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 20	98.51 4	99.77 8				99.19 34	95.12 7	99.97 23	99.90 1	99.92 3	99.99 1

PC_three_1452									94.60 21	99.41 2	99.12 48	95.50 6	99.96 30	99.84 2	99.92 3	99.97 7

SED-MVS		98.18 2	98.10 4	98.41 17	99.63 21	95.24 24	99.77 8	97.72 76	94.17 26	99.30 6	99.54 3	93.32 18	99.98 10	99.70 3	99.81 23	99.99 1

test_241102_TWO								97.72 76	94.17 26	99.23 8	99.54 3	93.14 23	99.98 10	99.70 3	99.82 19	99.99 1

IU-MVS					99.63 21	95.38 21		97.73 74	95.54 15	99.54 1				99.69 5	99.81 23	99.99 1

DVP-MVS		98.07 7	98.00 6	98.29 18	99.66 15	95.20 29	99.72 13	97.47 134	93.95 31	99.07 11	99.46 11	93.18 21	99.97 23	99.64 6	99.82 19	99.69 65
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 7	99.66 15	96.37 13	99.72 13	97.68 85					99.98 10	99.64 6	99.82 19	99.96 10

patch_mono-2		97.10 27	97.97 8	94.49 168	99.21 73	83.73 282	99.62 27	98.25 27	95.28 18	99.38 4	98.91 79	92.28 27	99.94 37	99.61 8	99.22 83	99.78 42

DPE-MVS		98.11 6	98.00 6	98.44 15	99.50 47	95.39 20	99.29 72	97.72 76	94.50 22	98.64 24	99.54 3	93.32 18	99.97 23	99.58 9	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
MSC_two_6792asdad				99.51 2	99.61 27	98.60 2		97.69 83					99.98 10	99.55 10	99.83 15	99.96 10

No_MVS				99.51 2	99.61 27	98.60 2		97.69 83					99.98 10	99.55 10	99.83 15	99.96 10

ETH3 D test6400		97.67 11	97.33 18	98.69 9	99.69 9	96.43 11	99.63 25	97.73 74	91.05 99	98.66 23	99.53 7	90.59 42	99.71 78	99.32 12	99.80 27	99.91 22

DeepPCF-MVS	93.56 1	96.55 43	97.84 10	92.68 219	98.71 99	78.11 333	99.70 16	97.71 80	98.18 1	97.36 59	99.76 1	90.37 50	99.94 37	99.27 13	99.54 61	99.99 1

APDe-MVS		97.53 12	97.47 12	97.70 39	99.58 33	93.63 66	99.56 34	97.52 123	93.59 45	98.01 46	99.12 48	90.80 39	99.55 99	99.26 14	99.79 29	99.93 21

DVP-MVS++		98.18 2	98.09 5	98.44 15	99.61 27	95.38 21	99.55 35	97.68 85	93.01 52	99.23 8	99.45 16	95.12 7	99.98 10	99.25 15	99.92 3	99.97 7

test_0728_THIRD									93.01 52	99.07 11	99.46 11	94.66 12	99.97 23	99.25 15	99.82 19	99.95 15

dcpmvs_2		95.67 73	96.18 47	94.12 182	98.82 95	84.22 275	97.37 241	95.45 281	90.70 108	95.77 99	98.63 102	90.47 44	98.68 158	99.20 17	99.22 83	99.45 92

TSAR-MVS + GP.		96.95 31	96.91 27	97.07 62	98.88 92	91.62 105	99.58 31	96.54 205	95.09 19	96.84 73	98.63 102	91.16 30	99.77 72	99.04 18	96.42 142	99.81 35

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 9	99.70 16	97.98 46	97.18 2	95.96 91	99.33 23	92.62 25	100.00 1	98.99 19	99.93 1	99.98 6

CNVR-MVS		98.46 1	98.38 1	98.72 8	99.80 4	96.19 14	99.80 7	97.99 45	97.05 3	99.41 2	99.59 2	92.89 24	100.00 1	98.99 19	99.90 7	99.96 10

ETH3D-3000-0.1		97.29 17	97.01 24	98.12 25	99.18 75	94.97 33	99.47 45	97.52 123	89.85 133	98.79 20	99.46 11	90.41 49	99.69 80	98.78 21	99.67 42	99.70 62

CANet		97.00 29	96.49 39	98.55 11	98.86 94	96.10 15	99.83 4	97.52 123	95.90 9	97.21 61	98.90 80	82.66 177	99.93 40	98.71 22	98.80 100	99.63 74

9.14			96.87 28		99.34 58		99.50 42	97.49 131	89.41 149	98.59 26	99.43 18	89.78 56	99.69 80	98.69 23	99.62 51

SD-MVS		97.51 13	97.40 16	97.81 35	99.01 85	93.79 65	99.33 70	97.38 148	93.73 42	98.83 19	99.02 60	90.87 37	99.88 49	98.69 23	99.74 32	99.77 49
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
test9_res														98.60 25	99.87 9	99.90 24

PS-MVSNAJ		96.87 35	96.40 41	98.29 18	97.35 137	97.29 5	99.03 102	97.11 173	95.83 10	98.97 14	99.14 45	82.48 180	99.60 96	98.60 25	99.08 86	98.00 187

xiu_mvs_v2_base		96.66 39	96.17 51	98.11 27	97.11 147	96.96 6	99.01 105	97.04 180	95.51 16	98.86 17	99.11 53	82.19 186	99.36 126	98.59 27	98.14 115	98.00 187

train_agg		97.20 23	97.08 21	97.57 45	99.57 37	93.17 76	99.38 62	97.66 88	90.18 124	98.39 31	99.18 37	90.94 34	99.66 85	98.58 28	99.85 13	99.88 28

agg_prior1		97.12 25	97.03 23	97.38 53	99.54 40	92.66 88	99.35 67	97.64 94	90.38 118	97.98 47	99.17 39	90.84 38	99.61 94	98.57 29	99.78 31	99.87 31

TSAR-MVS + MP.		97.44 16	97.46 13	97.39 52	99.12 78	93.49 71	98.52 159	97.50 129	94.46 23	98.99 13	98.64 100	91.58 29	99.08 144	98.49 30	99.83 15	99.60 78
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xxxxxxxxxxxxxcwj		97.51 13	97.42 15	97.78 37	99.34 58	93.85 63	99.65 23	95.45 281	95.69 11	98.70 21	99.42 19	90.42 47	99.72 76	98.47 31	99.65 44	99.77 49

SF-MVS		97.22 22	96.92 26	98.12 25	99.11 79	94.88 36	99.44 53	97.45 137	89.60 142	98.70 21	99.42 19	90.42 47	99.72 76	98.47 31	99.65 44	99.77 49

ETH3D cwj APD-0.16		96.94 33	96.58 38	98.01 29	98.62 102	94.73 45	99.13 93	97.38 148	88.44 179	98.53 28	99.39 21	89.66 60	99.69 80	98.43 33	99.61 55	99.61 77

PHI-MVS		96.65 40	96.46 40	97.21 59	99.34 58	91.77 100	99.70 16	98.05 41	86.48 228	98.05 41	99.20 33	89.33 62	99.96 30	98.38 34	99.62 51	99.90 24

ZD-MVS					99.67 13	93.28 74		97.61 102	87.78 199	97.41 57	99.16 41	90.15 52	99.56 98	98.35 35	99.70 39

test_prior3		97.07 28	97.09 20	97.01 65	99.58 33	91.77 100	99.57 32	97.57 113	91.43 91	98.12 38	98.97 66	90.43 45	99.49 109	98.33 36	99.81 23	99.79 38

test_prior2							99.57 32		91.43 91	98.12 38	98.97 66	90.43 45		98.33 36	99.81 23

SMA-MVS		97.24 19	96.99 25	98.00 30	99.30 65	94.20 57	99.16 81	97.65 93	89.55 146	99.22 10	99.52 9	90.34 51	99.99 5	98.32 38	99.83 15	99.82 34
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 37	96.85 29	96.66 94	97.85 122	94.42 53	94.76 309	98.36 24	92.50 66	95.62 103	97.52 147	97.92 1	97.38 225	98.31 39	98.80 100	98.20 183

NCCC		98.12 5	98.11 3	98.13 23	99.76 6	94.46 50	99.81 5	97.88 51	96.54 5	98.84 18	99.46 11	92.55 26	99.98 10	98.25 40	99.93 1	99.94 18

testtj		97.23 21	97.05 22	97.75 38	99.75 7	93.34 73	99.16 81	97.74 70	91.28 96	98.40 30	99.29 24	89.95 54	99.98 10	98.20 41	99.70 39	99.94 18

MSP-MVS		97.77 9	98.18 2	96.53 101	99.54 40	90.14 143	99.41 59	97.70 81	95.46 17	98.60 25	99.19 34	95.71 4	99.49 109	98.15 42	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 59	96.00 57	96.00 120	96.56 163	91.05 123	99.63 25	96.61 196	93.26 50	97.39 58	98.30 120	86.62 114	98.13 174	98.07 43	97.57 123	98.82 147

MSLP-MVS++		97.50 15	97.45 14	97.63 41	99.65 19	93.21 75	99.70 16	98.13 38	94.61 20	97.78 52	99.46 11	89.85 55	99.81 67	97.97 44	99.91 6	99.88 28

APD-MVS		96.95 31	96.72 34	97.63 41	99.51 46	93.58 67	99.16 81	97.44 141	90.08 129	98.59 26	99.07 54	89.06 64	99.42 120	97.92 45	99.66 43	99.88 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SteuartSystems-ACMMP		97.25 18	97.34 17	97.01 65	97.38 136	91.46 109	99.75 12	97.66 88	94.14 30	98.13 36	99.26 26	92.16 28	99.66 85	97.91 46	99.64 47	99.90 24
Skip Steuart: Steuart Systems R&D Blog.
agg_prior2														97.84 47	99.87 9	99.91 22

HPM-MVS++		97.72 10	97.59 11	98.14 22	99.53 45	94.76 43	99.19 76	97.75 68	95.66 13	98.21 34	99.29 24	91.10 32	99.99 5	97.68 48	99.87 9	99.68 66

SR-MVS		96.13 56	96.16 53	96.07 118	99.42 54	89.04 168	98.59 154	97.33 153	90.44 116	96.84 73	99.12 48	86.75 109	99.41 122	97.47 49	99.44 67	99.76 52

PVSNet_BlendedMVS		93.36 132	93.20 121	93.84 195	98.77 97	91.61 106	99.47 45	98.04 42	91.44 90	94.21 124	92.63 259	83.50 157	99.87 52	97.41 50	83.37 253	90.05 319

PVSNet_Blended		95.94 63	95.66 70	96.75 86	98.77 97	91.61 106	99.88 1	98.04 42	93.64 44	94.21 124	97.76 135	83.50 157	99.87 52	97.41 50	97.75 122	98.79 150

test1172		95.92 64	96.07 56	95.46 136	99.42 54	87.24 215	98.51 162	97.24 157	90.29 121	96.56 83	99.12 48	86.73 111	99.36 126	97.33 52	99.42 71	99.78 42

DROMVSNet		95.09 86	95.17 79	94.84 156	95.42 204	88.17 187	99.48 43	95.92 245	91.47 89	97.34 60	98.36 117	82.77 173	97.41 224	97.24 53	98.58 108	98.94 136

MVS_111021_HR		96.69 38	96.69 35	96.72 90	98.58 104	91.00 125	99.14 90	99.45 1	93.86 37	95.15 110	98.73 92	88.48 73	99.76 73	97.23 54	99.56 59	99.40 95

Regformer-1		96.97 30	96.80 32	97.47 47	99.46 52	93.11 78	98.89 117	97.94 47	92.89 58	96.90 66	99.02 60	89.78 56	99.53 102	97.06 55	99.26 80	99.75 53

xiu_mvs_v1_base_debu		94.73 95	93.98 103	96.99 68	95.19 211	95.24 24	98.62 148	96.50 207	92.99 54	97.52 54	98.83 85	72.37 256	99.15 138	97.03 56	96.74 137	96.58 217

xiu_mvs_v1_base		94.73 95	93.98 103	96.99 68	95.19 211	95.24 24	98.62 148	96.50 207	92.99 54	97.52 54	98.83 85	72.37 256	99.15 138	97.03 56	96.74 137	96.58 217

xiu_mvs_v1_base_debi		94.73 95	93.98 103	96.99 68	95.19 211	95.24 24	98.62 148	96.50 207	92.99 54	97.52 54	98.83 85	72.37 256	99.15 138	97.03 56	96.74 137	96.58 217

Regformer-2		96.94 33	96.78 33	97.42 49	99.46 52	92.97 85	98.89 117	97.93 48	92.86 60	96.88 67	99.02 60	89.74 58	99.53 102	97.03 56	99.26 80	99.75 53

lupinMVS		96.32 51	95.94 59	97.44 48	95.05 224	94.87 37	99.86 2	96.50 207	93.82 40	98.04 42	98.77 88	85.52 132	98.09 177	96.98 60	98.97 91	99.37 98

MVS_111021_LR		95.78 69	95.94 59	95.28 143	98.19 113	87.69 196	98.80 125	99.26 7	93.39 47	95.04 112	98.69 98	84.09 152	99.76 73	96.96 61	99.06 87	98.38 172

VNet		95.08 87	94.26 93	97.55 46	98.07 116	93.88 62	98.68 139	98.73 16	90.33 120	97.16 63	97.43 151	79.19 209	99.53 102	96.91 62	91.85 197	99.24 110

APD-MVS_3200maxsize		95.64 74	95.65 72	95.62 131	99.24 70	87.80 195	98.42 172	97.22 160	88.93 163	96.64 82	98.98 65	85.49 135	99.36 126	96.68 63	99.27 79	99.70 62

CS-MVS		95.63 75	96.18 47	93.97 190	97.87 120	84.94 265	99.66 22	96.15 231	92.70 62	98.02 44	98.88 82	87.11 101	97.59 212	96.64 64	98.62 106	99.38 96

CS-MVS-test		95.38 80	95.85 63	93.97 190	97.87 120	84.94 265	99.62 27	95.84 258	92.71 61	98.02 44	98.30 120	85.07 141	97.59 212	96.64 64	98.62 106	99.38 96

SR-MVS-dyc-post		95.75 72	95.86 62	95.41 139	99.22 71	87.26 213	98.40 177	97.21 161	89.63 140	96.67 80	98.97 66	86.73 111	99.36 126	96.62 66	99.31 76	99.60 78

RE-MVS-def			95.70 69		99.22 71	87.26 213	98.40 177	97.21 161	89.63 140	96.67 80	98.97 66	85.24 140		96.62 66	99.31 76	99.60 78

DeepC-MVS_fast	93.52 2	97.16 24	96.84 30	98.13 23	99.61 27	94.45 51	98.85 120	97.64 94	96.51 7	95.88 94	99.39 21	87.35 98	99.99 5	96.61 68	99.69 41	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		91.24 175	90.18 181	94.45 171	97.08 148	85.84 248	98.40 177	96.10 233	86.99 214	93.36 137	98.16 127	54.27 342	99.20 135	96.59 69	90.63 213	98.31 178

MP-MVS-pluss		95.80 68	95.30 75	97.29 55	98.95 89	92.66 88	98.59 154	97.14 169	88.95 161	93.12 140	99.25 27	85.62 131	99.94 37	96.56 70	99.48 63	99.28 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
diffmvs		94.59 103	94.19 96	95.81 126	95.54 200	90.69 132	98.70 136	95.68 267	91.61 85	95.96 91	97.81 132	80.11 201	98.06 181	96.52 71	95.76 156	98.67 158

ACMMP_NAP		96.59 41	96.18 47	97.81 35	98.82 95	93.55 68	98.88 119	97.59 108	90.66 109	97.98 47	99.14 45	86.59 115	100.00 1	96.47 72	99.46 64	99.89 27

Regformer-3		96.50 44	96.36 43	96.91 76	99.34 58	91.72 103	98.71 132	97.90 50	92.48 67	96.00 88	98.95 73	88.60 70	99.52 105	96.44 73	98.83 97	99.49 88

Regformer-4		96.45 47	96.33 45	96.81 83	99.34 58	91.44 110	98.71 132	97.88 51	92.43 68	95.97 90	98.95 73	88.42 74	99.51 106	96.40 74	98.83 97	99.49 88

PAPM		96.35 49	95.94 59	97.58 43	94.10 246	95.25 23	98.93 112	98.17 33	94.26 25	93.94 129	98.72 94	89.68 59	97.88 190	96.36 75	99.29 78	99.62 76

zzz-MVS		96.21 55	95.96 58	96.96 73	99.29 66	91.19 114	98.69 137	97.45 137	92.58 63	94.39 121	99.24 29	86.43 121	99.99 5	96.22 76	99.40 72	99.71 60

MTAPA		96.09 57	95.80 67	96.96 73	99.29 66	91.19 114	97.23 249	97.45 137	92.58 63	94.39 121	99.24 29	86.43 121	99.99 5	96.22 76	99.40 72	99.71 60

alignmvs		95.77 70	95.00 83	98.06 28	97.35 137	95.68 18	99.71 15	97.50 129	91.50 88	96.16 87	98.61 104	86.28 124	99.00 146	96.19 78	91.74 199	99.51 86

canonicalmvs		95.02 88	93.96 106	98.20 20	97.53 134	95.92 16	98.71 132	96.19 228	91.78 83	95.86 96	98.49 112	79.53 206	99.03 145	96.12 79	91.42 205	99.66 70

DELS-MVS		97.12 25	96.60 37	98.68 10	98.03 117	96.57 10	99.84 3	97.84 55	96.36 8	95.20 109	98.24 123	88.17 78	99.83 62	96.11 80	99.60 56	99.64 72
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 79	94.86 84	97.03 64	92.91 277	94.23 56	99.70 16	96.30 218	93.56 46	96.73 78	98.52 108	81.46 195	97.91 187	96.08 81	98.47 112	98.96 131
jason: jason.
CP-MVS		96.22 54	96.15 54	96.42 106	99.67 13	89.62 161	99.70 16	97.61 102	90.07 130	96.00 88	99.16 41	87.43 92	99.92 41	96.03 82	99.72 34	99.70 62

MP-MVS		96.00 59	95.82 64	96.54 100	99.47 51	90.13 145	99.36 66	97.41 145	90.64 112	95.49 104	98.95 73	85.51 134	99.98 10	96.00 83	99.59 58	99.52 84
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
#test#		96.48 45	96.34 44	96.90 77	99.69 9	90.96 126	99.53 40	97.81 60	90.94 103	96.88 67	99.05 57	87.57 88	99.96 30	95.87 84	99.72 34	99.78 42

h-mvs33		92.47 152	91.95 148	94.05 186	97.13 145	85.01 263	98.36 182	98.08 39	93.85 38	96.27 85	96.73 183	83.19 166	99.43 119	95.81 85	68.09 340	97.70 192

hse-mvs2		91.67 166	91.51 157	92.15 228	96.22 176	82.61 299	97.74 228	97.53 120	93.85 38	96.27 85	96.15 196	83.19 166	97.44 222	95.81 85	66.86 345	96.40 222

HFP-MVS		96.42 48	96.26 46	96.90 77	99.69 9	90.96 126	99.47 45	97.81 60	90.54 114	96.88 67	99.05 57	87.57 88	99.96 30	95.65 87	99.72 34	99.78 42

XVS		96.47 46	96.37 42	96.77 84	99.62 25	90.66 134	99.43 56	97.58 110	92.41 72	96.86 70	98.96 71	87.37 94	99.87 52	95.65 87	99.43 68	99.78 42

X-MVStestdata		90.69 185	88.66 205	96.77 84	99.62 25	90.66 134	99.43 56	97.58 110	92.41 72	96.86 70	29.59 377	87.37 94	99.87 52	95.65 87	99.43 68	99.78 42

ACMMPR		96.28 53	96.14 55	96.73 88	99.68 12	90.47 137	99.47 45	97.80 62	90.54 114	96.83 75	99.03 59	86.51 119	99.95 34	95.65 87	99.72 34	99.75 53

HPM-MVS		95.41 78	95.22 78	95.99 121	99.29 66	89.14 166	99.17 80	97.09 177	87.28 212	95.40 105	98.48 113	84.93 143	99.38 124	95.64 91	99.65 44	99.47 91
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_yl		95.27 82	94.60 87	97.28 56	98.53 105	92.98 83	99.05 100	98.70 17	86.76 222	94.65 118	97.74 137	87.78 84	99.44 117	95.57 92	92.61 183	99.44 93

DCV-MVSNet		95.27 82	94.60 87	97.28 56	98.53 105	92.98 83	99.05 100	98.70 17	86.76 222	94.65 118	97.74 137	87.78 84	99.44 117	95.57 92	92.61 183	99.44 93

region2R		96.30 52	96.17 51	96.70 91	99.70 8	90.31 139	99.46 50	97.66 88	90.55 113	97.07 64	99.07 54	86.85 107	99.97 23	95.43 94	99.74 32	99.81 35

EI-MVSNet-Vis-set		95.76 71	95.63 74	96.17 115	99.14 77	90.33 138	98.49 166	97.82 57	91.92 80	94.75 115	98.88 82	87.06 103	99.48 114	95.40 95	97.17 134	98.70 157

EPNet		96.82 36	96.68 36	97.25 58	98.65 100	93.10 79	99.48 43	98.76 13	96.54 5	97.84 51	98.22 124	87.49 91	99.66 85	95.35 96	97.78 121	99.00 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		97.24 19	96.83 31	98.47 14	99.79 5	95.71 17	99.07 97	99.06 9	94.45 24	96.42 84	98.70 97	88.81 68	99.74 75	95.35 96	99.86 12	99.97 7

HY-MVS	88.56 7	95.29 81	94.23 94	98.48 13	97.72 124	96.41 12	94.03 317	98.74 14	92.42 71	95.65 102	94.76 217	86.52 118	99.49 109	95.29 98	92.97 178	99.53 83

mPP-MVS		95.90 65	95.75 68	96.38 108	99.58 33	89.41 165	99.26 73	97.41 145	90.66 109	94.82 114	98.95 73	86.15 126	99.98 10	95.24 99	99.64 47	99.74 56

ZNCC-MVS		96.09 57	95.81 66	96.95 75	99.42 54	91.19 114	99.55 35	97.53 120	89.72 137	95.86 96	98.94 78	86.59 115	99.97 23	95.13 100	99.56 59	99.68 66

GG-mvs-BLEND				96.98 71	96.53 164	94.81 42	87.20 348	97.74 70		93.91 130	96.40 191	96.56 2	96.94 239	95.08 101	98.95 94	99.20 114

EIA-MVS		95.11 85	95.27 77	94.64 164	96.34 172	86.51 224	99.59 30	96.62 195	92.51 65	94.08 127	98.64 100	86.05 127	98.24 171	95.07 102	98.50 111	99.18 115

DeepC-MVS	91.02 4	94.56 104	93.92 109	96.46 103	97.16 143	90.76 130	98.39 180	97.11 173	93.92 33	88.66 196	98.33 118	78.14 217	99.85 59	95.02 103	98.57 109	98.78 152
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WTY-MVS		95.97 61	95.11 81	98.54 12	97.62 128	96.65 8	99.44 53	98.74 14	92.25 75	95.21 108	98.46 116	86.56 117	99.46 116	95.00 104	92.69 182	99.50 87

CSCG		94.87 90	94.71 85	95.36 140	99.54 40	86.49 225	99.34 69	98.15 36	82.71 286	90.15 183	99.25 27	89.48 61	99.86 57	94.97 105	98.82 99	99.72 59

EI-MVSNet-UG-set		95.43 76	95.29 76	95.86 125	99.07 83	89.87 154	98.43 171	97.80 62	91.78 83	94.11 126	98.77 88	86.25 125	99.48 114	94.95 106	96.45 141	98.22 181

CPTT-MVS		94.60 102	94.43 91	95.09 147	99.66 15	86.85 220	99.44 53	97.47 134	83.22 276	94.34 123	98.96 71	82.50 178	99.55 99	94.81 107	99.50 62	98.88 140

PVSNet_0	83.28 16	87.31 242	85.16 256	93.74 198	94.78 234	84.59 270	98.91 115	98.69 19	89.81 135	78.59 305	93.23 247	61.95 317	99.34 131	94.75 108	55.72 362	97.30 202

CLD-MVS		91.06 176	90.71 174	92.10 229	94.05 249	86.10 239	99.55 35	96.29 221	94.16 28	84.70 227	97.17 165	69.62 275	97.82 194	94.74 109	86.08 233	92.39 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
casdiffmvs		93.98 113	93.43 116	95.61 133	95.07 223	89.86 155	98.80 125	95.84 258	90.98 101	92.74 145	97.66 142	79.71 203	98.10 176	94.72 110	95.37 160	98.87 142

VDDNet		90.08 197	88.54 210	94.69 162	94.41 241	87.68 197	98.21 194	96.40 212	76.21 333	93.33 138	97.75 136	54.93 340	98.77 151	94.71 111	90.96 208	97.61 197

CDPH-MVS		96.56 42	96.18 47	97.70 39	99.59 31	93.92 61	99.13 93	97.44 141	89.02 158	97.90 50	99.22 31	88.90 67	99.49 109	94.63 112	99.79 29	99.68 66

GST-MVS		95.97 61	95.66 70	96.90 77	99.49 50	91.22 112	99.45 52	97.48 132	89.69 138	95.89 93	98.72 94	86.37 123	99.95 34	94.62 113	99.22 83	99.52 84

Effi-MVS+		93.87 117	93.15 122	96.02 119	95.79 191	90.76 130	96.70 271	95.78 260	86.98 216	95.71 100	97.17 165	79.58 204	98.01 185	94.57 114	96.09 150	99.31 103

abl_6		94.63 101	94.48 89	95.09 147	98.61 103	86.96 218	98.06 210	96.97 186	89.31 150	95.86 96	98.56 106	79.82 202	99.64 91	94.53 115	98.65 105	98.66 161

LFMVS		92.23 157	90.84 170	96.42 106	98.24 110	91.08 122	98.24 191	96.22 225	83.39 274	94.74 116	98.31 119	61.12 321	98.85 148	94.45 116	92.82 179	99.32 102

ET-MVSNet_ETH3D		92.56 150	91.45 158	95.88 124	96.39 170	94.13 59	99.46 50	96.97 186	92.18 77	66.94 353	98.29 122	94.65 13	94.28 331	94.34 117	83.82 249	99.24 110

baseline		93.91 115	93.30 118	95.72 129	95.10 221	90.07 147	97.48 237	95.91 250	91.03 100	93.54 135	97.68 140	79.58 204	98.02 184	94.27 118	95.14 161	99.08 123

PAPR		96.35 49	95.82 64	97.94 32	99.63 21	94.19 58	99.42 58	97.55 116	92.43 68	93.82 133	99.12 48	87.30 99	99.91 43	94.02 119	99.06 87	99.74 56

PGM-MVS		95.85 66	95.65 72	96.45 104	99.50 47	89.77 157	98.22 192	98.90 12	89.19 152	96.74 77	98.95 73	85.91 130	99.92 41	93.94 120	99.46 64	99.66 70

gg-mvs-nofinetune		90.00 198	87.71 219	96.89 82	96.15 182	94.69 47	85.15 354	97.74 70	68.32 356	92.97 144	60.16 366	96.10 3	96.84 241	93.89 121	98.87 95	99.14 117

MVS		93.92 114	92.28 138	98.83 6	95.69 195	96.82 7	96.22 286	98.17 33	84.89 252	84.34 231	98.61 104	79.32 208	99.83 62	93.88 122	99.43 68	99.86 32

旧先验2							98.67 141		85.75 236	98.96 15			98.97 147	93.84 123

ACMMP		94.67 99	94.30 92	95.79 127	99.25 69	88.13 189	98.41 174	98.67 20	90.38 118	91.43 160	98.72 94	82.22 185	99.95 34	93.83 124	95.76 156	99.29 105
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														93.82 125

HQP-MVS		91.50 168	91.23 161	92.29 223	93.95 250	86.39 229	99.16 81	96.37 214	93.92 33	87.57 203	96.67 185	73.34 247	97.77 198	93.82 125	86.29 228	92.72 238

DP-MVS Recon		95.85 66	95.15 80	97.95 31	99.87 2	94.38 54	99.60 29	97.48 132	86.58 225	94.42 120	99.13 47	87.36 97	99.98 10	93.64 127	98.33 114	99.48 90

CHOSEN 1792x2688		94.35 107	93.82 111	95.95 123	97.40 135	88.74 179	98.41 174	98.27 26	92.18 77	91.43 160	96.40 191	78.88 210	99.81 67	93.59 128	97.81 118	99.30 104

cascas		90.93 180	89.33 192	95.76 128	95.69 195	93.03 82	98.99 107	96.59 198	80.49 312	86.79 216	94.45 220	65.23 306	98.60 161	93.52 129	92.18 192	95.66 228

HQP_MVS		91.26 172	90.95 167	92.16 227	93.84 257	86.07 241	99.02 103	96.30 218	93.38 48	86.99 210	96.52 187	72.92 251	97.75 203	93.46 130	86.17 231	92.67 240

plane_prior5								96.30 218					97.75 203	93.46 130	86.17 231	92.67 240

PVSNet_Blended_VisFu		94.67 99	94.11 99	96.34 110	97.14 144	91.10 120	99.32 71	97.43 143	92.10 79	91.53 159	96.38 194	83.29 163	99.68 83	93.42 132	96.37 143	98.25 179

AdaColmap		93.82 118	93.06 123	96.10 117	99.88 1	89.07 167	98.33 184	97.55 116	86.81 221	90.39 180	98.65 99	75.09 229	99.98 10	93.32 133	97.53 126	99.26 109

HyFIR lowres test		93.68 123	93.29 119	94.87 154	97.57 132	88.04 191	98.18 196	98.47 22	87.57 207	91.24 165	95.05 213	85.49 135	97.46 220	93.22 134	92.82 179	99.10 121

HPM-MVS_fast		94.89 89	94.62 86	95.70 130	99.11 79	88.44 185	99.14 90	97.11 173	85.82 235	95.69 101	98.47 114	83.46 159	99.32 132	93.16 135	99.63 50	99.35 99

PMMVS		93.62 126	93.90 110	92.79 214	96.79 157	81.40 307	98.85 120	96.81 190	91.25 97	96.82 76	98.15 128	77.02 223	98.13 174	93.15 136	96.30 146	98.83 146

LCM-MVSNet-Re		88.59 223	88.61 206	88.51 304	95.53 201	72.68 351	96.85 263	88.43 367	88.45 176	73.14 333	90.63 296	75.82 225	94.38 330	92.95 137	95.71 158	98.48 167

EPP-MVSNet		93.75 120	93.67 113	94.01 188	95.86 190	85.70 250	98.67 141	97.66 88	84.46 257	91.36 163	97.18 164	91.16 30	97.79 196	92.93 138	93.75 171	98.53 164

CostFormer		92.89 142	92.48 136	94.12 182	94.99 226	85.89 245	92.89 326	97.00 185	86.98 216	95.00 113	90.78 288	90.05 53	97.51 218	92.92 139	91.73 200	98.96 131

XVG-OURS-SEG-HR		90.95 179	90.66 176	91.83 233	95.18 214	81.14 314	95.92 293	95.92 245	88.40 181	90.33 181	97.85 130	70.66 271	99.38 124	92.83 140	88.83 219	94.98 229

sss		94.85 91	93.94 108	97.58 43	96.43 167	94.09 60	98.93 112	99.16 8	89.50 147	95.27 107	97.85 130	81.50 193	99.65 89	92.79 141	94.02 170	98.99 128

MAR-MVS		94.43 105	94.09 100	95.45 137	99.10 81	87.47 203	98.39 180	97.79 64	88.37 182	94.02 128	99.17 39	78.64 215	99.91 43	92.48 142	98.85 96	98.96 131
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 96	99.21 73	90.06 150	98.80 125	97.78 65	83.59 271	93.85 131	99.21 32	83.79 154	99.97 23	92.37 143	99.00 90	99.74 56

nrg030		90.23 191	88.87 199	94.32 175	91.53 295	93.54 69	98.79 129	95.89 253	88.12 190	84.55 229	94.61 219	78.80 213	96.88 240	92.35 144	75.21 294	92.53 242

OMC-MVS		93.90 116	93.62 114	94.73 161	98.63 101	87.00 217	98.04 211	96.56 202	92.19 76	92.46 147	98.73 92	79.49 207	99.14 141	92.16 145	94.34 168	98.03 186

1314		93.44 128	91.98 147	97.84 33	95.24 208	94.38 54	96.22 286	97.92 49	90.18 124	82.28 256	97.71 139	77.63 220	99.80 69	91.94 146	98.67 104	99.34 101

DPM-MVS		97.86 8	97.25 19	99.68 1	98.25 109	99.10 1	99.76 11	97.78 65	96.61 4	98.15 35	99.53 7	93.62 16	100.00 1	91.79 147	99.80 27	99.94 18

mvs_anonymous		92.50 151	91.65 154	95.06 149	96.60 162	89.64 160	97.06 255	96.44 211	86.64 224	84.14 232	93.93 229	82.49 179	96.17 282	91.47 148	96.08 151	99.35 99

baseline2		94.04 111	93.80 112	94.74 160	93.07 275	90.25 140	98.12 201	98.16 35	89.86 132	86.53 217	96.95 174	95.56 5	98.05 182	91.44 149	94.53 165	95.93 226

bset_n11_16_dypcd		89.07 209	87.85 216	92.76 216	86.16 350	90.66 134	97.30 243	95.62 270	89.78 136	83.94 235	93.15 251	74.85 230	95.89 297	91.34 150	78.48 276	91.74 264

IB-MVS	89.43 6	92.12 158	90.83 172	95.98 122	95.40 206	90.78 129	99.81 5	98.06 40	91.23 98	85.63 221	93.66 237	90.63 41	98.78 150	91.22 151	71.85 330	98.36 175
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		91.05 177	89.17 194	96.69 92	95.96 188	91.72 103	92.62 330	97.23 159	85.61 237	89.74 188	93.89 231	68.55 280	99.42 120	91.09 152	87.84 222	98.92 138

XVG-OURS		90.83 181	90.49 178	91.86 232	95.23 209	81.25 311	95.79 301	95.92 245	88.96 160	90.02 185	98.03 129	71.60 265	99.35 130	91.06 153	87.78 223	94.98 229

3Dnovator	87.35 11	93.17 139	91.77 152	97.37 54	95.41 205	93.07 80	98.82 123	97.85 54	91.53 87	82.56 250	97.58 146	71.97 260	99.82 65	91.01 154	99.23 82	99.22 113

VPA-MVSNet		89.10 208	87.66 220	93.45 202	92.56 279	91.02 124	97.97 215	98.32 25	86.92 218	86.03 219	92.01 265	68.84 279	97.10 233	90.92 155	75.34 293	92.23 250

PAPM_NR		95.43 76	95.05 82	96.57 99	99.42 54	90.14 143	98.58 156	97.51 126	90.65 111	92.44 148	98.90 80	87.77 86	99.90 45	90.88 156	99.32 75	99.68 66

3Dnovator+	87.72 8	93.43 129	91.84 150	98.17 21	95.73 194	95.08 32	98.92 114	97.04 180	91.42 93	81.48 274	97.60 144	74.60 233	99.79 70	90.84 157	98.97 91	99.64 72

gm-plane-assit					94.69 236	88.14 188			88.22 187		97.20 162		98.29 169	90.79 158

MVSTER		92.71 144	92.32 137	93.86 194	97.29 139	92.95 86	99.01 105	96.59 198	90.09 128	85.51 222	94.00 227	94.61 14	96.56 253	90.77 159	83.03 255	92.08 256

ACMP	87.39 10	88.71 222	88.24 213	90.12 271	93.91 255	81.06 315	98.50 164	95.67 268	89.43 148	80.37 282	95.55 205	65.67 301	97.83 193	90.55 160	84.51 241	91.47 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT_test8_iter05		91.04 178	90.40 180	92.95 211	96.20 180	89.75 158	98.97 109	96.38 213	88.52 172	82.00 264	93.51 242	90.69 40	96.73 247	90.43 161	76.91 288	92.38 244

ECVR-MVS		92.29 154	91.33 159	95.15 145	96.41 168	87.84 194	98.10 205	94.84 305	90.82 106	91.42 162	97.28 154	65.61 303	98.49 162	90.33 162	97.19 132	99.12 119

testdata				95.26 144	98.20 111	87.28 210		97.60 104	85.21 243	98.48 29	99.15 43	88.15 79	98.72 156	90.29 163	99.45 66	99.78 42

LPG-MVS_test		88.86 214	88.47 211	90.06 272	93.35 270	80.95 316	98.22 192	95.94 242	87.73 203	83.17 242	96.11 198	66.28 299	97.77 198	90.19 164	85.19 237	91.46 277

LGP-MVS_train				90.06 272	93.35 270	80.95 316		95.94 242	87.73 203	83.17 242	96.11 198	66.28 299	97.77 198	90.19 164	85.19 237	91.46 277

MVSFormer		94.71 98	94.08 101	96.61 95	95.05 224	94.87 37	97.77 225	96.17 229	86.84 219	98.04 42	98.52 108	85.52 132	95.99 288	89.83 166	98.97 91	98.96 131

test_djsdf		88.26 229	87.73 218	89.84 278	88.05 336	82.21 301	97.77 225	96.17 229	86.84 219	82.41 254	91.95 268	72.07 259	95.99 288	89.83 166	84.50 242	91.32 284

test2506		94.80 92	94.21 95	96.58 97	96.41 168	92.18 98	98.01 212	98.96 10	90.82 106	93.46 136	97.28 154	85.92 128	98.45 163	89.82 168	97.19 132	99.12 119

tpmrst		92.78 143	92.16 142	94.65 163	96.27 174	87.45 204	91.83 334	97.10 176	89.10 156	94.68 117	90.69 292	88.22 77	97.73 205	89.78 169	91.80 198	98.77 153

PLC	91.07 3	94.23 109	94.01 102	94.87 154	99.17 76	87.49 202	99.25 74	96.55 203	88.43 180	91.26 164	98.21 126	85.92 128	99.86 57	89.77 170	97.57 123	97.24 204
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		92.12 158	91.19 162	94.94 153	96.15 182	87.36 207	98.12 201	94.84 305	90.85 105	90.97 168	97.26 157	65.60 304	98.37 165	89.74 171	97.14 135	99.07 125

CDS-MVSNet		93.47 127	93.04 125	94.76 158	94.75 235	89.45 164	98.82 123	97.03 182	87.91 196	90.97 168	96.48 189	89.06 64	96.36 267	89.50 172	92.81 181	98.49 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu		89.97 199	90.68 175	87.81 309	95.15 215	71.98 353	97.87 220	95.40 286	91.92 80	87.57 203	91.44 276	74.27 240	96.84 241	89.45 173	93.10 177	94.60 231

mvs-test1		91.57 167	92.20 141	89.70 282	95.15 215	74.34 343	99.51 41	95.40 286	91.92 80	91.02 167	97.25 158	74.27 240	98.08 180	89.45 173	95.83 155	96.67 214

jajsoiax		87.35 241	86.51 238	89.87 276	87.75 341	81.74 304	97.03 256	95.98 236	88.47 173	80.15 286	93.80 233	61.47 318	96.36 267	89.44 175	84.47 243	91.50 275

mvs_tets		87.09 244	86.22 241	89.71 281	87.87 337	81.39 308	96.73 270	95.90 251	88.19 188	79.99 288	93.61 238	59.96 324	96.31 275	89.40 176	84.34 244	91.43 279

PS-MVSNAJss		89.54 205	89.05 196	91.00 249	88.77 327	84.36 273	97.39 238	95.97 237	88.47 173	81.88 267	93.80 233	82.48 180	96.50 257	89.34 177	83.34 254	92.15 253

VPNet		88.30 227	86.57 236	93.49 201	91.95 288	91.35 111	98.18 196	97.20 165	88.61 169	84.52 230	94.89 214	62.21 316	96.76 246	89.34 177	72.26 327	92.36 245

114514_t		94.06 110	93.05 124	97.06 63	99.08 82	92.26 97	98.97 109	97.01 184	82.58 288	92.57 146	98.22 124	80.68 199	99.30 133	89.34 177	99.02 89	99.63 74

OPM-MVS		89.76 201	89.15 195	91.57 240	90.53 306	85.58 252	98.11 204	95.93 244	92.88 59	86.05 218	96.47 190	67.06 294	97.87 191	89.29 180	86.08 233	91.26 287
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_Test		93.67 124	92.67 132	96.69 92	96.72 159	92.66 88	97.22 250	96.03 235	87.69 205	95.12 111	94.03 225	81.55 192	98.28 170	89.17 181	96.46 140	99.14 117

BH-w/o		92.32 153	91.79 151	93.91 193	96.85 154	86.18 236	99.11 95	95.74 263	88.13 189	84.81 226	97.00 172	77.26 222	97.91 187	89.16 182	98.03 116	97.64 193

RRT_MVS		91.95 162	91.09 163	94.53 167	96.71 161	95.12 31	98.64 145	96.23 224	89.04 157	85.24 224	95.06 212	87.71 87	96.43 263	89.10 183	82.06 262	92.05 258

TAMVS		92.62 147	92.09 145	94.20 179	94.10 246	87.68 197	98.41 174	96.97 186	87.53 209	89.74 188	96.04 200	84.77 147	96.49 259	88.97 184	92.31 189	98.42 168

CNLPA		93.64 125	92.74 130	96.36 109	98.96 88	90.01 153	99.19 76	95.89 253	86.22 231	89.40 191	98.85 84	80.66 200	99.84 60	88.57 185	96.92 136	99.24 110

baseline1		92.61 148	91.28 160	96.58 97	97.05 150	94.63 48	97.72 229	96.20 226	89.82 134	88.56 197	96.85 179	86.85 107	97.82 194	88.42 186	80.10 270	97.30 202

CANet_DTU		94.31 108	93.35 117	97.20 60	97.03 151	94.71 46	98.62 148	95.54 276	95.61 14	97.21 61	98.47 114	71.88 261	99.84 60	88.38 187	97.46 128	97.04 211

thisisatest0515		94.75 94	94.19 96	96.43 105	96.13 187	92.64 92	99.47 45	97.60 104	87.55 208	93.17 139	97.59 145	94.71 11	98.42 164	88.28 188	93.20 175	98.24 180

原ACMM1				96.18 113	99.03 84	90.08 146		97.63 99	88.98 159	97.00 65	98.97 66	88.14 80	99.71 78	88.23 189	99.62 51	98.76 154

UGNet		91.91 163	90.85 169	95.10 146	97.06 149	88.69 180	98.01 212	98.24 29	92.41 72	92.39 149	93.61 238	60.52 322	99.68 83	88.14 190	97.25 130	96.92 213
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		90.17 194	89.50 187	92.19 226	96.21 177	82.67 297	97.76 227	97.53 120	88.05 191	91.67 154	96.15 196	83.10 168	97.47 219	88.11 191	66.91 344	96.43 221

Vis-MVSNet (Re-imp)		93.26 137	93.00 127	94.06 185	96.14 184	86.71 223	98.68 139	96.70 193	88.30 184	89.71 190	97.64 143	85.43 138	96.39 265	88.06 192	96.32 144	99.08 123

PVSNet	87.13 12	93.69 121	92.83 129	96.28 111	97.99 118	90.22 142	99.38 62	98.93 11	91.42 93	93.66 134	97.68 140	71.29 268	99.64 91	87.94 193	97.20 131	98.98 129

FIs		90.70 184	89.87 183	93.18 206	92.29 282	91.12 118	98.17 198	98.25 27	89.11 155	83.44 238	94.82 216	82.26 184	96.17 282	87.76 194	82.76 257	92.25 248

tpm2		91.77 164	91.09 163	93.82 196	94.83 233	85.56 253	92.51 331	97.16 168	84.00 263	93.83 132	90.66 294	87.54 90	97.17 229	87.73 195	91.55 203	98.72 155

无先验							98.52 159	97.82 57	87.20 213				99.90 45	87.64 196		99.85 33

1121		95.19 84	94.45 90	97.42 49	98.88 92	92.58 93	96.22 286	97.75 68	85.50 240	96.86 70	99.01 64	88.59 72	99.90 45	87.64 196	99.60 56	99.79 38

Anonymous202405211		88.84 215	87.03 230	94.27 176	98.14 115	84.18 276	98.44 170	95.58 274	76.79 332	89.34 192	96.88 178	53.42 345	99.54 101	87.53 198	87.12 226	99.09 122

IS-MVSNet		93.00 141	92.51 135	94.49 168	96.14 184	87.36 207	98.31 187	95.70 265	88.58 171	90.17 182	97.50 148	83.02 169	97.22 228	87.06 199	96.07 152	98.90 139

MDTV_nov1_ep13_2view						91.17 117	91.38 337		87.45 210	93.08 141		86.67 113		87.02 200		98.95 135

Anonymous20240529		87.66 238	85.58 251	93.92 192	97.59 131	85.01 263	98.13 199	97.13 171	66.69 360	88.47 198	96.01 201	55.09 339	99.51 106	87.00 201	84.12 245	97.23 205

UniMVSNet_NR-MVSNet		89.60 203	88.55 209	92.75 217	92.17 285	90.07 147	98.74 131	98.15 36	88.37 182	83.21 240	93.98 228	82.86 171	95.93 292	86.95 202	72.47 324	92.25 248

DU-MVS		88.83 217	87.51 221	92.79 214	91.46 296	90.07 147	98.71 132	97.62 101	88.87 165	83.21 240	93.68 235	74.63 231	95.93 292	86.95 202	72.47 324	92.36 245

ACMM	86.95 13	88.77 220	88.22 214	90.43 263	93.61 262	81.34 309	98.50 164	95.92 245	87.88 197	83.85 236	95.20 211	67.20 292	97.89 189	86.90 204	84.90 239	92.06 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		89.50 206	88.32 212	93.03 208	92.21 284	90.96 126	98.90 116	98.39 23	89.13 154	83.22 239	92.03 263	81.69 191	96.34 273	86.79 205	72.53 323	91.81 263

BH-untuned		91.46 170	90.84 170	93.33 204	96.51 166	84.83 268	98.84 122	95.50 278	86.44 230	83.50 237	96.70 184	75.49 228	97.77 198	86.78 206	97.81 118	97.40 199

miper_enhance_ethall		90.33 189	89.70 184	92.22 224	97.12 146	88.93 173	98.35 183	95.96 239	88.60 170	83.14 244	92.33 261	87.38 93	96.18 281	86.49 207	77.89 280	91.55 274

thisisatest0530		94.00 112	93.52 115	95.43 138	95.76 193	90.02 152	98.99 107	97.60 104	86.58 225	91.74 153	97.36 153	94.78 10	98.34 166	86.37 208	92.48 186	97.94 189

TESTMET0.1,1		93.82 118	93.26 120	95.49 135	95.21 210	90.25 140	99.15 87	97.54 119	89.18 153	91.79 152	94.87 215	89.13 63	97.63 209	86.21 209	96.29 147	98.60 162

anonymousdsp		86.69 250	85.75 249	89.53 287	86.46 347	82.94 290	96.39 277	95.71 264	83.97 264	79.63 293	90.70 291	68.85 278	95.94 291	86.01 210	84.02 246	89.72 324

F-COLMAP		92.07 160	91.75 153	93.02 209	98.16 114	82.89 293	98.79 129	95.97 237	86.54 227	87.92 201	97.80 133	78.69 214	99.65 89	85.97 211	95.93 154	96.53 220

cl22		89.57 204	88.79 202	91.91 231	97.94 119	87.62 199	97.98 214	96.51 206	85.03 248	82.37 255	91.79 269	83.65 155	96.50 257	85.96 212	77.89 280	91.61 271

test-LLR		93.11 140	92.68 131	94.40 172	94.94 229	87.27 211	99.15 87	97.25 155	90.21 122	91.57 156	94.04 223	84.89 144	97.58 214	85.94 213	96.13 148	98.36 175

test-mter		93.27 136	92.89 128	94.40 172	94.94 229	87.27 211	99.15 87	97.25 155	88.95 161	91.57 156	94.04 223	88.03 82	97.58 214	85.94 213	96.13 148	98.36 175

FC-MVSNet-test		90.22 192	89.40 190	92.67 220	91.78 292	89.86 155	97.89 217	98.22 30	88.81 166	82.96 245	94.66 218	81.90 190	95.96 290	85.89 215	82.52 260	92.20 252

DWT-MVSNet_test		94.36 106	93.95 107	95.62 131	96.99 152	89.47 163	96.62 273	97.38 148	90.96 102	93.07 142	97.27 156	93.73 15	98.09 177	85.86 216	93.65 173	99.29 105

Vis-MVSNet		92.64 146	91.85 149	95.03 151	95.12 217	88.23 186	98.48 167	96.81 190	91.61 85	92.16 151	97.22 161	71.58 266	98.00 186	85.85 217	97.81 118	98.88 140
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
WR-MVS		88.54 224	87.22 228	92.52 221	91.93 290	89.50 162	98.56 157	97.84 55	86.99 214	81.87 268	93.81 232	74.25 242	95.92 294	85.29 218	74.43 303	92.12 254

XXY-MVS		87.75 235	86.02 244	92.95 211	90.46 307	89.70 159	97.71 231	95.90 251	84.02 262	80.95 276	94.05 222	67.51 290	97.10 233	85.16 219	78.41 277	92.04 259

thres200		93.69 121	92.59 134	96.97 72	97.76 123	94.74 44	99.35 67	99.36 2	89.23 151	91.21 166	96.97 173	83.42 160	98.77 151	85.08 220	90.96 208	97.39 200

tttt0517		93.30 134	93.01 126	94.17 180	95.57 198	86.47 226	98.51 162	97.60 104	85.99 233	90.55 175	97.19 163	94.80 9	98.31 167	85.06 221	91.86 196	97.74 191

XVG-ACMP-BASELINE		85.86 264	84.95 260	88.57 302	89.90 312	77.12 336	94.30 313	95.60 273	87.40 211	82.12 259	92.99 255	53.42 345	97.66 207	85.02 222	83.83 247	90.92 295

test_part1		88.43 225	86.68 235	93.67 200	97.56 133	92.40 96	98.12 201	96.55 203	82.26 294	80.31 283	93.16 250	74.59 235	96.62 250	85.00 223	72.61 322	91.99 260

新几何1				97.40 51	98.92 90	92.51 95		97.77 67	85.52 238	96.69 79	99.06 56	88.08 81	99.89 48	84.88 224	99.62 51	99.79 38

1112_ss		92.71 144	91.55 156	96.20 112	95.56 199	91.12 118	98.48 167	94.69 311	88.29 185	86.89 213	98.50 110	87.02 104	98.66 159	84.75 225	89.77 217	98.81 148

miper_ehance_all_eth		88.94 212	88.12 215	91.40 241	95.32 207	86.93 219	97.85 221	95.55 275	84.19 260	81.97 265	91.50 275	84.16 151	95.91 295	84.69 226	77.89 280	91.36 282

Test_1112_low_res		92.27 156	90.97 166	96.18 113	95.53 201	91.10 120	98.47 169	94.66 312	88.28 186	86.83 215	93.50 243	87.00 105	98.65 160	84.69 226	89.74 218	98.80 149

TR-MVS		90.77 182	89.44 189	94.76 158	96.31 173	88.02 192	97.92 216	95.96 239	85.52 238	88.22 200	97.23 160	66.80 295	98.09 177	84.58 228	92.38 187	98.17 184

OpenMVS	85.28 14	90.75 183	88.84 200	96.48 102	93.58 263	93.51 70	98.80 125	97.41 145	82.59 287	78.62 303	97.49 149	68.00 286	99.82 65	84.52 229	98.55 110	96.11 225

UniMVSNet_ETH3D		85.65 271	83.79 277	91.21 244	90.41 308	80.75 318	95.36 304	95.78 260	78.76 322	81.83 271	94.33 221	49.86 353	96.66 248	84.30 230	83.52 252	96.22 224

NR-MVSNet		87.74 237	86.00 245	92.96 210	91.46 296	90.68 133	96.65 272	97.42 144	88.02 193	73.42 331	93.68 235	77.31 221	95.83 299	84.26 231	71.82 331	92.36 245

D2MVS		87.96 231	87.39 223	89.70 282	91.84 291	83.40 285	98.31 187	98.49 21	88.04 192	78.23 309	90.26 307	73.57 245	96.79 245	84.21 232	83.53 251	88.90 334

testdata2													99.88 49	84.16 233

Baseline_NR-MVSNet		85.83 265	84.82 263	88.87 301	88.73 328	83.34 286	98.63 147	91.66 353	80.41 315	82.44 252	91.35 278	74.63 231	95.42 310	84.13 234	71.39 333	87.84 340

thres100view900		93.34 133	92.15 143	96.90 77	97.62 128	94.84 39	99.06 99	99.36 2	87.96 194	90.47 178	96.78 181	83.29 163	98.75 153	84.11 235	90.69 210	97.12 206

tfpn200view9		93.43 129	92.27 139	96.90 77	97.68 126	94.84 39	99.18 78	99.36 2	88.45 176	90.79 170	96.90 176	83.31 161	98.75 153	84.11 235	90.69 210	97.12 206

thres400		93.39 131	92.27 139	96.73 88	97.68 126	94.84 39	99.18 78	99.36 2	88.45 176	90.79 170	96.90 176	83.31 161	98.75 153	84.11 235	90.69 210	96.61 215

c3_l		88.19 230	87.23 227	91.06 247	94.97 227	86.17 237	97.72 229	95.38 288	83.43 273	81.68 272	91.37 277	82.81 172	95.72 302	84.04 238	73.70 311	91.29 286

UA-Net		93.30 134	92.62 133	95.34 141	96.27 174	88.53 184	95.88 296	96.97 186	90.90 104	95.37 106	97.07 169	82.38 183	99.10 143	83.91 239	94.86 164	98.38 172

IterMVS-LS		88.34 226	87.44 222	91.04 248	94.10 246	85.85 247	98.10 205	95.48 279	85.12 244	82.03 263	91.21 281	81.35 196	95.63 305	83.86 240	75.73 292	91.63 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 200	89.38 191	91.36 243	94.32 242	85.87 246	97.61 234	96.59 198	85.10 245	85.51 222	97.10 167	81.30 197	96.56 253	83.85 241	83.03 255	91.64 266

tpm		89.67 202	88.95 198	91.82 234	92.54 280	81.43 306	92.95 325	95.92 245	87.81 198	90.50 177	89.44 320	84.99 142	95.65 304	83.67 242	82.71 258	98.38 172

eth_miper_zixun_eth		87.76 234	87.00 231	90.06 272	94.67 237	82.65 298	97.02 258	95.37 289	84.19 260	81.86 270	91.58 274	81.47 194	95.90 296	83.24 243	73.61 312	91.61 271

Fast-Effi-MVS+		91.72 165	90.79 173	94.49 168	95.89 189	87.40 206	99.54 39	95.70 265	85.01 250	89.28 193	95.68 204	77.75 219	97.57 217	83.22 244	95.06 162	98.51 165

test_post1							90.74 344				41.37 376	85.38 139	96.36 267	83.16 245

SCA		90.64 186	89.25 193	94.83 157	94.95 228	88.83 175	96.26 283	97.21 161	90.06 131	90.03 184	90.62 297	66.61 296	96.81 243	83.16 245	94.36 167	98.84 143

TranMVSNet+NR-MVSNet		87.75 235	86.31 240	92.07 230	90.81 303	88.56 181	98.33 184	97.18 166	87.76 200	81.87 268	93.90 230	72.45 255	95.43 309	83.13 247	71.30 334	92.23 250

CMPMVS	58.40 21	80.48 306	80.11 306	81.59 341	85.10 352	59.56 367	94.14 316	95.95 241	68.54 355	60.71 361	93.31 244	55.35 338	97.87 191	83.06 248	84.85 240	87.33 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres600view7		93.18 138	92.00 146	96.75 86	97.62 128	94.92 34	99.07 97	99.36 2	87.96 194	90.47 178	96.78 181	83.29 163	98.71 157	82.93 249	90.47 214	96.61 215

pmmvs4		87.58 240	86.17 243	91.80 235	89.58 317	88.92 174	97.25 247	95.28 292	82.54 289	80.49 281	93.17 249	75.62 227	96.05 287	82.75 250	78.90 274	90.42 310

CVMVSNet		90.30 190	90.91 168	88.46 305	94.32 242	73.58 347	97.61 234	97.59 108	90.16 127	88.43 199	97.10 167	76.83 224	92.86 340	82.64 251	93.54 174	98.93 137

Anonymous20231211		84.72 278	82.65 289	90.91 251	97.71 125	84.55 271	97.28 245	96.67 194	66.88 359	79.18 299	90.87 287	58.47 326	96.60 251	82.61 252	74.20 307	91.59 273

GA-MVS		90.10 196	88.69 204	94.33 174	92.44 281	87.97 193	99.08 96	96.26 222	89.65 139	86.92 212	93.11 252	68.09 284	96.96 237	82.54 253	90.15 215	98.05 185

QAPM		91.41 171	89.49 188	97.17 61	95.66 197	93.42 72	98.60 152	97.51 126	80.92 310	81.39 275	97.41 152	72.89 253	99.87 52	82.33 254	98.68 103	98.21 182

Patchmatch-RL test		81.90 302	80.13 305	87.23 314	80.71 364	70.12 359	84.07 360	88.19 368	83.16 278	70.57 341	82.18 353	87.18 100	92.59 345	82.28 255	62.78 350	98.98 129

v2v482		87.27 243	85.76 248	91.78 239	89.59 316	87.58 200	98.56 157	95.54 276	84.53 256	82.51 251	91.78 270	73.11 250	96.47 260	82.07 256	74.14 309	91.30 285

Fast-Effi-MVS+-dtu		88.84 215	88.59 208	89.58 286	93.44 268	78.18 331	98.65 143	94.62 313	88.46 175	84.12 233	95.37 210	68.91 277	96.52 256	82.06 257	91.70 201	94.06 232

pmmvs5		85.87 263	84.40 272	90.30 268	88.53 331	84.23 274	98.60 152	93.71 330	81.53 302	80.29 284	92.02 264	64.51 308	95.52 307	82.04 258	78.34 278	91.15 289

V42		87.00 245	85.68 250	90.98 250	89.91 311	86.08 240	98.32 186	95.61 272	83.67 270	82.72 247	90.67 293	74.00 244	96.53 255	81.94 259	74.28 306	90.32 312

EPMVS		92.59 149	91.59 155	95.59 134	97.22 141	90.03 151	91.78 335	98.04 42	90.42 117	91.66 155	90.65 295	86.49 120	97.46 220	81.78 260	96.31 145	99.28 107

DIV-MVS_self_test		87.82 232	86.81 233	90.87 254	94.87 232	85.39 256	97.81 222	95.22 300	82.92 284	80.76 278	91.31 279	81.99 187	95.81 300	81.36 261	75.04 296	91.42 280

cl____		87.82 232	86.79 234	90.89 253	94.88 231	85.43 254	97.81 222	95.24 296	82.91 285	80.71 279	91.22 280	81.97 189	95.84 298	81.34 262	75.06 295	91.40 281

RPSCF		85.33 273	85.55 252	84.67 329	94.63 238	62.28 365	93.73 319	93.76 328	74.38 340	85.23 225	97.06 170	64.09 309	98.31 167	80.98 263	86.08 233	93.41 237

OurMVSNet-221017-0		84.13 289	83.59 278	85.77 323	87.81 338	70.24 357	94.89 308	93.65 332	86.08 232	76.53 313	93.28 246	61.41 319	96.14 284	80.95 264	77.69 285	90.93 294

v148		86.38 257	85.06 257	90.37 267	89.47 321	84.10 277	98.52 159	95.48 279	83.80 266	80.93 277	90.22 311	74.60 233	96.31 275	80.92 265	71.55 332	90.69 305

PatchMatch-RL		91.47 169	90.54 177	94.26 177	98.20 111	86.36 231	96.94 259	97.14 169	87.75 201	88.98 194	95.75 203	71.80 263	99.40 123	80.92 265	97.39 129	97.02 212

miper_lstm_enhance		86.90 246	86.20 242	89.00 298	94.53 239	81.19 312	96.74 269	95.24 296	82.33 293	80.15 286	90.51 304	81.99 187	94.68 327	80.71 267	73.58 313	91.12 290

PCF-MVS	89.78 5	91.26 172	89.63 185	96.16 116	95.44 203	91.58 108	95.29 305	96.10 233	85.07 247	82.75 246	97.45 150	78.28 216	99.78 71	80.60 268	95.65 159	97.12 206
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		91.25 174	89.99 182	95.03 151	96.75 158	88.55 182	98.65 143	94.95 302	87.74 202	87.74 202	97.80 133	68.27 283	98.14 173	80.53 269	97.49 127	98.41 169

GeoE		90.60 187	89.56 186	93.72 199	95.10 221	85.43 254	99.41 59	94.94 303	83.96 265	87.21 209	96.83 180	74.37 238	97.05 235	80.50 270	93.73 172	98.67 158

CP-MVSNet		86.54 254	85.45 254	89.79 280	91.02 302	82.78 296	97.38 240	97.56 115	85.37 241	79.53 295	93.03 253	71.86 262	95.25 314	79.92 271	73.43 317	91.34 283

PatchmatchNet		92.05 161	91.04 165	95.06 149	96.17 181	89.04 168	91.26 339	97.26 154	89.56 145	90.64 174	90.56 301	88.35 76	97.11 231	79.53 272	96.07 152	99.03 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1144		86.83 248	85.31 255	91.40 241	89.75 314	87.21 216	98.31 187	95.45 281	83.22 276	82.70 248	90.78 288	73.36 246	96.36 267	79.49 273	74.69 300	90.63 307

IterMVS		85.81 266	84.67 266	89.22 293	93.51 264	83.67 283	96.32 280	94.80 307	85.09 246	78.69 301	90.17 314	66.57 298	93.17 339	79.48 274	77.42 286	90.81 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		85.73 269	84.64 267	89.00 298	93.46 267	82.90 292	96.27 281	94.70 310	85.02 249	78.62 303	90.35 306	66.61 296	93.33 336	79.38 275	77.36 287	90.76 301

GBi-Net		86.67 251	84.96 258	91.80 235	95.11 218	88.81 176	96.77 265	95.25 293	82.94 281	82.12 259	90.25 308	62.89 313	94.97 318	79.04 276	80.24 267	91.62 268

test1		86.67 251	84.96 258	91.80 235	95.11 218	88.81 176	96.77 265	95.25 293	82.94 281	82.12 259	90.25 308	62.89 313	94.97 318	79.04 276	80.24 267	91.62 268

FMVSNet3		88.81 219	87.08 229	93.99 189	96.52 165	94.59 49	98.08 208	96.20 226	85.85 234	82.12 259	91.60 273	74.05 243	95.40 311	79.04 276	80.24 267	91.99 260

LF4IMVS		81.94 301	81.17 300	84.25 331	87.23 344	68.87 362	93.35 323	91.93 351	83.35 275	75.40 322	93.00 254	49.25 356	96.65 249	78.88 279	78.11 279	87.22 347

v8		86.11 260	84.45 269	91.10 246	89.99 310	86.85 220	97.24 248	95.36 290	81.99 297	79.89 290	89.86 316	74.53 236	96.39 265	78.83 280	72.32 326	90.05 319

pm-mvs1		84.68 279	82.78 285	90.40 264	89.58 317	85.18 259	97.31 242	94.73 309	81.93 299	76.05 316	92.01 265	65.48 305	96.11 285	78.75 281	69.14 337	89.91 322

v144192		86.40 256	84.89 261	90.91 251	89.48 320	85.59 251	98.21 194	95.43 285	82.45 291	82.62 249	90.58 300	72.79 254	96.36 267	78.45 282	74.04 310	90.79 299

PS-CasMVS		85.81 266	84.58 268	89.49 290	90.77 304	82.11 302	97.20 251	97.36 151	84.83 253	79.12 300	92.84 256	67.42 291	95.16 316	78.39 283	73.25 318	91.21 288

tmp_tt		53.66 336	52.86 338	56.05 353	32.75 381	41.97 377	73.42 367	76.12 377	21.91 374	39.68 370	96.39 193	42.59 361	65.10 373	78.00 284	14.92 374	61.08 367

JIA-IIPM		85.97 262	84.85 262	89.33 292	93.23 272	73.68 346	85.05 355	97.13 171	69.62 352	91.56 158	68.03 364	88.03 82	96.96 237	77.89 285	93.12 176	97.34 201

MDTV_nov1_ep13			90.47 179		96.14 184	88.55 182	91.34 338	97.51 126	89.58 143	92.24 150	90.50 305	86.99 106	97.61 211	77.64 286	92.34 188

v1192		86.32 258	84.71 265	91.17 245	89.53 319	86.40 228	98.13 199	95.44 284	82.52 290	82.42 253	90.62 297	71.58 266	96.33 274	77.23 287	74.88 297	90.79 299

FMVSNet2		86.90 246	84.79 264	93.24 205	95.11 218	92.54 94	97.67 232	95.86 257	82.94 281	80.55 280	91.17 282	62.89 313	95.29 313	77.23 287	79.71 273	91.90 262

MVP-Stereo		86.61 253	85.83 247	88.93 300	88.70 329	83.85 281	96.07 291	94.41 319	82.15 296	75.64 321	91.96 267	67.65 289	96.45 262	77.20 289	98.72 102	86.51 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		88.89 213	87.27 226	93.76 197	95.79 191	85.32 257	90.76 343	97.09 177	76.14 334	85.72 220	88.59 326	82.92 170	98.04 183	76.96 290	91.43 204	97.90 190

v10		85.73 269	84.01 275	90.87 254	90.03 309	86.73 222	97.20 251	95.22 300	81.25 305	79.85 291	89.75 317	73.30 249	96.28 279	76.87 291	72.64 321	89.61 326

v1921920		86.02 261	84.44 270	90.77 256	89.32 322	85.20 258	98.10 205	95.35 291	82.19 295	82.25 257	90.71 290	70.73 269	96.30 278	76.85 292	74.49 302	90.80 298

MS-PatchMatch		86.75 249	85.92 246	89.22 293	91.97 287	82.47 300	96.91 260	96.14 232	83.74 267	77.73 310	93.53 241	58.19 327	97.37 227	76.75 293	98.35 113	87.84 340

K. test v3		81.04 304	79.77 307	84.83 327	87.41 342	70.23 358	95.60 303	93.93 327	83.70 269	67.51 351	89.35 322	55.76 333	93.58 335	76.67 294	68.03 341	90.67 306

PM-MVS		74.88 325	72.85 328	80.98 342	78.98 367	64.75 364	90.81 342	85.77 370	80.95 309	68.23 348	82.81 351	29.08 370	92.84 341	76.54 295	62.46 352	85.36 356

MVS_0304		84.13 289	82.66 288	88.52 303	93.07 275	80.15 319	95.81 300	98.21 31	79.27 317	86.85 214	86.40 342	41.33 364	94.69 326	76.36 296	86.69 227	90.73 303

WR-MVS_H		86.53 255	85.49 253	89.66 285	91.04 301	83.31 287	97.53 236	98.20 32	84.95 251	79.64 292	90.90 286	78.01 218	95.33 312	76.29 297	72.81 319	90.35 311

ACMH+	83.78 15	84.21 286	82.56 291	89.15 295	93.73 261	79.16 323	96.43 276	94.28 321	81.09 307	74.00 328	94.03 225	54.58 341	97.67 206	76.10 298	78.81 275	90.63 307

PEN-MVS		85.21 274	83.93 276	89.07 297	89.89 313	81.31 310	97.09 254	97.24 157	84.45 258	78.66 302	92.68 258	68.44 282	94.87 321	75.98 299	70.92 335	91.04 292

USDC		84.74 277	82.93 281	90.16 270	91.73 293	83.54 284	95.00 307	93.30 336	88.77 167	73.19 332	93.30 245	53.62 344	97.65 208	75.88 300	81.54 265	89.30 329

EU-MVSNet		84.19 287	84.42 271	83.52 334	88.64 330	67.37 363	96.04 292	95.76 262	85.29 242	78.44 306	93.18 248	70.67 270	91.48 356	75.79 301	75.98 290	91.70 265

v1240		85.77 268	84.11 273	90.73 257	89.26 323	85.15 261	97.88 219	95.23 299	81.89 300	82.16 258	90.55 302	69.60 276	96.31 275	75.59 302	74.87 298	90.72 304

ITE_SJBPF				87.93 307	92.26 283	76.44 337		93.47 335	87.67 206	79.95 289	95.49 208	56.50 332	97.38 225	75.24 303	82.33 261	89.98 321

dp		90.16 195	88.83 201	94.14 181	96.38 171	86.42 227	91.57 336	97.06 179	84.76 254	88.81 195	90.19 313	84.29 150	97.43 223	75.05 304	91.35 207	98.56 163

LS3D		90.19 193	88.72 203	94.59 166	98.97 86	86.33 232	96.90 261	96.60 197	74.96 337	84.06 234	98.74 91	75.78 226	99.83 62	74.93 305	97.57 123	97.62 196

TDRefinement		78.01 319	75.31 322	86.10 321	70.06 371	73.84 345	93.59 322	91.58 355	74.51 339	73.08 335	91.04 283	49.63 355	97.12 230	74.88 306	59.47 356	87.33 345

tpmvs		89.16 207	87.76 217	93.35 203	97.19 142	84.75 269	90.58 345	97.36 151	81.99 297	84.56 228	89.31 323	83.98 153	98.17 172	74.85 307	90.00 216	97.12 206

pmmvs6		79.90 309	77.31 314	87.67 310	84.17 355	78.13 332	95.86 298	93.68 331	67.94 357	72.67 338	89.62 319	50.98 351	95.75 301	74.80 308	66.04 346	89.14 332

SixPastTwentyTwo		82.63 297	81.58 295	85.79 322	88.12 335	71.01 356	95.17 306	92.54 342	84.33 259	72.93 337	92.08 262	60.41 323	95.61 306	74.47 309	74.15 308	90.75 302

ACMH	83.09 17	84.60 280	82.61 290	90.57 259	93.18 273	82.94 290	96.27 281	94.92 304	81.01 308	72.61 339	93.61 238	56.54 331	97.79 196	74.31 310	81.07 266	90.99 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ADS-MVSNet2		87.62 239	86.88 232	89.86 277	96.21 177	79.14 324	87.15 349	92.99 337	83.01 279	89.91 186	87.27 335	78.87 211	92.80 343	74.20 311	92.27 190	97.64 193

ADS-MVSNet		88.99 210	87.30 225	94.07 184	96.21 177	87.56 201	87.15 349	96.78 192	83.01 279	89.91 186	87.27 335	78.87 211	97.01 236	74.20 311	92.27 190	97.64 193

lessismore_v0				85.08 325	85.59 351	69.28 360		90.56 360		67.68 350	90.21 312	54.21 343	95.46 308	73.88 313	62.64 351	90.50 309

MIMVSNet		84.48 283	81.83 293	92.42 222	91.73 293	87.36 207	85.52 352	94.42 318	81.40 303	81.91 266	87.58 330	51.92 348	92.81 342	73.84 314	88.15 221	97.08 210

v7n		84.42 285	82.75 286	89.43 291	88.15 334	81.86 303	96.75 268	95.67 268	80.53 311	78.38 307	89.43 321	69.89 272	96.35 272	73.83 315	72.13 328	90.07 317

ambc				79.60 343	72.76 370	56.61 369	76.20 365	92.01 350		68.25 347	80.23 357	23.34 371	94.73 325	73.78 316	60.81 354	87.48 342

pmmvs-eth3d		78.71 316	76.16 320	86.38 318	80.25 365	81.19 312	94.17 315	92.13 348	77.97 325	66.90 354	82.31 352	55.76 333	92.56 346	73.63 317	62.31 353	85.38 355

FMVSNet1		83.94 291	81.32 299	91.80 235	91.94 289	88.81 176	96.77 265	95.25 293	77.98 324	78.25 308	90.25 308	50.37 352	94.97 318	73.27 318	77.81 284	91.62 268

MSDG		88.29 228	86.37 239	94.04 187	96.90 153	86.15 238	96.52 275	94.36 320	77.89 328	79.22 298	96.95 174	69.72 274	99.59 97	73.20 319	92.58 185	96.37 223

test0.0.03 1		88.96 211	88.61 206	90.03 275	91.09 300	84.43 272	98.97 109	97.02 183	90.21 122	80.29 284	96.31 195	84.89 144	91.93 354	72.98 320	85.70 236	93.73 233

UnsupCasMVSNet_eth		78.90 314	76.67 318	85.58 324	82.81 360	74.94 341	91.98 333	96.31 217	84.64 255	65.84 357	87.71 329	51.33 349	92.23 350	72.89 321	56.50 361	89.56 327

DTE-MVSNet		84.14 288	82.80 283	88.14 306	88.95 326	79.87 322	96.81 264	96.24 223	83.50 272	77.60 311	92.52 260	67.89 288	94.24 332	72.64 322	69.05 338	90.32 312

EPNet_dtu		92.28 155	92.15 143	92.70 218	97.29 139	84.84 267	98.64 145	97.82 57	92.91 57	93.02 143	97.02 171	85.48 137	95.70 303	72.25 323	94.89 163	97.55 198
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AllTest		84.97 276	83.12 280	90.52 261	96.82 155	78.84 326	95.89 294	92.17 346	77.96 326	75.94 317	95.50 206	55.48 335	99.18 136	71.15 324	87.14 224	93.55 235

TestCases				90.52 261	96.82 155	78.84 326		92.17 346	77.96 326	75.94 317	95.50 206	55.48 335	99.18 136	71.15 324	87.14 224	93.55 235

DP-MVS		88.75 221	86.56 237	95.34 141	98.92 90	87.45 204	97.64 233	93.52 334	70.55 348	81.49 273	97.25 158	74.43 237	99.88 49	71.14 326	94.09 169	98.67 158

CR-MVSNet		88.83 217	87.38 224	93.16 207	93.47 265	86.24 233	84.97 356	94.20 323	88.92 164	90.76 172	86.88 339	84.43 148	94.82 323	70.64 327	92.17 193	98.41 169

KD-MVS_2432*1600		82.98 295	80.52 303	90.38 265	94.32 242	88.98 170	92.87 327	95.87 255	80.46 313	73.79 329	87.49 332	82.76 175	93.29 337	70.56 328	46.53 367	88.87 335

miper_refine_blended		82.98 295	80.52 303	90.38 265	94.32 242	88.98 170	92.87 327	95.87 255	80.46 313	73.79 329	87.49 332	82.76 175	93.29 337	70.56 328	46.53 367	88.87 335

test_method		70.10 330	68.66 333	74.41 346	86.30 349	55.84 370	94.47 310	89.82 363	35.18 369	66.15 356	84.75 348	30.54 369	77.96 370	70.40 330	60.33 355	89.44 328

LTVRE_ROB	81.71 19	84.59 281	82.72 287	90.18 269	92.89 278	83.18 288	93.15 324	94.74 308	78.99 319	75.14 324	92.69 257	65.64 302	97.63 209	69.46 331	81.82 264	89.74 323
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
FMVSNet5		82.29 298	80.54 302	87.52 311	93.79 260	84.01 278	93.73 319	92.47 343	76.92 331	74.27 326	86.15 344	63.69 312	89.24 360	69.07 332	74.79 299	89.29 330

our_test_3		84.47 284	82.80 283	89.50 288	89.01 324	83.90 280	97.03 256	94.56 314	81.33 304	75.36 323	90.52 303	71.69 264	94.54 329	68.81 333	76.84 289	90.07 317

UnsupCasMVSNet_bld		73.85 327	70.14 330	84.99 326	79.44 366	75.73 338	88.53 347	95.24 296	70.12 351	61.94 360	74.81 361	41.41 363	93.62 334	68.65 334	51.13 366	85.62 354

Patchmtry		83.61 294	81.64 294	89.50 288	93.36 269	82.84 295	84.10 359	94.20 323	69.47 353	79.57 294	86.88 339	84.43 148	94.78 324	68.48 335	74.30 305	90.88 296

KD-MVS_self_test		77.47 322	75.88 321	82.24 336	81.59 361	68.93 361	92.83 329	94.02 326	77.03 330	73.14 333	83.39 350	55.44 337	90.42 357	67.95 336	57.53 359	87.38 343

TransMVSNet (Re)		81.97 300	79.61 308	89.08 296	89.70 315	84.01 278	97.26 246	91.85 352	78.84 320	73.07 336	91.62 272	67.17 293	95.21 315	67.50 337	59.46 357	88.02 339

COLMAP_ROB	82.69 18	84.54 282	82.82 282	89.70 282	96.72 159	78.85 325	95.89 294	92.83 340	71.55 346	77.54 312	95.89 202	59.40 325	99.14 141	67.26 338	88.26 220	91.11 291
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		79.92 308	77.59 312	86.90 316	87.06 345	77.90 335	96.20 289	94.06 325	74.61 338	66.53 355	88.76 325	40.40 366	96.20 280	67.02 339	83.66 250	86.61 349

DSMNet-mixed		81.60 303	81.43 297	82.10 338	84.36 354	60.79 366	93.63 321	86.74 369	79.00 318	79.32 297	87.15 337	63.87 311	89.78 359	66.89 340	91.92 195	95.73 227

testgi		82.29 298	81.00 301	86.17 320	87.24 343	74.84 342	97.39 238	91.62 354	88.63 168	75.85 320	95.42 209	46.07 359	91.55 355	66.87 341	79.94 271	92.12 254

MDA-MVSNet_test_wron		79.65 311	77.05 315	87.45 312	87.79 340	80.13 320	96.25 284	94.44 316	73.87 341	51.80 364	87.47 334	68.04 285	92.12 352	66.02 342	67.79 342	90.09 315

YYNet1		79.64 312	77.04 316	87.43 313	87.80 339	79.98 321	96.23 285	94.44 316	73.83 342	51.83 363	87.53 331	67.96 287	92.07 353	66.00 343	67.75 343	90.23 314

DeepMVS_CX				76.08 345	90.74 305	51.65 373		90.84 359	86.47 229	57.89 362	87.98 327	35.88 368	92.60 344	65.77 344	65.06 348	83.97 359

Anonymous20240521		78.63 317	76.90 317	83.82 332	82.82 359	72.86 349	95.72 302	93.57 333	73.55 343	72.17 340	84.79 347	49.69 354	92.51 347	65.29 345	74.50 301	86.09 353

TinyColmap		80.42 307	77.94 311	87.85 308	92.09 286	78.58 328	93.74 318	89.94 362	74.99 336	69.77 343	91.78 270	46.09 358	97.58 214	65.17 346	77.89 280	87.38 343

MVS-HIRNet		79.01 313	75.13 323	90.66 258	93.82 259	81.69 305	85.16 353	93.75 329	54.54 364	74.17 327	59.15 368	57.46 329	96.58 252	63.74 347	94.38 166	93.72 234

ppachtmachnet_test		83.63 293	81.57 296	89.80 279	89.01 324	85.09 262	97.13 253	94.50 315	78.84 320	76.14 315	91.00 284	69.78 273	94.61 328	63.40 348	74.36 304	89.71 325

CL-MVSNet_self_test		79.89 310	78.34 310	84.54 330	81.56 362	75.01 340	96.88 262	95.62 270	81.10 306	75.86 319	85.81 345	68.49 281	90.26 358	63.21 349	56.51 360	88.35 337

Patchmatch-test		86.25 259	84.06 274	92.82 213	94.42 240	82.88 294	82.88 363	94.23 322	71.58 345	79.39 296	90.62 297	89.00 66	96.42 264	63.03 350	91.37 206	99.16 116

pmmvs3		72.86 328	69.76 332	82.17 337	73.86 369	74.19 344	94.20 314	89.01 366	64.23 363	67.72 349	80.91 356	41.48 362	88.65 362	62.40 351	54.02 364	83.68 360

new_pmnet		76.02 323	73.71 326	82.95 335	83.88 356	72.85 350	91.26 339	92.26 345	70.44 349	62.60 359	81.37 354	47.64 357	92.32 349	61.85 352	72.10 329	83.68 360

tfpnnormal		83.65 292	81.35 298	90.56 260	91.37 298	88.06 190	97.29 244	97.87 53	78.51 323	76.20 314	90.91 285	64.78 307	96.47 260	61.71 353	73.50 314	87.13 348

MDA-MVSNet-bldmvs		77.82 321	74.75 325	87.03 315	88.33 332	78.52 329	96.34 279	92.85 339	75.57 335	48.87 366	87.89 328	57.32 330	92.49 348	60.79 354	64.80 349	90.08 316

Anonymous20231206		80.76 305	79.42 309	84.79 328	84.78 353	72.98 348	96.53 274	92.97 338	79.56 316	74.33 325	88.83 324	61.27 320	92.15 351	60.59 355	75.92 291	89.24 331

new-patchmatchnet		74.80 326	72.40 329	81.99 339	78.36 368	72.20 352	94.44 311	92.36 344	77.06 329	63.47 358	79.98 358	51.04 350	88.85 361	60.53 356	54.35 363	84.92 358

LCM-MVSNet		60.07 333	56.37 335	71.18 347	54.81 377	48.67 374	82.17 364	89.48 365	37.95 367	49.13 365	69.12 362	13.75 378	81.76 366	59.28 357	51.63 365	83.10 362

TAPA-MVS	87.50 9	90.35 188	89.05 196	94.25 178	98.48 107	85.17 260	98.42 172	96.58 201	82.44 292	87.24 208	98.53 107	82.77 173	98.84 149	59.09 358	97.88 117	98.72 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.03		78.51 318	77.48 313	81.62 340	83.07 358	71.03 355	96.11 290	92.83 340	81.66 301	69.31 344	89.68 318	57.53 328	87.29 365	58.65 359	68.47 339	86.53 350

PatchT		85.44 272	83.19 279	92.22 224	93.13 274	83.00 289	83.80 362	96.37 214	70.62 347	90.55 175	79.63 359	84.81 146	94.87 321	58.18 360	91.59 202	98.79 150

MIMVSNet1		75.92 324	73.30 327	83.81 333	81.29 363	75.57 339	92.26 332	92.05 349	73.09 344	67.48 352	86.18 343	40.87 365	87.64 364	55.78 361	70.68 336	88.21 338

OpenMVS_ROB	73.86 20	77.99 320	75.06 324	86.77 317	83.81 357	77.94 334	96.38 278	91.53 356	67.54 358	68.38 346	87.13 338	43.94 360	96.08 286	55.03 362	81.83 263	86.29 352

RPMNet		85.07 275	81.88 292	94.64 164	93.47 265	86.24 233	84.97 356	97.21 161	64.85 362	90.76 172	78.80 360	80.95 198	99.27 134	53.76 363	92.17 193	98.41 169

N_pmnet		70.19 329	69.87 331	71.12 348	88.24 333	30.63 381	95.85 299	28.70 381	70.18 350	68.73 345	86.55 341	64.04 310	93.81 333	53.12 364	73.46 315	88.94 333

PMMVS2		58.97 334	55.07 337	70.69 349	62.72 372	55.37 371	85.97 351	80.52 374	49.48 365	45.94 367	68.31 363	15.73 376	80.78 368	49.79 365	37.12 369	75.91 363

test_0402		78.81 315	76.33 319	86.26 319	91.18 299	78.44 330	95.88 296	91.34 357	68.55 354	70.51 342	89.91 315	52.65 347	94.99 317	47.14 366	79.78 272	85.34 357

FPMVS		61.57 331	60.32 334	65.34 350	60.14 375	42.44 376	91.02 341	89.72 364	44.15 366	42.63 368	80.93 355	19.02 372	80.59 369	42.50 367	72.76 320	73.00 364

EGC-MVSNET		60.70 332	55.37 336	76.72 344	86.35 348	71.08 354	89.96 346	84.44 373	0.38 378	1.50 379	84.09 349	37.30 367	88.10 363	40.85 368	73.44 316	70.97 366

ANet_high		50.71 337	46.17 340	64.33 351	44.27 379	52.30 372	76.13 366	78.73 375	64.95 361	27.37 372	55.23 369	14.61 377	67.74 372	36.01 369	18.23 372	72.95 365

Gipuma		54.77 335	52.22 339	62.40 352	86.50 346	59.37 368	50.20 370	90.35 361	36.52 368	41.20 369	49.49 370	18.33 374	81.29 367	32.10 370	65.34 347	46.54 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	41.42 23	45.67 338	42.50 341	55.17 354	34.28 380	32.37 379	66.24 368	78.71 376	30.72 370	22.04 375	59.59 367	4.59 379	77.85 371	27.49 371	58.84 358	55.29 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 339	37.64 344	53.90 355	49.46 378	43.37 375	65.09 369	66.66 378	26.19 373	25.77 374	48.53 371	3.58 381	63.35 374	26.15 372	27.28 370	54.97 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 340	40.93 342	41.29 356	61.97 373	33.83 378	84.00 361	65.17 379	27.17 371	27.56 371	46.72 372	17.63 375	60.41 375	19.32 373	18.82 371	29.61 371

EMVS		39.96 341	39.88 343	40.18 357	59.57 376	32.12 380	84.79 358	64.57 380	26.27 372	26.14 373	44.18 375	18.73 373	59.29 376	17.03 374	17.67 373	29.12 372

wuyk23d		16.71 344	16.73 348	16.65 358	60.15 374	25.22 382	41.24 371	5.17 382	6.56 375	5.48 378	3.61 378	3.64 380	22.72 377	15.20 375	9.52 375	1.99 375

testmvs		18.81 343	23.05 346	6.10 360	4.48 382	2.29 384	97.78 224	3.00 383	3.27 376	18.60 376	62.71 365	1.53 383	2.49 379	14.26 376	1.80 376	13.50 374

test123		16.58 345	19.47 347	7.91 359	3.59 383	5.37 383	94.32 312	1.39 384	2.49 377	13.98 377	44.60 374	2.91 382	2.65 378	11.35 377	0.57 377	15.70 373

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		22.52 342	30.03 345	0.00 361	0.00 384	0.00 385	0.00 372	97.17 167	0.00 379	0.00 380	98.77 88	74.35 239	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		6.87 347	9.16 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	82.48 180	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		8.21 346	10.94 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	98.50 110	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					99.50 47	88.94 172	99.55 35	97.47 134	91.32 95	98.12 38

test_one_0601					99.59 31	94.89 35		97.64 94	93.14 51	98.93 16	99.45 16	93.45 17

eth-test2					0.00 384

eth-test					0.00 384

test_241102_ONE					99.63 21	95.24 24		97.72 76	94.16 28	99.30 6	99.49 10	93.32 18	99.98 10

save fliter					99.34 58	93.85 63	99.65 23	97.63 99	95.69 11

test0726					99.66 15	95.20 29	99.77 8	97.70 81	93.95 31	99.35 5	99.54 3	93.18 21

GSMVS																98.84 143

test_part2					99.54 40	95.42 19				98.13 36

sam_mvs1												88.39 75				98.84 143

sam_mvs												87.08 102

MTGPA								97.45 137

test_post											46.00 373	87.37 94	97.11 231

patchmatchnet-post											84.86 346	88.73 69	96.81 243

MTMP							99.21 75	91.09 358

TEST9					99.57 37	93.17 76	99.38 62	97.66 88	89.57 144	98.39 31	99.18 37	90.88 36	99.66 85

test_8					99.55 39	93.07 80	99.37 65	97.64 94	90.18 124	98.36 33	99.19 34	90.94 34	99.64 91

agg_prior					99.54 40	92.66 88		97.64 94		97.98 47			99.61 94

test_prior4						92.00 99	99.41 59

test_prior				97.01 65	99.58 33	91.77 100		97.57 113					99.49 109			99.79 38

新几何2							98.26 190

旧先验1					98.97 86	92.90 87		97.74 70			99.15 43	91.05 33			99.33 74	99.60 78

原ACMM2							98.69 137

test222					98.32 108	91.21 113	98.08 208	97.58 110	83.74 267	95.87 95	99.02 60	86.74 110			99.64 47	99.81 35

segment_acmp												90.56 43

testdata1							97.89 217		92.43 68

test12				97.83 34	99.33 64	94.45 51		97.55 116		97.56 53		88.60 70	99.50 108		99.71 38	99.55 82

plane_prior7					93.84 257	85.73 249

plane_prior6					93.92 254	86.02 243						72.92 251

plane_prior4											96.52 187

plane_prior3						85.91 244			93.65 43	86.99 210

plane_prior2							99.02 103		93.38 48

plane_prior1					93.90 256

plane_prior						86.07 241	99.14 90		93.81 41						86.26 230

n2								0.00 385

nn								0.00 385

door-mid								84.90 372

test11								97.68 85

door								85.30 371

HQP5-MVS						86.39 229

HQP-NCC					93.95 250		99.16 81		93.92 33	87.57 203

ACMP_Plane					93.95 250		99.16 81		93.92 33	87.57 203

HQP4-MVS										87.57 203			97.77 198			92.72 238

HQP3-MVS								96.37 214							86.29 228

HQP2-MVS												73.34 247

NP-MVS					93.94 253	86.22 235					96.67 185

ACMMP++_ref															82.64 259

ACMMP++															83.83 247

Test By Simon												83.62 156

High-resolution multi-view benchmark