 269	84.62 309	87.45 290	95.15 235	81.88 209	97.45 306	87.70 229	87.87 272	94.27 321

MDA-MVSNet-bldmvs		85.00 317	82.95 321	91.17 312	93.13 329	83.33 318	94.56 287	95.00 311	84.57 310	65.13 365	92.65 318	70.45 322	95.85 341	73.57 349	77.49 347	94.33 317

QAPM		93.45 141	92.27 165	96.98 77	96.77 177	92.62 92	98.39 24	98.12 60	84.50 311	88.27 275	97.77 93	82.39 199	99.81 30	85.40 273	98.81 97	98.51 136

ppachtmachnet_test		88.35 292	87.29 291	91.53 303	92.45 340	83.57 317	93.75 314	95.97 270	84.28 312	85.32 318	94.18 284	79.00 259	96.93 326	75.71 341	84.99 307	94.10 323

pmmvs5		89.86 274	88.87 276	92.82 272	92.86 331	86.23 277	96.26 226	95.39 291	84.24 313	87.12 297	94.51 262	74.27 300	97.36 313	87.61 237	87.57 275	94.86 293

CostFormer		91.18 236	90.70 220	92.62 278	94.84 276	81.76 330	94.09 305	94.43 327	84.15 314	92.72 166	93.77 298	79.43 248	98.20 219	90.70 173	92.18 222	97.90 171

FMVSNet5		87.29 300	85.79 304	91.78 298	94.80 278	87.28 252	95.49 263	95.28 298	84.09 315	83.85 333	91.82 332	62.95 355	94.17 356	78.48 328	85.34 299	93.91 327

MIMVSNet1		84.93 318	83.05 320	90.56 321	89.56 358	84.84 301	95.40 266	95.35 294	83.91 316	80.38 347	92.21 330	57.23 360	93.34 361	70.69 359	82.75 334	93.50 331

RPSCF		90.75 251	90.86 210	90.42 323	96.84 172	76.29 359	95.61 259	96.34 257	83.89 317	91.38 190	97.87 83	76.45 286	98.78 169	87.16 247	92.23 219	96.20 221

MDTV_nov1_ep13_2view						70.35 366	93.10 329		83.88 318	93.55 145		82.47 197		86.25 257		98.38 152

无先验							95.79 252	97.87 114	83.87 319				99.65 57	87.68 232		98.89 111

PVSNet_0	82.17 19	85.46 316	83.64 319	90.92 314	95.27 251	79.49 349	90.55 349	95.60 285	83.76 320	83.00 338	89.95 345	71.09 318	97.97 253	82.75 300	60.79 367	95.31 268

Anonymous20240521		86.42 306	85.44 306	89.34 331	90.33 352	79.79 347	96.73 181	95.92 271	83.71 321	83.25 335	91.36 338	63.92 353	96.01 337	78.39 330	85.36 298	92.22 348

TinyColmap		86.82 303	85.35 309	91.21 310	94.91 273	82.99 321	93.94 309	94.02 337	83.58 322	81.56 341	94.68 256	62.34 357	98.13 226	75.78 340	87.35 280	92.52 344

Anonymous20231206		87.09 301	86.14 302	89.93 328	91.22 348	80.35 340	96.11 235	95.35 294	83.57 323	84.16 327	93.02 314	73.54 307	95.61 345	72.16 353	86.14 289	93.84 328

pmmvs-eth3d		86.22 309	84.45 315	91.53 303	88.34 363	87.25 254	94.47 289	95.01 310	83.47 324	79.51 352	89.61 348	69.75 329	95.71 344	83.13 295	76.73 350	91.64 351

EG-PatchMatch MVS		87.02 302	85.44 306	91.76 300	92.67 335	85.00 297	96.08 237	96.45 253	83.41 325	79.52 351	93.49 307	57.10 361	97.72 282	79.34 326	90.87 245	92.56 343

ADS-MVSNet2		89.45 277	88.59 279	92.03 289	95.86 219	82.26 327	90.93 346	94.32 332	83.23 326	91.28 198	91.81 333	79.01 257	95.99 338	79.52 321	91.39 235	97.84 175

ADS-MVSNet		89.89 272	88.68 278	93.53 245	95.86 219	84.89 300	90.93 346	95.07 309	83.23 326	91.28 198	91.81 333	79.01 257	97.85 269	79.52 321	91.39 235	97.84 175

COLMAP_ROB	87.81 15	90.40 261	89.28 270	93.79 233	97.95 118	87.13 260	96.92 165	95.89 274	82.83 328	86.88 305	97.18 130	73.77 305	99.29 122	78.44 329	93.62 204	94.95 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testdata				95.46 158	98.18 108	88.90 217		97.66 136	82.73 329	97.03 52	98.07 70	90.06 85	98.85 164	89.67 189	98.98 92	98.64 129

DP-MVS		92.76 171	91.51 190	96.52 88	98.77 60	90.99 147	97.38 120	96.08 268	82.38 330	89.29 250	97.87 83	83.77 166	99.69 48	81.37 312	96.69 155	98.89 111

MDA-MVSNet_test_wron		85.87 313	84.23 317	90.80 318	92.38 342	82.57 322	93.17 325	95.15 305	82.15 331	67.65 361	92.33 329	78.20 269	95.51 348	77.33 333	79.74 340	94.31 319

YYNet1		85.87 313	84.23 317	90.78 319	92.38 342	82.46 325	93.17 325	95.14 306	82.12 332	67.69 360	92.36 326	78.16 272	95.50 349	77.31 334	79.73 341	94.39 315

PatchT		88.87 285	87.42 290	93.22 259	94.08 303	85.10 296	89.51 355	94.64 324	81.92 333	92.36 171	88.15 354	80.05 237	97.01 324	72.43 352	93.65 203	97.54 192

TAPA-MVS	90.10 7	92.30 184	91.22 201	95.56 147	98.33 91	89.60 188	96.79 177	97.65 138	81.83 334	91.52 188	97.23 128	87.94 108	98.91 160	71.31 356	98.37 111	98.17 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
旧先验2							95.94 245		81.66 335	97.34 39			98.82 166	92.26 138

新几何1				97.32 59	98.60 74	93.59 65		97.75 123	81.58 336	95.75 100	97.85 86	90.04 86	99.67 53	86.50 254	99.13 82	98.69 126

1121		94.71 106	93.83 111	97.34 58	98.57 78	93.64 64	96.04 238	97.73 126	81.56 337	95.68 103	97.85 86	90.23 83	99.65 57	87.68 232	99.12 85	98.73 122

Patchmatch-test		89.42 278	87.99 285	93.70 237	95.27 251	85.11 295	88.98 357	94.37 330	81.11 338	87.10 299	93.69 300	82.28 200	97.50 302	74.37 346	94.76 188	98.48 141

test_0402		86.46 305	84.79 313	91.45 305	95.02 265	85.55 286	96.29 224	94.89 316	80.90 339	82.21 339	93.97 292	68.21 335	97.29 316	62.98 364	88.68 267	91.51 353

gg-mvs-nofinetune		87.82 296	85.61 305	94.44 200	94.46 291	89.27 208	91.21 345	84.61 371	80.88 340	89.89 231	74.98 364	71.50 315	97.53 299	85.75 269	97.21 144	96.51 215

JIA-IIPM		88.26 293	87.04 297	91.91 291	93.52 318	81.42 332	89.38 356	94.38 329	80.84 341	90.93 204	80.74 362	79.22 251	97.92 263	82.76 299	91.62 230	96.38 219

Patchmtry		88.64 289	87.25 292	92.78 274	94.09 302	86.64 268	89.82 354	95.68 283	80.81 342	87.63 289	92.36 326	80.91 221	97.03 321	78.86 327	85.12 303	94.67 308

tpm2		89.96 270	89.21 271	92.23 286	94.91 273	81.25 333	93.78 313	94.42 328	80.62 343	91.56 187	93.44 309	76.44 287	97.94 260	85.60 270	92.08 226	97.49 193

pmmvs6		87.81 297	86.19 301	92.69 276	91.32 347	86.30 275	97.34 122	96.41 255	80.59 344	84.05 331	94.37 271	67.37 339	97.67 285	84.75 280	79.51 343	94.09 325

Anonymous20231211		90.63 256	89.42 267	94.27 209	98.24 99	89.19 211	98.05 52	97.89 109	79.95 345	88.25 276	94.96 240	72.56 311	98.13 226	89.70 188	85.14 302	95.49 252

cascas		91.20 232	90.08 246	94.58 196	94.97 266	89.16 212	93.65 318	97.59 145	79.90 346	89.40 245	92.92 315	75.36 295	98.36 207	92.14 143	94.75 189	96.23 220

Anonymous20240529		91.98 197	90.73 219	95.73 138	98.14 111	89.40 199	97.99 55	97.72 129	79.63 347	93.54 146	97.41 121	69.94 328	99.56 85	91.04 168	91.11 239	98.22 158

PCF-MVS	89.48 11	91.56 211	89.95 251	96.36 104	96.60 182	92.52 95	92.51 337	97.26 188	79.41 348	88.90 257	96.56 167	84.04 164	99.55 88	77.01 338	97.30 141	97.01 200
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test222					98.24 99	92.21 105	95.33 269	97.60 143	79.22 349	95.25 114	97.84 89	88.80 98			99.15 80	98.72 123

UnsupCasMVSNet_bld		82.13 326	79.46 329	90.14 326	88.00 364	82.47 324	90.89 348	96.62 248	78.94 350	75.61 356	84.40 360	56.63 363	96.31 335	77.30 335	66.77 363	91.63 352

N_pmnet		78.73 328	78.71 330	78.79 346	92.80 333	46.50 377	94.14 303	43.71 380	78.61 351	80.83 343	91.66 336	74.94 297	96.36 334	67.24 361	84.45 315	93.50 331

ANet_high		63.94 335	59.58 338	77.02 347	61.24 378	66.06 369	85.66 362	87.93 367	78.53 352	42.94 370	71.04 367	25.42 376	80.71 370	52.60 368	30.83 371	84.28 362

114514_t		93.95 124	93.06 136	96.63 83	99.07 41	91.61 122	97.46 113	97.96 104	77.99 353	93.00 159	97.57 112	86.14 138	99.33 118	89.22 202	99.15 80	98.94 103

DSMNet-mixed		86.34 307	86.12 303	87.00 340	89.88 356	70.43 365	94.93 281	90.08 362	77.97 354	85.42 317	92.78 317	74.44 299	93.96 357	74.43 345	95.14 180	96.62 213

RPMNet		88.98 281	87.05 296	94.77 188	94.45 292	87.19 257	90.23 351	98.03 88	77.87 355	92.40 168	87.55 357	80.17 235	99.51 98	68.84 360	93.95 200	97.60 189

new_pmnet		82.89 324	81.12 328	88.18 336	89.63 357	80.18 344	91.77 340	92.57 349	76.79 356	75.56 358	88.23 353	61.22 358	94.48 354	71.43 355	82.92 332	89.87 359

tpm cat1		88.36 291	87.21 294	91.81 296	95.13 260	80.55 339	92.58 336	95.70 280	74.97 357	87.45 290	91.96 331	78.01 276	98.17 224	80.39 317	88.74 266	96.72 212

CMPMVS	62.92 21	85.62 315	84.92 312	87.74 337	89.14 360	73.12 364	94.17 302	96.80 232	73.98 358	73.65 359	94.93 242	66.36 344	97.61 292	83.95 290	91.28 237	92.48 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	81.14 20	84.42 321	82.28 324	90.83 315	90.06 354	84.05 310	95.73 254	94.04 336	73.89 359	80.17 350	91.53 337	59.15 359	97.64 288	66.92 362	89.05 262	90.80 357

MVS		91.71 203	90.44 229	95.51 151	95.20 257	91.59 124	96.04 238	97.45 166	73.44 360	87.36 294	95.60 218	85.42 145	99.10 138	85.97 265	97.46 132	95.83 238

pmmvs3		79.97 327	77.50 331	87.39 338	82.80 368	79.38 351	92.70 334	90.75 361	70.69 361	78.66 353	87.47 358	51.34 366	93.40 360	73.39 350	69.65 361	89.38 360

MVS-HIRNet		82.47 325	81.21 327	86.26 342	95.38 239	69.21 368	88.96 358	89.49 363	66.28 362	80.79 344	74.08 366	68.48 333	97.39 311	71.93 354	95.47 175	92.18 349

DeepMVS_CX				74.68 350	90.84 351	64.34 372		81.61 374	65.34 363	67.47 362	88.01 356	48.60 367	80.13 371	62.33 365	73.68 356	79.58 365

PMMVS2		70.19 331	66.92 334	80.01 345	76.35 370	65.67 370	86.22 360	87.58 368	64.83 364	62.38 366	80.29 363	26.78 375	88.49 366	63.79 363	54.07 368	85.88 361

FPMVS		71.27 330	69.85 332	75.50 348	74.64 371	59.03 373	91.30 342	91.50 357	58.80 365	57.92 367	88.28 352	29.98 373	85.53 368	53.43 367	82.84 333	81.95 364

LCM-MVSNet		72.55 329	69.39 333	82.03 344	70.81 376	65.42 371	90.12 353	94.36 331	55.02 366	65.88 363	81.72 361	24.16 377	89.96 364	74.32 347	68.10 362	90.71 358

Gipuma		67.86 333	65.41 335	75.18 349	92.66 336	73.45 363	66.50 368	94.52 326	53.33 367	57.80 368	66.07 368	30.81 371	89.20 365	48.15 369	78.88 346	62.90 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	53.92 22	58.58 336	55.40 339	68.12 352	51.00 379	48.64 375	78.86 365	87.10 370	46.77 368	35.84 374	74.28 365	8.76 378	86.34 367	42.07 370	73.91 355	69.38 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 337	52.56 341	55.43 354	74.43 372	47.13 376	83.63 364	76.30 375	42.23 369	42.59 371	62.22 370	28.57 374	74.40 372	31.53 372	31.51 370	44.78 369

EMVS		52.08 339	51.31 342	54.39 355	72.62 374	45.39 378	83.84 363	75.51 377	41.13 370	40.77 372	59.65 371	30.08 372	73.60 373	28.31 373	29.90 372	44.18 370

MVE	50.73 23	53.25 338	48.81 343	66.58 353	65.34 377	57.50 374	72.49 367	70.94 378	40.15 371	39.28 373	63.51 369	6.89 380	73.48 374	38.29 371	42.38 369	68.76 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		66.11 334	64.89 336	69.79 351	72.62 374	35.23 381	65.19 369	92.83 347	20.35 372	65.20 364	88.08 355	43.14 369	82.70 369	73.12 351	63.46 364	91.45 356

tmp_tt		51.94 340	53.82 340	46.29 356	33.73 380	45.30 379	78.32 366	67.24 379	18.02 373	50.93 369	87.05 359	52.99 365	53.11 375	70.76 358	25.29 373	40.46 371

wuyk23d		25.11 341	24.57 345	26.74 357	73.98 373	39.89 380	57.88 370	9.80 381	12.27 374	10.39 375	6.97 377	7.03 379	36.44 376	25.43 374	17.39 374	3.89 374

testmvs		13.36 343	16.33 346	4.48 359	5.04 381	2.26 383	93.18 324	3.28 382	2.70 375	8.24 376	21.66 373	2.29 382	2.19 377	7.58 375	2.96 375	9.00 373

test123		13.04 344	15.66 347	5.18 358	4.51 382	3.45 382	92.50 338	1.81 383	2.50 376	7.58 377	20.15 374	3.67 381	2.18 378	7.13 376	1.07 376	9.90 372

EGC-MVSNET		68.77 332	63.01 337	86.07 343	92.49 338	82.24 328	93.96 308	90.96 360	0.71 377	2.62 378	90.89 339	53.66 364	93.46 359	57.25 366	84.55 313	82.51 363

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

DCPMVS		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

cdsmvs_eth3d_5k		23.24 342	30.99 344	0.00 360	0.00 383	0.00 384	0.00 371	97.63 141	0.00 378	0.00 379	96.88 145	84.38 158	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		7.39 346	9.85 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	88.65 100	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		8.06 345	10.74 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	96.69 155	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

MSC_two_6792asdad				98.86 1	98.67 65	96.94 1		97.93 107					99.86 9	97.68 2	99.67 6	99.77 1

No_MVS				98.86 1	98.67 65	96.94 1		97.93 107					99.86 9	97.68 2	99.67 6	99.77 1

eth-test2					0.00 383

eth-test					0.00 383

OPU-MVS				98.55 3	98.82 59	96.86 3	98.25 34				98.26 57	96.04 2	99.24 125	95.36 75	99.59 17	99.56 27

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 41	98.28 28					99.86 9	97.52 5	99.67 6	99.75 5

GSMVS																98.45 144

test_part2					99.28 27	95.74 8				98.10 21

sam_mvs1												82.76 189				98.45 144

sam_mvs												81.94 208

ambc				86.56 341	83.60 367	70.00 367	85.69 361	94.97 313		80.60 346	88.45 350	37.42 370	96.84 329	82.69 301	75.44 352	92.86 338

MTGPA								98.08 68

test_post1							92.81 333				16.58 376	80.53 227	97.68 284	86.20 258

test_post											17.58 375	81.76 210	98.08 236

patchmatchnet-post											90.45 342	82.65 193	98.10 231

GG-mvs-BLEND				93.62 240	93.69 314	89.20 209	92.39 339	83.33 372		87.98 284	89.84 347	71.00 319	96.87 328	82.08 305	95.40 177	94.80 300

MTMP							97.86 67	82.03 373

test9_res														94.81 92	99.38 53	99.45 50

agg_prior2														93.94 109	99.38 53	99.50 42

agg_prior					98.67 65	93.79 59		98.00 97		95.68 103			99.57 83

test_prior4						93.66 63	96.42 208

test_prior				97.23 65	98.67 65	92.99 81		98.00 97					99.41 111			99.29 67

新几何2							95.79 252

旧先验1					98.38 87	93.38 71		97.75 123			98.09 69	92.30 42			99.01 91	99.16 78

原ACMM2							95.67 255

testdata2													99.67 53	85.96 266

segment_acmp												92.89 26

test12				97.65 47	98.46 80	94.26 41		97.66 136		95.52 112		90.89 74	99.46 105		99.25 70	99.22 75

plane_prior7					96.21 204	89.98 179

plane_prior6					96.10 214	90.00 175						81.32 216

plane_prior5								97.51 153					98.60 188	93.02 131	92.23 219	95.86 234

plane_prior4											96.64 158

plane_prior1					96.14 212

n2								0.00 384

nn								0.00 384

door-mid								91.06 359

lessismore_v0				90.45 322	91.96 345	79.09 354		87.19 369		80.32 348	94.39 269	66.31 346	97.55 296	84.00 289	76.84 349	94.70 307

test11								97.88 112

door								91.13 358

HQP5-MVS						89.33 203

BP-MVS														92.13 144

HQP4-MVS										90.14 216			98.50 196			95.78 241

HQP3-MVS								97.39 176							92.10 224

HQP2-MVS												80.95 219

NP-MVS					95.99 218	89.81 184					95.87 199

ACMMP++_ref															90.30 252

ACMMP++															91.02 241

Test By Simon												88.73 99

High-resolution multi-view benchmark