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.

AdaColmap		93.82 92	93.06 95	96.10 107	99.88 1	89.07 151	98.33 160	97.55 107	86.81 184	90.39 143	98.65 75	75.09 192	99.98 9	93.32 97	97.53 95	99.26 82

DP-MVS Recon		95.85 52	95.15 58	97.95 20	99.87 2	94.38 43	99.60 17	97.48 118	86.58 187	94.42 88	99.13 31	87.36 78	99.98 9	93.64 90	98.33 84	99.48 67

MCST-MVS		98.18 2	97.95 5	98.86 1	99.85 3	96.60 5	99.70 10	97.98 53	97.18 2	95.96 63	99.33 9	92.62 11	100.00 1	98.99 5	99.93 1	99.98 2

CNVR-MVS		98.46 1	98.38 1	98.72 3	99.80 4	96.19 9	99.80 7	97.99 52	97.05 3	99.41 1	99.59 2	92.89 10	100.00 1	98.99 5	99.90 4	99.96 4

MG-MVS		97.24 13	96.83 21	98.47 9	99.79 5	95.71 12	99.07 72	99.06 15	94.45 19	96.42 58	98.70 73	88.81 51	99.74 60	95.35 65	99.86 9	99.97 3

NCCC		98.12 3	98.11 3	98.13 15	99.76 6	94.46 39	99.81 5	97.88 58	96.54 4	98.84 7	99.46 6	92.55 12	99.98 9	98.25 22	99.93 1	99.94 7

region2R		96.30 42	96.17 38	96.70 78	99.70 7	90.31 126	99.46 30	97.66 85	90.55 84	97.07 42	99.07 36	86.85 87	99.97 14	95.43 63	99.74 20	99.81 21

HFP-MVS		96.42 38	96.26 35	96.90 63	99.69 8	90.96 113	99.47 27	97.81 67	90.54 85	96.88 45	99.05 39	87.57 69	99.96 17	95.65 57	99.72 22	99.78 29

#test#		96.48 35	96.34 33	96.90 63	99.69 8	90.96 113	99.53 24	97.81 67	90.94 78	96.88 45	99.05 39	87.57 69	99.96 17	95.87 56	99.72 22	99.78 29

ACMMPR		96.28 43	96.14 41	96.73 75	99.68 10	90.47 124	99.47 27	97.80 69	90.54 85	96.83 52	99.03 41	86.51 93	99.95 20	95.65 57	99.72 22	99.75 35

CP-MVS		96.22 44	96.15 40	96.42 94	99.67 11	89.62 144	99.70 10	97.61 97	90.07 99	96.00 60	99.16 25	87.43 73	99.92 26	96.03 54	99.72 22	99.70 44

CPTT-MVS		94.60 79	94.43 68	95.09 138	99.66 12	86.85 198	99.44 31	97.47 119	83.22 247	94.34 91	98.96 51	82.50 145	99.55 78	94.81 74	99.50 42	98.88 110

MSLP-MVS++		97.50 9	97.45 10	97.63 28	99.65 13	93.21 59	99.70 10	98.13 46	94.61 16	97.78 32	99.46 6	89.85 40	99.81 52	97.97 24	99.91 3	99.88 14

PAPR		96.35 39	95.82 46	97.94 21	99.63 14	94.19 47	99.42 35	97.55 107	92.43 50	93.82 101	99.12 32	87.30 80	99.91 28	94.02 82	99.06 60	99.74 38

XVS		96.47 36	96.37 31	96.77 71	99.62 15	90.66 122	99.43 33	97.58 101	92.41 54	96.86 48	98.96 51	87.37 75	99.87 37	95.65 57	99.43 47	99.78 29

X-MVStestdata		90.69 166	88.66 179	96.77 71	99.62 15	90.66 122	99.43 33	97.58 101	92.41 54	96.86 48	29.59 363	87.37 75	99.87 37	95.65 57	99.43 47	99.78 29

DeepC-MVS_fast	93.52 2	97.16 15	96.84 20	98.13 15	99.61 17	94.45 40	98.85 99	97.64 91	96.51 6	95.88 64	99.39 8	87.35 79	99.99 4	96.61 41	99.69 27	99.96 4
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		96.56 32	96.18 36	97.70 26	99.59 18	93.92 49	99.13 69	97.44 124	89.02 120	97.90 30	99.22 16	88.90 50	99.49 88	94.63 78	99.79 17	99.68 47

test_prior3		97.07 19	97.09 13	97.01 52	99.58 19	91.77 83	99.57 19	97.57 104	91.43 70	98.12 21	98.97 48	90.43 35	99.49 88	98.33 19	99.81 15	99.79 25

test_prior				97.01 52	99.58 19	91.77 83		97.57 104					99.49 88			99.79 25

APDe-MVS		97.53 7	97.47 8	97.70 26	99.58 19	93.63 52	99.56 21	97.52 111	93.59 33	98.01 26	99.12 32	90.80 31	99.55 78	99.26 3	99.79 17	99.93 8

mPP-MVS		95.90 51	95.75 49	96.38 96	99.58 19	89.41 149	99.26 50	97.41 128	90.66 80	94.82 83	98.95 53	86.15 99	99.98 9	95.24 68	99.64 30	99.74 38

TEST9					99.57 23	93.17 60	99.38 38	97.66 85	89.57 105	98.39 13	99.18 21	90.88 28	99.66 65

train_agg		97.20 14	97.08 14	97.57 32	99.57 23	93.17 60	99.38 38	97.66 85	90.18 93	98.39 13	99.18 21	90.94 26	99.66 65	98.58 13	99.85 10	99.88 14

agg_prior3		97.09 18	96.97 17	97.45 35	99.56 25	92.79 72	99.36 42	97.67 84	89.59 103	98.36 15	99.16 25	90.57 33	99.68 62	98.58 13	99.85 10	99.88 14

test_8					99.55 26	93.07 64	99.37 41	97.64 91	90.18 93	98.36 15	99.19 19	90.94 26	99.64 71

test_part2					99.54 27	95.42 14				98.13 18

v1.0		40.64 335	54.18 327	0.00 353	99.54 27	0.00 368	0.00 359	97.69 81	92.81 45	98.13 18	99.48 5	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

HSP-MVS		97.73 5	98.15 2	96.44 93	99.54 27	90.14 129	99.41 36	97.47 119	95.46 14	98.60 10	99.19 19	95.71 4	99.49 88	98.15 23	99.85 10	99.69 46

agg_prior1		97.12 16	97.03 15	97.38 41	99.54 27	92.66 73	99.35 44	97.64 91	90.38 88	97.98 27	99.17 23	90.84 30	99.61 74	98.57 15	99.78 19	99.87 18

agg_prior					99.54 27	92.66 73		97.64 91		97.98 27			99.61 74

CSCG		94.87 69	94.71 63	95.36 129	99.54 27	86.49 208	99.34 46	98.15 44	82.71 256	90.15 146	99.25 12	89.48 44	99.86 42	94.97 72	98.82 72	99.72 41

HPM-MVS++		97.72 6	97.59 7	98.14 14	99.53 33	94.76 30	99.19 53	97.75 74	95.66 11	98.21 17	99.29 10	91.10 18	99.99 4	97.68 28	99.87 6	99.68 47

APD-MVS		96.95 22	96.72 24	97.63 28	99.51 34	93.58 53	99.16 58	97.44 124	90.08 98	98.59 11	99.07 36	89.06 47	99.42 97	97.92 25	99.66 28	99.88 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ESAPD		98.11 4	98.00 4	98.44 10	99.50 35	95.39 15	99.29 49	97.72 79	94.50 17	98.64 9	99.54 3	93.32 9	99.97 14	99.58 1	99.90 4	99.95 6

PGM-MVS		95.85 52	95.65 51	96.45 92	99.50 35	89.77 141	98.22 175	98.90 17	89.19 114	96.74 54	98.95 53	85.91 101	99.92 26	93.94 83	99.46 44	99.66 50

MP-MVS		96.00 48	95.82 46	96.54 89	99.47 37	90.13 131	99.36 42	97.41 128	90.64 83	95.49 73	98.95 53	85.51 105	99.98 9	96.00 55	99.59 39	99.52 62
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Regformer-1		96.97 21	96.80 22	97.47 34	99.46 38	93.11 62	98.89 96	97.94 54	92.89 42	96.90 44	99.02 42	89.78 41	99.53 81	97.06 32	99.26 56	99.75 35

Regformer-2		96.94 24	96.78 23	97.42 37	99.46 38	92.97 68	98.89 96	97.93 55	92.86 44	96.88 45	99.02 42	89.74 42	99.53 81	97.03 33	99.26 56	99.75 35

PAPM_NR		95.43 59	95.05 60	96.57 88	99.42 40	90.14 129	98.58 132	97.51 113	90.65 82	92.44 112	98.90 58	87.77 68	99.90 30	90.88 119	99.32 53	99.68 47

Regformer-3		96.50 34	96.36 32	96.91 62	99.34 41	91.72 86	98.71 110	97.90 57	92.48 49	96.00 60	98.95 53	88.60 53	99.52 84	96.44 45	98.83 70	99.49 65

Regformer-4		96.45 37	96.33 34	96.81 70	99.34 41	91.44 94	98.71 110	97.88 58	92.43 50	95.97 62	98.95 53	88.42 57	99.51 85	96.40 46	98.83 70	99.49 65

PHI-MVS		96.65 30	96.46 29	97.21 46	99.34 41	91.77 83	99.70 10	98.05 48	86.48 189	98.05 23	99.20 18	89.33 45	99.96 17	98.38 18	99.62 34	99.90 10

test12				97.83 23	99.33 44	94.45 40		97.55 107		97.56 33		88.60 53	99.50 87		99.71 26	99.55 60

SMA-MVS		97.25 11	97.00 16	98.00 19	99.31 45	94.20 46	99.16 58	97.65 90	89.55 107	99.22 2	99.53 4	90.34 38	99.99 4	98.43 17	99.83 13	99.81 21

zzz-MVS		96.21 45	95.96 42	96.96 60	99.29 46	91.19 103	98.69 114	97.45 121	92.58 46	94.39 89	99.24 14	86.43 95	99.99 4	96.22 48	99.40 50	99.71 42

MTAPA		96.09 47	95.80 48	96.96 60	99.29 46	91.19 103	97.23 220	97.45 121	92.58 46	94.39 89	99.24 14	86.43 95	99.99 4	96.22 48	99.40 50	99.71 42

HPM-MVS		95.41 61	95.22 57	95.99 109	99.29 46	89.14 150	99.17 57	97.09 155	87.28 175	95.40 74	98.48 87	84.93 112	99.38 100	95.64 61	99.65 29	99.47 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		94.67 76	94.30 69	95.79 115	99.25 49	88.13 168	98.41 154	98.67 24	90.38 88	91.43 124	98.72 71	82.22 152	99.95 20	93.83 87	95.76 123	99.29 78
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
APD-MVS_3200maxsize		95.64 58	95.65 51	95.62 119	99.24 50	87.80 174	98.42 152	97.22 141	88.93 125	96.64 57	98.98 47	85.49 106	99.36 102	96.68 40	99.27 55	99.70 44

API-MVS		94.78 71	94.18 72	96.59 87	99.21 51	90.06 135	98.80 104	97.78 72	83.59 237	93.85 99	99.21 17	83.79 122	99.97 14	92.37 107	99.00 63	99.74 38

PLC	91.07 3	94.23 85	94.01 76	94.87 145	99.17 52	87.49 180	99.25 51	96.55 182	88.43 141	91.26 127	98.21 96	85.92 100	99.86 42	89.77 130	97.57 93	97.24 174
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EI-MVSNet-Vis-set		95.76 57	95.63 53	96.17 104	99.14 53	90.33 125	98.49 143	97.82 64	91.92 61	94.75 84	98.88 60	87.06 83	99.48 93	95.40 64	97.17 101	98.70 125

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 40	99.12 54	93.49 57	98.52 137	97.50 116	94.46 18	98.99 3	98.64 76	91.58 15	99.08 117	98.49 16	99.83 13	99.60 58

HPM-MVS_fast		94.89 68	94.62 64	95.70 118	99.11 55	88.44 165	99.14 66	97.11 151	85.82 195	95.69 69	98.47 88	83.46 126	99.32 106	93.16 99	99.63 33	99.35 72

MAR-MVS		94.43 81	94.09 74	95.45 128	99.10 56	87.47 181	98.39 158	97.79 71	88.37 143	94.02 96	99.17 23	78.64 176	99.91 28	92.48 106	98.85 69	98.96 102
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
114514_t		94.06 88	93.05 96	97.06 50	99.08 57	92.26 81	98.97 85	97.01 163	82.58 258	92.57 110	98.22 94	80.68 162	99.30 107	89.34 136	99.02 62	99.63 54

EI-MVSNet-UG-set		95.43 59	95.29 55	95.86 114	99.07 58	89.87 138	98.43 151	97.80 69	91.78 64	94.11 95	98.77 65	86.25 98	99.48 93	94.95 73	96.45 107	98.22 149

原ACMM1				96.18 102	99.03 59	90.08 132		97.63 95	88.98 121	97.00 43	98.97 48	88.14 63	99.71 61	88.23 147	99.62 34	98.76 122

SD-MVS		97.51 8	97.40 11	97.81 24	99.01 60	93.79 51	99.33 47	97.38 131	93.73 30	98.83 8	99.02 42	90.87 29	99.88 34	98.69 9	99.74 20	99.77 34

旧先验1					98.97 61	92.90 70		97.74 76			99.15 27	91.05 19			99.33 52	99.60 58

LS3D		90.19 171	88.72 177	94.59 152	98.97 61	86.33 215	96.90 233	96.60 176	74.96 314	84.06 204	98.74 68	75.78 189	99.83 47	74.93 276	97.57 93	97.62 167

CNLPA		93.64 99	92.74 101	96.36 97	98.96 63	90.01 137	99.19 53	95.89 228	86.22 192	89.40 159	98.85 61	80.66 163	99.84 45	88.57 145	96.92 102	99.24 83

MP-MVS-pluss		95.80 54	95.30 54	97.29 43	98.95 64	92.66 73	98.59 131	97.14 147	88.95 123	93.12 105	99.25 12	85.62 102	99.94 22	96.56 43	99.48 43	99.28 80
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
新几何1				97.40 39	98.92 65	92.51 79		97.77 73	85.52 198	96.69 56	99.06 38	88.08 64	99.89 33	84.88 178	99.62 34	99.79 25

DP-MVS		88.75 198	86.56 206	95.34 130	98.92 65	87.45 182	97.64 207	93.52 302	70.55 325	81.49 242	97.25 125	74.43 206	99.88 34	71.14 306	94.09 136	98.67 126

1121		95.19 65	94.45 67	97.42 37	98.88 67	92.58 77	96.22 259	97.75 74	85.50 200	96.86 48	99.01 46	88.59 55	99.90 30	87.64 153	99.60 37	99.79 25

TSAR-MVS + GP.		96.95 22	96.91 18	97.07 49	98.88 67	91.62 88	99.58 18	96.54 183	95.09 15	96.84 51	98.63 77	91.16 16	99.77 57	99.04 4	96.42 108	99.81 21

CANet		97.00 20	96.49 28	98.55 6	98.86 69	96.10 10	99.83 4	97.52 111	95.90 8	97.21 39	98.90 58	82.66 144	99.93 24	98.71 8	98.80 73	99.63 54

ACMMP_Plus		96.59 31	96.18 36	97.81 24	98.82 70	93.55 54	98.88 98	97.59 99	90.66 80	97.98 27	99.14 29	86.59 90	100.00 1	96.47 44	99.46 44	99.89 13

PVSNet_BlendedMVS		93.36 106	93.20 93	93.84 176	98.77 71	91.61 89	99.47 27	98.04 49	91.44 69	94.21 92	92.63 225	83.50 124	99.87 37	97.41 29	83.37 230	90.05 290

PVSNet_Blended		95.94 50	95.66 50	96.75 73	98.77 71	91.61 89	99.88 1	98.04 49	93.64 32	94.21 92	97.76 104	83.50 124	99.87 37	97.41 29	97.75 92	98.79 117

DeepPCF-MVS	93.56 1	96.55 33	97.84 6	92.68 197	98.71 73	78.11 310	99.70 10	97.71 80	98.18 1	97.36 38	99.76 1	90.37 37	99.94 22	99.27 2	99.54 41	99.99 1

EPNet		96.82 26	96.68 26	97.25 45	98.65 74	93.10 63	99.48 26	98.76 18	96.54 4	97.84 31	98.22 94	87.49 72	99.66 65	95.35 65	97.78 91	99.00 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		93.90 90	93.62 87	94.73 149	98.63 75	87.00 194	98.04 192	96.56 181	92.19 58	92.46 111	98.73 69	79.49 168	99.14 114	92.16 110	94.34 135	98.03 155

abl_6		94.63 78	94.48 66	95.09 138	98.61 76	86.96 195	98.06 191	96.97 165	89.31 110	95.86 66	98.56 80	79.82 165	99.64 71	94.53 80	98.65 79	98.66 127

MVS_111021_HR		96.69 28	96.69 25	96.72 77	98.58 77	91.00 112	99.14 66	99.45 1	93.86 27	95.15 79	98.73 69	88.48 56	99.76 58	97.23 31	99.56 40	99.40 70

0601test		95.27 64	94.60 65	97.28 44	98.53 78	92.98 67	99.05 77	98.70 22	86.76 185	94.65 87	97.74 106	87.78 67	99.44 96	95.57 62	92.61 148	99.44 69

TAPA-MVS	87.50 9	90.35 167	89.05 171	94.25 163	98.48 79	85.17 244	98.42 152	96.58 180	82.44 262	87.24 182	98.53 81	82.77 143	98.84 122	59.09 334	97.88 87	98.72 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		96.12 46	95.26 56	98.69 4	98.44 80	96.54 7	99.70 10	96.89 168	95.76 10	97.53 34	99.12 32	72.42 232	99.93 24	98.75 7	98.69 76	99.61 57

test222					98.32 81	91.21 102	98.08 189	97.58 101	83.74 233	95.87 65	99.02 42	86.74 88			99.64 30	99.81 21

LFMVS		92.23 135	90.84 149	96.42 94	98.24 82	91.08 110	98.24 173	96.22 202	83.39 245	94.74 85	98.31 92	61.12 304	98.85 121	94.45 81	92.82 144	99.32 75

testdata				95.26 133	98.20 83	87.28 190		97.60 98	85.21 204	98.48 12	99.15 27	88.15 62	98.72 130	90.29 124	99.45 46	99.78 29

PatchMatch-RL		91.47 150	90.54 156	94.26 162	98.20 83	86.36 214	96.94 231	97.14 147	87.75 161	88.98 162	95.75 172	71.80 240	99.40 99	80.92 219	97.39 98	97.02 182

MVS_111021_LR		95.78 55	95.94 43	95.28 132	98.19 85	87.69 175	98.80 104	99.26 13	93.39 35	95.04 81	98.69 74	84.09 120	99.76 58	96.96 38	99.06 60	98.38 141

F-COLMAP		92.07 141	91.75 127	93.02 189	98.16 86	82.89 272	98.79 107	95.97 215	86.54 188	87.92 175	97.80 102	78.69 175	99.65 69	85.97 168	95.93 120	96.53 198

Anonymous202405211		88.84 192	87.03 203	94.27 161	98.14 87	84.18 256	98.44 150	95.58 249	76.79 309	89.34 160	96.88 146	53.42 327	99.54 80	87.53 155	87.12 204	99.09 91

VNet		95.08 66	94.26 70	97.55 33	98.07 88	93.88 50	98.68 117	98.73 21	90.33 90	97.16 41	97.43 116	79.19 170	99.53 81	96.91 39	91.85 160	99.24 83

DELS-MVS		97.12 16	96.60 27	98.68 5	98.03 89	96.57 6	99.84 3	97.84 62	96.36 7	95.20 78	98.24 93	88.17 61	99.83 47	96.11 52	99.60 37	99.64 52
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
PVSNet	87.13 12	93.69 95	92.83 100	96.28 99	97.99 90	90.22 128	99.38 38	98.93 16	91.42 72	93.66 102	97.68 108	71.29 246	99.64 71	87.94 150	97.20 100	98.98 100

CHOSEN 280x420		96.80 27	96.85 19	96.66 81	97.85 91	94.42 42	94.76 289	98.36 27	92.50 48	95.62 72	97.52 112	97.92 1	97.38 198	98.31 21	98.80 73	98.20 151

thres200		93.69 95	92.59 105	96.97 59	97.76 92	94.74 31	99.35 44	99.36 2	89.23 113	91.21 129	96.97 142	83.42 127	98.77 124	85.08 176	90.96 171	97.39 171

tfpn_ndepth		93.28 110	92.32 108	96.16 105	97.74 93	92.86 71	99.01 81	98.19 40	85.50 200	89.84 151	97.12 134	93.57 7	97.58 183	79.39 233	90.50 179	98.04 154

HY-MVS	88.56 7	95.29 63	94.23 71	98.48 8	97.72 94	96.41 8	94.03 297	98.74 19	92.42 53	95.65 71	94.76 185	86.52 92	99.49 88	95.29 67	92.97 143	99.53 61

Anonymous20231211		84.72 254	82.65 264	90.91 230	97.71 95	84.55 252	97.28 216	96.67 173	66.88 337	79.18 264	90.87 250	58.47 309	96.60 220	82.61 202	74.20 282	91.59 244

tfpn200view9		93.43 103	92.27 111	96.90 63	97.68 96	94.84 24	99.18 55	99.36 2	88.45 138	90.79 132	96.90 144	83.31 128	98.75 126	84.11 186	90.69 173	97.12 176

thres400		93.39 105	92.27 111	96.73 75	97.68 96	94.84 24	99.18 55	99.36 2	88.45 138	90.79 132	96.90 144	83.31 128	98.75 126	84.11 186	90.69 173	96.61 189

tfpn111		93.20 113	92.00 120	96.83 69	97.62 98	94.84 24	99.06 74	99.36 2	87.96 153	90.47 139	96.78 147	83.29 130	98.71 131	82.93 198	90.47 180	96.94 183

conf200view11		93.32 108	92.15 116	96.84 68	97.62 98	94.84 24	99.06 74	99.36 2	87.96 153	90.47 139	96.78 147	83.29 130	98.75 126	84.11 186	90.69 173	96.94 183

thres100view900		93.34 107	92.15 116	96.90 63	97.62 98	94.84 24	99.06 74	99.36 2	87.96 153	90.47 139	96.78 147	83.29 130	98.75 126	84.11 186	90.69 173	97.12 176

thres600view7		93.18 114	92.00 120	96.75 73	97.62 98	94.92 21	99.07 72	99.36 2	87.96 153	90.47 139	96.78 147	83.29 130	98.71 131	82.93 198	90.47 180	96.61 189

WTY-MVS		95.97 49	95.11 59	98.54 7	97.62 98	96.65 4	99.44 31	98.74 19	92.25 57	95.21 77	98.46 90	86.56 91	99.46 95	95.00 71	92.69 147	99.50 64

tfpn1000		92.67 127	91.64 129	95.78 116	97.61 103	92.34 80	98.69 114	98.18 41	84.15 222	88.80 164	96.99 141	93.56 8	97.21 202	76.56 258	90.19 182	97.77 163

Anonymous20240529		87.66 209	85.58 225	93.92 173	97.59 104	85.01 247	98.13 184	97.13 149	66.69 338	88.47 166	96.01 170	55.09 321	99.51 85	87.00 160	84.12 224	97.23 175

HyFIR lowres test		93.68 97	93.29 91	94.87 145	97.57 105	88.04 170	98.18 180	98.47 25	87.57 167	91.24 128	95.05 181	85.49 106	97.46 189	93.22 98	92.82 144	99.10 90

canonicalmvs		95.02 67	93.96 80	98.20 12	97.53 106	95.92 11	98.71 110	96.19 205	91.78 64	95.86 66	98.49 86	79.53 167	99.03 118	96.12 51	91.42 168	99.66 50

view600		92.78 120	91.50 132	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 171	89.64 155	96.59 154	83.26 135	98.63 135	80.76 222	90.15 183	96.61 189

view800		92.78 120	91.50 132	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 171	89.64 155	96.59 154	83.26 135	98.63 135	80.76 222	90.15 183	96.61 189

conf0.05thres1000		92.78 120	91.50 132	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 171	89.64 155	96.59 154	83.26 135	98.63 135	80.76 222	90.15 183	96.61 189

tfpn		92.78 120	91.50 132	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 171	89.64 155	96.59 154	83.26 135	98.63 135	80.76 222	90.15 183	96.61 189

CHOSEN 1792x2688		94.35 83	93.82 85	95.95 111	97.40 111	88.74 158	98.41 154	98.27 29	92.18 59	91.43 124	96.40 162	78.88 171	99.81 52	93.59 91	97.81 88	99.30 77

SteuartSystems-ACMMP		97.25 11	97.34 12	97.01 52	97.38 112	91.46 92	99.75 8	97.66 85	94.14 22	98.13 18	99.26 11	92.16 13	99.66 65	97.91 26	99.64 30	99.90 10
Skip Steuart: Steuart Systems R&D Blog.
alignmvs		95.77 56	95.00 61	98.06 18	97.35 113	95.68 13	99.71 9	97.50 116	91.50 68	96.16 59	98.61 78	86.28 97	99.00 119	96.19 50	91.74 162	99.51 63

PS-MVSNAJ		96.87 25	96.40 30	98.29 11	97.35 113	97.29 1	99.03 78	97.11 151	95.83 9	98.97 4	99.14 29	82.48 147	99.60 76	98.60 10	99.08 59	98.00 156

EPNet_dtu		92.28 133	92.15 116	92.70 196	97.29 115	84.84 248	98.64 123	97.82 64	92.91 41	93.02 108	97.02 139	85.48 108	95.70 276	72.25 302	94.89 131	97.55 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSTER		92.71 125	92.32 108	93.86 175	97.29 115	92.95 69	99.01 81	96.59 177	90.09 97	85.51 193	94.00 195	94.61 5	96.56 223	90.77 122	83.03 233	92.08 231

EPMVS		92.59 130	91.59 130	95.59 121	97.22 117	90.03 136	91.78 316	98.04 49	90.42 87	91.66 118	90.65 263	86.49 94	97.46 189	81.78 213	96.31 111	99.28 80

tpmvs		89.16 186	87.76 190	93.35 182	97.19 118	84.75 250	90.58 326	97.36 133	81.99 266	84.56 198	89.31 293	83.98 121	98.17 145	74.85 278	90.00 188	97.12 176

DeepC-MVS	91.02 4	94.56 80	93.92 83	96.46 91	97.16 119	90.76 118	98.39 158	97.11 151	93.92 23	88.66 165	98.33 91	78.14 178	99.85 44	95.02 70	98.57 80	98.78 120
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_Blended_VisFu		94.67 76	94.11 73	96.34 98	97.14 120	91.10 108	99.32 48	97.43 126	92.10 60	91.53 122	96.38 165	83.29 130	99.68 62	93.42 95	96.37 109	98.25 148

xiu_mvs_v2_base		96.66 29	96.17 38	98.11 17	97.11 121	96.96 2	99.01 81	97.04 159	95.51 13	98.86 6	99.11 35	82.19 153	99.36 102	98.59 12	98.14 85	98.00 156

VDD-MVS		91.24 156	90.18 159	94.45 156	97.08 122	85.84 234	98.40 157	96.10 211	86.99 177	93.36 103	98.16 97	54.27 324	99.20 108	96.59 42	90.63 177	98.31 147

UGNet		91.91 145	90.85 148	95.10 137	97.06 123	88.69 159	98.01 193	98.24 31	92.41 54	92.39 113	93.61 207	60.52 305	99.68 62	88.14 148	97.25 99	96.92 187
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
CANet_DTU		94.31 84	93.35 90	97.20 47	97.03 124	94.71 32	98.62 125	95.54 250	95.61 12	97.21 39	98.47 88	71.88 238	99.84 45	88.38 146	97.46 97	97.04 181

DWT-MVSNet_test		94.36 82	93.95 81	95.62 119	96.99 125	89.47 147	96.62 244	97.38 131	90.96 77	93.07 107	97.27 124	93.73 6	98.09 150	85.86 172	93.65 139	99.29 78

PatchFormer-LS_test		94.08 87	93.60 88	95.53 126	96.92 126	89.57 145	96.51 248	97.34 135	91.29 74	92.22 115	97.18 130	91.66 14	98.02 155	87.05 158	92.21 155	99.00 97

MSDG		88.29 204	86.37 208	94.04 169	96.90 127	86.15 222	96.52 247	94.36 290	77.89 306	79.22 263	96.95 143	69.72 254	99.59 77	73.20 295	92.58 149	96.37 199

BH-w/o		92.32 132	91.79 125	93.91 174	96.85 128	86.18 220	99.11 70	95.74 233	88.13 150	84.81 196	97.00 140	77.26 183	97.91 158	89.16 142	98.03 86	97.64 164

conf0.01		92.06 142	90.99 139	95.24 135	96.84 129	91.39 95	98.31 163	98.20 33	83.57 238	88.08 169	97.34 118	91.05 19	97.40 192	75.80 264	89.74 190	96.94 183

conf0.002		92.06 142	90.99 139	95.24 135	96.84 129	91.39 95	98.31 163	98.20 33	83.57 238	88.08 169	97.34 118	91.05 19	97.40 192	75.80 264	89.74 190	96.94 183

thresconf0.02		92.14 136	90.99 139	95.58 122	96.84 129	91.39 95	98.31 163	98.20 33	83.57 238	88.08 169	97.34 118	91.05 19	97.40 192	75.80 264	89.74 190	97.94 158

tfpn_n400		92.14 136	90.99 139	95.58 122	96.84 129	91.39 95	98.31 163	98.20 33	83.57 238	88.08 169	97.34 118	91.05 19	97.40 192	75.80 264	89.74 190	97.94 158

tfpnconf		92.14 136	90.99 139	95.58 122	96.84 129	91.39 95	98.31 163	98.20 33	83.57 238	88.08 169	97.34 118	91.05 19	97.40 192	75.80 264	89.74 190	97.94 158

tfpnview11		92.14 136	90.99 139	95.58 122	96.84 129	91.39 95	98.31 163	98.20 33	83.57 238	88.08 169	97.34 118	91.05 19	97.40 192	75.80 264	89.74 190	97.94 158

AllTest		84.97 252	83.12 254	90.52 238	96.82 135	78.84 303	95.89 271	92.17 324	77.96 303	75.94 286	95.50 175	55.48 318	99.18 109	71.15 304	87.14 202	93.55 209

TestCases				90.52 238	96.82 135	78.84 303		92.17 324	77.96 303	75.94 286	95.50 175	55.48 318	99.18 109	71.15 304	87.14 202	93.55 209

PMMVS		93.62 100	93.90 84	92.79 193	96.79 137	81.40 283	98.85 99	96.81 169	91.25 75	96.82 53	98.15 98	77.02 184	98.13 147	93.15 100	96.30 112	98.83 114

BH-RMVSNet		91.25 155	89.99 161	95.03 143	96.75 138	88.55 162	98.65 121	94.95 274	87.74 162	87.74 176	97.80 102	68.27 265	98.14 146	80.53 227	97.49 96	98.41 138

MVS_Test		93.67 98	92.67 103	96.69 79	96.72 139	92.66 73	97.22 221	96.03 213	87.69 165	95.12 80	94.03 193	81.55 156	98.28 144	89.17 141	96.46 106	99.14 88

COLMAP_ROB	82.69 18	84.54 259	82.82 258	89.70 257	96.72 139	78.85 302	95.89 271	92.83 317	71.55 322	77.54 279	95.89 171	59.40 308	99.14 114	67.26 314	88.26 198	91.11 256
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		92.50 131	91.65 128	95.06 141	96.60 141	89.64 143	97.06 228	96.44 188	86.64 186	84.14 202	93.93 197	82.49 146	96.17 260	91.47 113	96.08 117	99.35 72

GG-mvs-BLEND				96.98 58	96.53 142	94.81 29	87.20 330	97.74 76		93.91 98	96.40 162	96.56 2	96.94 212	95.08 69	98.95 67	99.20 86

FMVSNet3		88.81 196	87.08 202	93.99 171	96.52 143	94.59 38	98.08 189	96.20 204	85.85 194	82.12 233	91.60 237	74.05 214	95.40 284	79.04 235	80.24 244	91.99 234

BH-untuned		91.46 151	90.84 149	93.33 183	96.51 144	84.83 249	98.84 101	95.50 253	86.44 191	83.50 206	96.70 151	75.49 191	97.77 169	86.78 165	97.81 88	97.40 170

sss		94.85 70	93.94 82	97.58 30	96.43 145	94.09 48	98.93 87	99.16 14	89.50 108	95.27 76	97.85 100	81.50 157	99.65 69	92.79 105	94.02 137	98.99 99

dp		90.16 172	88.83 176	94.14 165	96.38 146	86.42 210	91.57 317	97.06 158	84.76 214	88.81 163	90.19 283	84.29 119	97.43 191	75.05 275	91.35 170	98.56 132

casdiffmvs		94.10 86	93.40 89	96.20 100	96.31 147	91.46 92	97.65 206	96.22 202	88.49 134	95.69 69	94.11 189	83.01 140	98.10 149	93.33 96	95.82 122	99.04 94

TR-MVS		90.77 163	89.44 165	94.76 147	96.31 147	88.02 171	97.92 195	95.96 217	85.52 198	88.22 168	97.23 127	66.80 277	98.09 150	84.58 181	92.38 150	98.17 152

UA-Net		93.30 109	92.62 104	95.34 130	96.27 149	88.53 164	95.88 273	96.97 165	90.90 79	95.37 75	97.07 137	82.38 150	99.10 116	83.91 190	94.86 132	98.38 141

tpmrst		92.78 120	92.16 115	94.65 151	96.27 149	87.45 182	91.83 315	97.10 154	89.10 119	94.68 86	90.69 257	88.22 60	97.73 176	89.78 129	91.80 161	98.77 121

ADS-MVSNet2		87.62 210	86.88 204	89.86 252	96.21 151	79.14 299	87.15 331	92.99 308	83.01 251	89.91 149	87.27 308	78.87 172	92.80 316	74.20 283	92.27 153	97.64 164

ADS-MVSNet		88.99 188	87.30 197	94.07 167	96.21 151	87.56 179	87.15 331	96.78 171	83.01 251	89.91 149	87.27 308	78.87 172	97.01 209	74.20 283	92.27 153	97.64 164

PatchmatchNet		92.05 144	91.04 138	95.06 141	96.17 153	89.04 152	91.26 320	97.26 136	89.56 106	90.64 136	90.56 269	88.35 59	97.11 205	79.53 230	96.07 118	99.03 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
gg-mvs-nofinetune		90.00 176	87.71 192	96.89 67	96.15 154	94.69 33	85.15 336	97.74 76	68.32 334	92.97 109	60.16 349	96.10 3	96.84 214	93.89 84	98.87 68	99.14 88

MDTV_nov1_ep13			90.47 158		96.14 155	88.55 162	91.34 319	97.51 113	89.58 104	92.24 114	90.50 272	86.99 86	97.61 182	77.64 247	92.34 151

IS-MVSNet		93.00 117	92.51 106	94.49 154	96.14 155	87.36 188	98.31 163	95.70 237	88.58 133	90.17 145	97.50 113	83.02 139	97.22 201	87.06 157	96.07 118	98.90 109

Vis-MVSNet (Re-imp)		93.26 112	93.00 98	94.06 168	96.14 155	86.71 204	98.68 117	96.70 172	88.30 145	89.71 154	97.64 109	85.43 109	96.39 240	88.06 149	96.32 110	99.08 92

diffmvs		93.00 117	92.26 113	95.25 134	96.12 158	88.59 160	96.60 245	96.19 205	88.88 127	94.19 94	93.73 203	80.40 164	98.12 148	89.18 140	95.02 129	99.02 96

ab-mvs		91.05 158	89.17 169	96.69 79	95.96 159	91.72 86	92.62 309	97.23 140	85.61 197	89.74 152	93.89 199	68.55 263	99.42 97	91.09 115	87.84 200	98.92 108

Fast-Effi-MVS+		91.72 147	90.79 152	94.49 154	95.89 160	87.40 185	99.54 23	95.70 237	85.01 210	89.28 161	95.68 173	77.75 180	97.57 187	83.22 194	95.06 128	98.51 134

EPP-MVSNet		93.75 94	93.67 86	94.01 170	95.86 161	85.70 236	98.67 119	97.66 85	84.46 217	91.36 126	97.18 130	91.16 16	97.79 167	92.93 102	93.75 138	98.53 133

Effi-MVS+		93.87 91	93.15 94	96.02 108	95.79 162	90.76 118	96.70 241	95.78 231	86.98 179	95.71 68	97.17 132	79.58 166	98.01 156	94.57 79	96.09 116	99.31 76

tpm cat1		88.89 190	87.27 198	93.76 178	95.79 162	85.32 240	90.76 324	97.09 155	76.14 311	85.72 191	88.59 298	82.92 141	98.04 154	76.96 252	91.43 167	97.90 162

tpmp4_e23		91.05 158	90.07 160	93.97 172	95.77 164	85.30 241	92.64 308	97.09 155	84.42 219	91.53 122	90.31 275	87.38 74	97.82 165	80.86 221	90.62 178	98.79 117

3Dnovator+	87.72 8	93.43 103	91.84 124	98.17 13	95.73 165	95.08 20	98.92 89	97.04 159	91.42 72	81.48 243	97.60 110	74.60 199	99.79 55	90.84 120	98.97 64	99.64 52

MVS		93.92 89	92.28 110	98.83 2	95.69 166	96.82 3	96.22 259	98.17 42	84.89 212	84.34 201	98.61 78	79.32 169	99.83 47	93.88 85	99.43 47	99.86 19

cascas		90.93 161	89.33 168	95.76 117	95.69 166	93.03 66	98.99 84	96.59 177	80.49 280	86.79 188	94.45 188	65.23 287	98.60 139	93.52 92	92.18 156	95.66 202

QAPM		91.41 152	89.49 164	97.17 48	95.66 168	93.42 58	98.60 129	97.51 113	80.92 278	81.39 244	97.41 117	72.89 229	99.87 37	82.33 204	98.68 77	98.21 150

1112_ss		92.71 125	91.55 131	96.20 100	95.56 169	91.12 106	98.48 144	94.69 280	88.29 146	86.89 186	98.50 84	87.02 84	98.66 133	84.75 179	89.77 189	98.81 115

LCM-MVSNet-Re		88.59 200	88.61 180	88.51 280	95.53 170	72.68 326	96.85 234	88.43 349	88.45 138	73.14 300	90.63 264	75.82 188	94.38 302	92.95 101	95.71 124	98.48 136

Test_1112_low_res		92.27 134	90.97 145	96.18 102	95.53 170	91.10 108	98.47 146	94.66 281	88.28 147	86.83 187	93.50 211	87.00 85	98.65 134	84.69 180	89.74 190	98.80 116

PCF-MVS	89.78 5	91.26 153	89.63 163	96.16 105	95.44 172	91.58 91	95.29 285	96.10 211	85.07 208	82.75 221	97.45 115	78.28 177	99.78 56	80.60 226	95.65 125	97.12 176
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	87.35 11	93.17 115	91.77 126	97.37 42	95.41 173	93.07 64	98.82 102	97.85 61	91.53 67	82.56 225	97.58 111	71.97 237	99.82 50	91.01 117	99.23 58	99.22 85

IB-MVS	89.43 6	92.12 140	90.83 151	95.98 110	95.40 174	90.78 117	99.81 5	98.06 47	91.23 76	85.63 192	93.66 206	90.63 32	98.78 123	91.22 114	71.85 305	98.36 144
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
1314		93.44 102	91.98 122	97.84 22	95.24 175	94.38 43	96.22 259	97.92 56	90.18 93	82.28 230	97.71 107	77.63 181	99.80 54	91.94 112	98.67 78	99.34 74

XVG-OURS		90.83 162	90.49 157	91.86 207	95.23 176	81.25 287	95.79 278	95.92 222	88.96 122	90.02 148	98.03 99	71.60 242	99.35 104	91.06 116	87.78 201	94.98 203

TESTMET0.1,1		93.82 92	93.26 92	95.49 127	95.21 177	90.25 127	99.15 63	97.54 110	89.18 116	91.79 117	94.87 183	89.13 46	97.63 180	86.21 166	96.29 113	98.60 128

xiu_mvs_v1_base_debu		94.73 72	93.98 77	96.99 55	95.19 178	95.24 17	98.62 125	96.50 184	92.99 38	97.52 35	98.83 62	72.37 233	99.15 111	97.03 33	96.74 103	96.58 195

xiu_mvs_v1_base		94.73 72	93.98 77	96.99 55	95.19 178	95.24 17	98.62 125	96.50 184	92.99 38	97.52 35	98.83 62	72.37 233	99.15 111	97.03 33	96.74 103	96.58 195

xiu_mvs_v1_base_debi		94.73 72	93.98 77	96.99 55	95.19 178	95.24 17	98.62 125	96.50 184	92.99 38	97.52 35	98.83 62	72.37 233	99.15 111	97.03 33	96.74 103	96.58 195

XVG-OURS-SEG-HR		90.95 160	90.66 155	91.83 208	95.18 181	81.14 289	95.92 270	95.92 222	88.40 142	90.33 144	97.85 100	70.66 249	99.38 100	92.83 104	88.83 197	94.98 203

Effi-MVS+-dtu		89.97 177	90.68 154	87.81 294	95.15 182	71.98 328	97.87 199	95.40 261	91.92 61	87.57 177	91.44 238	74.27 209	96.84 214	89.45 132	93.10 142	94.60 205

mvs-test1		91.57 148	92.20 114	89.70 257	95.15 182	74.34 319	99.51 25	95.40 261	91.92 61	91.02 130	97.25 125	74.27 209	98.08 153	89.45 132	95.83 121	96.67 188

Vis-MVSNet		92.64 128	91.85 123	95.03 143	95.12 184	88.23 166	98.48 144	96.81 169	91.61 66	92.16 116	97.22 128	71.58 243	98.00 157	85.85 173	97.81 88	98.88 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		86.67 228	84.96 233	91.80 210	95.11 185	88.81 155	96.77 236	95.25 268	82.94 253	82.12 233	90.25 277	62.89 296	94.97 291	79.04 235	80.24 244	91.62 241

test1		86.67 228	84.96 233	91.80 210	95.11 185	88.81 155	96.77 236	95.25 268	82.94 253	82.12 233	90.25 277	62.89 296	94.97 291	79.04 235	80.24 244	91.62 241

FMVSNet2		86.90 224	84.79 239	93.24 184	95.11 185	92.54 78	97.67 205	95.86 230	82.94 253	80.55 247	91.17 241	62.89 296	95.29 286	77.23 249	79.71 250	91.90 235

MVSFormer		94.71 75	94.08 75	96.61 86	95.05 188	94.87 22	97.77 202	96.17 207	86.84 182	98.04 24	98.52 82	85.52 103	95.99 266	89.83 127	98.97 64	98.96 102

lupinMVS		96.32 41	95.94 43	97.44 36	95.05 188	94.87 22	99.86 2	96.50 184	93.82 28	98.04 24	98.77 65	85.52 103	98.09 150	96.98 37	98.97 64	99.37 71

CostFormer		92.89 119	92.48 107	94.12 166	94.99 190	85.89 230	92.89 307	97.00 164	86.98 179	95.00 82	90.78 252	90.05 39	97.51 188	92.92 103	91.73 163	98.96 102

Patchmatch-test1		90.10 173	88.61 180	94.57 153	94.95 191	88.83 154	96.26 255	97.21 142	90.06 100	90.03 147	90.68 259	66.61 279	95.83 273	77.31 248	94.36 134	99.05 93

test-LLR		93.11 116	92.68 102	94.40 157	94.94 192	87.27 191	99.15 63	97.25 137	90.21 91	91.57 119	94.04 191	84.89 113	97.58 183	85.94 169	96.13 114	98.36 144

test-mter		93.27 111	92.89 99	94.40 157	94.94 192	87.27 191	99.15 63	97.25 137	88.95 123	91.57 119	94.04 191	88.03 65	97.58 183	85.94 169	96.13 114	98.36 144

tpm2		91.77 146	91.09 137	93.82 177	94.83 194	85.56 239	92.51 310	97.16 146	84.00 224	93.83 100	90.66 262	87.54 71	97.17 203	87.73 152	91.55 166	98.72 123

PVSNet_0	83.28 16	87.31 213	85.16 231	93.74 179	94.78 195	84.59 251	98.91 90	98.69 23	89.81 101	78.59 270	93.23 215	61.95 300	99.34 105	94.75 75	55.72 344	97.30 173

CDS-MVSNet		93.47 101	93.04 97	94.76 147	94.75 196	89.45 148	98.82 102	97.03 161	87.91 157	90.97 131	96.48 160	89.06 47	96.36 242	89.50 131	92.81 146	98.49 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gm-plane-assit					94.69 197	88.14 167			88.22 148		97.20 129		98.29 143	90.79 121

RPSCF		85.33 250	85.55 226	84.67 314	94.63 198	62.28 340	93.73 300	93.76 297	74.38 317	85.23 195	97.06 138	64.09 290	98.31 142	80.98 217	86.08 211	93.41 211

Patchmatch-test		86.25 236	84.06 248	92.82 192	94.42 199	82.88 273	82.88 346	94.23 292	71.58 321	79.39 261	90.62 265	89.00 49	96.42 237	63.03 324	91.37 169	99.16 87

VDDNet		90.08 175	88.54 185	94.69 150	94.41 200	87.68 176	98.21 178	96.40 189	76.21 310	93.33 104	97.75 105	54.93 322	98.77 124	94.71 77	90.96 171	97.61 168

EI-MVSNet		89.87 178	89.38 167	91.36 223	94.32 201	85.87 231	97.61 208	96.59 177	85.10 206	85.51 193	97.10 135	81.30 160	96.56 223	83.85 192	83.03 233	91.64 239

CVMVSNet		90.30 168	90.91 147	88.46 281	94.32 201	73.58 323	97.61 208	97.59 99	90.16 96	88.43 167	97.10 135	76.83 185	92.86 312	82.64 201	93.54 140	98.93 107

testpf		80.59 294	80.13 281	81.97 322	94.25 203	71.65 329	60.37 356	95.46 257	70.99 323	76.97 280	87.74 302	73.58 219	91.67 333	76.86 254	84.97 217	82.60 343

IterMVS-LS		88.34 202	87.44 195	91.04 227	94.10 204	85.85 233	98.10 187	95.48 255	85.12 205	82.03 237	91.21 240	81.35 159	95.63 278	83.86 191	75.73 264	91.63 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		92.62 129	92.09 119	94.20 164	94.10 204	87.68 176	98.41 154	96.97 165	87.53 168	89.74 152	96.04 169	84.77 116	96.49 231	88.97 143	92.31 152	98.42 137

PAPM		96.35 39	95.94 43	97.58 30	94.10 204	95.25 16	98.93 87	98.17 42	94.26 20	93.94 97	98.72 71	89.68 43	97.88 161	96.36 47	99.29 54	99.62 56

CLD-MVS		91.06 157	90.71 153	92.10 204	94.05 207	86.10 223	99.55 22	96.29 198	94.16 21	84.70 197	97.17 132	69.62 255	97.82 165	94.74 76	86.08 211	92.39 217
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					93.95 208		99.16 58		93.92 23	87.57 177

ACMP_Plane					93.95 208		99.16 58		93.92 23	87.57 177

HQP-MVS		91.50 149	91.23 136	92.29 201	93.95 208	86.39 212	99.16 58	96.37 190	93.92 23	87.57 177	96.67 152	73.34 222	97.77 169	93.82 88	86.29 206	92.72 212

NP-MVS					93.94 211	86.22 219					96.67 152

plane_prior6					93.92 212	86.02 228						72.92 227

ACMP	87.39 10	88.71 199	88.24 188	90.12 246	93.91 213	81.06 290	98.50 141	95.67 239	89.43 109	80.37 249	95.55 174	65.67 284	97.83 164	90.55 123	84.51 220	91.47 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior1					93.90 214

HQP_MVS		91.26 153	90.95 146	92.16 203	93.84 215	86.07 225	99.02 79	96.30 195	93.38 36	86.99 183	96.52 158	72.92 227	97.75 174	93.46 93	86.17 209	92.67 214

plane_prior7					93.84 215	85.73 235

MVS-HIRNet		79.01 301	75.13 308	90.66 235	93.82 217	81.69 281	85.16 335	93.75 298	54.54 348	74.17 296	59.15 351	57.46 312	96.58 221	63.74 322	94.38 133	93.72 208

FMVSNet5		82.29 275	80.54 280	87.52 296	93.79 218	84.01 258	93.73 300	92.47 321	76.92 308	74.27 295	86.15 316	63.69 293	89.24 337	69.07 309	74.79 271	89.29 302

ACMH+	83.78 15	84.21 263	82.56 267	89.15 269	93.73 219	79.16 298	96.43 249	94.28 291	81.09 275	74.00 297	94.03 193	54.58 323	97.67 177	76.10 261	78.81 252	90.63 278

ACMM	86.95 13	88.77 197	88.22 189	90.43 240	93.61 220	81.34 285	98.50 141	95.92 222	87.88 158	83.85 205	95.20 180	67.20 274	97.89 160	86.90 163	84.90 218	92.06 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS	85.28 14	90.75 164	88.84 175	96.48 90	93.58 221	93.51 56	98.80 104	97.41 128	82.59 257	78.62 268	97.49 114	68.00 268	99.82 50	84.52 182	98.55 81	96.11 200

IterMVS		85.81 243	84.67 241	89.22 267	93.51 222	83.67 263	96.32 253	94.80 276	85.09 207	78.69 266	90.17 284	66.57 280	93.17 308	79.48 232	77.42 259	90.81 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		88.83 194	87.38 196	93.16 186	93.47 223	86.24 217	84.97 338	94.20 293	88.92 126	90.76 134	86.88 312	84.43 117	94.82 296	70.64 307	92.17 157	98.41 138

RPMNet		84.62 256	81.78 270	93.16 186	93.47 223	86.24 217	84.97 338	96.28 199	64.85 342	90.76 134	78.80 341	80.95 161	94.82 296	53.76 339	92.17 157	98.41 138

semantic-postprocess				89.00 272	93.46 225	82.90 271		94.70 279	85.02 209	78.62 268	90.35 273	66.63 278	93.33 307	79.38 234	77.36 260	90.76 272

Fast-Effi-MVS+-dtu		88.84 192	88.59 183	89.58 260	93.44 226	78.18 308	98.65 121	94.62 282	88.46 137	84.12 203	95.37 179	68.91 260	96.52 229	82.06 207	91.70 164	94.06 206

Patchmtry		83.61 273	81.64 272	89.50 262	93.36 227	82.84 274	84.10 341	94.20 293	69.47 331	79.57 259	86.88 312	84.43 117	94.78 298	68.48 312	74.30 280	90.88 267

LPG-MVS_test		88.86 191	88.47 186	90.06 247	93.35 228	80.95 291	98.22 175	95.94 219	87.73 163	83.17 211	96.11 167	66.28 281	97.77 169	90.19 125	85.19 215	91.46 247

LGP-MVS_train				90.06 247	93.35 228	80.95 291		95.94 219	87.73 163	83.17 211	96.11 167	66.28 281	97.77 169	90.19 125	85.19 215	91.46 247

JIA-IIPM		85.97 239	84.85 237	89.33 266	93.23 230	73.68 322	85.05 337	97.13 149	69.62 330	91.56 121	68.03 347	88.03 65	96.96 210	77.89 246	93.12 141	97.34 172

ACMH	83.09 17	84.60 257	82.61 265	90.57 236	93.18 231	82.94 269	96.27 254	94.92 275	81.01 276	72.61 306	93.61 207	56.54 314	97.79 167	74.31 281	81.07 243	90.99 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchT		85.44 249	83.19 253	92.22 202	93.13 232	83.00 268	83.80 344	96.37 190	70.62 324	90.55 137	79.63 339	84.81 115	94.87 294	58.18 336	91.59 165	98.79 117

jason		95.40 62	94.86 62	97.03 51	92.91 233	94.23 45	99.70 10	96.30 195	93.56 34	96.73 55	98.52 82	81.46 158	97.91 158	96.08 53	98.47 82	98.96 102
jason: jason.
LTVRE_ROB	81.71 19	84.59 258	82.72 263	90.18 244	92.89 234	83.18 267	93.15 305	94.74 277	78.99 289	75.14 292	92.69 223	65.64 285	97.63 180	69.46 308	81.82 241	89.74 296
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
VPA-MVSNet		89.10 187	87.66 193	93.45 181	92.56 235	91.02 111	97.97 194	98.32 28	86.92 181	86.03 190	92.01 230	68.84 262	97.10 207	90.92 118	75.34 266	92.23 224

tpm		89.67 180	88.95 173	91.82 209	92.54 236	81.43 282	92.95 306	95.92 222	87.81 159	90.50 138	89.44 290	84.99 111	95.65 277	83.67 193	82.71 236	98.38 141

GA-MVS		90.10 173	88.69 178	94.33 159	92.44 237	87.97 172	99.08 71	96.26 200	89.65 102	86.92 185	93.11 218	68.09 266	96.96 210	82.54 203	90.15 183	98.05 153

FIs		90.70 165	89.87 162	93.18 185	92.29 238	91.12 106	98.17 183	98.25 30	89.11 118	83.44 207	94.82 184	82.26 151	96.17 260	87.76 151	82.76 235	92.25 222

ITE_SJBPF				87.93 292	92.26 239	76.44 314		93.47 303	87.67 166	79.95 254	95.49 177	56.50 315	97.38 198	75.24 274	82.33 239	89.98 293

UniMVSNet (Re)		89.50 183	88.32 187	93.03 188	92.21 240	90.96 113	98.90 95	98.39 26	89.13 117	83.22 208	92.03 228	81.69 155	96.34 249	86.79 164	72.53 296	91.81 236

UniMVSNet_NR-MVSNet		89.60 181	88.55 184	92.75 195	92.17 241	90.07 133	98.74 109	98.15 44	88.37 143	83.21 209	93.98 196	82.86 142	95.93 270	86.95 161	72.47 297	92.25 222

TinyColmap		80.42 296	77.94 296	87.85 293	92.09 242	78.58 305	93.74 299	89.94 344	74.99 313	69.77 312	91.78 234	46.09 338	97.58 183	65.17 321	77.89 256	87.38 315

MS-PatchMatch		86.75 226	85.92 214	89.22 267	91.97 243	82.47 276	96.91 232	96.14 210	83.74 233	77.73 276	93.53 210	58.19 310	97.37 200	76.75 256	98.35 83	87.84 310

VPNet		88.30 203	86.57 205	93.49 180	91.95 244	91.35 101	98.18 180	97.20 143	88.61 132	84.52 200	94.89 182	62.21 299	96.76 218	89.34 136	72.26 301	92.36 218

FMVSNet1		83.94 269	81.32 277	91.80 210	91.94 245	88.81 155	96.77 236	95.25 268	77.98 301	78.25 275	90.25 277	50.37 334	94.97 291	73.27 294	77.81 257	91.62 241

WR-MVS		88.54 201	87.22 200	92.52 199	91.93 246	89.50 146	98.56 133	97.84 62	86.99 177	81.87 240	93.81 200	74.25 211	95.92 272	85.29 174	74.43 275	92.12 229

LP		77.80 309	74.39 311	88.01 290	91.93 246	79.02 301	80.88 348	92.90 314	65.43 340	72.00 307	81.29 331	65.78 283	92.73 321	43.76 348	75.58 265	92.27 221

FC-MVSNet-test		90.22 170	89.40 166	92.67 198	91.78 248	89.86 139	97.89 196	98.22 32	88.81 129	82.96 216	94.66 186	81.90 154	95.96 268	85.89 171	82.52 238	92.20 227

MIMVSNet		84.48 260	81.83 269	92.42 200	91.73 249	87.36 188	85.52 334	94.42 288	81.40 272	81.91 238	87.58 304	51.92 330	92.81 315	73.84 288	88.15 199	97.08 180

USDC		84.74 253	82.93 255	90.16 245	91.73 249	83.54 264	95.00 287	93.30 304	88.77 130	73.19 299	93.30 213	53.62 326	97.65 179	75.88 263	81.54 242	89.30 301

nrg030		90.23 169	88.87 174	94.32 160	91.53 251	93.54 55	98.79 107	95.89 228	88.12 151	84.55 199	94.61 187	78.80 174	96.88 213	92.35 108	75.21 267	92.53 216

DU-MVS		88.83 194	87.51 194	92.79 193	91.46 252	90.07 133	98.71 110	97.62 96	88.87 128	83.21 209	93.68 204	74.63 197	95.93 270	86.95 161	72.47 297	92.36 218

NR-MVSNet		87.74 208	86.00 213	92.96 190	91.46 252	90.68 121	96.65 243	97.42 127	88.02 152	73.42 298	93.68 204	77.31 182	95.83 273	84.26 183	71.82 306	92.36 218

tfpnnormal		83.65 271	81.35 276	90.56 237	91.37 254	88.06 169	97.29 215	97.87 60	78.51 295	76.20 283	90.91 248	64.78 288	96.47 234	61.71 327	73.50 289	87.13 320

test_0402		78.81 303	76.33 305	86.26 304	91.18 255	78.44 307	95.88 273	91.34 335	68.55 332	70.51 310	89.91 285	52.65 329	94.99 290	47.14 344	79.78 249	85.34 337

test0.0.03 1		88.96 189	88.61 180	90.03 250	91.09 256	84.43 253	98.97 85	97.02 162	90.21 91	80.29 250	96.31 166	84.89 113	91.93 332	72.98 298	85.70 214	93.73 207

WR-MVS_H		86.53 232	85.49 227	89.66 259	91.04 257	83.31 266	97.53 210	98.20 33	84.95 211	79.64 257	90.90 249	78.01 179	95.33 285	76.29 260	72.81 293	90.35 282

CP-MVSNet		86.54 231	85.45 228	89.79 255	91.02 258	82.78 275	97.38 213	97.56 106	85.37 202	79.53 260	93.03 219	71.86 239	95.25 287	79.92 228	73.43 291	91.34 250

TranMVSNet+NR-MVSNet		87.75 206	86.31 209	92.07 205	90.81 259	88.56 161	98.33 160	97.18 144	87.76 160	81.87 240	93.90 198	72.45 231	95.43 282	83.13 196	71.30 309	92.23 224

PS-CasMVS		85.81 243	84.58 242	89.49 264	90.77 260	82.11 278	97.20 222	97.36 133	84.83 213	79.12 265	92.84 222	67.42 273	95.16 289	78.39 242	73.25 292	91.21 254

DeepMVS_CX				76.08 329	90.74 261	51.65 352		90.84 337	86.47 190	57.89 341	87.98 300	35.88 350	92.60 323	65.77 320	65.06 322	83.97 339

Anonymous20240521		85.45 248	83.91 251	90.05 249	90.73 262	83.74 262	97.13 225	96.15 209	82.08 265	76.93 281	90.84 251	71.53 245	96.36 242	75.26 273	74.57 273	90.04 292

OPM-MVS		89.76 179	89.15 170	91.57 218	90.53 263	85.58 238	98.11 186	95.93 221	92.88 43	86.05 189	96.47 161	67.06 276	97.87 162	89.29 139	86.08 211	91.26 253

XXY-MVS		87.75 206	86.02 212	92.95 191	90.46 264	89.70 142	97.71 204	95.90 226	84.02 223	80.95 245	94.05 190	67.51 272	97.10 207	85.16 175	78.41 253	92.04 233

v1neww		87.29 214	85.88 215	91.50 219	90.07 265	86.87 196	98.45 147	95.66 242	83.84 230	83.07 214	90.99 244	74.58 201	96.56 223	81.96 210	74.33 278	91.07 260

v7new		87.29 214	85.88 215	91.50 219	90.07 265	86.87 196	98.45 147	95.66 242	83.84 230	83.07 214	90.99 244	74.58 201	96.56 223	81.96 210	74.33 278	91.07 260

v7		86.91 223	85.45 228	91.29 224	90.06 267	86.73 202	98.26 171	95.49 254	83.08 250	82.95 217	90.96 247	73.37 220	96.42 237	79.90 229	74.97 268	90.71 275

v18		82.00 277	79.76 285	88.72 275	90.03 268	86.81 201	96.17 264	93.12 305	78.70 292	68.39 315	82.10 321	74.64 195	93.00 309	74.21 282	60.45 332	86.35 324

v10		85.73 246	84.01 249	90.87 232	90.03 268	86.73 202	97.20 222	95.22 273	81.25 274	79.85 256	89.75 287	73.30 225	96.28 257	76.87 253	72.64 295	89.61 299

v16		81.90 280	79.65 286	88.65 276	90.02 270	86.66 205	96.01 268	93.07 307	78.53 294	68.27 317	82.05 322	74.39 207	92.96 310	74.02 286	60.48 331	86.33 326

v8		86.11 237	84.45 243	91.10 226	89.99 271	86.85 198	97.24 219	95.36 263	81.99 266	79.89 255	89.86 286	74.53 203	96.39 240	78.83 239	72.32 299	90.05 290

v6		87.27 216	85.86 217	91.50 219	89.97 272	86.84 200	98.45 147	95.67 239	83.85 229	83.11 213	90.97 246	74.46 204	96.58 221	81.97 209	74.34 277	91.09 257

v17		81.87 282	79.61 287	88.64 277	89.91 273	86.64 206	96.01 268	93.08 306	78.54 293	68.27 317	81.96 323	74.44 205	92.95 311	74.03 285	60.22 334	86.34 325

V42		87.00 222	85.68 224	90.98 229	89.91 273	86.08 224	98.32 162	95.61 247	83.67 236	82.72 222	90.67 260	74.00 215	96.53 227	81.94 212	74.28 281	90.32 283

XVG-ACMP-BASELINE		85.86 241	84.95 235	88.57 278	89.90 275	77.12 313	94.30 293	95.60 248	87.40 170	82.12 233	92.99 221	53.42 327	97.66 178	85.02 177	83.83 226	90.92 266

PEN-MVS		85.21 251	83.93 250	89.07 271	89.89 276	81.31 286	97.09 227	97.24 139	84.45 218	78.66 267	92.68 224	68.44 264	94.87 294	75.98 262	70.92 310	91.04 263

v1141		87.23 218	85.75 221	91.67 215	89.88 277	87.43 184	98.52 137	95.62 245	83.91 226	82.83 220	90.69 257	74.70 194	96.49 231	81.53 216	74.08 285	91.07 260

divwei89l23v2f112		87.23 218	85.75 221	91.66 216	89.88 277	87.40 185	98.53 136	95.62 245	83.91 226	82.84 219	90.67 260	74.75 193	96.49 231	81.55 214	74.05 287	91.08 258

v1		87.23 218	85.76 219	91.66 216	89.88 277	87.37 187	98.54 135	95.64 244	83.91 226	82.88 218	90.70 255	74.64 195	96.53 227	81.54 215	74.08 285	91.08 258

v15		81.62 283	79.32 290	88.52 279	89.80 280	86.56 207	95.83 277	92.96 310	78.50 296	67.88 321	81.68 325	74.22 212	92.82 314	73.46 292	59.55 335	86.18 329

V14		81.55 285	79.26 291	88.42 282	89.80 280	86.33 215	95.72 280	92.96 310	78.35 297	67.82 322	81.70 324	74.13 213	92.78 318	73.32 293	59.50 337	86.16 331

v1144		86.83 225	85.31 230	91.40 222	89.75 282	87.21 193	98.31 163	95.45 258	83.22 247	82.70 223	90.78 252	73.36 221	96.36 242	79.49 231	74.69 272	90.63 278

V9		81.46 286	79.15 292	88.39 285	89.75 282	86.17 221	95.62 281	92.92 312	78.22 298	67.65 326	81.64 326	73.95 216	92.80 316	73.15 296	59.43 340	86.21 328

TransMVSNet (Re)		81.97 278	79.61 287	89.08 270	89.70 284	84.01 258	97.26 217	91.85 330	78.84 290	73.07 302	91.62 236	67.17 275	95.21 288	67.50 313	59.46 339	88.02 309

v12		81.37 288	79.05 293	88.33 286	89.68 285	86.05 227	95.48 283	92.92 312	78.08 299	67.55 327	81.58 327	73.75 217	92.75 319	73.05 297	59.37 341	86.18 329

v11		81.38 287	79.03 294	88.41 283	89.68 285	86.43 209	95.74 279	92.82 319	78.03 300	67.74 323	81.45 329	73.33 224	92.69 322	72.23 303	60.27 333	86.11 333

v13		81.30 289	78.99 295	88.25 287	89.61 287	85.87 231	95.39 284	92.90 314	77.93 305	67.45 330	81.52 328	73.66 218	92.75 319	72.91 299	59.53 336	86.14 332

v2v482		87.27 216	85.76 219	91.78 214	89.59 288	87.58 178	98.56 133	95.54 250	84.53 216	82.51 226	91.78 234	73.11 226	96.47 234	82.07 206	74.14 284	91.30 252

pm-mvs1		84.68 255	82.78 261	90.40 241	89.58 289	85.18 243	97.31 214	94.73 278	81.93 268	76.05 285	92.01 230	65.48 286	96.11 263	78.75 240	69.14 313	89.91 294

pmmvs4		87.58 211	86.17 211	91.80 210	89.58 289	88.92 153	97.25 218	95.28 267	82.54 259	80.49 248	93.17 217	75.62 190	96.05 265	82.75 200	78.90 251	90.42 281

v1192		86.32 235	84.71 240	91.17 225	89.53 291	86.40 211	98.13 184	95.44 259	82.52 260	82.42 228	90.62 265	71.58 243	96.33 250	77.23 249	74.88 269	90.79 270

pcd1.5k->3k		35.91 337	37.64 337	30.74 349	89.49 292	0.00 368	0.00 359	96.36 193	0.00 363	0.00 365	0.00 365	69.17 259	0.00 365	0.00 362	83.71 228	92.21 226

v144192		86.40 233	84.89 236	90.91 230	89.48 293	85.59 237	98.21 178	95.43 260	82.45 261	82.62 224	90.58 268	72.79 230	96.36 242	78.45 241	74.04 288	90.79 270

v148		86.38 234	85.06 232	90.37 242	89.47 294	84.10 257	98.52 137	95.48 255	83.80 232	80.93 246	90.22 280	74.60 199	96.31 253	80.92 219	71.55 307	90.69 276

v1921920		86.02 238	84.44 244	90.77 233	89.32 295	85.20 242	98.10 187	95.35 265	82.19 263	82.25 231	90.71 254	70.73 247	96.30 256	76.85 255	74.49 274	90.80 269

v1240		85.77 245	84.11 247	90.73 234	89.26 296	85.15 245	97.88 198	95.23 272	81.89 269	82.16 232	90.55 270	69.60 256	96.31 253	75.59 272	74.87 270	90.72 274

DI_MVS_plusplus_test		89.41 184	87.24 199	95.92 113	89.06 297	90.75 120	98.18 180	96.63 174	89.29 112	70.54 309	90.31 275	63.50 294	98.40 140	92.25 109	95.44 126	98.60 128

our_test_3		84.47 261	82.80 259	89.50 262	89.01 298	83.90 260	97.03 229	94.56 283	81.33 273	75.36 291	90.52 271	71.69 241	94.54 301	68.81 310	76.84 261	90.07 288

ppachtmachnet_test		83.63 272	81.57 274	89.80 254	89.01 298	85.09 246	97.13 225	94.50 284	78.84 290	76.14 284	91.00 243	69.78 253	94.61 300	63.40 323	74.36 276	89.71 298

DTE-MVSNet		84.14 267	82.80 259	88.14 288	88.95 300	79.87 297	96.81 235	96.24 201	83.50 244	77.60 278	92.52 226	67.89 270	94.24 303	72.64 301	69.05 314	90.32 283

test_normal		89.37 185	87.18 201	95.93 112	88.94 301	90.83 116	98.24 173	96.62 175	89.31 110	70.38 311	90.20 282	63.50 294	98.37 141	92.06 111	95.41 127	98.59 131

PS-MVSNAJss		89.54 182	89.05 171	91.00 228	88.77 302	84.36 254	97.39 211	95.97 215	88.47 135	81.88 239	93.80 201	82.48 147	96.50 230	89.34 136	83.34 231	92.15 228

Baseline_NR-MVSNet		85.83 242	84.82 238	88.87 274	88.73 303	83.34 265	98.63 124	91.66 331	80.41 281	82.44 227	91.35 239	74.63 197	95.42 283	84.13 185	71.39 308	87.84 310

MVP-Stereo		86.61 230	85.83 218	88.93 273	88.70 304	83.85 261	96.07 266	94.41 289	82.15 264	75.64 289	91.96 232	67.65 271	96.45 236	77.20 251	98.72 75	86.51 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		84.19 265	84.42 245	83.52 317	88.64 305	67.37 337	96.04 267	95.76 232	85.29 203	78.44 273	93.18 216	70.67 248	91.48 335	75.79 270	75.98 262	91.70 238

pmmvs5		85.87 240	84.40 246	90.30 243	88.53 306	84.23 255	98.60 129	93.71 299	81.53 271	80.29 250	92.02 229	64.51 289	95.52 280	82.04 208	78.34 254	91.15 255

MDA-MVSNet-bldmvs		77.82 308	74.75 310	87.03 300	88.33 307	78.52 306	96.34 252	92.85 316	75.57 312	48.87 348	87.89 301	57.32 313	92.49 326	60.79 329	64.80 323	90.08 287

N_pmnet		70.19 319	69.87 318	71.12 333	88.24 308	30.63 364	95.85 276	28.70 366	70.18 328	68.73 314	86.55 314	64.04 291	93.81 304	53.12 340	73.46 290	88.94 305

v7n		84.42 262	82.75 262	89.43 265	88.15 309	81.86 279	96.75 239	95.67 239	80.53 279	78.38 274	89.43 291	69.89 251	96.35 248	73.83 289	72.13 303	90.07 288

SixPastTwentyTwo		82.63 274	81.58 273	85.79 307	88.12 310	71.01 331	95.17 286	92.54 320	84.33 220	72.93 303	92.08 227	60.41 306	95.61 279	74.47 280	74.15 283	90.75 273

test_djsdf		88.26 205	87.73 191	89.84 253	88.05 311	82.21 277	97.77 202	96.17 207	86.84 182	82.41 229	91.95 233	72.07 236	95.99 266	89.83 127	84.50 221	91.32 251

mvs_tets		87.09 221	86.22 210	89.71 256	87.87 312	81.39 284	96.73 240	95.90 226	88.19 149	79.99 253	93.61 207	59.96 307	96.31 253	89.40 135	84.34 223	91.43 249

OurMVSNet-221017-0		84.13 268	83.59 252	85.77 308	87.81 313	70.24 332	94.89 288	93.65 301	86.08 193	76.53 282	93.28 214	61.41 302	96.14 262	80.95 218	77.69 258	90.93 265

YYNet1		79.64 300	77.04 302	87.43 298	87.80 314	79.98 294	96.23 257	94.44 286	73.83 319	51.83 345	87.53 306	67.96 269	92.07 331	66.00 319	67.75 319	90.23 285

MDA-MVSNet_test_wron		79.65 299	77.05 301	87.45 297	87.79 315	80.13 293	96.25 256	94.44 286	73.87 318	51.80 346	87.47 307	68.04 267	92.12 330	66.02 318	67.79 318	90.09 286

jajsoiax		87.35 212	86.51 207	89.87 251	87.75 316	81.74 280	97.03 229	95.98 214	88.47 135	80.15 252	93.80 201	61.47 301	96.36 242	89.44 134	84.47 222	91.50 245

v748		83.84 270	82.31 268	88.41 283	87.65 317	79.10 300	96.66 242	95.51 252	80.09 282	77.65 277	88.53 299	69.81 252	96.23 258	75.67 271	69.25 312	89.91 294

v52		84.19 265	82.92 256	88.01 290	87.64 318	79.92 295	96.23 257	95.32 266	79.87 284	78.51 271	89.05 294	69.50 258	96.32 251	77.95 245	72.24 302	87.79 313

V4		84.20 264	82.92 256	88.02 289	87.59 319	79.91 296	96.21 262	95.36 263	79.88 283	78.51 271	89.00 295	69.52 257	96.32 251	77.96 244	72.29 300	87.83 312

K. test v3		81.04 290	79.77 284	84.83 312	87.41 320	70.23 333	95.60 282	93.93 296	83.70 235	67.51 328	89.35 292	55.76 316	93.58 306	76.67 257	68.03 317	90.67 277

testgi		82.29 275	81.00 279	86.17 305	87.24 321	74.84 318	97.39 211	91.62 332	88.63 131	75.85 288	95.42 178	46.07 339	91.55 334	66.87 317	79.94 247	92.12 229

LF4IMVS		81.94 279	81.17 278	84.25 315	87.23 322	68.87 336	93.35 304	91.93 329	83.35 246	75.40 290	93.00 220	49.25 336	96.65 219	78.88 238	78.11 255	87.22 319

EG-PatchMatch MVS		79.92 297	77.59 297	86.90 301	87.06 323	77.90 312	96.20 263	94.06 295	74.61 315	66.53 332	88.76 297	40.40 347	96.20 259	67.02 315	83.66 229	86.61 321

Gipuma		54.77 327	52.22 329	62.40 340	86.50 324	59.37 343	50.20 357	90.35 341	36.52 353	41.20 352	49.49 355	18.33 357	81.29 350	32.10 354	65.34 321	46.54 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		86.69 227	85.75 221	89.53 261	86.46 325	82.94 269	96.39 250	95.71 236	83.97 225	79.63 258	90.70 255	68.85 261	95.94 269	86.01 167	84.02 225	89.72 297

lessismore_v0				85.08 310	85.59 326	69.28 335		90.56 339		67.68 325	90.21 281	54.21 325	95.46 281	73.88 287	62.64 326	90.50 280

CMPMVS	58.40 21	80.48 295	80.11 283	81.59 324	85.10 327	59.56 342	94.14 296	95.95 218	68.54 333	60.71 338	93.31 212	55.35 320	97.87 162	83.06 197	84.85 219	87.33 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		80.76 293	79.42 289	84.79 313	84.78 328	72.98 324	96.53 246	92.97 309	79.56 286	74.33 294	88.83 296	61.27 303	92.15 329	60.59 330	75.92 263	89.24 303

Test4		85.71 247	82.59 266	95.07 140	84.45 329	89.84 140	97.20 222	95.73 234	89.19 114	64.59 334	87.58 304	40.59 346	96.77 217	88.95 144	95.01 130	98.60 128

DSMNet-mixed		81.60 284	81.43 275	82.10 320	84.36 330	60.79 341	93.63 302	86.74 351	79.00 288	79.32 262	87.15 310	63.87 292	89.78 336	66.89 316	91.92 159	95.73 201

pmmvs6		79.90 298	77.31 299	87.67 295	84.17 331	78.13 309	95.86 275	93.68 300	67.94 335	72.67 305	89.62 289	50.98 333	95.75 275	74.80 279	66.04 320	89.14 304

new_pmnet		76.02 311	73.71 312	82.95 318	83.88 332	72.85 325	91.26 320	92.26 323	70.44 326	62.60 336	81.37 330	47.64 337	92.32 327	61.85 326	72.10 304	83.68 340

OpenMVS_ROB	73.86 20	77.99 307	75.06 309	86.77 302	83.81 333	77.94 311	96.38 251	91.53 334	67.54 336	68.38 316	87.13 311	43.94 340	96.08 264	55.03 338	81.83 240	86.29 327

test20.03		78.51 305	77.48 298	81.62 323	83.07 334	71.03 330	96.11 265	92.83 317	81.66 270	69.31 313	89.68 288	57.53 311	87.29 341	58.65 335	68.47 315	86.53 322

UnsupCasMVSNet_eth		78.90 302	76.67 304	85.58 309	82.81 335	74.94 317	91.98 314	96.31 194	84.64 215	65.84 333	87.71 303	51.33 331	92.23 328	72.89 300	56.50 343	89.56 300

MIMVSNet1		75.92 312	73.30 313	83.81 316	81.29 336	75.57 316	92.26 313	92.05 327	73.09 320	67.48 329	86.18 315	40.87 345	87.64 340	55.78 337	70.68 311	88.21 306

test2356		80.96 291	81.77 271	78.52 327	81.02 337	62.33 339	98.22 175	94.49 285	79.38 287	74.56 293	90.34 274	70.65 250	85.10 345	60.83 328	86.42 205	88.14 307

Patchmatch-RL test		81.90 280	80.13 281	87.23 299	80.71 338	70.12 334	84.07 342	88.19 350	83.16 249	70.57 308	82.18 320	87.18 81	92.59 324	82.28 205	62.78 325	98.98 100

pmmvs-eth3d		78.71 304	76.16 306	86.38 303	80.25 339	81.19 288	94.17 295	92.13 326	77.97 302	66.90 331	82.31 319	55.76 316	92.56 325	73.63 291	62.31 328	85.38 335

testus		77.11 310	76.95 303	77.58 328	80.02 340	58.93 344	97.78 200	90.48 340	79.68 285	72.84 304	90.61 267	37.72 349	86.57 344	60.28 332	83.18 232	87.23 318

UnsupCasMVSNet_bld		73.85 315	70.14 317	84.99 311	79.44 341	75.73 315	88.53 329	95.24 271	70.12 329	61.94 337	74.81 343	41.41 344	93.62 305	68.65 311	51.13 349	85.62 334

PM-MVS		74.88 313	72.85 314	80.98 325	78.98 342	64.75 338	90.81 323	85.77 353	80.95 277	68.23 320	82.81 318	29.08 352	92.84 313	76.54 259	62.46 327	85.36 336

testing_2		80.92 292	77.24 300	91.98 206	78.88 343	87.83 173	93.96 298	95.72 235	84.27 221	56.20 343	80.42 334	38.64 348	96.40 239	87.20 156	79.85 248	91.72 237

new-patchmatchnet		74.80 314	72.40 315	81.99 321	78.36 344	72.20 327	94.44 290	92.36 322	77.06 307	63.47 335	79.98 338	51.04 332	88.85 338	60.53 331	54.35 345	84.92 338

pmmvs3		72.86 316	69.76 319	82.17 319	73.86 345	74.19 320	94.20 294	89.01 347	64.23 343	67.72 324	80.91 333	41.48 343	88.65 339	62.40 325	54.02 346	83.68 340

1111		72.28 317	71.36 316	75.02 331	73.04 346	57.38 346	92.30 311	90.22 342	62.27 344	59.46 339	80.36 335	76.23 186	87.07 342	44.29 346	64.08 324	80.59 344

.test1245		61.50 322	64.44 322	52.65 346	73.04 346	57.38 346	92.30 311	90.22 342	62.27 344	59.46 339	80.36 335	76.23 186	87.07 342	44.29 346	1.80 360	13.50 360

ambc				79.60 326	72.76 348	56.61 348	76.20 350	92.01 328		68.25 319	80.23 337	23.34 353	94.73 299	73.78 290	60.81 330	87.48 314

test1235678		71.07 318	69.53 320	75.71 330	71.87 349	55.27 350	94.32 291	90.76 338	70.23 327	57.61 342	79.06 340	43.13 341	83.72 347	50.48 341	68.30 316	88.14 307

TDRefinement		78.01 306	75.31 307	86.10 306	70.06 350	73.84 321	93.59 303	91.58 333	74.51 316	73.08 301	91.04 242	49.63 335	97.12 204	74.88 277	59.47 338	87.33 316

test12356		66.36 320	65.12 321	70.08 336	66.92 351	50.46 353	89.96 327	88.58 348	66.00 339	53.38 344	78.13 342	32.89 351	82.87 348	48.36 343	61.87 329	76.92 345

PMMVS2		58.97 325	55.07 326	70.69 335	62.72 352	55.37 349	85.97 333	80.52 357	49.48 349	45.94 349	68.31 346	15.73 360	80.78 351	49.79 342	37.12 350	75.91 347

E-PMN		41.02 334	40.93 334	41.29 347	61.97 353	33.83 361	84.00 343	65.17 364	27.17 356	27.56 355	46.72 357	17.63 359	60.41 360	19.32 357	18.82 355	29.61 357

PNet_i23d		48.05 330	44.98 332	57.28 342	60.15 354	42.39 359	80.85 349	73.14 362	36.78 352	27.46 356	56.66 352	6.38 363	68.34 356	36.65 352	26.72 352	61.10 352

wuyk23d		16.71 340	16.73 342	16.65 350	60.15 354	25.22 365	41.24 358	5.17 367	6.56 360	5.48 364	3.61 364	3.64 365	22.72 362	15.20 359	9.52 359	1.99 362

FPMVS		61.57 321	60.32 323	65.34 338	60.14 356	42.44 358	91.02 322	89.72 345	44.15 350	42.63 351	80.93 332	19.02 355	80.59 352	42.50 349	72.76 294	73.00 348

EMVS		39.96 336	39.88 335	40.18 348	59.57 357	32.12 363	84.79 340	64.57 365	26.27 357	26.14 358	44.18 360	18.73 356	59.29 361	17.03 358	17.67 357	29.12 358

no-one		56.69 326	51.89 330	71.08 334	59.35 358	58.65 345	83.78 345	84.81 356	61.73 346	36.46 354	56.52 353	18.15 358	84.78 346	47.03 345	19.19 354	69.81 350

testmv		60.41 323	57.98 324	67.69 337	58.16 359	47.14 355	89.09 328	86.74 351	61.52 347	44.30 350	68.44 345	20.98 354	79.92 353	40.94 350	51.67 347	76.01 346

LCM-MVSNet		60.07 324	56.37 325	71.18 332	54.81 360	48.67 354	82.17 347	89.48 346	37.95 351	49.13 347	69.12 344	13.75 362	81.76 349	59.28 333	51.63 348	83.10 342

MVE	44.00 22	41.70 333	37.64 337	53.90 345	49.46 361	43.37 357	65.09 355	66.66 363	26.19 358	25.77 359	48.53 356	3.58 367	63.35 359	26.15 356	27.28 351	54.97 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		43.53 332	37.95 336	60.27 341	45.36 362	44.79 356	68.27 353	74.26 361	33.48 354	18.21 362	40.16 362	3.64 365	71.01 355	38.85 351	19.31 353	65.02 351

ANet_high		50.71 329	46.17 331	64.33 339	44.27 363	52.30 351	76.13 351	78.73 358	64.95 341	27.37 357	55.23 354	14.61 361	67.74 357	36.01 353	18.23 356	72.95 349

PMVS	41.42 23	45.67 331	42.50 333	55.17 344	34.28 364	32.37 362	66.24 354	78.71 359	30.72 355	22.04 360	59.59 350	4.59 364	77.85 354	27.49 355	58.84 342	55.29 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 328	52.86 328	56.05 343	32.75 365	41.97 360	73.42 352	76.12 360	21.91 359	39.68 353	96.39 164	42.59 342	65.10 358	78.00 243	14.92 358	61.08 353

testmvs		18.81 339	23.05 340	6.10 352	4.48 366	2.29 367	97.78 200	3.00 368	3.27 361	18.60 361	62.71 348	1.53 369	2.49 364	14.26 360	1.80 360	13.50 360

test123		16.58 341	19.47 341	7.91 351	3.59 367	5.37 366	94.32 291	1.39 369	2.49 362	13.98 363	44.60 359	2.91 368	2.65 363	11.35 361	0.57 362	15.70 359

cdsmvs_eth3d_5k		22.52 338	30.03 339	0.00 353	0.00 368	0.00 368	0.00 359	97.17 145	0.00 363	0.00 365	98.77 65	74.35 208	0.00 365	0.00 362	0.00 363	0.00 363

pcd_1.5k_mvsjas		6.87 343	9.16 344	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	82.48 147	0.00 365	0.00 362	0.00 363	0.00 363

sosnet-low-res		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sosnet		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uncertanet		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Regformer		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

ab-mvs-re		8.21 342	10.94 343	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	98.50 84	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uanet		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

GSMVS																98.84 112

test_part1				0.00 353		0.00 368	0.00 359	97.69 81				0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sam_mvs1												88.39 58				98.84 112

sam_mvs												87.08 82

MTGPA								97.45 121

test_post1							90.74 325				41.37 361	85.38 110	96.36 242	83.16 195

test_post											46.00 358	87.37 75	97.11 205

patchmatchnet-post											84.86 317	88.73 52	96.81 216

MTMP							99.21 52	91.09 336

test9_res														98.60 10	99.87 6	99.90 10

agg_prior2														97.84 27	99.87 6	99.91 9

test_prior4						92.00 82	99.41 36

test_prior2							99.57 19		91.43 70	98.12 21	98.97 48	90.43 35		98.33 19	99.81 15

旧先验2							98.67 119		85.75 196	98.96 5			98.97 120	93.84 86

新几何2							98.26 171

无先验							98.52 137	97.82 64	87.20 176				99.90 30	87.64 153		99.85 20

原ACMM2							98.69 114

testdata2													99.88 34	84.16 184

segment_acmp												90.56 34

testdata1							97.89 196		92.43 50

plane_prior5								96.30 195					97.75 174	93.46 93	86.17 209	92.67 214

plane_prior4											96.52 158

plane_prior3						85.91 229			93.65 31	86.99 183

plane_prior2							99.02 79		93.38 36

plane_prior						86.07 225	99.14 66		93.81 29						86.26 208

n2								0.00 370

nn								0.00 370

door-mid								84.90 355

test11								97.68 83

door								85.30 354

HQP5-MVS						86.39 212

BP-MVS														93.82 88

HQP4-MVS										87.57 177			97.77 169			92.72 212

HQP3-MVS								96.37 190							86.29 206

HQP2-MVS												73.34 222

MDTV_nov1_ep13_2view						91.17 105	91.38 318		87.45 169	93.08 106		86.67 89		87.02 159		98.95 106

ACMMP++_ref															82.64 237

ACMMP++															83.83 226

Test By Simon												83.62 123

High-resolution multi-view benchmark