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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SMA-MVS		99.14 13	99.79 4	98.39 16	99.68 2	99.94 17	99.74 13	96.86 11	99.97 6	94.36 28	99.22 40	100.00 1	99.89 5	99.84 12	99.58 13	99.83 34	99.95 108

ESAPD		99.25 6	99.69 18	98.74 8	99.62 6	99.94 17	99.79 2	96.87 9	99.93 24	96.33 14	99.59 23	100.00 1	99.84 8	99.88 8	98.50 53	100.00 1	100.00 1

CHOSEN 280x420		97.16 46	99.58 29	94.35 78	96.95 56	99.97 3	97.19 80	81.55 140	99.92 29	91.75 42	100.00 1	100.00 1	98.84 64	98.55 48	98.65 47	99.79 54	99.97 80

HSP-MVS		99.36 4	99.79 4	98.85 6	99.61 10	99.96 7	99.71 19	96.94 4	99.97 6	97.11 8	99.60 22	100.00 1	99.70 16	99.96 1	99.12 30	100.00 1	99.96 99

TSAR-MVS + MP.		98.99 20	99.61 26	98.27 19	97.88 49	99.92 35	99.71 19	96.80 14	99.96 16	95.58 20	98.71 61	100.00 1	99.68 19	99.91 5	98.78 44	99.99 6	100.00 1

zzz-MVS		99.12 15	99.52 34	98.65 10	99.58 15	99.93 29	99.74 13	96.72 20	99.44 83	96.47 12	99.62 21	100.00 1	99.63 24	99.74 15	97.97 63	99.77 66	99.94 113

MTAPA										96.61 10		100.00 1

MTMP										97.42 7		100.00 1

HFP-MVS		99.19 10	99.77 7	98.51 14	99.55 16	99.94 17	99.76 5	96.84 12	99.88 34	95.27 22	99.67 11	100.00 1	99.85 7	99.56 21	99.36 20	99.79 54	99.97 80

HPM-MVS++		98.98 21	99.62 25	98.22 20	99.62 6	99.94 17	99.74 13	96.95 3	99.87 37	93.76 30	99.49 31	100.00 1	99.39 35	99.73 16	98.35 55	99.89 22	99.96 99

SD-MVS		99.16 12	99.73 14	98.49 15	97.93 48	99.95 12	99.74 13	96.94 4	99.96 16	96.60 11	99.47 32	100.00 1	99.88 6	99.15 29	99.59 12	99.84 29	100.00 1

MSLP-MVS++		99.39 2	99.76 8	98.95 2	99.60 12	99.99 1	99.83 1	96.82 13	99.92 29	97.58 6	99.58 25	100.00 1	99.93 1	98.98 31	99.86 7	99.96 11	100.00 1

APDe-MVS		99.40 1	99.81 2	98.92 3	99.62 6	99.96 7	99.76 5	96.87 9	99.95 20	97.66 4	99.57 26	100.00 1	99.63 24	99.88 8	99.28 25	100.00 1	100.00 1

MCST-MVS		99.08 17	99.72 16	98.33 18	99.59 14	99.97 3	99.78 3	96.96 2	99.95 20	93.72 31	99.67 11	100.00 1	99.90 4	99.91 5	98.55 51	100.00 1	100.00 1

TSAR-MVS + GP.		98.06 36	99.55 32	96.32 43	94.72 74	99.92 35	99.22 35	89.98 54	99.97 6	94.77 25	99.94 9	100.00 1	99.43 31	98.52 55	98.53 52	99.79 54	100.00 1

abl_6				97.06 32	99.17 35	99.82 56	98.68 49	90.86 47	100.00 1	94.53 27	97.40 82	100.00 1	99.17 50			99.93 16	99.99 47

CNVR-MVS		99.39 2	99.75 11	98.98 1	99.69 1	99.95 12	99.76 5	96.91 6	99.98 3	97.59 5	99.64 19	100.00 1	99.93 1	99.94 2	98.75 46	99.97 10	99.97 80

NCCC		99.24 7	99.75 11	98.65 10	99.63 5	99.96 7	99.76 5	96.91 6	99.97 6	95.86 18	99.67 11	100.00 1	99.75 13	99.85 10	98.80 42	99.98 9	99.97 80

CP-MVS		99.14 13	99.67 20	98.53 13	99.45 20	99.94 17	99.63 25	96.62 24	99.82 46	95.92 17	99.65 16	100.00 1	99.71 15	99.76 14	98.56 50	99.83 34	100.00 1

SteuartSystems-ACMMP		98.95 22	99.80 3	97.95 24	99.43 23	99.96 7	99.76 5	96.45 28	99.82 46	93.63 32	99.64 19	100.00 1	98.56 72	99.90 7	99.31 23	99.84 29	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.08 17	99.53 33	98.57 12	99.44 22	99.93 29	99.60 26	95.92 32	99.77 53	97.01 9	99.67 11	100.00 1	99.72 14	99.56 21	97.76 73	99.70 110	99.98 67

CNLPA		99.24 7	99.58 29	98.85 6	99.34 28	99.95 12	99.32 31	96.65 22	99.96 16	98.44 2	98.97 51	100.00 1	99.57 27	98.66 39	99.56 15	99.76 73	99.97 80

PHI-MVS		98.85 23	99.67 20	97.89 25	98.63 44	99.93 29	98.95 42	95.20 34	99.84 44	94.94 23	99.74 10	100.00 1	99.69 17	98.40 58	99.75 10	99.93 16	99.99 47

PLC	98.06 1	99.17 11	99.38 37	98.92 3	99.47 18	99.90 43	99.48 28	96.47 27	99.96 16	98.73 1	99.52 29	100.00 1	99.55 29	98.54 51	97.73 76	99.84 29	99.99 47
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator+	95.21 7	98.17 32	99.08 38	97.12 31	99.28 31	99.78 70	98.61 50	89.93 56	99.93 24	95.36 21	95.50 98	100.00 1	99.56 28	98.58 46	99.80 9	99.95 13	99.97 80

ACMMP_Plus		98.68 25	99.58 29	97.62 27	99.62 6	99.92 35	99.72 18	96.78 16	99.71 61	90.13 68	99.66 15	99.99 26	99.64 23	99.78 13	98.14 60	99.82 39	99.89 137

TSAR-MVS + ACMM		98.30 30	99.64 22	96.74 36	99.08 36	99.94 17	99.67 22	96.73 19	99.97 6	86.30 96	98.30 67	99.99 26	98.78 66	99.73 16	99.57 14	99.88 25	99.98 67

XVS					95.09 69	99.94 17	97.49 71			88.58 83		99.98 28				99.78 61

X-MVStestdata					95.09 69	99.94 17	97.49 71			88.58 83		99.98 28				99.78 61

X-MVS		98.62 26	99.75 11	97.29 28	99.50 17	99.94 17	99.71 19	96.55 25	99.85 41	88.58 83	99.65 16	99.98 28	99.67 20	99.60 20	99.26 26	99.77 66	99.97 80

UA-Net		94.95 94	98.66 52	90.63 109	94.60 77	98.94 115	96.03 100	85.28 97	98.01 143	78.92 124	97.42 81	99.96 31	89.09 198	98.95 32	98.80 42	99.82 39	98.57 199

ACMMPR		99.12 15	99.76 8	98.36 17	99.45 20	99.94 17	99.75 11	96.70 21	99.93 24	94.65 26	99.65 16	99.96 31	99.84 8	99.51 23	99.35 21	99.79 54	99.96 99

APD-MVS		99.33 5	99.85 1	98.73 9	99.61 10	99.92 35	99.77 4	96.91 6	99.93 24	96.31 15	99.59 23	99.95 33	99.84 8	99.73 16	99.84 8	99.95 13	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		98.82 24	99.63 23	97.88 26	99.41 24	99.91 42	99.74 13	96.76 18	99.88 34	91.89 41	99.50 30	99.94 34	99.65 22	99.71 19	98.49 54	99.82 39	99.97 80
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSTER		97.00 51	98.85 46	94.83 66	92.71 99	97.43 146	99.03 40	85.52 95	99.82 46	92.74 37	99.15 42	99.94 34	99.19 49	98.66 39	96.99 99	99.79 54	99.98 67

DeepC-MVS_fast	98.03 2	99.05 19	99.78 6	98.21 21	99.47 18	99.97 3	99.75 11	96.80 14	99.97 6	93.58 34	98.68 62	99.94 34	99.69 17	99.93 4	99.95 2	99.96 11	99.98 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS					99.23 33							99.87 37

DeepPCF-MVS	97.16 4	97.58 43	99.72 16	95.07 57	98.45 45	99.96 7	93.83 134	95.93 31	100.00 1	90.79 55	98.38 66	99.85 38	95.28 124	99.94 2	99.97 1	96.15 221	99.97 80

train_agg		98.62 26	99.76 8	97.28 29	99.03 37	99.93 29	99.65 23	96.37 29	99.98 3	89.24 78	99.53 27	99.83 39	99.59 26	99.85 10	99.19 28	99.80 50	100.00 1

tpmrst		92.52 130	97.45 83	86.77 149	92.15 114	99.36 95	92.53 144	65.95 216	99.53 73	72.50 143	92.22 131	99.83 39	97.81 90	95.18 145	96.05 119	99.69 116	100.00 1

ACMMP		98.16 33	99.01 40	97.18 30	98.86 39	99.92 35	98.77 47	95.73 33	99.31 94	91.15 53	100.00 1	99.81 41	98.82 65	98.11 72	95.91 122	99.77 66	99.97 80
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
QAPM		97.90 39	98.89 43	96.74 36	99.35 27	99.80 68	98.84 44	90.20 53	99.94 22	92.85 35	94.17 115	99.78 42	99.42 32	98.71 37	99.87 6	99.79 54	99.98 67

EPNet		98.11 35	99.63 23	96.34 42	98.44 46	99.88 49	98.55 51	90.25 52	99.93 24	92.60 38	100.00 1	99.73 43	98.41 73	98.87 33	99.02 33	99.82 39	99.97 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		97.94 38	99.59 27	96.02 45	99.27 32	99.97 3	97.03 84	90.44 49	99.89 31	90.75 56	100.00 1	99.73 43	98.68 71	98.67 38	98.89 37	99.95 13	99.97 80

OMC-MVS		98.59 28	99.07 39	98.03 23	99.41 24	99.90 43	99.26 34	94.33 36	99.94 22	96.03 16	96.68 87	99.72 45	99.42 32	98.86 34	98.84 39	99.72 106	99.58 175

PGM-MVS		98.47 29	99.73 14	97.00 33	99.68 2	99.94 17	99.76 5	91.74 41	99.84 44	91.17 52	100.00 1	99.69 46	99.81 11	99.38 25	99.30 24	99.82 39	99.95 108

AdaColmap		99.21 9	99.45 35	98.92 3	99.67 4	99.95 12	99.65 23	96.77 17	99.97 6	97.67 3	100.00 1	99.69 46	99.93 1	99.26 27	97.25 85	99.85 27	100.00 1

MDTV_nov1_ep13		94.32 101	98.77 49	89.14 124	91.70 122	99.52 85	95.21 116	72.09 202	99.80 49	78.91 125	96.32 91	99.62 48	97.71 93	98.39 59	97.71 77	99.22 204	100.00 1

PatchmatchNet		93.48 114	98.84 47	87.22 144	91.93 117	99.39 93	92.55 143	66.06 215	99.71 61	75.61 135	98.24 71	99.59 49	97.35 97	97.87 83	97.64 78	99.83 34	99.43 185
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PCF-MVS	97.20 3	97.49 44	98.20 66	96.66 38	97.62 51	99.92 35	98.93 43	96.64 23	98.53 125	88.31 86	94.04 117	99.58 50	98.94 58	97.53 91	97.79 71	99.54 135	99.97 80
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_LR		98.15 34	99.69 18	96.36 41	99.23 33	99.93 29	97.79 61	91.84 40	99.87 37	90.53 63	100.00 1	99.57 51	98.93 59	99.44 24	99.08 32	99.85 27	99.95 108

CANet		97.62 41	98.94 42	96.08 44	97.19 53	99.93 29	99.29 33	90.38 50	99.87 37	91.00 54	95.79 97	99.51 52	98.72 70	98.53 52	99.00 34	99.90 21	99.99 47

GG-mvs-BLEND		69.85 227	99.39 36	35.39 237	3.67 242	99.94 17	99.10 38	1.69 239	99.85 41	3.19 244	98.13 74	99.46 53	4.92 239	99.23 28	99.14 29	99.80 50	100.00 1

DWT-MVSNet_training		96.26 63	98.44 59	93.72 82	92.58 101	99.34 96	96.15 98	83.00 123	99.76 55	93.63 32	97.89 77	99.46 53	97.23 100	94.43 155	98.19 58	99.70 110	100.00 1

CDPH-MVS		97.88 40	99.59 27	95.89 46	98.90 38	99.95 12	99.40 30	92.86 39	99.86 40	85.33 99	98.62 63	99.45 55	99.06 57	99.29 26	99.94 3	99.81 47	100.00 1

3Dnovator	95.01 8	97.98 37	98.89 43	96.92 35	99.36 26	99.76 72	98.72 48	89.98 54	99.98 3	93.99 29	94.60 112	99.43 56	99.50 30	98.55 48	99.91 4	99.99 6	99.98 67

EPMVS		94.08 106	98.54 56	88.87 126	92.51 107	99.47 89	94.18 130	66.53 211	99.68 63	82.40 113	95.24 101	99.40 57	97.86 88	98.12 71	97.99 62	99.75 86	99.88 140

tpm cat1		93.29 116	96.53 108	89.50 121	91.84 118	99.18 101	94.70 123	67.70 206	98.38 131	86.67 94	89.16 141	99.38 58	96.66 111	94.33 156	95.30 133	99.43 166	100.00 1

UGNet		96.05 65	98.55 55	93.13 89	94.64 75	99.65 78	94.70 123	87.78 87	99.40 88	89.69 74	98.25 70	99.25 59	92.12 156	96.50 115	97.08 95	99.84 29	99.72 164
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
PVSNet_Blended_VisFu		95.37 87	97.44 84	92.95 94	95.20 67	99.80 68	92.68 141	88.41 84	99.12 100	87.64 87	88.31 146	99.10 60	94.07 139	98.27 65	97.51 81	99.73 102	100.00 1

RPMNet		92.64 128	97.88 75	86.53 151	90.79 130	98.95 113	95.13 117	64.44 224	99.09 102	72.36 144	93.58 122	99.01 61	96.74 110	98.05 75	96.45 105	99.71 108	100.00 1

RPSCF		95.86 70	96.94 99	94.61 72	96.52 57	98.67 123	98.54 52	88.43 83	99.56 72	90.51 66	99.39 33	98.70 62	97.72 91	93.77 168	92.00 176	95.93 222	96.50 217

dps		94.29 103	97.33 85	90.75 108	92.02 116	99.21 99	94.31 128	66.97 210	99.50 75	95.61 19	96.22 93	98.64 63	96.08 115	93.71 170	94.03 151	99.52 139	99.98 67

TAPA-MVS	96.62 5	97.60 42	98.46 58	96.60 39	98.73 42	99.90 43	99.30 32	94.96 35	99.46 82	87.57 88	96.05 96	98.53 64	99.26 46	98.04 77	97.33 84	99.77 66	99.88 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CANet_DTU		94.90 95	98.98 41	90.13 115	94.74 73	99.81 64	98.53 53	82.23 131	99.97 6	66.76 178	100.00 1	98.50 65	98.74 68	97.52 92	97.19 93	99.76 73	99.88 140

OpenMVS	94.03 11	96.87 52	98.10 69	95.44 52	99.29 30	99.78 70	98.46 56	89.92 57	99.47 81	85.78 97	91.05 136	98.50 65	99.30 39	98.49 56	99.41 17	99.89 22	99.98 67

EPP-MVSNet		96.29 62	98.34 62	93.90 80	91.77 120	99.38 94	95.45 114	87.25 92	99.38 89	91.36 49	94.86 111	98.49 67	97.83 89	98.01 80	98.23 57	99.75 86	99.99 47

Vis-MVSNet (Re-imp)		95.60 85	98.52 57	92.19 101	92.37 111	99.56 83	96.37 96	87.41 91	98.95 108	84.77 104	94.88 110	98.48 68	92.44 153	98.63 43	99.37 18	99.76 73	99.77 159

IS_MVSNet		96.66 55	98.62 53	94.38 75	92.41 109	99.70 75	97.19 80	87.67 89	99.05 105	91.27 51	95.09 104	98.46 69	97.95 86	98.64 41	99.37 18	99.79 54	100.00 1

diffmvs		96.35 61	98.76 50	93.54 85	92.41 109	99.55 84	97.22 79	83.75 115	99.57 70	89.64 76	96.86 83	98.33 70	98.37 74	98.42 57	98.61 48	99.88 25	99.99 47

ADS-MVSNet		92.91 122	97.97 73	87.01 146	92.07 115	99.27 98	92.70 140	65.39 220	99.85 41	75.40 136	94.93 109	98.26 71	96.86 105	96.09 129	97.52 80	99.65 120	99.84 149

testpf		91.26 140	97.28 87	84.23 186	89.52 139	97.45 145	88.08 199	56.08 233	99.76 55	78.71 126	95.06 108	98.26 71	93.44 145	94.72 151	95.69 126	99.57 130	99.99 47

EPNet_dtu		95.10 93	98.81 48	90.78 107	98.38 47	98.47 125	96.54 92	89.36 61	99.78 52	65.65 190	99.31 37	98.24 73	94.79 129	98.28 64	99.35 21	99.93 16	98.27 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		97.04 49	98.72 51	95.08 56	96.32 60	99.90 43	99.15 36	89.61 60	99.89 31	87.22 93	95.47 99	98.22 74	98.22 78	98.63 43	98.90 36	99.93 16	100.00 1

CR-MVSNet		92.32 133	97.97 73	85.74 163	90.63 133	98.95 113	95.46 112	65.50 218	99.09 102	67.51 169	94.20 114	98.18 75	95.59 122	98.16 69	97.20 91	99.74 90	100.00 1

FMVSNet5		93.53 110	96.09 121	90.56 111	86.74 154	92.84 208	92.64 142	77.50 168	99.41 87	88.97 80	98.02 75	97.81 76	98.00 84	94.85 149	95.43 132	99.50 146	94.25 222

IterMVS		91.65 137	96.62 102	85.85 160	90.27 136	95.80 180	95.32 115	74.15 185	98.91 111	60.95 207	88.79 145	97.76 77	94.69 132	98.04 77	97.07 96	99.73 102	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test0.0.03 1		95.15 92	97.87 76	91.99 102	91.69 123	98.82 119	93.04 139	83.60 116	99.65 64	88.80 81	94.15 116	97.67 78	94.97 126	96.62 114	98.16 59	99.83 34	100.00 1

PatchT		91.06 141	97.66 78	83.36 199	90.32 135	98.96 112	82.30 215	64.72 223	98.45 129	67.51 169	93.28 125	97.60 79	95.59 122	98.16 69	97.20 91	99.70 110	100.00 1

DI_MVS_plusplus_trai		95.29 88	97.02 94	93.28 88	91.76 121	99.52 85	97.84 60	85.67 94	99.08 104	87.29 91	87.76 149	97.46 80	97.31 98	97.83 85	97.48 82	99.83 34	100.00 1

gg-mvs-nofinetune		86.69 193	91.30 159	81.30 207	90.42 134	99.64 79	98.50 54	61.68 229	79.23 230	40.35 233	66.58 224	97.14 81	96.92 103	98.64 41	97.94 64	99.91 20	99.97 80

thresconf0.02		96.46 58	98.87 45	93.64 83	92.77 98	99.11 102	97.05 83	89.36 61	99.64 66	85.14 100	99.07 44	96.84 82	97.72 91	98.72 36	98.76 45	99.78 61	99.95 108

COLMAP_ROB	93.56 12	96.03 66	96.83 100	95.11 55	97.87 50	99.52 85	98.81 46	91.40 44	99.42 85	84.97 101	90.46 138	96.82 83	98.05 81	96.46 119	96.19 112	99.54 135	98.92 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DELS-MVS		97.05 47	98.05 70	95.88 48	97.09 54	99.99 1	98.82 45	90.30 51	98.44 130	91.40 47	92.91 126	96.57 84	97.68 94	98.56 47	99.88 5	100.00 1	100.00 1
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
test-LLR		93.71 107	97.23 88	89.60 119	91.69 123	99.10 103	94.68 125	83.60 116	99.36 90	71.94 148	93.82 119	96.51 85	95.96 117	97.42 96	94.37 145	99.74 90	99.99 47

TESTMET0.1,1		92.87 123	97.23 88	87.79 140	86.96 153	99.10 103	94.68 125	77.46 169	99.36 90	71.94 148	93.82 119	96.51 85	95.96 117	97.42 96	94.37 145	99.74 90	99.99 47

CDS-MVSNet		94.32 101	97.00 96	91.19 106	89.82 138	98.71 121	95.51 111	85.14 101	96.85 152	82.33 114	92.48 130	96.40 87	94.71 130	96.86 111	97.76 73	99.63 122	99.92 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn_ndepth		96.84 53	98.58 54	94.81 67	93.18 82	99.62 81	96.83 88	88.75 76	99.73 59	92.38 39	98.45 65	96.34 88	97.90 87	98.34 63	97.59 79	99.84 29	99.99 47

test-mter		92.67 127	97.13 91	87.47 143	86.72 155	99.07 105	94.28 129	76.90 173	99.21 96	71.53 152	93.63 121	96.32 89	95.67 119	97.32 105	94.36 147	99.74 90	99.99 47

MS-PatchMatch		93.46 115	95.91 124	90.61 110	95.48 65	99.31 97	95.62 108	77.23 170	99.42 85	81.88 117	88.92 143	96.06 90	93.80 141	96.45 120	93.11 163	99.65 120	98.10 206

CSCG		98.22 31	98.37 60	98.04 22	99.60 12	99.82 56	99.45 29	93.59 37	99.16 98	96.46 13	98.22 73	95.86 91	99.41 34	96.33 123	99.22 27	99.75 86	99.94 113

tfpn1000		96.58 56	98.37 60	94.50 74	93.04 90	99.59 82	96.53 93	88.54 80	99.73 59	91.59 43	98.28 69	95.76 92	97.46 96	98.19 68	97.10 94	99.82 39	99.96 99

HyFIR lowres test		93.13 117	94.48 137	91.56 104	96.12 63	99.68 76	93.52 136	79.98 148	97.24 149	81.73 119	72.66 216	95.74 93	98.29 77	98.27 65	97.79 71	99.70 110	100.00 1

FC-MVSNet-test		92.78 124	96.19 120	88.80 129	88.00 150	97.54 143	93.60 135	82.36 130	98.16 138	79.71 121	91.55 135	95.41 94	89.65 193	96.09 129	95.23 134	99.49 147	99.31 188

MSDG		97.29 45	97.55 81	97.00 33	98.66 43	99.71 74	99.03 40	96.15 30	99.59 69	89.67 75	92.77 129	94.86 95	98.75 67	98.22 67	97.94 64	99.72 106	99.76 160

PMMVS		96.45 59	98.24 63	94.36 77	92.58 101	99.01 109	97.08 82	87.42 90	99.88 34	90.06 69	99.39 33	94.63 96	99.33 38	97.85 84	96.99 99	99.70 110	99.96 99

GBi-Net		95.19 90	96.99 97	93.09 91	89.11 141	96.47 160	96.90 85	84.17 110	99.48 78	89.76 71	95.09 104	94.35 97	98.87 60	96.50 115	97.21 87	99.74 90	99.81 153

test1		95.19 90	96.99 97	93.09 91	89.11 141	96.47 160	96.90 85	84.17 110	99.48 78	89.76 71	95.09 104	94.35 97	98.87 60	96.50 115	97.21 87	99.74 90	99.81 153

FMVSNet3		95.59 86	97.51 82	93.34 87	89.48 140	96.57 158	97.67 63	84.17 110	99.48 78	89.76 71	95.09 104	94.35 97	99.14 53	98.37 60	98.86 38	99.82 39	99.89 137

tfpn_n400		95.76 78	98.21 64	92.90 95	92.57 105	99.05 106	96.42 94	88.50 81	99.49 76	83.08 109	98.90 52	94.24 100	97.07 101	98.10 73	97.93 66	99.74 90	99.76 160

tfpnconf		95.76 78	98.21 64	92.90 95	92.57 105	99.05 106	96.42 94	88.50 81	99.49 76	83.08 109	98.90 52	94.24 100	97.07 101	98.10 73	97.93 66	99.74 90	99.76 160

tfpnview11		95.78 74	98.17 68	93.01 93	92.58 101	99.04 108	96.64 91	88.72 78	99.63 68	83.08 109	98.90 52	94.24 100	97.25 99	98.35 62	97.21 87	99.77 66	99.80 157

IterMVS-LS		93.50 111	96.22 118	90.33 114	90.93 128	95.50 190	94.83 121	80.54 144	98.92 110	79.11 122	90.64 137	93.70 103	96.79 107	96.93 109	97.85 70	99.78 61	99.99 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_Test		95.74 80	98.18 67	92.90 95	92.16 113	99.49 88	97.36 77	84.30 109	99.79 50	84.94 102	96.65 88	93.63 104	98.85 63	98.61 45	99.10 31	99.81 47	100.00 1

PatchMatch-RL		96.84 53	98.03 71	95.47 49	98.84 40	99.81 64	95.61 109	89.20 64	99.65 64	91.28 50	99.39 33	93.46 105	98.18 79	98.05 75	96.28 109	99.69 116	99.55 180

Fast-Effi-MVS+-dtu		92.73 125	97.62 79	87.02 145	88.91 145	98.83 118	95.79 102	73.98 189	99.89 31	68.62 163	97.73 79	93.30 106	95.21 125	97.67 87	95.96 121	99.59 127	100.00 1

Vis-MVSNet		93.08 119	96.76 101	88.78 130	91.14 127	99.63 80	94.85 120	83.34 119	97.19 150	74.78 139	91.92 134	93.15 107	88.81 201	97.59 89	98.35 55	99.78 61	99.49 184
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DeepC-MVS	96.33 6	97.05 47	97.59 80	96.42 40	97.37 52	99.92 35	99.10 38	96.54 26	99.34 93	86.64 95	91.93 133	93.15 107	99.11 55	99.11 30	99.68 11	99.73 102	99.97 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FMVSNet2		94.48 99	95.95 122	92.77 98	89.11 141	96.47 160	96.90 85	83.38 118	99.11 101	88.64 82	87.50 154	92.26 109	98.87 60	97.91 82	98.60 49	99.74 90	99.81 153

conf0.002		96.51 57	97.75 77	95.07 57	93.11 83	99.83 52	97.67 63	89.10 66	98.62 117	91.47 46	99.39 33	91.68 110	99.28 41	97.49 93	97.24 86	99.76 73	100.00 1

CVMVSNet		92.13 134	95.40 128	88.32 137	91.29 126	97.29 148	91.85 148	86.42 93	96.71 154	71.84 150	89.56 140	91.18 111	88.98 200	96.17 127	97.76 73	99.51 143	99.14 193

CHOSEN 1792x2688		93.69 108	94.89 133	92.28 100	96.17 61	99.84 51	95.69 105	83.17 121	98.54 124	82.04 115	77.58 205	91.15 112	96.90 104	98.36 61	98.82 41	99.73 102	99.98 67

MIMVSNet		91.01 142	96.22 118	84.93 174	85.24 172	98.09 134	90.40 167	64.96 222	97.55 147	72.65 141	96.23 92	90.81 113	96.79 107	96.69 112	97.06 97	99.52 139	97.09 213

MAR-MVS		97.03 50	98.00 72	95.89 46	99.32 29	99.74 73	96.76 90	84.89 102	99.97 6	94.86 24	98.29 68	90.58 114	99.67 20	98.02 79	99.50 16	99.82 39	99.92 121
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
LP		88.31 159	93.18 144	82.63 202	90.66 132	97.98 137	87.32 202	63.49 227	97.17 151	63.02 201	82.08 167	90.47 115	91.92 158	92.75 187	93.42 159	99.38 188	98.37 201

MVS-HIRNet		88.27 160	94.05 141	81.51 206	88.90 146	98.93 116	83.38 213	60.52 232	98.06 142	63.78 197	80.67 179	90.36 116	92.94 148	97.29 106	96.41 107	99.56 132	96.66 215

Effi-MVS+-dtu		93.13 117	97.13 91	88.47 134	88.86 147	99.19 100	96.79 89	79.08 157	99.64 66	70.01 158	97.51 80	89.38 117	96.53 113	97.60 88	96.55 102	99.57 130	100.00 1

FC-MVSNet-train		94.61 96	96.27 117	92.68 99	92.35 112	97.14 149	93.45 138	87.73 88	98.93 109	87.31 90	96.42 90	89.35 118	95.67 119	96.06 132	96.01 120	99.56 132	99.98 67

gm-plane-assit		84.93 205	91.61 156	77.14 215	84.14 202	91.29 220	66.18 231	69.70 204	85.22 226	47.95 228	78.58 201	89.24 119	94.90 128	98.82 35	98.12 61	99.99 6	100.00 1

LS3D		96.44 60	97.31 86	95.41 53	97.06 55	99.87 50	99.51 27	97.48 1	99.57 70	79.00 123	95.39 100	89.19 120	99.81 11	98.55 48	98.84 39	99.62 124	99.78 158

conf0.01		96.20 64	97.19 90	95.05 59	93.11 83	99.83 52	97.67 63	89.06 67	98.62 117	91.38 48	99.19 41	89.09 121	99.28 41	97.48 94	96.10 113	99.76 73	100.00 1

CostFormer		93.50 111	96.50 109	90.00 116	91.69 123	98.65 124	93.88 133	67.64 207	98.97 106	89.16 79	97.79 78	88.92 122	97.97 85	95.14 146	96.06 118	99.63 122	100.00 1

tfpn		95.93 69	97.06 93	94.62 71	92.94 97	99.81 64	97.25 78	88.71 79	98.32 137	89.98 70	98.79 60	88.55 123	99.11 55	97.26 108	96.71 101	99.75 86	99.98 67

tpmp4_e23		92.95 121	96.28 116	89.06 125	91.80 119	98.81 120	94.95 119	67.56 209	99.21 96	82.97 112	96.54 89	88.52 124	97.47 95	94.47 154	96.42 106	99.61 125	100.00 1

MDTV_nov1_ep13_2view		87.75 170	93.32 143	81.26 208	83.74 206	96.64 156	85.66 206	66.20 214	98.36 135	61.61 205	84.34 159	87.95 125	91.12 186	94.01 161	92.66 169	99.22 204	99.27 190

FMVSNet1		92.55 129	93.66 142	91.26 105	87.91 151	96.12 167	94.75 122	81.69 139	97.67 145	85.63 98	80.56 180	87.88 126	98.15 80	96.50 115	97.21 87	99.41 184	99.71 165

canonicalmvs		95.80 72	97.02 94	94.37 76	92.96 93	99.47 89	97.49 71	84.58 104	99.44 83	92.05 40	98.54 64	86.65 127	99.37 36	96.18 126	98.93 35	99.77 66	99.92 121

TAMVS		92.43 132	94.21 140	90.35 113	88.68 148	98.85 117	94.15 131	81.53 141	95.58 161	83.61 107	87.05 155	86.45 128	94.71 130	96.27 125	95.91 122	99.42 172	99.38 187

IB-MVS	90.59 15	92.70 126	95.70 125	89.21 123	94.62 76	99.45 91	83.77 210	88.92 69	99.53 73	92.82 36	98.86 55	86.08 129	75.24 222	92.81 186	93.17 161	99.89 22	100.00 1
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
testgi		92.47 131	95.68 127	88.73 131	90.68 131	98.35 128	91.67 151	79.50 153	98.96 107	77.12 131	95.17 103	85.84 130	93.95 140	95.75 135	96.47 104	99.45 160	99.21 191

N_pmnet		87.31 180	91.51 157	82.41 205	85.13 174	95.57 186	80.59 218	81.79 137	96.20 157	58.52 213	78.62 200	85.66 131	89.36 196	94.64 152	92.14 175	99.08 209	97.72 211

tfpn111		95.79 73	96.55 104	94.89 61	93.10 85	99.82 56	97.67 63	88.85 70	98.62 117	90.69 57	99.07 44	84.86 132	99.28 41	97.41 98	96.10 113	99.76 73	99.99 47

conf200view11		95.78 74	96.54 106	94.89 61	93.10 85	99.82 56	97.67 63	88.85 70	98.62 117	90.69 57	99.00 47	84.86 132	99.28 41	97.41 98	96.10 113	99.76 73	99.99 47

thres100view900		95.86 70	96.62 102	94.97 60	93.10 85	99.83 52	97.76 62	89.15 65	98.62 117	90.69 57	99.00 47	84.86 132	99.30 39	97.57 90	96.48 103	99.81 47	100.00 1

tfpn200view9		95.78 74	96.54 106	94.89 61	93.10 85	99.82 56	97.67 63	88.85 70	98.62 117	90.69 57	99.00 47	84.86 132	99.28 41	97.41 98	96.10 113	99.76 73	99.99 47

thres200		95.77 77	96.55 104	94.86 64	93.09 89	99.82 56	97.63 69	88.85 70	98.49 126	90.66 61	98.99 50	84.86 132	99.20 47	97.41 98	96.28 109	99.76 73	100.00 1

thres400		95.72 81	96.48 110	94.84 65	93.00 92	99.83 52	97.55 70	88.93 68	98.49 126	90.61 62	98.86 55	84.63 137	99.20 47	97.45 95	96.10 113	99.77 66	99.99 47

view600		95.64 82	96.38 113	94.79 68	92.96 93	99.82 56	97.48 74	88.85 70	98.38 131	90.52 64	98.84 57	84.61 138	99.15 51	97.41 98	95.60 130	99.76 73	99.99 47

thres600view7		95.64 82	96.38 113	94.79 68	92.96 93	99.82 56	97.48 74	88.85 70	98.38 131	90.52 64	98.84 57	84.61 138	99.15 51	97.41 98	95.60 130	99.76 73	99.99 47

view800		95.62 84	96.38 113	94.73 70	92.96 93	99.81 64	97.38 76	88.75 76	98.35 136	90.43 67	98.81 59	84.54 140	99.13 54	97.35 104	95.82 125	99.76 73	99.98 67

conf0.05thres1000		94.50 98	95.70 125	93.11 90	92.68 100	99.67 77	96.04 99	87.81 86	97.52 148	83.71 105	96.20 94	84.52 141	98.73 69	96.39 121	95.66 128	99.71 108	99.92 121

tpm		89.60 150	94.93 132	83.39 197	89.94 137	97.11 150	90.09 181	65.28 221	98.67 115	60.03 211	96.79 86	84.38 142	95.66 121	91.90 191	95.65 129	99.32 194	99.98 67

GA-MVS		90.38 145	94.59 136	85.46 168	88.30 149	98.44 126	92.18 145	83.30 120	97.89 144	58.05 214	92.86 127	84.25 143	91.27 181	96.65 113	92.61 171	99.66 119	99.43 185

anonymousdsp		87.98 163	92.38 149	82.85 200	83.68 207	96.79 153	90.78 158	74.06 188	95.29 166	57.91 215	83.33 162	83.12 144	91.15 185	95.96 133	92.37 173	99.52 139	99.76 160

PVSNet_BlendedMVS		96.01 67	96.48 110	95.46 50	96.47 58	99.89 47	95.64 106	91.23 45	99.75 57	91.59 43	96.80 84	82.44 145	98.05 81	98.53 52	97.92 68	99.80 50	100.00 1

PVSNet_Blended		96.01 67	96.48 110	95.46 50	96.47 58	99.89 47	95.64 106	91.23 45	99.75 57	91.59 43	96.80 84	82.44 145	98.05 81	98.53 52	97.92 68	99.80 50	100.00 1

pm-mvs1		89.68 149	92.00 151	86.96 147	86.23 159	96.62 157	90.36 169	83.05 122	93.97 186	72.15 147	81.77 175	82.10 147	90.69 187	95.38 141	94.50 141	99.29 198	99.65 167

pmmvs4		91.41 139	93.05 145	89.49 122	85.85 162	96.52 159	91.70 150	82.49 125	98.14 139	83.17 108	87.57 151	81.76 148	94.39 134	95.47 139	92.62 170	99.33 193	99.29 189

Effi-MVS+		93.06 120	95.94 123	89.70 118	90.82 129	99.45 91	95.71 104	78.94 160	98.72 113	74.71 140	97.92 76	80.73 149	98.35 75	97.72 86	97.05 98	99.70 110	100.00 1

UniMVSNet_NR-MVSNet		90.50 143	92.31 150	88.38 135	85.04 177	96.34 163	90.94 153	85.32 96	95.87 160	75.69 133	87.68 150	78.49 150	93.78 142	93.21 179	94.60 138	99.53 138	99.97 80

EU-MVSNet		87.20 182	90.47 165	83.38 198	85.11 176	93.85 206	86.10 205	79.76 151	93.30 206	65.39 193	84.41 158	78.43 151	85.04 212	92.20 190	93.03 165	98.86 211	98.05 207

Anonymous20240521		89.92 148	92.00 151	87.50 142	83.54 208	97.11 150	90.82 156	78.98 158	93.57 198	81.83 118	86.91 156	77.84 152	90.34 190	95.68 136	92.96 166	99.51 143	99.93 116

pmmvs6		85.75 203	86.97 211	84.34 183	84.88 183	95.59 185	87.41 201	79.19 156	87.81 223	67.56 168	63.05 227	77.76 153	89.15 197	93.45 176	91.90 178	97.83 218	99.21 191

Fast-Effi-MVS+		92.11 135	94.33 139	89.52 120	89.06 144	99.00 110	95.13 117	76.72 175	98.59 123	78.21 129	89.99 139	77.35 154	98.34 76	97.97 81	97.44 83	99.67 118	99.96 99

HQP-MVS		94.48 99	95.39 129	93.42 86	95.10 68	98.35 128	98.19 57	91.41 43	99.77 53	79.79 120	99.30 38	77.08 155	96.25 114	96.93 109	96.28 109	99.76 73	99.99 47

CLD-MVS		94.53 97	94.45 138	94.61 72	93.85 80	98.36 127	98.12 58	89.68 58	99.35 92	89.62 77	95.19 102	77.08 155	96.66 111	95.51 138	95.67 127	99.74 90	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test2356		83.84 211	91.77 154	74.59 219	78.71 219	89.10 224	78.24 223	72.07 203	96.78 153	45.18 231	96.19 95	76.77 157	74.87 223	93.17 181	94.01 152	98.44 215	96.38 219

ACMM	94.44 10	94.26 104	94.62 135	93.84 81	94.86 72	97.73 141	93.48 137	90.76 48	99.27 95	87.46 89	99.04 46	76.60 158	96.76 109	96.37 122	93.76 154	99.74 90	99.55 180
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LGP-MVS_train		93.60 109	95.05 130	91.90 103	94.90 71	98.29 131	97.93 59	88.06 85	99.14 99	74.83 138	99.26 39	76.50 159	96.07 116	96.31 124	95.90 124	99.59 127	99.97 80

new_pmnet		84.12 209	87.89 205	79.72 210	80.43 217	94.14 205	80.26 219	74.14 186	96.01 159	56.30 219	74.94 215	76.45 160	88.59 203	93.11 183	89.31 213	98.59 214	91.27 225

UniMVSNet (Re)		90.41 144	91.96 153	88.59 133	85.71 163	96.73 155	90.82 156	84.11 114	95.23 167	78.54 127	88.91 144	76.41 161	92.84 150	93.40 177	93.05 164	99.55 134	100.00 1

ACMP	94.49 9	94.19 105	94.74 134	93.56 84	94.25 78	98.32 130	96.02 101	89.35 63	98.90 112	87.28 92	99.14 43	76.41 161	94.94 127	96.07 131	94.35 148	99.49 147	99.99 47
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		93.50 111	93.00 148	94.07 79	95.82 64	98.26 132	98.49 55	91.62 42	94.69 174	81.93 116	92.82 128	76.18 163	96.82 106	96.12 128	94.57 139	99.74 90	98.39 200

SixPastTwentyTwo		88.35 157	91.51 157	84.66 178	85.39 168	96.96 152	86.57 203	79.62 152	96.57 155	63.73 198	87.86 148	75.18 164	93.43 146	94.03 160	90.37 208	99.24 203	99.58 175

TSAR-MVS + COLMAP		95.20 89	95.03 131	95.41 53	96.17 61	98.69 122	99.11 37	93.40 38	99.97 6	84.89 103	98.23 72	75.01 165	99.34 37	97.27 107	96.37 108	99.58 129	99.64 170

ACMH+	92.61 13	91.80 136	93.03 146	90.37 112	93.03 91	98.17 133	94.00 132	84.13 113	98.12 140	77.39 130	91.95 132	74.62 166	94.36 136	94.62 153	93.82 153	99.32 194	99.87 144

tmp_tt				78.81 211	98.80 41	85.73 227	70.08 228	77.87 166	98.68 114	83.71 105	99.53 27	74.55 167	54.97 233	78.28 228	72.43 231	87.45 232

test20.03		83.86 210	88.73 196	78.16 213	82.60 215	93.00 207	81.61 217	74.68 182	92.36 211	57.50 216	83.01 165	74.48 168	73.30 225	92.40 189	91.14 205	99.29 198	94.75 221

WR-MVS_H		88.47 156	90.55 164	86.04 154	85.13 174	96.07 173	89.86 189	79.80 150	94.37 183	72.32 146	83.12 163	74.44 169	89.60 194	93.52 174	92.40 172	99.51 143	99.96 99

WR-MVS		88.23 161	90.15 166	86.00 156	84.39 198	95.64 183	89.96 185	81.80 136	94.46 181	71.60 151	82.10 166	74.36 170	88.76 202	92.48 188	92.20 174	99.46 157	99.83 151

CMPMVS	65.66 17	84.62 206	85.02 214	84.15 188	95.40 66	97.79 140	88.35 197	79.22 155	89.66 221	60.71 209	72.20 217	73.94 171	87.32 208	86.73 221	84.55 226	93.90 228	90.31 226
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		84.28 208	89.53 177	78.17 212	82.31 216	94.16 204	82.57 214	76.51 177	93.38 205	52.98 220	79.47 192	73.74 172	75.45 221	95.07 147	94.41 144	99.18 207	96.46 218

v7		87.72 171	89.75 169	85.35 170	85.01 178	95.79 181	90.43 166	78.98 158	94.50 179	66.39 181	78.87 196	73.65 173	91.85 161	93.69 171	91.86 180	99.45 160	99.92 121

v10		87.40 178	89.62 172	84.80 176	84.93 181	95.07 198	90.44 164	75.63 179	94.51 176	66.52 179	78.87 196	73.47 174	91.86 160	93.69 171	91.87 179	99.45 160	99.86 147

v1144		87.49 173	89.64 171	84.97 173	84.73 190	95.84 179	90.17 179	79.30 154	93.96 187	64.65 195	78.83 198	73.38 175	91.51 171	93.77 168	91.77 181	99.45 160	99.93 116

v11		86.74 191	89.01 184	84.09 191	84.79 188	91.79 217	90.39 168	72.53 201	94.47 180	65.75 189	78.64 199	72.96 176	91.66 163	93.92 163	91.69 184	99.42 172	99.61 171

v16		87.15 184	89.13 183	84.83 175	85.55 165	91.94 212	90.50 161	74.13 187	95.06 169	67.72 166	81.84 172	72.55 177	91.65 164	91.50 200	91.42 192	99.42 172	99.60 172

TransMVSNet (Re)		88.33 158	89.55 176	86.91 148	86.65 156	95.56 187	90.48 163	84.44 108	92.02 216	71.07 156	80.13 182	72.48 178	89.41 195	95.05 148	94.44 142	99.39 186	97.14 212

Baseline_NR-MVSNet		89.13 153	89.53 177	88.66 132	84.71 191	94.43 201	91.79 149	84.49 107	95.54 162	78.28 128	78.52 202	72.46 179	93.29 147	91.10 209	94.82 137	99.42 172	99.86 147

ACMH	92.34 14	91.59 138	93.02 147	89.92 117	93.97 79	97.98 137	90.10 180	84.70 103	98.46 128	76.80 132	93.38 124	71.94 180	94.39 134	95.34 142	94.04 150	99.54 135	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V42		87.84 168	89.42 181	85.99 157	85.16 173	96.01 176	90.52 160	81.78 138	94.43 182	67.59 167	81.32 176	71.87 181	91.48 172	91.25 208	91.16 203	99.43 166	99.92 121

pmmvs5		87.33 179	90.01 167	84.20 187	84.31 200	96.04 175	87.63 200	76.59 176	93.17 207	65.35 194	84.30 160	71.68 182	91.91 159	95.41 140	91.37 196	99.39 186	98.13 204

1111		73.79 221	78.62 221	68.16 224	69.34 229	81.48 230	59.42 235	52.46 235	78.55 231	50.42 223	62.43 228	71.67 183	80.43 217	86.79 219	88.22 217	96.87 219	81.17 235

.test1245		70.78 225	79.90 220	60.13 230	69.34 229	81.48 230	59.42 235	52.46 235	78.55 231	50.42 223	62.43 228	71.67 183	80.43 217	86.79 219	78.71 228	48.74 239	99.65 167

v6		87.96 164	89.58 173	86.08 153	85.34 169	96.14 166	90.44 164	82.19 132	94.56 175	67.43 173	81.90 170	71.57 185	91.62 168	91.54 196	91.43 189	99.43 166	99.92 121

EG-PatchMatch MVS		86.96 187	89.56 175	83.93 193	86.29 158	97.61 142	90.75 159	73.31 194	95.43 165	66.08 186	75.88 213	71.31 186	87.55 207	94.79 150	92.74 168	99.61 125	99.13 194

v18		87.14 185	88.96 188	85.01 172	85.57 164	92.03 210	90.89 155	74.62 183	94.80 173	67.90 164	82.02 168	71.28 187	91.63 167	91.53 197	91.44 188	99.47 154	99.60 172

v1neww		87.88 165	89.51 179	85.97 158	85.32 170	96.12 167	90.33 171	82.17 133	94.51 176	66.96 175	81.84 172	71.21 188	91.64 165	91.52 198	91.43 189	99.42 172	99.92 121

v7new		87.88 165	89.51 179	85.97 158	85.32 170	96.12 167	90.33 171	82.17 133	94.51 176	66.96 175	81.84 172	71.21 188	91.64 165	91.52 198	91.43 189	99.42 172	99.92 121

v17		86.99 186	88.90 191	84.76 177	85.52 166	91.96 211	90.50 161	74.17 184	94.88 171	67.33 174	81.94 169	71.21 188	91.57 170	91.49 201	91.20 201	99.48 151	99.60 172

v1921920		86.81 189	88.93 189	84.33 184	84.23 201	95.41 193	90.09 181	78.10 165	93.74 193	62.17 204	76.98 208	71.14 191	92.05 157	93.69 171	91.69 184	99.32 194	99.88 140

V14		86.54 195	88.41 199	84.35 181	84.94 180	91.83 214	90.28 176	73.48 192	93.73 194	66.50 180	79.89 185	71.12 192	91.46 173	91.48 203	91.25 198	99.42 172	99.58 175

v8		87.54 172	89.33 182	85.45 169	85.41 167	95.50 190	90.32 174	78.94 160	94.35 184	66.93 177	81.90 170	70.99 193	91.62 168	91.49 201	91.22 200	99.48 151	99.87 144

v1192		86.93 188	89.01 184	84.50 179	84.46 197	95.51 189	89.93 187	78.65 162	93.75 191	62.29 203	77.19 206	70.88 194	92.28 155	93.84 165	91.96 177	99.38 188	99.90 133

v144192		86.80 190	88.90 191	84.35 181	84.33 199	95.56 187	89.34 192	77.74 167	93.60 197	64.03 196	77.82 203	70.76 195	91.28 180	92.91 185	91.74 183	99.37 190	99.90 133

divwei89l23v2f112		87.46 175	88.97 187	85.70 165	84.85 186	96.08 171	90.23 177	82.46 126	93.69 195	65.83 188	79.57 190	70.54 196	91.39 177	91.60 194	91.39 193	99.43 166	99.92 121

v1141		87.45 177	88.98 186	85.67 166	84.86 185	96.08 171	90.23 177	82.46 126	93.75 191	65.64 192	79.57 190	70.52 197	91.41 176	91.63 193	91.39 193	99.42 172	99.92 121

v13		86.27 199	88.16 204	84.06 192	84.85 186	91.77 218	90.00 183	72.77 200	93.56 199	66.06 187	79.25 194	70.50 198	91.25 182	91.35 207	91.15 204	99.42 172	99.55 180

v1		87.48 174	88.91 190	85.81 161	84.93 181	96.07 173	90.33 171	82.45 128	93.65 196	66.39 181	79.38 193	70.40 199	91.33 178	91.58 195	91.38 195	99.42 172	99.93 116

v15		86.50 196	88.32 200	84.37 180	85.00 179	91.86 213	90.30 175	73.76 190	93.90 189	66.28 184	79.78 187	70.37 200	91.45 174	91.48 203	91.27 197	99.43 166	99.58 175

V9		86.42 197	88.26 201	84.27 185	84.88 183	91.80 215	90.34 170	73.18 196	93.92 188	66.37 183	79.68 189	70.25 201	91.42 175	91.43 205	91.23 199	99.42 172	99.55 180

TranMVSNet+NR-MVSNet		88.88 155	89.90 168	87.69 141	84.06 203	95.68 182	91.88 147	85.23 98	95.16 168	72.54 142	83.06 164	70.14 202	92.93 149	90.81 212	94.53 140	99.48 151	99.89 137

CP-MVSNet		88.09 162	89.57 174	86.36 152	84.63 194	95.46 192	89.48 191	80.53 145	93.42 202	71.26 155	81.25 177	69.90 203	92.78 151	93.30 178	93.69 155	99.47 154	99.96 99

V4		85.78 202	87.74 207	83.50 196	82.90 211	95.33 195	88.62 196	77.05 171	92.14 215	63.45 200	76.91 209	69.85 204	89.72 192	90.07 213	90.05 211	99.27 201	99.81 153

v1240		86.24 200	88.56 197	83.54 194	84.05 204	95.21 197	89.27 193	76.76 174	93.42 202	60.68 210	75.99 212	69.80 205	91.21 184	93.83 167	91.76 182	99.29 198	99.91 132

v52		85.80 201	87.74 207	83.53 195	82.87 212	95.31 196	88.71 195	77.04 172	92.23 213	63.53 199	76.91 209	69.80 205	89.78 191	90.05 214	90.07 210	99.26 202	99.82 152

v12		86.32 198	88.22 202	84.10 189	84.76 189	91.80 215	89.94 186	72.97 198	93.85 190	66.18 185	79.98 184	69.72 207	91.33 178	91.40 206	91.20 201	99.42 172	99.56 179

tfpnnormal		89.09 154	89.71 170	88.38 135	87.37 152	96.78 154	91.46 152	85.20 99	90.33 217	72.35 145	83.45 161	69.30 208	94.45 133	95.29 143	92.86 167	99.44 165	99.93 116

v2v482		87.46 175	88.90 191	85.78 162	84.58 195	95.95 178	89.90 188	82.43 129	94.19 185	65.65 190	79.80 186	69.12 209	92.67 152	91.88 192	91.46 187	99.45 160	99.93 116

DU-MVS		89.49 152	90.60 163	88.19 138	84.71 191	96.20 164	90.94 153	84.58 104	95.54 162	75.69 133	87.52 152	68.74 210	93.78 142	91.10 209	95.13 135	99.47 154	99.97 80

NR-MVSNet		89.52 151	90.71 162	88.14 139	86.19 160	96.20 164	92.07 146	84.58 104	95.54 162	75.27 137	87.52 152	67.96 211	91.24 183	94.33 156	93.45 158	99.49 147	99.97 80

USDC		90.36 146	91.68 155	88.82 128	92.58 101	98.02 135	96.27 97	79.83 149	98.37 134	70.61 157	89.05 142	67.50 212	94.17 137	95.77 134	94.43 143	99.46 157	98.62 198

TinyColmap		89.94 147	90.88 161	88.84 127	92.43 108	97.91 139	95.59 110	80.10 147	98.12 140	71.33 154	84.56 157	67.46 213	94.15 138	95.57 137	94.27 149	99.43 166	98.26 203

PS-CasMVS		87.24 181	88.52 198	85.73 164	84.58 195	95.35 194	89.03 194	80.17 146	93.11 208	68.86 162	77.71 204	66.89 214	92.30 154	93.13 182	93.50 157	99.46 157	99.96 99

DeepMVS_CX						97.31 147	79.48 220	89.65 59	98.66 116	60.89 208	94.40 113	66.89 214	87.65 206	81.69 227		92.76 230	94.24 223

v148		86.63 194	87.79 206	85.28 171	84.65 193	95.97 177	86.46 204	82.84 124	92.91 209	71.52 153	78.99 195	66.74 216	86.83 209	89.28 217	90.69 206	99.41 184	99.94 113

TDRefinement		87.79 169	88.76 195	86.66 150	93.54 81	98.02 135	95.76 103	85.18 100	96.57 155	67.90 164	80.51 181	66.51 217	78.37 219	93.20 180	89.73 212	99.22 204	96.75 214

LTVRE_ROB	88.65 16	87.87 167	91.11 160	84.10 189	86.64 157	97.47 144	94.40 127	78.41 164	96.13 158	52.02 222	87.95 147	65.92 218	93.59 144	95.29 143	95.09 136	99.52 139	99.95 108
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
PEN-MVS		87.20 182	88.22 202	86.01 155	84.01 205	94.93 200	90.00 183	81.52 143	93.46 201	69.29 160	79.69 188	65.51 219	91.72 162	91.01 211	93.12 162	99.49 147	99.84 149

MIMVSNet1		80.64 216	83.97 216	76.76 216	68.91 232	91.15 222	78.32 222	75.47 180	89.58 222	56.64 218	65.10 225	65.17 220	82.14 214	93.51 175	91.64 186	99.10 208	91.66 224

v7n		85.39 204	87.70 209	82.70 201	82.77 214	95.64 183	88.27 198	74.83 181	92.30 212	62.58 202	76.37 211	64.80 221	88.38 204	94.29 158	90.61 207	99.34 191	99.87 144

DTE-MVSNet		86.70 192	87.66 210	85.58 167	83.30 209	94.29 202	89.74 190	81.53 141	92.77 210	68.93 161	80.13 182	64.00 222	90.62 188	89.45 216	93.34 160	99.32 194	99.67 166

pmmvs3		80.91 215	85.62 213	75.42 217	75.01 223	89.09 225	75.31 225	68.70 205	86.99 224	46.74 230	81.18 178	62.91 223	87.95 205	93.84 165	89.06 215	98.80 213	96.23 220

v748		84.47 207	86.06 212	82.62 203	82.85 213	95.02 199	83.73 211	78.48 163	90.20 219	67.45 172	75.86 214	61.27 224	83.84 213	89.87 215	90.28 209	99.34 191	99.90 133

FPMVS		73.80 220	74.62 225	72.84 222	83.09 210	84.44 228	83.89 208	73.64 191	92.20 214	48.50 226	72.19 218	59.51 225	63.16 228	69.13 231	66.26 236	84.74 233	78.59 236

testus		82.22 214	88.82 194	74.52 220	79.14 218	89.37 223	78.38 221	72.99 197	97.57 146	44.54 232	93.44 123	58.13 226	74.20 224	92.96 184	93.67 156	97.89 217	96.58 216

PMVS	60.14 18	62.67 229	64.05 231	61.06 229	68.32 233	53.27 243	52.23 240	67.63 208	75.07 234	48.30 227	58.27 230	57.43 227	49.99 236	67.20 233	62.42 237	79.87 237	74.68 238
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs-eth3d		82.92 212	83.31 217	82.47 204	76.97 221	91.76 219	83.79 209	76.10 178	90.33 217	69.95 159	71.04 220	48.09 228	89.02 199	93.85 164	89.14 214	99.02 210	98.96 196

MDA-MVSNet-bldmvs		80.30 217	82.83 218	77.34 214	69.16 231	94.29 202	72.16 226	81.97 135	90.14 220	57.32 217	94.01 118	47.97 229	86.81 210	68.74 232	86.82 222	96.63 220	97.86 209

new-patchmatchnet		78.17 218	80.82 219	75.07 218	76.93 222	91.20 221	71.90 227	73.32 193	86.59 225	48.91 225	67.11 223	47.85 230	81.19 215	88.18 218	87.02 221	98.19 216	97.79 210

PM-MVS		82.79 213	84.51 215	80.77 209	77.22 220	92.13 209	83.61 212	73.31 194	93.50 200	61.06 206	77.15 207	46.52 231	90.55 189	94.14 159	89.05 216	98.85 212	99.12 195

testmv		71.50 222	77.62 222	64.36 225	72.64 225	81.28 232	59.32 237	66.24 212	83.91 227	35.02 237	69.74 221	46.18 232	57.12 231	85.60 224	87.48 219	95.84 223	89.16 228

test1235678		71.50 222	77.61 223	64.36 225	72.64 225	81.26 233	59.31 238	66.22 213	83.90 228	35.02 237	69.74 221	46.18 232	57.12 231	85.60 224	87.47 220	95.84 223	89.15 229

Anonymous20231211		74.10 219	74.22 227	73.97 221	74.36 224	87.76 226	75.92 224	72.78 199	74.83 235	52.25 221	44.18 234	42.42 234	73.07 226	86.16 222	86.24 224	95.44 226	97.94 208

test12356		69.94 226	75.85 224	63.04 227	70.04 228	79.32 235	61.62 233	65.84 217	80.56 229	36.30 236	71.45 219	39.38 235	48.79 237	83.64 226	88.02 218	95.64 225	88.56 231

Gipuma		71.02 224	72.60 229	69.19 223	71.31 227	75.11 236	66.36 230	61.65 230	94.93 170	47.29 229	38.74 235	38.52 236	75.52 220	86.09 223	85.92 225	93.01 229	88.87 230
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		65.18 228	68.25 230	61.59 228	61.37 235	79.72 234	59.18 239	61.80 228	64.72 236	37.33 234	53.82 231	35.59 237	54.46 235	73.94 230	80.52 227	95.40 227	89.43 227

testmvs		61.76 230	72.90 228	48.76 234	21.21 240	68.61 238	66.11 232	37.38 237	94.83 172	33.06 239	64.31 226	29.72 238	86.08 211	74.44 229	78.71 228	48.74 239	99.65 167

EMVS		55.14 232	55.29 234	54.97 231	60.87 236	57.52 240	38.58 242	63.57 226	64.54 237	23.36 242	36.96 236	27.99 239	60.69 229	51.17 236	66.61 235	82.73 236	82.25 233

E-PMN		55.33 231	55.79 233	54.81 232	59.81 237	57.23 241	38.83 241	63.59 225	64.06 238	24.66 241	35.33 237	26.40 240	58.69 230	55.41 235	70.54 233	83.26 234	81.56 234

no-one		52.34 233	53.36 236	51.14 233	57.63 238	69.39 237	35.07 244	61.58 231	44.14 240	37.06 235	34.80 238	26.36 241	32.65 238	50.68 237	70.83 232	82.88 235	77.30 237

MVE	58.81 19	52.07 234	55.15 235	48.48 235	42.45 239	62.35 239	36.41 243	54.70 234	49.88 239	27.65 240	29.98 239	18.08 242	54.87 234	65.93 234	77.26 230	74.79 238	82.59 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ambc			74.33 226		66.84 234	84.26 229	84.17 207		93.39 204	58.99 212	45.93 233	18.06 243	70.61 227	93.94 162	86.62 223	92.61 231	98.13 204

test123		48.14 235	58.11 232	36.51 236	8.71 241	56.81 242	59.55 234	24.08 238	77.50 233	14.41 243	49.20 232	11.94 244	80.98 216	41.62 238	69.81 234	31.32 241	99.90 133

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					85.89 161	96.09 170	82.15 216

Patchmatch-RL test							68.01 229

NP-MVS									99.79 50

Patchmtry						99.00 110	95.46 112	65.50 218		67.51 169

High-resolution multi-view benchmark