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.

DP-MVS Recon		91.72 59	90.85 65	94.34 24	99.50 1	85.00 46	98.51 16	95.96 125	80.57 189	88.08 97	97.63 56	76.84 96	99.89 6	85.67 106	94.88 91	98.13 55

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 32	95.17 2	92.11 48	98.46 11	87.33 8	99.97 1	97.21 6	99.31 1	99.63 2

MG-MVS		94.25 18	93.72 25	95.85 7	99.38 3	89.35 6	97.98 32	98.09 14	89.99 29	92.34 47	96.97 82	81.30 48	98.99 86	88.54 84	98.88 15	99.20 8

AdaColmap		88.81 106	87.61 112	92.39 96	99.33 4	79.95 149	96.70 122	95.58 142	77.51 236	83.05 144	96.69 92	61.90 237	99.72 23	84.29 116	93.47 104	97.50 100

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 25	94.66 3	96.79 4	98.78 4	86.42 12	99.95 2	97.59 4	99.18 3	99.00 14

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 43	99.16 3	96.96 42	94.11 6	95.59 12	98.64 7	85.07 15	99.91 3	95.61 19	99.10 5	99.00 14

test_part2					98.90 7	85.14 44				96.07 8

ESAPD		95.32 5	95.52 6	94.70 19	98.90 7	85.14 44	98.15 25	96.77 53	84.95 103	96.07 8	98.83 2	89.33 6	99.80 14	97.78 2	98.95 12	99.18 10

PAPR		92.74 42	92.17 51	94.45 22	98.89 9	84.87 51	97.20 78	96.20 111	87.73 57	88.40 92	98.12 27	78.71 74	99.76 16	87.99 92	96.28 75	98.74 23

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 33	98.06 29	96.64 70	93.64 8	91.74 53	98.54 8	80.17 58	99.90 4	92.28 51	98.75 19	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		94.56 13	94.75 10	93.96 34	98.84 11	83.40 77	98.04 30	96.41 93	85.79 80	95.00 20	98.28 15	84.32 25	99.18 73	97.35 5	98.77 18	99.28 5

APD-MVS		93.61 30	93.59 27	93.69 43	98.76 12	83.26 78	97.21 76	96.09 118	82.41 159	94.65 25	98.21 17	81.96 40	98.81 97	94.65 26	98.36 36	99.01 13
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		92.89 40	92.86 39	92.98 73	98.71 13	81.12 122	97.58 54	96.70 62	85.20 95	91.75 51	97.97 41	78.47 76	99.71 24	90.95 60	98.41 31	98.12 56

#test#		92.99 38	92.99 36	92.98 73	98.71 13	81.12 122	97.77 42	96.70 62	85.75 81	91.75 51	97.97 41	78.47 76	99.71 24	91.36 56	98.41 31	98.12 56

region2R		92.72 45	92.70 42	92.79 80	98.68 15	80.53 137	97.53 58	96.51 83	85.22 93	91.94 49	97.98 39	77.26 90	99.67 32	90.83 63	98.37 35	98.18 49

test_prior3		94.03 24	94.34 18	93.09 70	98.68 15	81.91 103	98.37 18	96.40 95	86.08 75	94.57 26	98.02 34	83.14 33	99.06 82	95.05 21	98.79 16	98.29 44

test_prior				93.09 70	98.68 15	81.91 103		96.40 95					99.06 82			98.29 44

ACMMPR		92.69 46	92.67 43	92.75 82	98.66 18	80.57 134	97.58 54	96.69 64	85.20 95	91.57 54	97.92 43	77.01 94	99.67 32	90.95 60	98.41 31	98.00 67

API-MVS		90.18 85	88.97 91	93.80 38	98.66 18	82.95 87	97.50 60	95.63 141	75.16 265	86.31 109	97.69 50	72.49 149	99.90 4	81.26 143	96.07 78	98.56 32

CDPH-MVS		93.12 36	92.91 38	93.74 40	98.65 20	83.88 65	97.67 50	96.26 107	83.00 149	93.22 39	98.24 16	81.31 47	99.21 66	89.12 80	98.74 20	98.14 54

TEST9					98.64 21	83.71 70	97.82 37	96.65 67	84.29 123	95.16 15	98.09 29	84.39 21	99.36 56

train_agg		94.28 16	94.45 15	93.74 40	98.64 21	83.71 70	97.82 37	96.65 67	84.50 115	95.16 15	98.09 29	84.33 22	99.36 56	95.91 15	98.96 10	98.16 51

test_8					98.63 23	83.64 73	97.81 39	96.63 73	84.50 115	95.10 17	98.11 28	84.33 22	99.23 62

agg_prior3		94.10 21	94.29 21	93.53 53	98.62 24	83.03 84	97.80 41	96.64 70	84.28 124	95.01 19	98.03 33	83.40 31	99.41 53	95.91 15	98.96 10	98.16 51

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 24	86.04 27	97.81 39	96.93 45	92.45 11	95.69 11	98.50 9	85.38 14	99.85 10	94.75 24	99.18 3	98.65 28

agg_prior1		94.10 21	94.31 19	93.48 56	98.59 26	83.13 80	97.77 42	96.56 78	84.38 119	94.19 29	98.13 24	84.66 19	99.16 75	95.74 18	98.74 20	98.15 53

agg_prior					98.59 26	83.13 80		96.56 78		94.19 29			99.16 75

CSCG		92.02 55	91.65 57	93.12 68	98.53 28	80.59 133	97.47 61	97.18 23	77.06 244	84.64 124	97.98 39	83.98 27	99.52 44	90.72 64	97.33 61	99.23 7

XVS		92.69 46	92.71 40	92.63 89	98.52 29	80.29 141	97.37 71	96.44 90	87.04 67	91.38 57	97.83 47	77.24 92	99.59 38	90.46 66	98.07 43	98.02 62

X-MVStestdata		86.26 156	84.14 173	92.63 89	98.52 29	80.29 141	97.37 71	96.44 90	87.04 67	91.38 57	20.73 359	77.24 92	99.59 38	90.46 66	98.07 43	98.02 62

CP-MVS		92.54 51	92.60 45	92.34 97	98.50 31	79.90 151	98.40 17	96.40 95	84.75 108	90.48 70	98.09 29	77.40 89	99.21 66	91.15 59	98.23 40	97.92 73

PAPM_NR		91.46 65	90.82 66	93.37 60	98.50 31	81.81 108	95.03 201	96.13 115	84.65 111	86.10 112	97.65 55	79.24 67	99.75 19	83.20 132	96.88 70	98.56 32

MAR-MVS		90.63 76	90.22 71	91.86 118	98.47 33	78.20 215	97.18 80	96.61 74	83.87 133	88.18 96	98.18 18	68.71 176	99.75 19	83.66 126	97.15 63	97.63 92
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
Regformer-1		94.00 25	94.04 23	93.87 36	98.41 34	84.29 60	97.43 67	97.04 36	89.50 33	92.75 44	98.13 24	82.60 37	99.26 61	93.55 34	96.99 65	98.06 59

Regformer-2		93.92 26	94.01 24	93.67 44	98.41 34	83.75 69	97.43 67	97.00 38	89.43 35	92.69 45	98.13 24	82.48 38	99.22 64	93.51 35	96.99 65	98.04 60

mPP-MVS		91.88 57	91.82 53	92.07 107	98.38 36	78.63 197	97.29 74	96.09 118	85.12 97	88.45 91	97.66 51	75.53 115	99.68 30	89.83 72	98.02 46	97.88 74

test12				94.25 27	98.34 37	85.55 35		96.35 101		92.36 46		80.84 49	99.22 64		98.31 37	97.98 69

CPTT-MVS		89.72 91	89.87 80	89.29 182	98.33 38	73.30 266	97.70 48	95.35 157	75.68 257	87.40 100	97.44 66	70.43 168	98.25 112	89.56 77	96.90 68	96.33 144

HSP-MVS		95.55 4	96.51 2	92.66 86	98.31 39	80.10 148	97.42 69	96.46 88	92.20 13	97.11 3	98.29 14	93.46 1	99.10 80	96.01 13	99.30 2	98.77 22

MSLP-MVS++		94.28 16	94.39 17	93.97 33	98.30 40	84.06 64	98.64 13	96.93 45	90.71 22	93.08 40	98.70 5	79.98 60	99.21 66	94.12 31	99.07 6	98.63 29

PGM-MVS		91.93 56	91.80 54	92.32 99	98.27 41	79.74 155	95.28 187	97.27 20	83.83 134	90.89 67	97.78 49	76.12 109	99.56 42	88.82 82	97.93 49	97.66 89

114514_t		88.79 108	87.57 113	92.45 93	98.21 42	81.74 110	96.99 101	95.45 152	75.16 265	82.48 147	95.69 105	68.59 177	98.50 104	80.33 146	95.18 89	97.10 118

DP-MVS		81.47 231	78.28 241	91.04 137	98.14 43	78.48 202	95.09 200	86.97 325	61.14 330	71.12 261	92.78 167	59.59 244	99.38 55	53.11 319	86.61 155	95.27 164

MP-MVS		92.61 49	92.67 43	92.42 94	98.13 44	79.73 156	97.33 73	96.20 111	85.63 83	90.53 68	97.66 51	78.14 81	99.70 27	92.12 53	98.30 38	97.85 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Regformer-3		93.19 34	93.19 33	93.19 65	98.10 45	83.01 85	97.08 97	96.98 40	88.98 37	91.35 61	97.89 44	80.80 50	99.23 62	92.30 50	95.20 87	97.32 108

Regformer-4		93.06 37	93.12 34	92.89 76	98.10 45	82.20 98	97.08 97	96.92 47	88.87 39	91.23 63	97.89 44	80.57 52	99.19 71	92.21 52	95.20 87	97.29 113

PHI-MVS		93.59 31	93.63 26	93.48 56	98.05 47	81.76 109	98.64 13	97.13 24	82.60 157	94.09 32	98.49 10	80.35 53	99.85 10	94.74 25	98.62 24	98.83 19

SMA-MVS		94.64 11	94.66 11	94.58 21	98.02 48	85.42 38	97.47 61	96.74 57	85.49 88	98.01 1	98.70 5	82.85 35	99.84 12	95.79 17	98.92 14	98.49 35

PLC	83.97 7	88.00 126	87.38 118	89.83 175	98.02 48	76.46 244	97.16 84	94.43 200	79.26 219	81.98 161	96.28 96	69.36 173	99.27 59	77.71 172	92.25 118	93.77 190
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
zzz-MVS		92.74 42	92.71 40	92.86 77	97.90 50	80.85 128	96.47 133	96.33 103	87.92 52	90.20 72	98.18 18	76.71 101	99.76 16	92.57 48	98.09 41	97.96 70

MTAPA		92.45 52	92.31 48	92.86 77	97.90 50	80.85 128	92.88 253	96.33 103	87.92 52	90.20 72	98.18 18	76.71 101	99.76 16	92.57 48	98.09 41	97.96 70

APD-MVS_3200maxsize		91.23 69	91.35 60	90.89 143	97.89 52	76.35 246	96.30 152	95.52 146	79.82 208	91.03 66	97.88 46	74.70 137	98.54 102	92.11 54	96.89 69	97.77 83

HPM-MVS		91.62 62	91.53 59	91.89 113	97.88 53	79.22 174	96.99 101	95.73 136	82.07 163	89.50 82	97.19 75	75.59 114	98.93 94	90.91 62	97.94 47	97.54 96
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SD-MVS		94.84 9	95.02 9	94.29 26	97.87 54	84.61 53	97.76 45	96.19 113	89.59 32	96.66 5	98.17 22	84.33 22	99.60 37	96.09 12	98.50 27	98.66 27

原ACMM1				91.22 134	97.77 55	78.10 217		96.61 74	81.05 177	91.28 62	97.42 67	77.92 84	98.98 87	79.85 153	98.51 26	96.59 135

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 45	97.66 56	84.10 63	95.85 174	96.42 92	91.26 17	97.49 2	96.80 89	86.50 11	98.49 105	95.54 20	99.03 7	98.33 40

HPM-MVS_fast		90.38 83	90.17 73	91.03 138	97.61 57	77.35 234	97.15 85	95.48 148	79.51 213	88.79 88	96.90 83	71.64 157	98.81 97	87.01 101	97.44 56	96.94 122

EI-MVSNet-Vis-set		91.84 58	91.77 55	92.04 109	97.60 58	81.17 121	96.61 127	96.87 49	88.20 48	89.19 84	97.55 61	78.69 75	99.14 77	90.29 68	90.94 128	95.80 152

CNLPA		86.96 142	85.37 147	91.72 123	97.59 59	79.34 169	97.21 76	91.05 289	74.22 279	78.90 192	96.75 91	67.21 184	98.95 91	74.68 202	90.77 129	96.88 126

ACMMP		90.39 82	89.97 75	91.64 124	97.58 60	78.21 214	96.78 115	96.72 60	84.73 109	84.72 122	97.23 73	71.22 160	99.63 35	88.37 89	92.41 114	97.08 119
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
CANet		94.89 8	94.64 12	95.63 8	97.55 61	88.12 11	99.06 5	96.39 98	94.07 7	95.34 14	97.80 48	76.83 97	99.87 8	97.08 7	97.64 52	98.89 17

PVSNet_BlendedMVS		90.05 87	89.96 76	90.33 153	97.47 62	83.86 66	98.02 31	96.73 58	87.98 51	89.53 80	89.61 206	76.42 103	99.57 40	94.29 29	79.59 215	87.57 276

PVSNet_Blended		93.13 35	92.98 37	93.57 49	97.47 62	83.86 66	99.32 1	96.73 58	91.02 20	89.53 80	96.21 97	76.42 103	99.57 40	94.29 29	95.81 84	97.29 113

新几何1				93.12 68	97.44 64	81.60 115		96.71 61	74.54 277	91.22 64	97.57 57	79.13 69	99.51 47	77.40 176	98.46 28	98.26 47

LS3D		82.22 224	79.94 232	89.06 184	97.43 65	74.06 264	93.20 248	92.05 273	61.90 325	73.33 247	95.21 120	59.35 248	99.21 66	54.54 315	92.48 113	93.90 185

EI-MVSNet-UG-set		91.35 67	91.22 61	91.73 122	97.39 66	80.68 131	96.47 133	96.83 51	87.92 52	88.30 95	97.36 68	77.84 85	99.13 78	89.43 79	89.45 134	95.37 162

旧先验1					97.39 66	79.58 166		96.54 81			98.08 32	84.00 26			97.42 58	97.62 93

1121		90.66 75	89.82 81	93.16 67	97.39 66	81.71 113	93.33 240	96.66 66	74.45 278	91.38 57	97.55 61	79.27 65	99.52 44	79.95 151	98.43 30	98.26 47

TSAR-MVS + GP.		94.35 15	94.50 13	93.89 35	97.38 69	83.04 83	98.10 28	95.29 160	91.57 15	93.81 33	97.45 63	86.64 9	99.43 52	96.28 11	94.01 97	99.20 8

MVS_111021_HR		93.41 33	93.39 30	93.47 59	97.34 70	82.83 88	97.56 56	98.27 12	89.16 36	89.71 75	97.14 76	79.77 62	99.56 42	93.65 33	97.94 47	98.02 62

MP-MVS-pluss		92.58 50	92.35 47	93.29 61	97.30 71	82.53 92	96.44 138	96.04 122	84.68 110	89.12 85	98.37 12	77.48 88	99.74 21	93.31 40	98.38 34	97.59 95
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EPNet		94.06 23	94.15 22	93.76 39	97.27 72	84.35 58	98.29 20	97.64 17	94.57 4	95.36 13	96.88 85	79.96 61	99.12 79	91.30 57	96.11 77	97.82 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_Plus		93.46 32	93.23 32	94.17 29	97.16 73	84.28 61	96.82 113	96.65 67	86.24 72	94.27 28	97.99 37	77.94 83	99.83 13	93.39 36	98.57 25	98.39 38

LFMVS		89.27 97	87.64 109	94.16 31	97.16 73	85.52 36	97.18 80	94.66 188	79.17 220	89.63 78	96.57 93	55.35 286	98.22 113	89.52 78	89.54 133	98.74 23

DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 172	97.09 75	70.21 296	98.99 8	96.69 64	95.57 1	95.08 18	99.23 1	86.40 13	99.87 8	97.84 1	98.66 23	99.65 1

VNet		92.11 54	91.22 61	94.79 17	96.91 76	86.98 22	97.91 33	97.96 15	86.38 71	93.65 35	95.74 102	70.16 171	98.95 91	93.39 36	88.87 138	98.43 36

MVS_0304		93.82 29	93.11 35	95.95 5	96.79 77	89.15 7	98.56 15	95.30 159	93.61 9	94.82 23	98.02 34	66.60 198	99.88 7	96.94 8	97.39 59	98.81 20

TAPA-MVS	81.61 12	85.02 177	83.67 177	89.06 184	96.79 77	73.27 268	95.92 167	94.79 181	74.81 272	80.47 173	96.83 87	71.07 162	98.19 115	49.82 329	92.57 111	95.71 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous202405211		84.41 187	81.93 202	91.85 120	96.78 79	78.41 206	97.44 63	91.34 284	70.29 304	84.06 128	94.26 141	41.09 327	98.96 88	79.46 155	82.65 205	98.17 50

abl_6		89.80 89	89.71 84	90.07 163	96.53 80	75.52 252	94.48 210	95.04 167	81.12 176	89.22 83	97.00 81	68.83 175	98.96 88	89.86 71	95.27 86	95.73 154

DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 81	90.59 3	99.21 2	97.02 37	94.40 5	91.46 56	97.08 79	83.32 32	99.69 28	92.83 44	98.70 22	99.04 12
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
thres200		88.92 103	87.65 108	92.73 83	96.30 82	85.62 34	97.85 35	98.86 1	84.38 119	84.82 119	93.99 146	75.12 133	98.01 117	70.86 229	86.67 154	94.56 178

tfpn200view9		88.48 114	87.15 122	92.47 92	96.21 83	85.30 40	97.44 63	98.85 2	83.37 142	83.99 130	93.82 149	75.36 128	97.93 119	69.04 239	86.24 159	94.17 179

thres400		88.42 117	87.15 122	92.23 101	96.21 83	85.30 40	97.44 63	98.85 2	83.37 142	83.99 130	93.82 149	75.36 128	97.93 119	69.04 239	86.24 159	93.45 195

test222					96.15 85	78.41 206	95.87 172	96.46 88	71.97 295	89.66 77	97.45 63	76.33 106			98.24 39	98.30 43

HY-MVS	84.06 6	91.63 61	90.37 69	95.39 12	96.12 86	88.25 10	90.22 285	97.58 18	88.33 46	90.50 69	91.96 172	79.26 66	99.06 82	90.29 68	89.07 136	98.88 18

tfpn111		88.08 123	86.70 131	92.20 103	96.10 87	84.90 48	97.14 86	98.85 2	82.69 154	83.41 137	93.66 152	75.43 123	97.82 128	67.13 254	85.88 164	93.89 186

conf200view11		88.27 121	86.95 127	92.24 100	96.10 87	84.90 48	97.14 86	98.85 2	82.69 154	83.41 137	93.66 152	75.43 123	97.93 119	69.04 239	86.24 159	93.89 186

thres100view900		88.30 119	86.95 127	92.33 98	96.10 87	84.90 48	97.14 86	98.85 2	82.69 154	83.41 137	93.66 152	75.43 123	97.93 119	69.04 239	86.24 159	94.17 179

thres600view7		88.06 124	86.70 131	92.15 105	96.10 87	85.17 42	97.14 86	98.85 2	82.70 153	83.41 137	93.66 152	75.43 123	97.82 128	67.13 254	85.88 164	93.45 195

WTY-MVS		92.65 48	91.68 56	95.56 9	96.00 91	88.90 8	98.23 22	97.65 16	88.57 40	89.82 74	97.22 74	79.29 64	99.06 82	89.57 76	88.73 140	98.73 25

MVSTER		89.25 98	88.92 93	90.24 155	95.98 92	84.66 52	96.79 114	95.36 155	87.19 65	80.33 176	90.61 193	90.02 5	95.97 208	85.38 109	78.64 224	90.09 223

testdata				90.13 162	95.92 93	74.17 262		96.49 87	73.49 285	94.82 23	97.99 37	78.80 73	97.93 119	83.53 129	97.52 53	98.29 44

view600		87.45 135	85.98 138	91.88 114	95.90 94	84.52 54	96.68 123	98.85 2	81.85 166	82.30 150	93.39 156	75.44 119	97.66 132	64.02 274	85.36 171	93.45 195

view800		87.45 135	85.98 138	91.88 114	95.90 94	84.52 54	96.68 123	98.85 2	81.85 166	82.30 150	93.39 156	75.44 119	97.66 132	64.02 274	85.36 171	93.45 195

conf0.05thres1000		87.45 135	85.98 138	91.88 114	95.90 94	84.52 54	96.68 123	98.85 2	81.85 166	82.30 150	93.39 156	75.44 119	97.66 132	64.02 274	85.36 171	93.45 195

tfpn		87.45 135	85.98 138	91.88 114	95.90 94	84.52 54	96.68 123	98.85 2	81.85 166	82.30 150	93.39 156	75.44 119	97.66 132	64.02 274	85.36 171	93.45 195

tfpn_ndepth		87.25 140	86.00 137	91.01 140	95.86 98	81.46 117	96.53 130	97.09 33	77.35 239	81.36 164	95.07 129	84.74 18	95.86 217	60.88 289	85.14 177	95.72 155

PatchMatch-RL		85.00 178	83.66 178	89.02 186	95.86 98	74.55 259	92.49 261	93.60 244	79.30 218	79.29 191	91.47 177	58.53 256	98.45 107	70.22 232	92.17 119	94.07 183

canonicalmvs		92.27 53	91.22 61	95.41 11	95.80 100	88.31 9	97.09 95	94.64 191	88.49 43	92.99 42	97.31 69	72.68 148	98.57 101	93.38 38	88.58 142	99.36 4

MVS_111021_LR		91.60 63	91.64 58	91.47 127	95.74 101	78.79 194	96.15 157	96.77 53	88.49 43	88.64 90	97.07 80	72.33 151	99.19 71	93.13 42	96.48 74	96.43 139

DWT-MVSNet_test		90.52 81	89.80 82	92.70 85	95.73 102	82.20 98	93.69 230	96.55 80	88.34 45	87.04 106	95.34 111	86.53 10	97.55 142	76.32 188	88.66 141	98.34 39

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 103	92.35 1	98.21 23	95.79 134	92.42 12	96.24 6	98.18 18	71.04 163	99.17 74	96.77 9	97.39 59	96.79 128

tfpn1000		86.43 154	85.10 152	90.41 151	95.56 104	80.51 138	95.90 170	97.09 33	75.91 254	80.02 180	94.82 132	84.78 17	95.47 245	57.36 299	84.46 180	95.26 165

alignmvs		92.97 39	92.26 49	95.12 13	95.54 105	87.77 15	98.67 11	96.38 99	88.04 50	93.01 41	97.45 63	79.20 68	98.60 99	93.25 41	88.76 139	98.99 16

PVSNet	82.34 9	89.02 100	87.79 106	92.71 84	95.49 106	81.50 116	97.70 48	97.29 19	87.76 56	85.47 114	95.12 127	56.90 273	98.90 95	80.33 146	94.02 96	97.71 86

tpmvs		83.04 209	80.77 219	89.84 174	95.43 107	77.96 220	85.59 320	95.32 158	75.31 263	76.27 225	83.70 290	73.89 141	97.41 148	59.53 291	81.93 207	94.14 181

PatchFormer-LS_test		90.14 86	89.30 89	92.65 88	95.43 107	82.46 93	93.46 236	96.35 101	88.56 41	84.82 119	95.22 118	84.63 20	97.55 142	78.40 164	86.81 153	97.94 72

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 64	95.41 109	81.35 119	99.02 7	96.59 76	89.50 33	94.18 31	98.36 13	83.68 30	99.45 51	94.77 23	98.45 29	98.81 20
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		87.47 134	85.90 143	92.18 104	95.41 109	82.26 97	87.00 311	96.28 106	85.88 79	84.23 127	85.57 266	75.07 134	96.26 194	71.14 227	92.50 112	98.03 61

BH-RMVSNet		86.84 145	85.28 148	91.49 126	95.35 111	80.26 144	96.95 107	92.21 271	82.86 151	81.77 163	95.46 109	59.34 249	97.64 136	69.79 236	93.81 101	96.57 136

OMC-MVS		88.80 107	88.16 100	90.72 145	95.30 112	77.92 223	94.81 206	94.51 195	86.80 69	84.97 117	96.85 86	67.53 180	98.60 99	85.08 110	87.62 148	95.63 157

MVS_Test		90.29 84	89.18 90	93.62 47	95.23 113	84.93 47	94.41 213	94.66 188	84.31 121	90.37 71	91.02 186	75.13 132	97.82 128	83.11 134	94.42 93	98.12 56

F-COLMAP		84.50 186	83.44 184	87.67 217	95.22 114	72.22 273	95.95 165	93.78 236	75.74 255	76.30 224	95.18 122	59.50 246	98.45 107	72.67 212	86.59 156	92.35 203

CHOSEN 1792x2688		91.07 70	90.21 72	93.64 45	95.18 115	83.53 74	96.26 154	96.13 115	88.92 38	84.90 118	93.10 164	72.86 147	99.62 36	88.86 81	95.67 85	97.79 82

UGNet		87.73 131	86.55 133	91.27 131	95.16 116	79.11 178	96.35 146	96.23 109	88.14 49	87.83 99	90.48 194	50.65 298	99.09 81	80.13 150	94.03 95	95.60 158
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
VDD-MVS		88.28 120	87.02 126	92.06 108	95.09 117	80.18 147	97.55 57	94.45 199	83.09 146	89.10 86	95.92 101	47.97 309	98.49 105	93.08 43	86.91 152	97.52 99

PVSNet_Blended_VisFu		91.24 68	90.77 67	92.66 86	95.09 117	82.40 94	97.77 42	95.87 131	88.26 47	86.39 108	93.94 147	76.77 98	99.27 59	88.80 83	94.00 98	96.31 145

xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 119	92.02 2	98.19 24	95.68 138	92.06 14	96.01 10	98.14 23	70.83 166	98.96 88	96.74 10	96.57 73	96.76 131

BH-w/o		88.24 122	87.47 116	90.54 149	95.03 120	78.54 200	97.41 70	93.82 231	84.08 127	78.23 198	94.51 138	69.34 174	97.21 158	80.21 149	94.58 92	95.87 151

CHOSEN 280x420		91.71 60	91.85 52	91.29 130	94.94 121	82.69 90	87.89 303	96.17 114	85.94 77	87.27 103	94.31 139	90.27 4	95.65 234	94.04 32	95.86 82	95.53 159

GG-mvs-BLEND				93.49 55	94.94 121	86.26 25	81.62 328	97.00 38		88.32 94	94.30 140	91.23 2	96.21 197	88.49 86	97.43 57	98.00 67

HyFIR lowres test		89.36 95	88.60 96	91.63 125	94.91 123	80.76 130	95.60 181	95.53 144	82.56 158	84.03 129	91.24 182	78.03 82	96.81 178	87.07 100	88.41 143	97.32 108

mvs_anonymous		88.68 109	87.62 111	91.86 118	94.80 124	81.69 114	93.53 235	94.92 172	82.03 164	78.87 194	90.43 196	75.77 113	95.34 250	85.04 111	93.16 109	98.55 34

CANet_DTU		90.98 71	90.04 74	93.83 37	94.76 125	86.23 26	96.32 148	93.12 262	93.11 10	93.71 34	96.82 88	63.08 226	99.48 49	84.29 116	95.12 90	95.77 153

PMMVS		89.46 94	89.92 78	88.06 205	94.64 126	69.57 302	96.22 155	94.95 171	87.27 61	91.37 60	96.54 94	65.88 203	97.39 149	88.54 84	93.89 99	97.23 116

TR-MVS		86.30 155	84.93 157	90.42 150	94.63 127	77.58 229	96.57 129	93.82 231	80.30 196	82.42 149	95.16 123	58.74 254	97.55 142	74.88 200	87.82 147	96.13 147

casdiffmvs		90.98 71	90.24 70	93.19 65	94.60 128	84.15 62	95.01 202	94.98 170	84.98 102	91.53 55	91.14 184	76.72 100	97.62 137	89.78 74	93.42 108	97.81 80

EPNet_dtu		87.65 132	87.89 103	86.93 232	94.57 129	71.37 287	96.72 118	96.50 85	88.56 41	87.12 104	95.02 130	75.91 112	94.01 282	66.62 258	90.00 132	95.42 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		84.71 182	82.71 193	90.70 146	94.55 130	87.71 16	95.92 167	94.67 187	81.73 171	75.82 231	88.08 224	66.99 192	94.47 273	71.23 224	75.38 237	89.91 227

conf0.01		85.70 169	84.35 165	89.77 177	94.53 131	79.70 157	95.17 191	97.11 26	75.97 248	79.44 183	95.31 112	81.90 41	95.73 228	56.78 304	82.91 195	93.89 186

conf0.002		85.70 169	84.35 165	89.77 177	94.53 131	79.70 157	95.17 191	97.11 26	75.97 248	79.44 183	95.31 112	81.90 41	95.73 228	56.78 304	82.91 195	93.89 186

thresconf0.02		85.80 162	84.35 165	90.17 158	94.53 131	79.70 157	95.17 191	97.11 26	75.97 248	79.44 183	95.31 112	81.90 41	95.73 228	56.78 304	82.91 195	95.09 166

tfpn_n400		85.80 162	84.35 165	90.17 158	94.53 131	79.70 157	95.17 191	97.11 26	75.97 248	79.44 183	95.31 112	81.90 41	95.73 228	56.78 304	82.91 195	95.09 166

tfpnconf		85.80 162	84.35 165	90.17 158	94.53 131	79.70 157	95.17 191	97.11 26	75.97 248	79.44 183	95.31 112	81.90 41	95.73 228	56.78 304	82.91 195	95.09 166

tfpnview11		85.80 162	84.35 165	90.17 158	94.53 131	79.70 157	95.17 191	97.11 26	75.97 248	79.44 183	95.31 112	81.90 41	95.73 228	56.78 304	82.91 195	95.09 166

BH-untuned		86.95 143	85.94 142	89.99 167	94.52 137	77.46 231	96.78 115	93.37 255	81.80 170	76.62 219	93.81 151	66.64 197	97.02 168	76.06 190	93.88 100	95.48 160

DeepC-MVS	86.58 3	91.53 64	91.06 64	92.94 75	94.52 137	81.89 105	95.95 165	95.98 124	90.76 21	83.76 135	96.76 90	73.24 145	99.71 24	91.67 55	96.96 67	97.22 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
gg-mvs-nofinetune		85.48 173	82.90 189	93.24 63	94.51 139	85.82 30	79.22 332	96.97 41	61.19 329	87.33 102	53.01 346	90.58 3	96.07 201	86.07 104	97.23 62	97.81 80

3Dnovator+	82.88 8	89.63 92	87.85 104	94.99 15	94.49 140	86.76 23	97.84 36	95.74 135	86.10 74	75.47 235	96.02 99	65.00 215	99.51 47	82.91 136	97.07 64	98.72 26

tpmrst		88.36 118	87.38 118	91.31 128	94.36 141	79.92 150	87.32 307	95.26 162	85.32 91	88.34 93	86.13 259	80.60 51	96.70 182	83.78 120	85.34 176	97.30 111

diffmvs		87.96 128	86.47 134	92.42 94	94.26 142	82.70 89	92.79 257	94.03 222	77.94 230	88.99 87	89.98 203	70.72 167	97.56 140	77.75 166	91.80 123	96.98 120

MVS		90.60 77	88.64 95	96.50 1	94.25 143	90.53 4	93.33 240	97.21 22	77.59 235	78.88 193	97.31 69	71.52 158	99.69 28	89.60 75	98.03 45	99.27 6

dp		84.30 189	82.31 197	90.28 154	94.24 144	77.97 219	86.57 314	95.53 144	79.94 206	80.75 170	85.16 273	71.49 159	96.39 189	63.73 279	83.36 189	96.48 138

sss		90.87 73	89.96 76	93.60 48	94.15 145	83.84 68	97.14 86	98.13 13	85.93 78	89.68 76	96.09 98	71.67 155	99.30 58	87.69 94	89.16 135	97.66 89

PatchmatchNet		86.83 146	85.12 151	91.95 111	94.12 146	82.27 96	86.55 315	95.64 140	84.59 113	82.98 145	84.99 276	77.26 90	95.96 212	68.61 247	91.34 126	97.64 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.69 176		94.09 147	81.01 124	86.78 313	96.09 118	83.81 135	84.75 121	84.32 281	74.44 139	96.54 184	63.88 278	85.07 178

UA-Net		88.92 103	88.48 98	90.24 155	94.06 148	77.18 238	93.04 250	94.66 188	87.39 60	91.09 65	93.89 148	74.92 135	98.18 116	75.83 192	91.43 125	95.35 163

Fast-Effi-MVS+		87.93 129	86.94 129	90.92 142	94.04 149	79.16 176	98.26 21	93.72 239	81.29 174	83.94 133	92.90 165	69.83 172	96.68 183	76.70 184	91.74 124	96.93 123

QAPM		86.88 144	84.51 160	93.98 32	94.04 149	85.89 29	97.19 79	96.05 121	73.62 283	75.12 238	95.62 107	62.02 233	99.74 21	70.88 228	96.06 79	96.30 146

Vis-MVSNet (Re-imp)		88.88 105	88.87 94	88.91 188	93.89 151	74.43 260	96.93 109	94.19 209	84.39 118	83.22 142	95.67 106	78.24 79	94.70 269	78.88 161	94.40 94	97.61 94

ADS-MVSNet2		79.57 245	77.53 247	85.71 245	93.78 152	72.13 275	79.48 330	86.11 330	73.09 288	80.14 178	79.99 308	62.15 231	90.14 326	59.49 292	83.52 186	94.85 172

ADS-MVSNet		81.26 234	78.36 240	89.96 170	93.78 152	79.78 152	79.48 330	93.60 244	73.09 288	80.14 178	79.99 308	62.15 231	95.24 255	59.49 292	83.52 186	94.85 172

EPP-MVSNet		89.76 90	89.72 83	89.87 173	93.78 152	76.02 249	97.22 75	96.51 83	79.35 215	85.11 116	95.01 131	84.82 16	97.10 165	87.46 97	88.21 145	96.50 137

tpmp4_e23		86.46 152	84.95 155	90.98 141	93.74 155	78.60 199	88.13 301	95.90 129	79.65 211	85.42 115	85.67 261	80.08 59	97.06 166	71.71 219	84.26 183	97.28 115

3Dnovator	82.32 10	89.33 96	87.64 109	94.42 23	93.73 156	85.70 32	97.73 47	96.75 56	86.73 70	76.21 226	95.93 100	62.17 230	99.68 30	81.67 141	97.81 50	97.88 74

Effi-MVS+		90.70 74	89.90 79	93.09 70	93.61 157	83.48 75	95.20 190	92.79 266	83.22 144	91.82 50	95.70 104	71.82 154	97.48 147	91.25 58	93.67 102	98.32 41

IS-MVSNet		88.67 110	88.16 100	90.20 157	93.61 157	76.86 240	96.77 117	93.07 263	84.02 129	83.62 136	95.60 108	74.69 138	96.24 196	78.43 163	93.66 103	97.49 101

LCM-MVSNet-Re		83.75 194	83.54 181	84.39 275	93.54 159	64.14 317	92.51 260	84.03 339	83.90 132	66.14 286	86.59 249	67.36 182	92.68 294	84.89 113	92.87 110	96.35 141

tpm cat1		83.63 196	81.38 213	90.39 152	93.53 160	78.19 216	85.56 321	95.09 164	70.78 302	78.51 195	83.28 294	74.80 136	97.03 167	66.77 257	84.05 184	95.95 148

MSDG		80.62 240	77.77 246	89.14 183	93.43 161	77.24 235	91.89 273	90.18 303	69.86 306	68.02 277	91.94 174	52.21 296	98.84 96	59.32 294	83.12 190	91.35 205

ab-mvs		87.08 141	84.94 156	93.48 56	93.34 162	83.67 72	88.82 295	95.70 137	81.18 175	84.55 125	90.14 201	62.72 227	98.94 93	85.49 108	82.54 206	97.85 77

1314		88.94 102	87.20 120	94.17 29	93.21 163	85.73 31	93.33 240	96.64 70	82.89 150	75.98 228	96.36 95	66.83 194	99.39 54	83.52 130	96.02 80	97.39 106

1112_ss		88.60 113	87.47 116	92.00 110	93.21 163	80.97 125	96.47 133	92.46 269	83.64 139	80.86 169	97.30 71	80.24 56	97.62 137	77.60 173	85.49 169	97.40 105

Test_1112_low_res		88.03 125	86.73 130	91.94 112	93.15 165	80.88 127	96.44 138	92.41 270	83.59 141	80.74 171	91.16 183	80.18 57	97.59 139	77.48 175	85.40 170	97.36 107

CostFormer		89.08 99	88.39 99	91.15 135	93.13 166	79.15 177	88.61 298	96.11 117	83.14 145	89.58 79	86.93 239	83.83 29	96.87 175	88.22 90	85.92 163	97.42 104

IB-MVS	85.34 4	88.67 110	87.14 124	93.26 62	93.12 167	84.32 59	98.76 10	97.27 20	87.19 65	79.36 190	90.45 195	83.92 28	98.53 103	84.41 115	69.79 271	96.93 123
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
MVSFormer		91.36 66	90.57 68	93.73 42	93.00 168	88.08 12	94.80 207	94.48 196	80.74 185	94.90 21	97.13 77	78.84 71	95.10 259	83.77 121	97.46 54	98.02 62

lupinMVS		93.87 28	93.58 28	94.75 18	93.00 168	88.08 12	99.15 4	95.50 147	91.03 19	94.90 21	97.66 51	78.84 71	97.56 140	94.64 27	97.46 54	98.62 30

tpm2		87.35 139	86.26 135	90.62 147	92.93 170	78.67 195	88.06 302	95.99 123	79.33 216	87.40 100	86.43 255	80.28 55	96.40 188	80.23 148	85.73 168	96.79 128

Vis-MVSNet		88.67 110	87.82 105	91.24 133	92.68 171	78.82 191	96.95 107	93.85 230	87.55 58	87.07 105	95.13 126	63.43 224	97.21 158	77.58 174	96.15 76	97.70 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		82.42 219	80.43 224	88.39 198	92.66 172	81.95 100	94.30 217	93.38 252	79.06 222	75.82 231	85.66 262	56.38 281	93.84 284	71.23 224	75.38 237	89.38 234

test1		82.42 219	80.43 224	88.39 198	92.66 172	81.95 100	94.30 217	93.38 252	79.06 222	75.82 231	85.66 262	56.38 281	93.84 284	71.23 224	75.38 237	89.38 234

FMVSNet2		82.79 214	80.44 223	89.83 175	92.66 172	85.43 37	95.42 186	94.35 202	79.06 222	74.46 239	87.28 230	56.38 281	94.31 276	69.72 237	74.68 241	89.76 228

cascas		86.50 151	84.48 162	92.55 91	92.64 175	85.95 28	97.04 100	95.07 166	75.32 262	80.50 172	91.02 186	54.33 293	97.98 118	86.79 102	87.62 148	93.71 191

TESTMET0.1,1		89.83 88	89.34 88	91.31 128	92.54 176	80.19 146	97.11 91	96.57 77	86.15 73	86.85 107	91.83 176	79.32 63	96.95 170	81.30 142	92.35 115	96.77 130

COLMAP_ROB	73.24 19	75.74 284	73.00 289	83.94 280	92.38 177	69.08 304	91.85 274	86.93 326	61.48 328	65.32 292	90.27 198	42.27 323	96.93 173	50.91 326	75.63 236	85.80 300
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
xiu_mvs_v1_base_debu		90.54 78	89.54 85	93.55 50	92.31 178	87.58 18	96.99 101	94.87 174	87.23 62	93.27 36	97.56 58	57.43 268	98.32 109	92.72 45	93.46 105	94.74 175

xiu_mvs_v1_base		90.54 78	89.54 85	93.55 50	92.31 178	87.58 18	96.99 101	94.87 174	87.23 62	93.27 36	97.56 58	57.43 268	98.32 109	92.72 45	93.46 105	94.74 175

xiu_mvs_v1_base_debi		90.54 78	89.54 85	93.55 50	92.31 178	87.58 18	96.99 101	94.87 174	87.23 62	93.27 36	97.56 58	57.43 268	98.32 109	92.72 45	93.46 105	94.74 175

Patchmatch-test1		84.89 180	82.76 192	91.27 131	92.30 181	81.86 106	92.88 253	95.56 143	84.85 106	82.52 146	85.19 271	58.04 262	94.21 278	65.93 264	87.58 150	97.74 84

gm-plane-assit					92.27 182	79.64 164			84.47 117		95.15 124		97.93 119	85.81 105

test-LLR		88.48 114	87.98 102	89.98 168	92.26 183	77.23 236	97.11 91	95.96 125	83.76 136	86.30 110	91.38 179	72.30 152	96.78 180	80.82 144	91.92 121	95.94 149

test-mter		88.95 101	88.60 96	89.98 168	92.26 183	77.23 236	97.11 91	95.96 125	85.32 91	86.30 110	91.38 179	76.37 105	96.78 180	80.82 144	91.92 121	95.94 149

PAPM		92.87 41	92.40 46	94.30 25	92.25 185	87.85 14	96.40 144	96.38 99	91.07 18	88.72 89	96.90 83	82.11 39	97.37 150	90.05 70	97.70 51	97.67 88

AllTest		75.92 283	73.06 288	84.47 271	92.18 186	67.29 308	91.07 281	84.43 336	67.63 310	63.48 297	90.18 199	38.20 331	97.16 161	57.04 300	73.37 245	88.97 244

TestCases				84.47 271	92.18 186	67.29 308		84.43 336	67.63 310	63.48 297	90.18 199	38.20 331	97.16 161	57.04 300	73.37 245	88.97 244

CLD-MVS		87.97 127	87.48 115	89.44 180	92.16 188	80.54 136	98.14 27	94.92 172	91.41 16	79.43 189	95.40 110	62.34 229	97.27 156	90.60 65	82.90 201	90.50 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					92.08 189		97.63 51		90.52 24	82.30 150

ACMP_Plane					92.08 189		97.63 51		90.52 24	82.30 150

HQP-MVS		87.91 130	87.55 114	88.98 187	92.08 189	78.48 202	97.63 51	94.80 179	90.52 24	82.30 150	94.56 136	65.40 211	97.32 151	87.67 95	83.01 192	91.13 206

PCF-MVS	84.09 5	86.77 149	85.00 154	92.08 106	92.06 192	83.07 82	92.14 269	94.47 198	79.63 212	76.90 216	94.78 133	71.15 161	99.20 70	72.87 210	91.05 127	93.98 184
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					92.04 193	78.22 211					94.56 136

plane_prior6					91.98 194	77.92 223						64.77 216

Effi-MVS+-dtu		84.61 184	84.90 158	83.72 285	91.96 195	63.14 321	94.95 203	93.34 256	85.57 84	79.79 181	87.12 236	61.99 234	95.61 238	83.55 127	85.83 166	92.41 202

mvs-test1		86.83 146	87.17 121	85.81 244	91.96 195	65.24 314	97.90 34	93.34 256	85.57 84	84.51 126	95.14 125	61.99 234	97.19 160	83.55 127	90.55 130	95.00 170

plane_prior1					91.95 197

CDS-MVSNet		89.50 93	88.96 92	91.14 136	91.94 198	80.93 126	97.09 95	95.81 133	84.26 125	84.72 122	94.20 142	80.31 54	95.64 235	83.37 131	88.96 137	96.85 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		87.50 133	87.09 125	88.74 192	91.86 199	77.96 220	97.18 80	94.69 184	89.89 30	81.33 165	94.15 143	64.77 216	97.30 153	87.08 98	82.82 202	90.96 208

plane_prior7					91.86 199	77.55 230

VDDNet		86.44 153	84.51 160	92.22 102	91.56 201	81.83 107	97.10 94	94.64 191	69.50 307	87.84 98	95.19 121	48.01 308	97.92 125	89.82 73	86.92 151	96.89 125

EI-MVSNet		85.80 162	85.20 149	87.59 220	91.55 202	77.41 232	95.13 197	95.36 155	80.43 193	80.33 176	94.71 134	73.72 143	95.97 208	76.96 183	78.64 224	89.39 232

CVMVSNet		84.83 181	85.57 144	82.63 295	91.55 202	60.38 327	95.13 197	95.03 168	80.60 188	82.10 160	94.71 134	66.40 200	90.19 325	74.30 204	90.32 131	97.31 110

ACMP	81.66 11	84.00 191	83.22 186	86.33 236	91.53 204	72.95 271	95.91 169	93.79 235	83.70 138	73.79 242	92.22 169	54.31 294	96.89 174	83.98 118	79.74 214	89.16 237
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 192	82.80 191	87.31 227	91.46 205	77.39 233	95.66 179	93.43 249	80.44 191	75.51 234	87.26 232	73.72 143	95.16 256	76.99 181	70.72 258	89.39 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		78.25 258	74.72 274	88.83 190	91.20 206	74.10 263	73.91 344	88.70 317	59.89 334	66.82 282	85.12 275	78.38 78	94.54 272	48.84 331	79.58 216	97.86 76

ACMM	80.70 13	83.72 195	82.85 190	86.31 239	91.19 207	72.12 276	95.88 171	94.29 204	80.44 191	77.02 214	91.96 172	55.24 287	97.14 164	79.30 157	80.38 211	89.67 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		88.48 114	87.79 106	90.56 148	91.09 208	79.18 175	96.45 136	95.88 130	83.64 139	83.12 143	93.33 160	75.94 111	95.74 227	82.40 137	88.27 144	96.75 132

ACMH+	76.62 16	77.47 268	74.94 271	85.05 252	91.07 209	71.58 284	93.26 245	90.01 304	71.80 297	64.76 294	88.55 216	41.62 325	96.48 186	62.35 285	71.00 255	87.09 285

OpenMVS	79.58 14	86.09 157	83.62 179	93.50 54	90.95 210	86.71 24	97.44 63	95.83 132	75.35 261	72.64 254	95.72 103	57.42 271	99.64 34	71.41 222	95.85 83	94.13 182

LPG-MVS_test		84.20 190	83.49 183	86.33 236	90.88 211	73.06 269	95.28 187	94.13 214	82.20 161	76.31 222	93.20 161	54.83 291	96.95 170	83.72 123	80.83 209	88.98 242

LGP-MVS_train				86.33 236	90.88 211	73.06 269		94.13 214	82.20 161	76.31 222	93.20 161	54.83 291	96.95 170	83.72 123	80.83 209	88.98 242

PVSNet_0	77.72 15	81.70 229	78.95 239	89.94 171	90.77 213	76.72 243	95.96 164	96.95 43	85.01 101	70.24 270	88.53 218	52.32 295	98.20 114	86.68 103	44.08 346	94.89 171

ACMH	75.40 17	77.99 260	74.96 270	87.10 230	90.67 214	76.41 245	93.19 249	91.64 280	72.47 294	63.44 299	87.61 228	43.34 319	97.16 161	58.34 296	73.94 243	87.72 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS-HIRNet		71.36 303	67.00 306	84.46 273	90.58 215	69.74 300	79.15 333	87.74 321	46.09 345	61.96 314	50.50 347	45.14 315	95.64 235	53.74 317	88.11 146	88.00 268

jason		92.73 44	92.23 50	94.21 28	90.50 216	87.30 21	98.65 12	95.09 164	90.61 23	92.76 43	97.13 77	75.28 131	97.30 153	93.32 39	96.75 72	98.02 62
jason: jason.
LTVRE_ROB	73.68 18	77.99 260	75.74 262	84.74 260	90.45 217	72.02 277	86.41 316	91.12 286	72.57 293	66.63 283	87.27 231	54.95 290	96.98 169	56.29 310	75.98 233	85.21 303
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
XVG-OURS		85.18 176	84.38 164	87.59 220	90.42 218	71.73 282	91.06 282	94.07 221	82.00 165	83.29 141	95.08 128	56.42 280	97.55 142	83.70 125	83.42 188	93.49 194

VPA-MVSNet		85.32 174	83.83 175	89.77 177	90.25 219	82.63 91	96.36 145	97.07 35	83.03 148	81.21 167	89.02 211	61.58 238	96.31 192	85.02 112	70.95 256	90.36 214

XVG-OURS-SEG-HR		85.74 168	85.16 150	87.49 224	90.22 220	71.45 286	91.29 279	94.09 220	81.37 173	83.90 134	95.22 118	60.30 241	97.53 146	85.58 107	84.42 182	93.50 193

tpm		85.55 171	84.47 163	88.80 191	90.19 221	75.39 254	88.79 296	94.69 184	84.83 107	83.96 132	85.21 270	78.22 80	94.68 270	76.32 188	78.02 229	96.34 142

CR-MVSNet		83.53 197	81.36 214	90.06 164	90.16 222	79.75 153	79.02 334	91.12 286	84.24 126	82.27 158	80.35 306	75.45 117	93.67 288	63.37 282	86.25 157	96.75 132

RPMNet		79.32 249	75.75 261	90.06 164	90.16 222	79.75 153	79.02 334	93.92 227	58.43 336	82.27 158	72.55 334	73.03 146	93.67 288	46.10 335	86.25 157	96.75 132

FIs		86.73 150	86.10 136	88.61 194	90.05 224	80.21 145	96.14 158	96.95 43	85.56 87	78.37 197	92.30 168	76.73 99	95.28 253	79.51 154	79.27 219	90.35 215

FMVSNet5		76.46 281	74.16 283	83.35 290	90.05 224	76.17 247	89.58 289	89.85 305	71.39 301	65.29 293	80.42 305	50.61 299	87.70 332	61.05 288	69.24 279	86.18 296

IterMVS		80.67 239	79.16 237	85.20 251	89.79 226	76.08 248	92.97 252	91.86 275	80.28 197	71.20 260	85.14 274	57.93 266	91.34 316	72.52 213	70.74 257	88.18 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		85.31 175	84.23 172	88.55 195	89.75 227	80.55 135	96.72 118	96.89 48	85.42 89	78.40 196	88.93 212	75.38 127	95.52 242	78.58 162	68.02 287	89.57 230

Patchmtry		77.36 270	74.59 277	85.67 246	89.75 227	75.75 251	77.85 337	91.12 286	60.28 332	71.23 259	80.35 306	75.45 117	93.56 290	57.94 297	67.34 295	87.68 273

JIA-IIPM		79.00 252	77.20 249	84.40 274	89.74 229	64.06 318	75.30 340	95.44 153	62.15 324	81.90 162	59.08 344	78.92 70	95.59 239	66.51 261	85.78 167	93.54 192

MS-PatchMatch		83.05 208	81.82 204	86.72 235	89.64 230	79.10 179	94.88 205	94.59 194	79.70 210	70.67 264	89.65 205	50.43 300	96.82 177	70.82 231	95.99 81	84.25 310

semantic-postprocess				84.73 261	89.63 231	74.66 257		91.81 277	80.05 203	71.06 262	85.18 272	57.98 265	91.40 315	72.48 214	70.70 259	88.12 266

Fast-Effi-MVS+-dtu		83.33 206	82.60 194	85.50 248	89.55 232	69.38 303	96.09 162	91.38 281	82.30 160	75.96 229	91.41 178	56.71 274	95.58 240	75.13 199	84.90 179	91.54 204

PatchT		79.75 243	76.85 254	88.42 196	89.55 232	75.49 253	77.37 338	94.61 193	63.07 319	82.46 148	73.32 333	75.52 116	93.41 291	51.36 323	84.43 181	96.36 140

GA-MVS		85.79 167	84.04 174	91.02 139	89.47 234	80.27 143	96.90 110	94.84 177	85.57 84	80.88 168	89.08 209	56.56 279	96.47 187	77.72 171	85.35 175	96.34 142

UniMVSNet_NR-MVSNet		85.49 172	84.59 159	88.21 204	89.44 235	79.36 167	96.71 120	96.41 93	85.22 93	78.11 199	90.98 188	76.97 95	95.14 257	79.14 158	68.30 284	90.12 221

FC-MVSNet-test		85.96 158	85.39 146	87.66 218	89.38 236	78.02 218	95.65 180	96.87 49	85.12 97	77.34 209	91.94 174	76.28 107	94.74 268	77.09 180	78.82 222	90.21 218

WR-MVS		84.32 188	82.96 187	88.41 197	89.38 236	80.32 140	96.59 128	96.25 108	83.97 130	76.63 218	90.36 197	67.53 180	94.86 265	75.82 193	70.09 266	90.06 225

VPNet		84.69 183	82.92 188	90.01 166	89.01 238	83.45 76	96.71 120	95.46 149	85.71 82	79.65 182	92.18 170	56.66 277	96.01 207	83.05 135	67.84 290	90.56 212

testpf		70.88 304	70.47 298	72.08 326	88.92 239	59.57 330	48.62 354	93.15 261	63.05 320	63.07 302	79.51 311	58.33 258	86.63 335	66.93 256	72.69 250	70.05 346

nrg030		86.79 148	85.43 145	90.87 144	88.76 240	85.34 39	97.06 99	94.33 203	84.31 121	80.45 174	91.98 171	72.36 150	96.36 190	88.48 87	71.13 254	90.93 210

DU-MVS		84.57 185	83.33 185	88.28 202	88.76 240	79.36 167	96.43 142	95.41 154	85.42 89	78.11 199	90.82 189	67.61 178	95.14 257	79.14 158	68.30 284	90.33 216

NR-MVSNet		83.35 205	81.52 211	88.84 189	88.76 240	81.31 120	94.45 212	95.16 163	84.65 111	67.81 278	90.82 189	70.36 169	94.87 264	74.75 201	66.89 297	90.33 216

test_0402		72.68 298	69.54 302	82.09 300	88.67 243	71.81 280	92.72 258	86.77 327	61.52 327	62.21 310	83.91 284	43.22 320	93.76 287	34.60 346	72.23 252	80.72 336

RPSCF		77.73 266	76.63 256	81.06 304	88.66 244	55.76 336	87.77 304	87.88 320	64.82 318	74.14 241	92.79 166	49.22 305	96.81 178	67.47 253	76.88 232	90.62 211

FMVSNet1		79.50 246	76.54 257	88.39 198	88.47 245	81.95 100	94.30 217	93.38 252	73.14 287	72.04 257	85.66 262	43.86 316	93.84 284	65.48 266	72.53 251	89.38 234

OPM-MVS		85.84 160	85.10 152	88.06 205	88.34 246	77.83 226	95.72 177	94.20 207	87.89 55	80.45 174	94.05 145	58.57 255	97.26 157	83.88 119	82.76 204	89.09 238

tfpnnormal		78.14 259	75.42 265	86.31 239	88.33 247	79.24 171	94.41 213	96.22 110	73.51 284	69.81 271	85.52 268	55.43 285	95.75 224	47.65 333	67.86 289	83.95 314

TinyColmap		72.41 299	68.99 304	82.68 294	88.11 248	69.59 301	88.41 299	85.20 333	65.55 316	57.91 327	84.82 278	30.80 344	95.94 213	51.38 322	68.70 280	82.49 331

WR-MVS_H		81.02 236	80.09 227	83.79 282	88.08 249	71.26 290	94.46 211	96.54 81	80.08 202	72.81 253	86.82 244	70.36 169	92.65 295	64.18 272	67.50 293	87.46 281

LP		68.54 311	63.92 313	82.39 296	87.93 250	71.79 281	72.37 347	86.01 332	55.89 339	58.33 326	71.46 338	49.58 304	90.10 327	32.25 348	61.48 313	85.27 301

CP-MVSNet		81.01 237	80.08 228	83.79 282	87.91 251	70.51 293	94.29 220	95.65 139	80.83 181	72.54 255	88.84 213	63.71 220	92.32 298	68.58 248	68.36 283	88.55 254

Anonymous20240521		79.73 244	78.10 243	84.63 266	87.90 252	71.58 284	93.91 225	94.39 201	76.69 246	70.27 269	87.00 238	58.97 253	94.76 267	64.38 271	69.43 277	87.54 279

TranMVSNet+NR-MVSNet		83.24 207	81.71 208	87.83 213	87.71 253	78.81 193	96.13 160	94.82 178	84.52 114	76.18 227	90.78 191	64.07 219	94.60 271	74.60 203	66.59 301	90.09 223

USDC		78.65 256	76.25 258	85.85 243	87.58 254	74.60 258	89.58 289	90.58 302	84.05 128	63.13 301	88.23 221	40.69 329	96.86 176	66.57 260	75.81 235	86.09 298

PS-CasMVS		80.27 241	79.18 236	83.52 288	87.56 255	69.88 298	94.08 222	95.29 160	80.27 198	72.08 256	88.51 219	59.22 251	92.23 300	67.49 252	68.15 286	88.45 258

MIMVSNet		79.18 251	75.99 260	88.72 193	87.37 256	80.66 132	79.96 329	91.82 276	77.38 238	74.33 240	81.87 299	41.78 324	90.74 321	66.36 263	83.10 191	94.76 174

XXY-MVS		83.84 193	82.00 198	89.35 181	87.13 257	81.38 118	95.72 177	94.26 205	80.15 201	75.92 230	90.63 192	61.96 236	96.52 185	78.98 160	73.28 248	90.14 219

ITE_SJBPF				82.38 297	87.00 258	65.59 313		89.55 307	79.99 205	69.37 274	91.30 181	41.60 326	95.33 251	62.86 284	74.63 242	86.24 295

test0.0.03 1		82.79 214	82.48 195	83.74 284	86.81 259	72.22 273	96.52 131	95.03 168	83.76 136	73.00 250	93.20 161	72.30 152	88.88 328	64.15 273	77.52 231	90.12 221

pcd1.5k->3k		34.11 331	35.46 331	30.05 347	86.70 260	0.00 366	0.00 357	94.74 183	0.00 361	0.00 362	0.00 363	58.13 260	0.00 364	0.00 361	79.56 217	90.14 219

v1neww		83.45 199	81.95 199	87.95 210	86.66 261	79.04 182	96.32 148	94.17 210	80.76 182	77.56 202	87.25 233	67.02 190	96.08 199	77.73 168	70.07 267	88.74 252

v7new		83.45 199	81.95 199	87.95 210	86.66 261	79.04 182	96.32 148	94.17 210	80.76 182	77.56 202	87.25 233	67.02 190	96.08 199	77.73 168	70.07 267	88.74 252

v18		77.96 262	75.61 263	84.98 254	86.66 261	79.01 186	93.02 251	90.94 291	75.69 256	63.19 300	77.62 315	67.11 186	92.07 303	70.05 233	56.24 322	83.87 315

v8		81.88 227	80.06 230	87.32 226	86.63 264	79.04 182	94.41 213	93.65 242	78.77 226	73.19 249	85.57 266	66.87 193	95.81 220	73.84 208	67.61 292	87.11 284

v16		77.84 264	75.47 264	84.93 256	86.62 265	78.93 188	92.84 255	90.89 292	75.50 259	63.03 304	77.54 316	66.82 195	92.04 304	69.82 234	56.22 323	83.82 317

v6		83.45 199	81.94 201	87.95 210	86.62 265	79.03 185	96.32 148	94.17 210	80.76 182	77.57 201	87.23 235	67.03 189	96.09 198	77.73 168	70.06 269	88.75 250

v1141		83.36 203	81.81 206	88.01 207	86.61 267	79.26 170	96.44 138	94.12 217	80.88 178	77.48 206	86.87 241	67.08 187	96.03 203	77.14 178	69.69 274	88.75 250

divwei89l23v2f112		83.36 203	81.81 206	88.01 207	86.60 268	79.23 173	96.44 138	94.12 217	80.88 178	77.49 204	86.87 241	67.08 187	96.03 203	77.14 178	69.67 275	88.76 248

v1		83.37 202	81.82 204	88.01 207	86.58 269	79.24 171	96.45 136	94.13 214	80.88 178	77.48 206	86.88 240	67.15 185	96.04 202	77.15 177	69.67 275	88.76 248

v17		77.79 265	75.41 266	84.94 255	86.53 270	78.94 187	92.83 256	90.88 293	75.51 258	62.97 305	77.50 317	66.69 196	92.03 305	69.80 235	56.01 324	83.83 316

DI_MVS_plusplus_test		85.92 159	83.61 180	92.86 77	86.43 271	83.20 79	95.57 182	95.46 149	85.10 100	65.99 287	86.84 243	56.70 275	97.89 126	88.10 91	92.33 116	97.48 102

v7		82.99 212	81.41 212	87.73 216	86.41 272	78.86 190	96.10 161	93.98 224	79.88 207	77.49 204	87.11 237	65.44 209	95.97 208	75.69 195	70.59 260	88.36 260

v10		81.43 232	79.53 235	87.11 229	86.38 273	78.87 189	94.31 216	93.43 249	77.88 232	73.24 248	85.26 269	65.44 209	95.75 224	72.14 215	67.71 291	86.72 289

v15		77.52 267	75.09 268	84.82 258	86.37 274	78.82 191	92.58 259	90.78 295	75.47 260	62.53 307	77.17 318	66.58 199	91.92 306	69.18 238	55.16 326	83.73 318

PEN-MVS		79.47 247	78.26 242	83.08 291	86.36 275	68.58 305	93.85 227	94.77 182	79.76 209	71.37 258	88.55 216	59.79 242	92.46 296	64.50 270	65.40 302	88.19 264

test_normal		85.83 161	83.51 182	92.78 81	86.33 276	83.01 85	95.56 184	95.46 149	85.11 99	65.73 289	86.63 248	56.62 278	97.86 127	87.87 93	92.29 117	97.47 103

V14		77.43 269	74.99 269	84.75 259	86.32 277	78.67 195	92.44 263	90.77 296	75.28 264	62.42 308	77.13 319	66.40 200	91.88 307	69.01 243	55.14 327	83.70 319

v1144		82.90 213	81.27 215	87.78 215	86.29 278	79.07 181	96.14 158	93.93 226	80.05 203	77.38 208	86.80 245	65.50 207	95.93 214	75.21 198	70.13 263	88.33 262

v11		77.21 272	74.72 274	84.68 263	86.29 278	78.62 198	92.30 267	90.63 301	74.86 271	62.32 309	76.73 324	65.49 208	91.83 309	68.17 250	55.72 325	83.59 322

V42		83.04 209	81.53 210	87.57 222	86.27 280	79.09 180	95.87 172	94.11 219	80.35 195	77.22 212	86.79 246	65.32 213	96.02 206	77.74 167	70.14 262	87.61 275

V9		77.32 271	74.87 272	84.69 262	86.26 281	78.52 201	92.33 266	90.72 297	75.11 267	62.21 310	77.08 321	66.19 202	91.87 308	68.84 244	55.06 329	83.69 320

v2v482		83.46 198	81.86 203	88.25 203	86.19 282	79.65 163	96.34 147	94.02 223	81.56 172	77.32 210	88.23 221	65.62 206	96.03 203	77.77 165	69.72 273	89.09 238

v148		82.41 221	80.89 217	86.99 231	86.18 283	76.81 241	96.27 153	93.82 231	80.49 190	75.28 237	86.11 260	67.32 183	95.75 224	75.48 196	67.03 296	88.42 259

v12		77.20 273	74.73 273	84.63 266	86.15 284	78.41 206	92.17 268	90.71 298	74.92 270	62.05 312	77.00 322	65.83 204	91.83 309	68.69 246	55.01 330	83.64 321

pmmvs4		82.54 217	80.79 218	87.79 214	86.11 285	80.49 139	93.55 234	93.18 259	77.29 240	73.35 246	89.40 208	65.26 214	95.05 262	75.32 197	73.61 244	87.83 270

MVP-Stereo		82.65 216	81.67 209	85.59 247	86.10 286	78.29 209	93.33 240	92.82 265	77.75 233	69.17 276	87.98 225	59.28 250	95.76 223	71.77 218	96.88 70	82.73 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v13		77.11 276	74.63 276	84.55 269	86.08 287	78.27 210	92.06 270	90.68 300	74.73 273	61.86 315	76.93 323	65.73 205	91.81 312	68.55 249	55.07 328	83.59 322

v1192		82.31 222	80.55 222	87.60 219	85.94 288	78.47 205	95.85 174	93.80 234	79.33 216	76.97 215	86.51 250	63.33 225	95.87 216	73.11 209	70.13 263	88.46 257

TransMVSNet (Re)		76.94 278	74.38 280	84.62 268	85.92 289	75.25 255	95.28 187	89.18 311	73.88 282	67.22 279	86.46 252	59.64 243	94.10 280	59.24 295	52.57 336	84.50 309

PS-MVSNAJss		84.91 179	84.30 171	86.74 233	85.89 290	74.40 261	94.95 203	94.16 213	83.93 131	76.45 221	90.11 202	71.04 163	95.77 222	83.16 133	79.02 221	90.06 225

v144192		82.43 218	80.73 220	87.54 223	85.81 291	78.22 211	95.98 163	93.78 236	79.09 221	77.11 213	86.49 251	64.66 218	95.91 215	74.20 205	69.42 278	88.49 255

v1921920		82.02 226	80.23 226	87.41 225	85.62 292	77.92 223	95.79 176	93.69 240	78.86 225	76.67 217	86.44 253	62.50 228	95.83 219	72.69 211	69.77 272	88.47 256

v1240		81.70 229	79.83 233	87.30 228	85.50 293	77.70 228	95.48 185	93.44 247	78.46 229	76.53 220	86.44 253	60.85 240	95.84 218	71.59 221	70.17 261	88.35 261

pm-mvs1		80.05 242	78.02 244	86.15 241	85.42 294	75.81 250	95.11 199	92.69 268	77.13 241	70.36 266	87.43 229	58.44 257	95.27 254	71.36 223	64.25 306	87.36 282

our_test_3		77.90 263	75.37 267	85.48 249	85.39 295	76.74 242	93.63 231	91.67 278	73.39 286	65.72 290	84.65 279	58.20 259	93.13 293	57.82 298	67.87 288	86.57 291

ppachtmachnet_test		77.19 274	74.22 282	86.13 242	85.39 295	78.22 211	93.98 224	91.36 283	71.74 298	67.11 281	84.87 277	56.67 276	93.37 292	52.21 321	64.59 304	86.80 288

MDA-MVSNet-bldmvs		71.45 302	67.94 305	81.98 301	85.33 297	68.50 306	92.35 265	88.76 315	70.40 303	42.99 342	81.96 298	46.57 313	91.31 317	48.75 332	54.39 332	86.11 297

Baseline_NR-MVSNet		81.22 235	80.07 229	84.68 263	85.32 298	75.12 256	96.48 132	88.80 314	76.24 247	77.28 211	86.40 256	67.61 178	94.39 275	75.73 194	66.73 300	84.54 308

DTE-MVSNet		78.37 257	77.06 250	82.32 299	85.22 299	67.17 310	93.40 237	93.66 241	78.71 227	70.53 265	88.29 220	59.06 252	92.23 300	61.38 287	63.28 310	87.56 277

pmmvs5		81.34 233	79.54 234	86.73 234	85.02 300	76.91 239	96.22 155	91.65 279	77.65 234	73.55 243	88.61 215	55.70 284	94.43 274	74.12 206	73.35 247	88.86 247

XVG-ACMP-BASELINE		79.38 248	77.90 245	83.81 281	84.98 301	67.14 311	89.03 294	93.18 259	80.26 199	72.87 252	88.15 223	38.55 330	96.26 194	76.05 191	78.05 228	88.02 267

MDA-MVSNet_test_wron		73.54 293	70.43 299	82.86 292	84.55 302	71.85 278	91.74 276	91.32 285	67.63 310	46.73 341	81.09 303	55.11 288	90.42 324	55.91 312	59.76 316	86.31 294

SixPastTwentyTwo		76.04 282	74.32 281	81.22 303	84.54 303	61.43 326	91.16 280	89.30 310	77.89 231	64.04 296	86.31 257	48.23 306	94.29 277	63.54 281	63.84 308	87.93 269

YYNet1		73.53 294	70.43 299	82.85 293	84.52 304	71.73 282	91.69 277	91.37 282	67.63 310	46.79 340	81.21 302	55.04 289	90.43 323	55.93 311	59.70 317	86.38 293

N_pmnet		61.30 316	60.20 317	64.60 333	84.32 305	17.00 363	91.67 278	10.98 363	61.77 326	58.45 325	78.55 312	49.89 302	91.83 309	42.27 338	63.94 307	84.97 306

mvs_tets		81.74 228	80.71 221	84.84 257	84.22 306	70.29 295	93.91 225	93.78 236	82.77 152	73.37 245	89.46 207	47.36 312	95.31 252	81.99 140	79.55 218	88.92 246

jajsoiax		82.12 225	81.15 216	85.03 253	84.19 307	70.70 292	94.22 221	93.95 225	83.07 147	73.48 244	89.75 204	49.66 303	95.37 249	82.24 139	79.76 212	89.02 241

EU-MVSNet		76.92 279	76.95 251	76.83 316	84.10 308	54.73 337	91.77 275	92.71 267	72.74 291	69.57 272	88.69 214	58.03 264	87.43 333	64.91 269	70.00 270	88.33 262

test_djsdf		83.00 211	82.45 196	84.64 265	84.07 309	69.78 299	94.80 207	94.48 196	80.74 185	75.41 236	87.70 227	61.32 239	95.10 259	83.77 121	79.76 212	89.04 240

v7n		79.32 249	77.34 248	85.28 250	84.05 310	72.89 272	93.38 238	93.87 229	75.02 268	70.68 263	84.37 280	59.58 245	95.62 237	67.60 251	67.50 293	87.32 283

OurMVSNet-221017-0		77.18 275	76.06 259	80.55 306	83.78 311	60.00 328	90.35 284	91.05 289	77.01 245	66.62 284	87.92 226	47.73 310	94.03 281	71.63 220	68.44 282	87.62 274

EG-PatchMatch MVS		74.92 287	72.02 291	83.62 286	83.76 312	73.28 267	93.62 232	92.04 274	68.57 309	58.88 323	83.80 286	31.87 342	95.57 241	56.97 302	78.67 223	82.00 334

v748		78.69 255	76.79 255	84.39 275	83.40 313	70.78 291	93.25 246	93.62 243	74.96 269	69.40 273	83.74 287	59.40 247	95.39 247	68.74 245	64.59 304	86.99 287

K. test v3		73.62 291	71.59 293	79.69 309	82.98 314	59.85 329	90.85 283	88.83 313	77.13 241	58.90 322	82.11 297	43.62 317	91.72 313	65.83 265	54.10 333	87.50 280

Test4		82.30 223	79.15 238	91.78 121	81.84 315	81.74 110	94.04 223	94.20 207	84.86 105	59.75 321	83.88 285	37.14 333	96.28 193	84.60 114	92.00 120	97.30 111

anonymousdsp		80.98 238	79.97 231	84.01 277	81.73 316	70.44 294	92.49 261	93.58 246	77.10 243	72.98 251	86.31 257	57.58 267	94.90 263	79.32 156	78.63 226	86.69 290

v52		78.70 253	76.95 251	83.95 278	81.71 317	71.34 288	91.93 272	93.43 249	74.69 275	70.36 266	83.71 289	58.04 262	95.50 243	71.84 216	66.82 299	85.00 305

V4		78.70 253	76.95 251	83.95 278	81.66 318	71.34 288	91.94 271	93.44 247	74.69 275	70.35 268	83.73 288	58.07 261	95.50 243	71.84 216	66.86 298	85.02 304

Anonymous20231206		75.29 286	73.64 285	80.22 307	80.75 319	63.38 320	93.36 239	90.71 298	73.09 288	67.12 280	83.70 290	50.33 301	90.85 320	53.63 318	70.10 265	86.44 292

Gipuma		45.11 326	42.05 326	54.30 339	80.69 320	51.30 342	35.80 355	83.81 341	28.13 351	27.94 351	34.53 353	11.41 359	76.70 351	21.45 353	54.65 331	34.90 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
lessismore_v0				79.98 308	80.59 321	58.34 332		80.87 347		58.49 324	83.46 292	43.10 321	93.89 283	63.11 283	48.68 339	87.72 271

OpenMVS_ROB	68.52 20	73.02 297	69.57 301	83.37 289	80.54 322	71.82 279	93.60 233	88.22 318	62.37 323	61.98 313	83.15 295	35.31 337	95.47 245	45.08 336	75.88 234	82.82 326

testgi		74.88 288	73.40 286	79.32 311	80.13 323	61.75 324	93.21 247	86.64 328	79.49 214	66.56 285	91.06 185	35.51 336	88.67 329	56.79 303	71.25 253	87.56 277

CMPMVS	54.94 21	75.71 285	74.56 278	79.17 312	79.69 324	55.98 334	89.59 288	93.30 258	60.28 332	53.85 333	89.07 210	47.68 311	96.33 191	76.55 185	81.02 208	85.22 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		72.36 300	70.82 295	76.95 315	79.18 325	56.33 333	86.12 317	86.11 330	69.30 308	63.06 303	86.66 247	33.03 340	92.25 299	65.33 267	68.64 281	82.28 332

pmmvs6		74.65 289	71.67 292	83.60 287	79.13 326	69.94 297	93.31 244	90.88 293	61.05 331	65.83 288	84.15 283	43.43 318	94.83 266	66.62 258	60.63 314	86.02 299

DeepMVS_CX				64.06 334	78.53 327	43.26 350		68.11 356	69.94 305	38.55 344	76.14 325	18.53 351	79.34 347	43.72 337	41.62 348	69.57 347

test20.03		72.36 300	71.15 294	75.98 320	77.79 328	59.16 331	92.40 264	89.35 309	74.09 280	61.50 316	84.32 281	48.09 307	85.54 341	50.63 327	62.15 312	83.24 324

UnsupCasMVSNet_eth		73.25 295	70.57 297	81.30 302	77.53 329	66.33 312	87.24 308	93.89 228	80.38 194	57.90 328	81.59 300	42.91 322	90.56 322	65.18 268	48.51 340	87.01 286

DSMNet-mixed		73.13 296	72.45 290	75.19 321	77.51 330	46.82 345	85.09 322	82.01 345	67.61 314	69.27 275	81.33 301	50.89 297	86.28 336	54.54 315	83.80 185	92.46 201

Patchmatch-RL test		76.65 280	74.01 284	84.55 269	77.37 331	64.23 316	78.49 336	82.84 344	78.48 228	64.63 295	73.40 332	76.05 110	91.70 314	76.99 181	57.84 318	97.72 85

MIMVSNet1		69.44 306	66.65 308	77.84 313	76.48 332	62.84 322	87.42 306	88.97 312	66.96 315	57.75 329	79.72 310	32.77 341	85.83 338	46.32 334	63.42 309	84.85 307

pmmvs-eth3d		73.59 292	70.66 296	82.38 297	76.40 333	73.38 265	89.39 293	89.43 308	72.69 292	60.34 320	77.79 314	46.43 314	91.26 318	66.42 262	57.06 319	82.51 329

new_pmnet		66.18 312	63.18 314	75.18 322	76.27 334	61.74 325	83.79 324	84.66 335	56.64 338	51.57 334	71.85 336	31.29 343	87.93 331	49.98 328	62.55 311	75.86 341

test2356		74.41 290	74.53 279	74.07 323	76.13 335	54.45 338	94.74 209	92.08 272	71.96 296	65.51 291	83.05 296	56.96 272	83.71 343	52.74 320	77.58 230	84.06 312

testing_2		76.96 277	73.18 287	88.30 201	75.87 336	79.64 164	89.92 287	94.21 206	80.16 200	51.23 335	75.94 326	33.94 338	95.81 220	82.28 138	75.12 240	89.46 231

UnsupCasMVSNet_bld		68.60 310	64.50 311	80.92 305	74.63 337	67.80 307	83.97 323	92.94 264	65.12 317	54.63 332	68.23 341	35.97 334	92.17 302	60.13 290	44.83 344	82.78 327

testus		70.06 305	69.09 303	72.98 325	74.54 338	51.28 343	93.78 228	87.34 322	71.49 300	62.69 306	83.46 292	24.44 347	84.77 342	51.22 325	72.85 249	82.90 325

PM-MVS		69.32 307	66.93 307	76.49 317	73.60 339	55.84 335	85.91 318	79.32 351	74.72 274	61.09 317	78.18 313	21.76 348	91.10 319	70.86 229	56.90 320	82.51 329

new-patchmatchnet		68.85 309	65.93 309	77.61 314	73.57 340	63.94 319	90.11 286	88.73 316	71.62 299	55.08 331	73.60 329	40.84 328	87.22 334	51.35 324	48.49 341	81.67 335

Anonymous20231211		61.03 317	56.76 319	73.82 324	71.24 341	53.47 339	87.60 305	81.65 346	44.19 346	51.08 338	71.82 337	20.79 349	88.46 330	35.45 345	47.07 343	79.52 337

1111		65.60 314	64.33 312	69.41 328	68.26 342	45.11 348	87.06 309	87.32 323	54.99 340	51.20 336	73.45 330	63.57 221	85.70 339	36.53 343	56.59 321	77.42 340

.test1245		54.61 320	58.07 318	44.24 343	68.26 342	45.11 348	87.06 309	87.32 323	54.99 340	51.20 336	73.45 330	63.57 221	85.70 339	36.53 343	0.21 359	1.91 359

ambc				76.02 319	68.11 344	51.43 341	64.97 349	89.59 306		60.49 319	74.49 327	17.17 352	92.46 296	61.50 286	52.85 335	84.17 311

pmmvs3		65.75 313	62.18 316	76.45 318	67.12 345	64.54 315	88.68 297	85.05 334	54.77 343	57.54 330	73.79 328	29.40 346	86.21 337	55.49 314	47.77 342	78.62 338

test1235678		64.50 315	62.19 315	71.42 327	66.82 346	48.00 344	89.44 291	87.90 319	62.82 322	49.12 339	71.31 339	30.14 345	82.19 345	41.88 339	60.32 315	84.06 312

TDRefinement		69.20 308	65.78 310	79.48 310	66.04 347	62.21 323	88.21 300	86.12 329	62.92 321	61.03 318	85.61 265	33.23 339	94.16 279	55.82 313	53.02 334	82.08 333

test12356		58.24 318	56.06 320	64.77 331	60.65 348	39.42 354	82.78 327	84.34 338	57.47 337	42.65 343	69.10 340	19.21 350	81.18 346	38.97 342	49.40 337	73.69 342

FPMVS		55.09 319	52.93 321	61.57 336	55.98 349	40.51 353	83.11 326	83.41 343	37.61 348	34.95 347	71.95 335	14.40 355	76.95 349	29.81 350	65.16 303	67.25 348

PMMVS2		50.90 324	46.31 324	64.67 332	55.53 350	46.67 346	77.30 339	71.02 353	40.89 347	34.16 348	59.32 343	9.83 360	76.14 352	40.09 340	28.63 349	71.21 344

PNet_i23d		41.20 328	38.13 329	50.41 340	55.23 351	36.24 357	73.80 345	65.45 358	29.75 350	21.36 353	47.05 351	3.43 362	63.23 356	28.17 352	18.92 351	51.76 351

wuyk23d		14.10 336	13.89 337	14.72 348	55.23 351	22.91 362	33.83 356	3.56 364	4.94 358	4.11 359	2.28 362	2.06 365	19.66 361	10.23 358	8.74 358	1.59 361

testmv		54.58 321	51.53 322	63.74 335	53.58 353	40.82 352	83.26 325	83.92 340	54.07 344	36.35 346	61.26 342	14.80 354	77.07 348	33.00 347	43.53 347	73.33 343

E-PMN		32.70 333	32.39 332	33.65 345	53.35 354	25.70 360	74.07 343	53.33 361	21.08 354	17.17 356	33.63 355	11.85 358	54.84 358	12.98 356	14.04 354	20.42 356

no-one		51.12 323	45.81 325	67.03 329	53.16 355	52.22 340	75.21 341	80.40 348	54.89 342	28.26 350	48.50 349	15.54 353	82.81 344	39.29 341	17.06 352	66.07 349

EMVS		31.70 334	31.45 334	32.48 346	50.72 356	23.95 361	74.78 342	52.30 362	20.36 355	16.08 357	31.48 356	12.80 356	53.60 359	11.39 357	13.10 357	19.88 357

LCM-MVSNet		52.52 322	48.24 323	65.35 330	47.63 357	41.45 351	72.55 346	83.62 342	31.75 349	37.66 345	57.92 345	9.19 361	76.76 350	49.26 330	44.60 345	77.84 339

MVE	35.65 22	33.85 332	29.49 335	46.92 342	41.86 358	36.28 356	50.45 353	56.52 360	18.75 356	18.28 354	37.84 352	2.41 364	58.41 357	18.71 354	20.62 350	46.06 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		37.75 330	31.85 333	55.46 338	40.00 359	38.01 355	59.81 351	69.47 354	25.46 353	12.42 358	30.55 357	2.06 365	67.08 354	31.81 349	15.03 353	61.29 350

ANet_high		46.22 325	41.28 328	61.04 337	39.91 360	46.25 347	70.59 348	76.18 352	58.87 335	23.09 352	48.00 350	12.58 357	66.54 355	28.65 351	13.62 355	70.35 345

PMVS	34.80 23	39.19 329	35.53 330	50.18 341	29.72 361	30.30 358	59.60 352	66.20 357	26.06 352	17.91 355	49.53 348	3.12 363	74.09 353	18.19 355	49.40 337	46.14 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		41.54 327	41.93 327	40.38 344	20.10 362	26.84 359	61.93 350	59.09 359	14.81 357	28.51 349	80.58 304	35.53 335	48.33 360	63.70 280	13.11 356	45.96 354

testmvs		9.92 337	12.94 338	0.84 350	0.65 363	0.29 365	93.78 228	0.39 365	0.42 359	2.85 360	15.84 360	0.17 368	0.30 363	2.18 359	0.21 359	1.91 359

test123		9.07 338	11.73 339	1.11 349	0.50 364	0.77 364	89.44 291	0.20 366	0.34 360	2.15 361	10.72 361	0.34 367	0.32 362	1.79 360	0.08 361	2.23 358

cdsmvs_eth3d_5k		21.43 335	28.57 336	0.00 351	0.00 365	0.00 366	0.00 357	95.93 128	0.00 361	0.00 362	97.66 51	63.57 221	0.00 364	0.00 361	0.00 362	0.00 362

pcd_1.5k_mvsjas		5.92 340	7.89 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	71.04 163	0.00 364	0.00 361	0.00 362	0.00 362

sosnet-low-res		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

sosnet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uncertanet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

Regformer		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

ab-mvs-re		8.11 339	10.81 340	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	97.30 71	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uanet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

GSMVS																97.54 96

test_part3							98.15 25		84.95 103		98.83 2		99.80 14	97.78 2

test_part1								96.77 53				89.33 6			98.95 12	99.18 10

sam_mvs1												77.59 87				97.54 96

sam_mvs												75.35 130

MTGPA								96.33 103

test_post1							85.88 319				30.24 358	73.77 142	95.07 261	73.89 207

test_post											33.80 354	76.17 108	95.97 208

patchmatchnet-post											77.09 320	77.78 86	95.39 247

MTMP								68.16 355

test9_res														96.00 14	99.03 7	98.31 42

agg_prior2														94.30 28	99.00 9	98.57 31

test_prior4						82.34 95	97.75 46

test_prior2							98.37 18		86.08 75	94.57 26	98.02 34	83.14 33		95.05 21	98.79 16

旧先验2							96.97 106		74.06 281	96.10 7			97.76 131	88.38 88

新几何2							96.42 143

无先验							96.87 111	96.78 52	77.39 237				99.52 44	79.95 151		98.43 36

原ACMM2							96.84 112

testdata2													99.48 49	76.45 187

segment_acmp												82.69 36

testdata1							95.57 182		87.44 59

plane_prior5								94.69 184					97.30 153	87.08 98	82.82 202	90.96 208

plane_prior4											94.15 143

plane_prior3						77.75 227			90.17 28	81.33 165

plane_prior2							97.18 80		89.89 30

plane_prior						77.96 220	97.52 59		90.36 27						82.96 194

n2								0.00 367

nn								0.00 367

door-mid								79.75 350

test11								96.50 85

door								80.13 349

HQP5-MVS						78.48 202

BP-MVS														87.67 95

HQP4-MVS										82.30 150			97.32 151			91.13 206

HQP3-MVS								94.80 179							83.01 192

HQP2-MVS												65.40 211

MDTV_nov1_ep13_2view						81.74 110	86.80 312		80.65 187	85.65 113		74.26 140		76.52 186		96.98 120

ACMMP++_ref															78.45 227

ACMMP++															79.05 220

Test By Simon												71.65 156

High-resolution multi-view benchmark