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.

CNVR-MVS		97.60 3	98.08 3	97.03 2	99.14 1	99.55 1	98.67 2	95.32 2	97.91 6	92.55 7	97.11 5	97.23 9	97.49 1	98.16 2	97.05 4	99.04 11	99.55 15

ESAPD		97.61 1	98.19 2	96.94 3	99.03 2	99.49 2	99.00 1	95.35 1	97.97 5	92.21 10	97.50 3	99.73 1	96.95 3	97.13 10	95.61 22	99.11 6	99.87 4

MCST-MVS		96.93 6	98.07 4	95.61 15	98.98 4	99.44 3	98.04 7	95.04 4	98.10 3	86.55 28	97.65 2	97.56 7	95.60 19	97.67 5	96.45 11	99.43 1	99.61 14

HSP-MVS		97.61 1	98.30 1	96.81 4	98.66 10	99.35 4	98.00 8	94.75 8	98.45 2	92.78 5	97.99 1	98.58 5	97.41 2	98.24 1	96.48 10	99.27 4	98.99 44

GG-mvs-BLEND		65.67 212	93.78 34	32.89 232	0.47 240	99.35 4	96.92 26	0.22 239	93.28 51	0.51 242	84.07 48	92.50 33	0.62 239	93.59 54	93.86 48	98.59 32	99.79 6

NCCC		97.01 5	97.74 5	96.16 7	99.02 3	99.35 4	98.63 3	95.04 4	97.84 9	88.95 21	96.83 10	97.02 12	96.39 9	97.44 6	96.51 9	98.90 20	99.16 36

SMA-MVS		96.67 10	97.36 10	95.86 11	98.90 7	99.34 7	97.86 13	94.75 8	97.31 14	89.22 19	92.39 28	99.04 3	96.29 13	97.23 9	96.87 5	98.15 53	99.34 20

DeepPCF-MVS	91.00 2	94.15 28	96.87 15	90.97 46	96.82 37	99.33 8	89.40 89	92.76 25	98.76 1	82.36 43	88.74 36	95.49 21	90.58 63	98.13 3	97.80 3	93.88 195	99.88 3

APDe-MVS		97.31 4	97.51 8	97.08 1	98.95 6	99.29 9	98.58 4	95.11 3	97.69 12	94.16 1	96.91 8	96.81 13	96.57 5	96.71 16	95.39 25	99.08 10	99.79 6

SD-MVS		96.87 8	97.69 7	95.92 10	96.38 42	99.25 10	97.76 16	94.75 8	97.72 10	92.46 9	95.94 13	99.09 2	96.48 8	96.01 24	96.08 16	97.68 87	99.73 9

ACMMP_Plus		95.81 15	96.50 18	95.01 19	98.79 8	99.17 11	97.52 21	94.20 14	96.19 25	85.71 32	93.80 26	96.20 15	95.89 15	96.62 18	94.98 33	97.93 75	98.52 66

HPM-MVS++		96.91 7	97.70 6	96.00 9	98.97 5	99.16 12	97.82 15	94.81 7	98.04 4	89.61 17	96.56 12	98.60 4	96.39 9	97.09 11	95.22 27	98.39 40	99.22 30

SteuartSystems-ACMMP		96.20 12	97.22 11	95.01 19	98.40 17	99.11 13	97.93 11	93.62 18	96.28 23	87.45 24	97.05 7	96.00 16	94.23 25	96.83 15	95.97 17	98.40 39	99.27 26
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		92.73 37	94.83 30	90.28 51	96.27 43	99.10 14	92.77 57	86.15 51	93.41 50	77.11 72	93.82 25	87.39 51	90.61 61	95.60 27	95.15 29	98.79 25	99.32 21

CANet		93.23 32	93.72 35	92.65 34	95.48 49	99.09 15	96.55 29	86.74 46	95.28 36	85.22 34	77.30 67	91.25 41	92.60 39	97.06 12	96.63 7	99.31 2	99.45 18

CSCG		93.16 34	92.65 40	93.76 25	98.32 19	99.09 15	96.12 32	89.91 34	93.15 53	89.64 16	83.62 49	88.91 47	92.40 41	91.09 97	93.70 51	96.14 168	98.99 44

DeepC-MVS_fast	91.53 1	95.57 18	95.67 23	95.45 16	98.57 14	99.00 17	97.76 16	94.41 12	97.06 15	86.84 27	86.39 42	92.27 37	96.38 11	97.89 4	98.06 2	98.73 30	99.01 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		96.50 11	97.08 12	95.82 13	96.12 46	98.97 18	98.00 8	94.13 15	97.89 7	91.49 12	95.11 21	97.52 8	96.26 14	96.27 22	94.07 46	98.91 19	99.74 8

train_agg		95.72 16	97.37 9	93.80 24	97.82 26	98.92 19	97.84 14	93.50 19	96.86 19	81.35 47	97.10 6	97.71 6	94.19 26	96.02 23	95.37 26	98.07 59	99.64 12

PHI-MVS		94.49 26	96.72 16	91.88 38	97.06 34	98.88 20	94.99 42	89.13 36	96.15 26	79.70 56	96.91 8	95.78 19	91.87 45	94.65 45	95.68 20	98.53 34	98.98 47

APD-MVS		96.79 9	96.99 14	96.56 5	98.76 9	98.87 21	98.42 5	94.93 6	97.70 11	91.83 11	95.52 16	95.94 17	96.63 4	95.94 25	95.47 23	98.80 24	99.47 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		96.09 13	96.41 19	95.72 14	98.58 13	98.84 22	97.95 10	93.08 22	96.96 17	90.24 15	96.60 11	94.40 25	96.52 7	95.13 34	94.33 40	97.93 75	98.59 63

zzz-MVS		95.87 14	95.63 25	96.15 8	98.60 12	98.83 23	97.89 12	93.65 17	96.24 24	93.08 4	91.13 31	95.46 22	95.72 18	95.64 26	93.67 53	97.97 72	98.46 69

MVS_0304		91.90 43	92.93 39	90.69 50	93.66 57	98.78 24	96.73 28	85.43 58	93.13 54	78.11 68	77.02 70	89.09 45	91.10 50	96.98 13	96.54 8	99.11 6	98.96 48

ACMMPR		95.59 17	95.89 21	95.25 17	98.41 16	98.74 25	97.69 19	92.73 26	96.88 18	88.95 21	95.33 18	92.91 32	95.79 16	94.73 44	94.33 40	97.92 78	98.32 74

QAPM		91.68 44	91.97 42	91.34 42	97.86 25	98.72 26	95.60 37	85.72 53	90.86 67	77.14 71	76.06 71	90.35 42	92.69 38	94.10 51	94.60 37	99.04 11	99.09 37

TSAR-MVS + ACMM		94.99 22	97.02 13	92.61 35	97.19 32	98.71 27	97.74 18	93.21 21	96.97 16	79.27 59	94.09 24	97.14 10	90.84 57	96.64 17	95.94 18	97.42 101	99.67 11

abl_6				93.25 30	97.12 33	98.71 27	94.40 46	87.81 41	97.86 8	87.19 26	91.07 32	95.80 18	94.18 27			98.78 26	99.36 19

MSLP-MVS++		95.49 19	94.84 29	96.25 6	98.64 11	98.63 29	98.35 6	92.37 28	95.04 41	92.62 6	87.12 39	93.79 26	96.55 6	93.53 55	96.78 6	98.98 15	98.99 44

CP-MVS		95.43 20	95.67 23	95.14 18	98.24 22	98.60 30	97.45 22	92.80 24	95.98 28	89.21 20	95.22 19	93.60 27	95.43 20	94.37 48	93.22 58	97.68 87	98.72 55

DELS-MVS		91.09 46	90.56 55	91.71 40	95.82 47	98.59 31	95.74 36	86.68 47	85.86 92	85.12 35	72.71 91	81.36 73	88.06 83	97.31 7	98.27 1	98.86 22	99.82 5
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
XVS					92.16 64	98.56 32	91.04 77			81.00 53		93.49 28				98.00 68

X-MVStestdata					92.16 64	98.56 32	91.04 77			81.00 53		93.49 28				98.00 68

X-MVS		94.70 23	95.71 22	93.52 28	98.38 18	98.56 32	96.99 25	92.62 27	95.58 32	81.00 53	94.57 23	93.49 28	94.16 28	94.82 40	94.29 42	97.99 71	98.68 57

DeepC-MVS	88.77 4	92.39 40	91.74 45	93.14 32	96.21 44	98.55 35	96.30 30	93.84 16	93.06 57	81.09 51	74.69 82	85.20 67	93.48 34	95.41 30	96.13 15	97.92 78	99.18 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_BlendedMVS		90.74 48	90.66 51	90.82 48	94.75 52	98.54 36	91.30 73	86.53 48	95.43 34	85.75 30	78.66 62	70.67 108	87.60 84	96.37 20	95.08 31	98.98 15	99.90 1

PVSNet_Blended		90.74 48	90.66 51	90.82 48	94.75 52	98.54 36	91.30 73	86.53 48	95.43 34	85.75 30	78.66 62	70.67 108	87.60 84	96.37 20	95.08 31	98.98 15	99.90 1

PGM-MVS		94.64 24	95.49 26	93.66 26	98.55 15	98.51 38	97.63 20	87.77 42	94.45 45	84.92 36	97.23 4	91.90 39	95.22 21	94.56 46	93.80 49	97.87 82	97.97 82

OpenMVS	83.41 11	89.84 53	88.89 70	90.95 47	97.63 28	98.51 38	94.64 44	85.47 57	88.14 80	78.39 66	65.06 122	85.42 65	91.04 51	93.06 65	93.70 51	98.53 34	98.37 71

CDPH-MVS		93.22 33	95.08 28	91.04 45	97.57 29	98.49 40	96.74 27	89.35 35	95.19 38	73.57 82	90.26 33	91.59 40	90.68 60	95.09 37	96.15 14	98.31 45	98.81 52

EPNet		93.69 30	95.34 27	91.76 39	96.98 36	98.47 41	95.40 39	86.79 45	95.47 33	82.84 41	95.66 15	89.17 44	90.47 64	95.25 33	94.69 36	98.10 55	98.68 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	85.78 8	92.53 39	91.96 43	93.20 31	97.99 23	98.47 41	95.78 35	85.94 52	93.07 56	86.40 29	73.43 88	89.00 46	94.08 29	94.74 43	96.44 12	99.01 14	98.57 64

3Dnovator+	86.26 7	92.90 36	92.45 41	93.42 29	97.25 31	98.45 43	95.82 34	85.71 54	93.83 48	89.55 18	72.31 95	92.28 36	94.01 31	95.10 36	95.92 19	98.17 50	99.23 29

MP-MVS		95.24 21	95.96 20	94.40 22	98.32 19	98.38 44	97.12 24	92.87 23	95.17 39	85.50 33	95.68 14	94.91 23	94.58 23	95.11 35	93.76 50	98.05 62	98.68 57
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_LR		93.05 35	94.53 31	91.32 43	96.43 41	98.38 44	92.81 56	87.20 44	95.94 30	81.45 45	94.75 22	86.08 58	92.12 44	94.83 39	93.34 56	97.89 81	98.42 70

AdaColmap		94.28 27	92.94 38	95.84 12	98.32 19	98.33 46	96.06 33	94.62 11	96.29 22	91.22 13	89.89 35	85.50 64	96.38 11	91.85 88	90.89 74	98.44 36	97.81 86

ACMMP		93.32 31	93.59 36	93.00 33	97.03 35	98.24 47	95.27 40	91.66 32	95.20 37	83.25 40	95.39 17	85.52 62	92.80 37	92.60 78	90.21 86	98.01 67	97.99 81
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
CHOSEN 1792x2688		84.59 102	84.30 108	84.93 91	93.71 56	98.23 48	89.91 85	77.96 108	84.81 101	65.93 110	45.19 206	71.76 105	83.13 111	95.46 29	95.13 30	98.94 18	99.53 16

PCF-MVS	88.14 5	90.42 52	89.56 64	91.41 41	94.44 54	98.18 49	94.35 47	94.33 13	84.55 105	76.61 76	75.84 73	88.47 48	91.29 48	90.37 108	90.66 80	97.46 95	98.88 51
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + GP.		94.59 25	96.60 17	92.25 36	90.25 90	98.17 50	96.22 31	86.53 48	97.49 13	87.26 25	95.21 20	97.06 11	94.07 30	94.34 50	94.20 44	99.18 5	99.71 10

CHOSEN 280x420		90.61 50	94.27 32	86.35 80	93.12 61	98.16 51	89.99 84	69.62 173	92.48 61	76.89 75	87.28 38	96.72 14	90.31 66	94.81 41	92.33 62	98.17 50	98.08 79

OMC-MVS		92.05 41	91.88 44	92.25 36	96.51 40	97.94 52	93.18 53	88.97 38	96.53 20	84.47 38	80.79 57	87.85 49	93.25 36	92.48 80	91.81 65	97.12 111	95.73 128

MAR-MVS		90.44 51	91.17 48	89.59 55	97.48 30	97.92 53	90.96 79	79.80 93	95.07 40	77.03 73	80.83 53	79.10 81	94.68 22	93.16 62	94.46 39	97.59 93	97.63 88
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
CPTT-MVS		94.11 29	93.99 33	94.25 23	96.58 39	97.66 54	97.31 23	91.94 29	94.84 42	88.72 23	92.51 27	93.04 31	95.78 17	91.51 91	89.97 90	95.15 186	98.37 71

TAPA-MVS	87.40 6	90.98 47	90.71 50	91.30 44	96.14 45	97.66 54	94.80 43	89.00 37	94.74 44	77.42 70	80.22 58	86.70 54	92.27 42	91.65 90	90.17 88	98.15 53	93.83 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNLPA		91.53 45	89.74 59	93.63 27	96.75 38	97.63 56	91.16 76	91.70 30	96.38 21	90.82 14	69.66 107	85.52 62	93.76 32	90.44 106	91.14 73	97.55 94	97.40 97

IB-MVS	79.58 12	83.83 106	84.81 98	82.68 104	91.85 68	97.35 57	75.75 195	82.57 77	86.55 89	84.01 39	70.90 98	65.43 127	63.18 204	84.19 157	89.92 94	98.74 29	99.31 23
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
PVSNet_Blended_VisFu		87.44 67	88.72 71	85.95 85	92.02 66	97.26 58	86.88 113	82.66 76	83.86 115	79.16 60	66.96 116	84.91 68	77.26 147	94.97 38	93.48 54	97.73 84	99.64 12

gg-mvs-nofinetune		77.08 146	79.79 135	73.92 183	85.95 127	97.23 59	92.18 60	52.65 227	46.19 226	27.79 233	38.27 218	85.63 61	85.67 95	96.95 14	95.62 21	99.30 3	98.67 60

CANet_DTU		87.91 61	91.57 46	83.64 100	90.96 71	97.12 60	91.90 62	75.97 123	92.83 59	53.16 179	86.02 44	79.02 82	90.80 58	95.40 31	94.15 45	99.03 13	96.47 124

HyFIR lowres test		83.43 109	82.94 120	84.01 96	93.41 59	97.10 61	87.21 110	74.04 138	80.15 128	64.98 113	41.09 214	76.61 88	86.51 92	93.31 58	93.01 60	97.91 80	99.30 24

MVS_Test		89.02 57	90.20 57	87.64 67	89.83 97	97.05 62	92.30 59	77.59 112	92.89 58	75.01 79	77.36 66	76.10 90	92.27 42	95.30 32	95.42 24	98.83 23	97.30 99

canonicalmvs		89.62 54	89.87 58	89.33 57	90.47 81	97.02 63	93.46 52	79.67 96	92.45 62	81.05 52	82.84 50	73.00 94	93.71 33	90.38 107	94.85 34	97.65 90	98.54 65

thres100view900		86.48 78	85.08 97	88.12 64	90.54 76	96.90 64	92.39 58	84.82 59	84.16 109	71.65 87	70.86 99	60.49 137	91.23 49	93.65 53	90.19 87	98.10 55	99.32 21

conf0.002		87.85 62	87.85 76	87.84 65	90.63 74	96.81 65	91.35 68	83.36 64	84.16 109	72.61 84	78.06 64	71.90 103	90.91 52	93.29 59	91.47 68	98.20 48	99.28 25

IS_MVSNet		87.83 63	90.66 51	84.53 93	90.08 92	96.79 66	88.16 98	79.89 92	85.44 95	72.20 86	75.50 77	87.14 52	80.21 126	95.53 28	95.22 27	96.65 133	99.02 42

conf0.01		87.22 69	86.71 86	87.81 66	90.61 75	96.75 67	91.35 68	83.33 65	84.16 109	72.45 85	75.61 74	68.65 114	90.91 52	93.23 60	89.34 98	98.17 50	99.27 26

thres400		85.59 88	84.08 109	87.36 72	90.45 82	96.60 68	90.95 80	83.67 62	80.99 122	71.17 93	69.08 109	60.25 142	89.88 69	93.14 63	89.34 98	98.02 66	99.17 32

TSAR-MVS + COLMAP		89.59 55	89.64 61	89.53 56	93.32 60	96.51 69	95.03 41	88.53 39	95.98 28	69.10 101	91.81 29	64.53 131	93.40 35	93.53 55	91.35 71	97.77 83	93.75 161

Vis-MVSNet		82.88 115	86.04 89	79.20 130	87.77 119	96.42 70	86.10 118	76.70 114	74.82 142	61.38 123	70.70 102	77.91 85	64.83 198	93.22 61	93.19 59	98.43 37	96.01 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tfpn111		87.30 68	87.03 83	87.61 68	90.54 76	96.39 71	91.35 68	83.15 66	84.16 109	71.65 87	86.75 40	60.49 137	90.91 52	92.89 69	89.34 98	98.05 62	99.17 32

conf200view11		86.07 82	84.76 99	87.61 68	90.54 76	96.39 71	91.35 68	83.15 66	84.16 109	71.65 87	70.86 99	60.49 137	90.91 52	92.89 69	89.34 98	98.05 62	99.17 32

tfpn200view9		86.07 82	84.76 99	87.61 68	90.54 76	96.39 71	91.35 68	83.15 66	84.16 109	71.65 87	70.86 99	60.49 137	90.91 52	92.89 69	89.34 98	98.05 62	99.17 32

view600		85.15 91	83.59 115	86.96 76	90.38 85	96.39 71	90.33 81	83.15 66	80.46 123	70.61 96	67.96 112	60.04 143	89.22 74	92.89 69	88.30 119	98.10 55	99.08 38

thres600view7		85.14 92	83.58 116	86.96 76	90.37 87	96.39 71	90.33 81	83.15 66	80.46 123	70.60 97	67.96 112	60.04 143	89.22 74	92.89 69	88.28 120	98.06 60	99.08 38

thres200		85.80 86	84.38 106	87.46 71	90.51 80	96.39 71	91.64 63	83.15 66	81.59 119	71.54 91	70.24 104	60.41 141	89.88 69	92.89 69	89.85 95	98.06 60	99.26 28

diffmvs		88.92 59	90.30 56	87.32 73	89.46 105	96.38 77	91.21 75	77.89 109	93.11 55	79.09 62	74.17 85	87.41 50	88.55 81	90.20 110	92.70 61	97.71 86	98.13 75

PatchMatch-RL		86.75 76	85.43 94	88.29 62	94.06 55	96.37 78	86.82 114	82.94 73	88.94 74	79.59 57	79.83 60	59.17 147	89.46 73	91.12 96	88.81 114	96.88 117	93.78 158

view800		84.86 98	83.35 118	86.63 79	90.31 89	96.17 79	89.86 86	82.67 75	79.95 129	70.04 98	67.25 115	59.75 145	88.72 77	92.64 77	88.72 116	98.19 49	98.95 49

PLC	89.12 3	92.67 38	90.84 49	94.81 21	97.69 27	96.10 80	95.42 38	91.70 30	95.82 31	92.52 8	81.24 52	86.01 59	94.36 24	92.44 82	90.27 83	97.19 110	93.99 151
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Vis-MVSNet (Re-imp)		85.89 84	89.62 62	81.55 115	89.85 96	96.08 81	87.55 105	79.80 93	84.80 102	66.55 108	73.70 87	86.71 53	68.25 192	94.40 47	94.53 38	97.32 105	97.09 102

tfpn		85.32 90	84.47 104	86.31 83	90.24 91	95.99 82	89.39 90	82.28 79	79.44 130	69.50 99	66.59 118	67.71 116	88.20 82	92.47 81	90.22 85	98.26 46	98.89 50

UGNet		87.04 73	89.59 63	84.07 95	90.94 72	95.95 83	86.02 119	81.65 81	85.94 91	78.54 65	78.00 65	85.40 66	69.62 185	91.83 89	91.53 67	97.63 91	98.51 67
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
DI_MVS_plusplus_trai		87.63 66	87.13 81	88.22 63	88.61 111	95.92 84	94.09 50	81.41 84	87.00 87	78.38 67	59.70 139	80.52 78	89.08 76	94.37 48	93.34 56	97.73 84	99.05 40

Effi-MVS+		84.80 99	85.71 92	83.73 97	87.94 116	95.76 85	90.08 83	73.45 141	85.12 99	62.66 120	72.39 94	64.97 130	90.59 62	92.95 68	90.69 79	97.67 89	98.12 76

UA-Net		84.69 100	87.64 78	81.25 117	90.38 85	95.67 86	87.33 109	79.41 98	72.07 150	66.48 109	75.09 79	92.48 34	66.88 194	94.03 52	94.25 43	97.01 116	89.88 191

thresconf0.02		86.84 75	89.56 64	83.67 99	90.08 92	95.66 87	89.03 91	83.62 63	87.45 84	62.19 121	86.75 40	80.81 74	78.48 135	92.24 86	91.27 72	98.60 31	92.72 176

DWT-MVSNet_training		87.65 65	88.45 72	86.71 78	90.32 88	95.64 88	87.91 101	75.69 127	93.27 52	81.43 46	74.99 80	76.48 89	86.92 88	87.74 129	92.29 63	98.00 68	98.74 54

EPMVS		83.71 108	86.76 85	80.16 121	89.72 99	95.64 88	84.68 126	59.73 211	89.61 72	62.67 119	72.65 93	81.80 72	86.22 93	86.23 138	88.03 125	97.96 73	93.35 167

PMMVS		88.56 60	91.22 47	85.47 88	90.04 94	95.60 90	86.62 115	78.49 105	93.86 47	70.62 95	90.00 34	80.08 80	91.64 46	92.36 83	89.80 96	95.40 181	96.84 106

EPP-MVSNet		87.72 64	89.74 59	85.37 89	89.11 108	95.57 91	86.31 117	79.44 97	85.83 93	75.73 78	77.23 68	90.05 43	84.78 100	91.22 95	90.25 84	96.83 118	98.04 80

LS3D		87.19 70	85.48 93	89.18 58	94.96 51	95.47 92	92.02 61	93.36 20	88.69 76	67.01 106	70.56 103	72.10 98	92.47 40	89.96 115	89.93 92	95.25 183	91.68 180

tfpn_ndepth		86.61 77	87.92 75	85.08 90	90.39 84	95.45 93	88.21 97	82.30 78	90.79 68	71.22 92	82.59 51	72.09 100	80.42 125	91.37 93	88.61 117	97.93 75	94.56 143

tfpn1000		84.98 94	86.47 87	83.24 101	89.93 95	94.98 94	86.58 116	81.22 87	88.54 78	67.35 105	79.39 61	70.93 107	76.07 167	90.70 100	87.37 131	98.32 44	93.37 166

PatchmatchNet		83.28 112	87.57 79	78.29 137	87.46 121	94.95 95	83.36 135	59.43 214	90.20 70	58.10 134	74.29 84	86.20 57	84.13 103	85.27 149	87.39 130	97.25 107	94.67 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CLD-MVS		88.99 58	88.07 73	90.07 53	89.61 101	94.94 96	93.82 51	85.70 55	92.73 60	82.73 42	79.97 59	69.59 111	90.44 65	90.32 109	89.93 92	98.10 55	99.04 41
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn_n400		83.32 110	84.61 102	81.81 113	89.50 103	94.81 97	87.41 107	81.65 81	80.24 126	58.99 128	80.80 54	66.64 121	75.84 168	90.09 112	89.33 105	97.46 95	90.37 186

tfpnconf		83.32 110	84.61 102	81.81 113	89.50 103	94.81 97	87.41 107	81.65 81	80.24 126	58.99 128	80.80 54	66.64 121	75.84 168	90.09 112	89.33 105	97.46 95	90.37 186

HQP-MVS		89.57 56	90.57 54	88.41 61	92.77 62	94.71 99	94.24 48	87.97 40	93.44 49	68.18 104	91.75 30	71.54 106	89.90 68	92.31 85	91.43 69	97.39 102	98.80 53

tfpnview11		83.86 105	85.36 95	82.10 112	89.66 100	94.55 100	87.73 102	81.81 80	85.72 94	58.99 128	80.80 54	66.64 121	76.13 166	90.79 98	88.15 122	98.26 46	90.90 184

test-LLR		85.11 93	89.49 66	80.00 123	85.32 131	94.49 101	82.27 165	74.18 136	87.83 82	56.70 142	75.55 75	86.26 55	82.75 113	93.06 65	90.60 81	98.77 27	98.65 61

TESTMET0.1,1		84.62 101	89.49 66	78.94 132	82.18 144	94.49 101	82.27 165	70.94 162	87.83 82	56.70 142	75.55 75	86.26 55	82.75 113	93.06 65	90.60 81	98.77 27	98.65 61

ACMP	85.16 9	87.15 72	87.04 82	87.27 74	90.80 73	94.45 103	89.41 88	83.09 72	89.15 73	76.98 74	86.35 43	65.80 125	86.94 87	88.45 123	87.52 129	96.42 158	97.56 93
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MDTV_nov1_ep13		84.17 103	88.03 74	79.66 125	86.00 126	94.41 104	85.05 125	66.01 195	90.36 69	64.34 117	77.13 69	84.56 69	82.71 115	87.12 133	88.92 112	93.84 198	93.69 162

test-mter		84.06 104	89.00 69	78.29 137	81.92 146	94.23 105	81.07 176	70.38 166	87.12 86	56.10 152	74.75 81	85.80 60	81.81 118	92.52 79	90.10 89	98.43 37	98.49 68

OPM-MVS		85.69 87	82.79 121	89.06 59	93.42 58	94.21 106	94.21 49	87.61 43	72.68 147	70.79 94	71.09 97	67.27 119	90.74 59	91.29 94	89.05 110	97.61 92	93.94 153

conf0.05thres1000		81.86 121	79.55 136	84.56 92	89.39 106	94.15 107	87.57 104	81.36 85	69.95 163	65.78 111	56.38 150	59.38 146	86.04 94	90.58 102	88.49 118	97.22 108	97.97 82

CostFormer		85.47 89	86.98 84	83.71 98	88.70 110	94.02 108	88.07 100	62.72 204	89.78 71	78.68 63	72.69 92	78.37 84	87.35 86	85.96 142	89.32 107	96.73 124	98.72 55

LGP-MVS_train		86.95 74	87.65 77	86.12 84	91.77 69	93.84 109	93.04 54	82.77 74	88.04 81	65.33 112	87.69 37	67.09 120	86.79 89	90.20 110	88.99 111	97.05 113	97.71 87

tpmrst		81.71 122	83.87 113	79.20 130	89.01 109	93.67 110	84.22 128	60.14 209	87.45 84	59.49 127	64.97 123	71.86 104	85.30 98	84.72 153	86.30 138	97.04 114	98.09 78

MS-PatchMatch		82.16 120	82.18 129	82.12 111	91.65 70	93.50 111	89.51 87	71.95 153	81.48 120	64.45 116	59.58 141	77.54 86	77.23 148	89.88 116	85.62 147	97.94 74	87.68 197

CMPMVS	54.54 17	71.74 196	67.94 209	76.16 167	90.41 83	93.25 112	78.32 187	75.60 131	59.81 210	53.95 174	44.64 209	51.22 163	70.70 180	74.59 216	75.88 216	88.01 218	76.23 220
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+		82.61 118	82.51 126	82.72 103	85.49 130	93.06 113	87.17 112	71.39 159	84.18 108	64.59 115	63.03 131	58.89 148	90.22 67	91.39 92	90.83 75	97.44 98	96.21 125

CDS-MVSNet		83.13 113	83.73 114	82.43 110	84.52 136	92.92 114	88.26 96	77.67 111	72.08 149	69.08 102	66.96 116	74.66 92	78.61 132	90.70 100	91.96 64	96.46 157	96.86 105
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPNet_dtu		84.87 97	89.01 68	80.05 122	95.25 50	92.88 115	88.84 93	84.11 60	91.69 63	49.28 196	85.69 45	78.95 83	65.39 197	92.22 87	91.66 66	97.43 100	89.95 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dps		82.63 116	82.64 124	82.62 106	87.81 118	92.81 116	84.39 127	61.96 205	86.43 90	81.63 44	69.72 106	67.60 118	84.42 102	82.51 185	83.90 183	95.52 176	95.50 134

MSDG		85.81 85	82.29 128	89.93 54	95.52 48	92.61 117	91.51 64	91.46 33	85.12 99	78.56 64	63.25 130	69.01 112	85.31 97	88.45 123	88.23 121	97.21 109	89.33 193

ADS-MVSNet		80.25 129	82.96 119	77.08 155	87.86 117	92.60 118	81.82 173	56.19 221	86.95 88	56.16 150	68.19 110	72.42 96	83.70 108	82.05 189	85.45 152	96.75 122	93.08 172

Effi-MVS+-dtu		81.18 125	82.77 122	79.33 128	84.70 135	92.54 119	85.81 121	71.55 157	78.84 132	57.06 140	71.98 96	63.77 133	85.09 99	88.94 120	87.62 128	91.79 213	95.68 129

tpmp4_e23		83.72 107	84.45 105	82.86 102	88.25 113	92.54 119	88.95 92	63.01 202	88.20 79	74.83 80	68.07 111	71.99 102	86.65 90	84.11 159	88.74 115	95.47 178	97.51 95

TAMVS		79.23 136	78.95 139	79.56 126	81.89 147	92.52 121	82.97 150	73.70 140	67.27 185	64.97 114	61.66 136	65.06 128	78.61 132	87.12 133	88.07 124	95.23 184	90.95 183

tpm cat1		82.39 119	82.32 127	82.47 108	88.13 115	92.42 122	87.43 106	62.79 203	85.30 96	78.05 69	60.14 137	72.10 98	83.20 110	82.26 188	85.67 146	95.23 184	98.35 73

MVSTER		91.91 42	93.43 37	90.14 52	89.81 98	92.32 123	94.53 45	81.32 86	96.00 27	84.77 37	85.41 47	92.39 35	91.32 47	96.41 19	94.01 47	99.11 6	97.45 96

GA-MVS		78.86 140	80.42 134	77.05 156	83.27 139	92.17 124	83.24 138	75.73 126	73.75 143	46.27 207	62.43 132	57.12 150	76.94 155	93.14 63	89.34 98	96.83 118	95.00 140

CR-MVSNet		81.44 124	85.29 96	76.94 159	86.53 124	92.12 125	83.86 129	58.37 216	85.21 97	56.28 147	59.60 140	80.39 79	80.50 123	92.77 75	89.32 107	96.12 169	97.59 91

RPMNet		81.47 123	86.24 88	75.90 175	86.72 123	92.12 125	82.82 156	55.76 222	85.21 97	53.73 175	63.45 129	83.16 71	80.13 127	92.34 84	89.52 97	96.23 165	97.90 84

Patchmtry						92.08 127	83.86 129	58.37 216		56.28 147

ACMM	84.23 10	86.40 79	84.64 101	88.46 60	91.90 67	91.93 128	88.11 99	85.59 56	88.61 77	79.13 61	75.31 78	66.25 124	89.86 71	89.88 116	87.64 127	96.16 167	92.86 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		84.88 96	84.08 109	85.82 87	89.21 107	91.74 129	85.87 120	81.20 88	81.71 118	74.66 81	73.38 89	64.99 129	86.60 91	90.75 99	88.08 123	97.36 103	97.90 84

USDC		80.10 131	79.33 137	81.00 119	86.36 125	91.71 130	88.74 94	75.77 125	81.90 117	54.90 160	67.67 114	52.05 161	83.94 106	88.44 125	86.25 139	96.31 161	87.28 201

Fast-Effi-MVS+-dtu		80.57 127	83.44 117	77.22 152	83.98 138	91.52 131	85.78 123	64.54 200	80.38 125	50.28 192	74.06 86	62.89 135	82.00 117	89.10 119	88.91 113	96.75 122	97.21 101

PatchT		79.28 134	83.88 112	73.93 182	85.54 129	90.95 132	66.14 215	56.53 220	83.21 116	56.28 147	56.50 149	76.80 87	80.50 123	92.77 75	89.32 107	98.57 33	97.59 91

COLMAP_ROB	75.69 15	79.47 132	76.90 149	82.46 109	92.20 63	90.53 133	85.30 124	83.69 61	78.27 135	61.47 122	58.26 144	62.75 136	78.28 137	82.41 186	82.13 198	93.83 200	83.98 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH	78.51 14	79.27 135	78.08 140	80.65 120	89.52 102	90.40 134	80.45 178	79.77 95	69.54 167	54.85 161	64.83 124	56.16 154	83.94 106	84.58 155	86.01 143	95.41 180	95.03 139
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		78.00 142	77.52 144	78.57 135	79.66 160	90.36 135	82.09 171	77.86 110	76.38 139	60.26 124	54.63 155	52.07 160	75.31 170	84.97 152	86.10 141	96.22 166	98.11 77

ACMH+	79.09 13	79.12 137	77.22 147	81.35 116	88.50 112	90.36 135	82.14 170	79.38 100	72.78 146	58.59 131	62.31 134	56.44 153	84.10 104	82.03 190	84.05 181	95.40 181	92.55 177

FMVSNet3		87.19 70	87.32 80	87.04 75	82.82 140	90.21 137	92.88 55	76.53 115	91.69 63	81.31 48	64.81 125	80.64 75	89.79 72	94.80 42	94.76 35	98.88 21	94.32 146

tpm		78.87 139	81.33 133	76.00 172	85.57 128	90.19 138	82.81 157	59.66 212	78.35 134	51.40 186	66.30 119	67.92 115	80.94 121	83.28 175	85.73 144	95.65 175	97.56 93

MVS-HIRNet		72.32 192	73.45 179	71.00 203	80.58 154	89.97 139	68.51 212	55.28 223	70.89 157	52.27 181	39.09 216	57.11 151	75.02 171	85.76 144	86.33 137	94.36 194	85.00 205

EG-PatchMatch MVS		71.81 195	71.54 196	72.12 197	80.53 155	89.94 140	78.51 185	66.56 193	57.38 214	47.46 203	44.28 211	52.22 159	63.10 205	85.22 150	84.42 179	96.56 147	87.35 200

test0.0.03 1		80.99 126	84.37 107	77.05 156	85.32 131	89.79 141	78.43 186	74.18 136	84.78 103	57.98 137	76.06 71	72.88 95	69.14 189	88.02 127	87.70 126	97.27 106	91.37 181

UniMVSNet_NR-MVSNet		78.89 138	78.04 142	79.88 124	79.40 161	89.70 142	82.92 152	80.17 90	76.37 140	58.56 132	57.10 147	54.92 156	81.44 119	83.51 163	87.12 133	96.76 121	97.60 89

TDRefinement		75.54 164	73.22 181	78.25 140	87.65 120	89.65 143	85.81 121	79.28 101	71.14 154	56.06 153	52.17 162	51.96 162	68.74 191	81.60 191	80.58 204	91.94 211	85.45 203

GBi-Net		86.16 80	86.00 90	86.35 80	81.81 148	89.52 144	91.40 65	76.53 115	91.69 63	81.31 48	64.81 125	80.64 75	88.72 77	90.54 103	90.72 76	98.34 41	94.08 148

test1		86.16 80	86.00 90	86.35 80	81.81 148	89.52 144	91.40 65	76.53 115	91.69 63	81.31 48	64.81 125	80.64 75	88.72 77	90.54 103	90.72 76	98.34 41	94.08 148

FMVSNet2		84.89 95	84.02 111	85.91 86	81.81 148	89.52 144	91.40 65	75.79 124	84.45 106	79.39 58	58.75 142	74.35 93	88.72 77	93.51 57	93.46 55	98.34 41	94.08 148

v1neww		76.39 151	75.09 158	77.91 144	79.08 167	89.49 147	83.21 139	75.62 128	70.20 159	55.81 156	50.43 173	50.74 171	77.05 152	83.33 168	84.99 163	96.66 130	96.48 122

v7new		76.39 151	75.09 158	77.91 144	79.08 167	89.49 147	83.21 139	75.62 128	70.20 159	55.81 156	50.43 173	50.74 171	77.05 152	83.33 168	84.99 163	96.66 130	96.48 122

v6		76.41 150	75.11 157	77.93 143	79.08 167	89.48 149	83.25 137	75.62 128	70.21 158	55.94 155	50.48 172	50.81 170	77.01 154	83.32 170	84.97 166	96.66 130	96.50 119

pmmvs5		75.46 167	75.12 156	75.87 176	79.39 162	89.44 150	78.12 188	72.27 151	65.98 192	51.54 184	55.83 151	46.23 200	76.80 156	88.77 121	85.73 144	97.07 112	93.84 155

CVMVSNet		76.86 148	79.09 138	74.26 180	85.29 133	89.44 150	79.91 181	78.47 106	68.94 171	44.45 212	62.35 133	69.70 110	64.50 200	85.82 143	87.03 134	92.94 206	90.33 188

divwei89l23v2f112		76.03 156	74.64 166	77.65 148	78.78 172	89.33 152	83.15 144	76.21 120	68.26 174	54.55 169	50.08 179	49.86 179	76.73 158	83.39 165	85.06 160	96.51 151	96.51 116

v1		76.04 155	74.65 165	77.66 146	78.77 174	89.33 152	83.18 142	76.22 118	68.17 175	54.58 167	50.10 178	49.99 176	76.70 159	83.38 166	85.05 161	96.50 152	96.51 116

V42		76.21 154	75.04 160	77.58 149	78.68 178	89.33 152	82.93 151	74.64 133	69.84 164	56.13 151	50.42 176	50.93 167	76.30 164	83.32 170	84.89 172	96.83 118	96.54 113

v1141		76.03 156	74.64 166	77.66 146	78.78 172	89.32 155	83.14 146	76.22 118	68.27 173	54.56 168	50.06 180	49.84 181	76.78 157	83.40 164	85.07 158	96.50 152	96.51 116

anonymousdsp		75.14 171	77.25 146	72.69 191	76.68 202	89.26 156	75.26 199	68.44 180	65.53 195	46.65 206	58.16 145	56.67 152	73.96 173	87.84 128	86.05 142	95.13 187	97.22 100

DU-MVS		77.98 143	76.71 150	79.46 127	78.68 178	89.26 156	82.92 152	79.06 102	76.52 137	58.56 132	54.89 153	48.35 194	81.44 119	83.16 178	87.21 132	96.08 171	97.60 89

NR-MVSNet		77.21 145	76.41 151	78.14 141	80.18 156	89.26 156	83.38 134	79.06 102	76.52 137	56.59 145	54.89 153	45.32 207	72.89 175	85.39 148	86.12 140	96.71 125	97.36 98

Anonymous20240521		74.52 178	75.39 154	73.50 186	76.79 201	89.15 159	74.95 201	72.58 146	67.39 184	57.14 139	51.33 165	52.48 158	64.12 201	85.45 147	84.92 169	95.42 179	96.99 103

v2v482		76.25 153	74.78 162	77.96 142	78.50 187	89.14 160	83.05 148	76.02 122	68.78 172	54.11 171	51.36 164	48.59 191	79.49 129	83.53 162	85.60 150	96.59 142	96.49 121

v148		74.98 172	73.52 178	76.69 161	78.84 171	89.02 161	78.78 184	76.82 113	67.22 186	59.61 126	49.18 193	47.94 196	70.57 184	80.76 195	83.99 182	95.52 176	96.52 115

FC-MVSNet-test		77.95 144	81.85 131	73.39 188	82.31 142	88.99 162	79.33 182	74.24 135	78.75 133	47.40 204	70.22 105	72.09 100	60.78 210	86.66 135	85.62 147	96.30 162	90.61 185

pmmvs4		79.32 133	77.78 143	81.11 118	80.18 156	88.96 163	83.39 133	76.07 121	81.27 121	69.35 100	58.66 143	51.19 164	82.01 116	87.16 131	84.39 180	95.66 174	92.82 175

v7		76.00 158	75.01 161	77.15 154	78.73 175	88.87 164	83.15 144	72.40 149	69.20 169	53.57 176	49.73 184	49.23 185	78.49 134	86.15 141	85.17 157	96.53 149	96.73 109

pm-mvs1		75.61 162	74.19 170	77.26 150	80.16 158	88.79 165	81.49 175	75.49 132	59.49 211	58.09 135	48.32 196	55.53 155	72.35 176	88.61 122	85.48 151	95.99 172	93.12 171

v1144		75.54 164	74.55 169	76.69 161	78.33 191	88.77 166	82.89 154	72.76 145	67.18 187	51.73 183	49.34 191	48.37 192	78.10 140	86.22 139	85.24 154	96.35 160	96.74 108

MIMVSNet		75.71 161	77.26 145	73.90 184	70.93 216	88.71 167	79.98 180	57.67 219	73.58 144	58.08 136	53.93 159	58.56 149	79.41 130	90.04 114	89.97 90	97.34 104	86.04 202

tfpnnormal		75.27 169	72.12 193	78.94 132	82.30 143	88.52 168	82.41 162	79.41 98	58.03 212	55.59 158	43.83 212	44.71 209	77.35 144	87.70 130	85.45 152	96.60 141	96.61 111

RPSCF		82.91 114	81.86 130	84.13 94	88.25 113	88.32 169	87.67 103	80.86 89	84.78 103	76.57 77	85.56 46	76.00 91	84.61 101	78.20 208	76.52 215	86.81 224	83.63 211

TranMVSNet+NR-MVSNet		77.02 147	75.76 153	78.49 136	78.46 189	88.24 170	83.03 149	79.97 91	73.49 145	54.73 164	54.00 158	48.74 189	78.15 139	82.36 187	86.90 135	96.59 142	96.55 112

v144192		74.76 174	73.64 175	76.06 169	77.58 195	88.23 171	81.87 172	71.63 156	66.03 191	51.08 187	48.63 195	46.77 199	77.59 143	84.53 156	84.76 173	96.64 135	96.54 113

v1192		74.96 173	73.92 172	76.17 166	77.76 194	88.19 172	82.54 160	71.94 154	66.84 188	50.07 194	48.10 197	46.14 201	78.28 137	86.30 137	85.23 155	96.41 159	96.67 110

v8		75.89 159	74.74 163	77.23 151	79.09 166	88.00 173	83.19 141	71.08 161	70.03 162	56.29 146	50.50 170	50.88 169	77.06 151	83.32 170	84.99 163	96.68 129	95.49 135

v1921920		74.60 176	73.56 177	75.81 177	77.43 197	87.94 174	82.18 169	71.33 160	66.48 190	49.23 198	47.84 199	45.56 205	78.03 141	85.70 145	84.92 169	96.65 133	96.50 119

WR-MVS_H		72.69 188	72.80 190	72.56 193	77.94 193	87.83 175	75.26 199	71.53 158	64.75 198	52.19 182	49.83 181	48.62 190	61.96 208	81.12 193	82.44 195	96.50 152	95.00 140

WR-MVS		72.93 186	73.57 176	72.19 196	78.14 192	87.71 176	76.21 193	73.02 143	67.78 181	50.09 193	50.35 177	50.53 173	61.27 209	80.42 198	83.10 192	94.43 193	95.11 138

v1240		74.04 183	73.04 186	75.20 179	77.19 199	87.69 177	80.93 177	70.72 165	65.08 197	48.47 199	47.31 200	44.71 209	77.33 145	85.50 146	85.07 158	96.59 142	95.94 127

TinyColmap		75.75 160	73.19 183	78.74 134	84.82 134	87.69 177	81.59 174	74.62 134	71.81 151	54.01 173	55.79 152	44.42 212	82.89 112	84.61 154	83.76 185	94.50 192	84.22 208

SixPastTwentyTwo		72.65 189	73.22 181	71.98 199	78.40 190	87.64 179	70.09 208	70.37 167	66.49 189	47.60 202	65.09 121	45.94 202	73.09 174	78.94 201	78.66 210	92.33 209	89.82 192

LTVRE_ROB	71.82 16	72.62 190	71.77 194	73.62 185	80.74 153	87.59 180	80.42 179	70.37 167	49.73 222	37.12 222	59.76 138	42.52 219	80.92 122	83.20 176	85.61 149	92.13 210	93.95 152
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
FMVSNet1		80.18 130	78.07 141	82.65 105	78.55 183	87.57 181	88.41 95	73.93 139	70.16 161	73.57 82	49.80 182	64.45 132	85.35 96	90.54 103	90.72 76	96.10 170	93.21 170

IterMVS-LS		82.62 117	82.75 123	82.48 107	87.09 122	87.48 182	87.19 111	72.85 144	79.09 131	66.63 107	65.22 120	72.14 97	84.06 105	88.33 126	91.39 70	97.03 115	95.60 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		75.57 163	74.67 164	76.62 163	78.73 175	87.46 183	83.14 146	69.41 174	69.27 168	53.44 177	49.73 184	49.21 186	78.44 136	86.17 140	85.18 156	96.53 149	95.65 132

v7n		72.11 194	71.66 195	72.63 192	75.26 208	86.85 184	76.74 190	68.77 176	62.70 204	49.40 195	45.92 205	43.51 215	70.63 183	84.16 158	83.21 190	94.99 188	95.25 136

V4		71.67 197	71.15 200	72.27 194	73.91 213	86.82 185	75.73 196	68.04 183	62.49 207	50.47 190	46.20 202	47.74 198	70.70 180	78.54 203	81.76 199	94.76 190	94.52 145

MDTV_nov1_ep13_2view		71.65 199	73.08 184	69.97 205	75.22 209	86.81 186	73.98 202	59.61 213	69.75 165	48.01 201	54.21 157	53.06 157	69.19 188	78.50 206	80.43 205	93.84 198	88.79 194

v52		71.67 197	71.16 199	72.26 195	73.90 214	86.80 187	75.72 197	68.04 183	62.53 206	50.43 191	46.15 204	47.83 197	70.73 179	78.53 205	81.76 199	94.75 191	94.53 144

CP-MVSNet		73.19 185	72.37 192	74.15 181	77.54 196	86.77 188	76.34 191	72.05 152	65.66 194	51.47 185	50.49 171	43.66 214	70.90 178	80.93 194	83.40 188	96.59 142	95.66 131

PS-CasMVS		72.37 191	71.47 197	73.43 187	77.32 198	86.43 189	75.99 194	71.94 154	63.37 201	49.24 197	49.07 194	42.42 220	69.60 186	80.59 197	83.18 191	96.48 156	95.23 137

v748		70.94 203	70.25 203	71.75 201	75.58 206	86.28 190	72.12 203	70.25 170	60.25 209	54.08 172	46.18 203	44.41 213	64.61 199	77.92 210	82.49 194	93.87 196	94.19 147

IterMVS		78.85 141	81.36 132	75.93 173	84.27 137	85.74 191	83.83 131	66.35 194	76.82 136	50.48 189	63.48 128	68.82 113	73.99 172	89.68 118	89.34 98	96.63 137	95.67 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		72.24 193	71.30 198	73.33 189	77.08 200	85.57 192	76.75 189	72.52 148	63.89 200	48.12 200	50.79 166	43.09 217	69.03 190	78.54 203	83.46 187	96.50 152	93.76 160

testgi		73.22 184	75.84 152	70.16 204	81.67 151	85.50 193	71.45 205	70.81 163	69.56 166	44.74 211	74.52 83	49.25 184	58.45 211	84.10 160	83.37 189	93.86 197	84.56 207

pmmvs6		70.29 204	67.90 210	73.07 190	76.17 204	85.31 194	76.29 192	70.75 164	47.39 225	55.33 159	37.15 222	50.49 174	69.55 187	82.96 183	80.85 201	90.34 216	91.18 182

TransMVSNet (Re)		72.90 187	70.51 201	75.69 178	80.88 152	85.26 195	79.25 183	78.43 107	56.13 218	52.81 180	46.81 201	48.20 195	66.77 195	85.18 151	83.70 186	95.98 173	88.28 196

our_test_3					78.55 183	84.98 196	70.12 207

DTE-MVSNet		71.19 201	70.45 202	72.06 198	76.61 203	84.59 197	75.61 198	72.32 150	63.12 203	45.70 209	50.72 167	43.02 218	65.89 196	77.53 212	82.23 197	96.26 163	91.93 179

Baseline_NR-MVSNet		76.71 149	74.56 168	79.23 129	78.68 178	84.15 198	82.45 161	78.87 104	75.83 141	60.05 125	47.92 198	50.18 175	79.06 131	83.16 178	83.86 184	96.26 163	96.80 107

LP		68.35 208	68.20 208	68.53 207	82.61 141	82.93 199	69.42 209	53.36 226	71.06 155	45.32 210	41.19 213	49.10 187	67.20 193	73.89 217	78.16 211	93.25 203	81.04 215

Anonymous20231206		68.09 209	68.68 207	67.39 208	75.16 210	82.55 200	69.33 210	70.06 171	63.34 202	42.28 214	37.91 220	43.12 216	52.67 215	83.56 161	82.71 193	94.84 189	87.59 198

v18		75.49 166	74.04 171	77.18 153	79.31 163	82.47 201	83.66 132	68.68 177	71.77 152	57.43 138	50.71 168	51.01 165	77.31 146	83.35 167	85.03 162	96.70 127	93.91 154

MDA-MVSNet-bldmvs		62.23 215	61.13 217	63.52 212	58.94 231	82.44 202	60.71 221	73.28 142	57.22 215	38.42 219	49.63 188	27.64 232	62.83 206	54.98 229	74.16 218	86.96 223	81.83 213

v17		75.24 170	73.83 174	76.89 160	79.15 165	82.38 203	83.16 143	68.48 178	70.93 156	56.69 144	50.53 169	50.98 166	77.13 150	83.29 174	84.93 168	96.71 125	93.77 159

v16		75.32 168	73.90 173	76.98 158	79.23 164	82.37 204	83.27 136	68.48 178	71.54 153	57.06 140	50.43 173	50.93 167	77.18 149	83.30 173	84.92 169	96.70 127	93.79 157

v15		74.54 177	73.06 185	76.26 164	78.70 177	82.14 205	82.89 154	68.05 182	68.07 177	54.77 162	49.76 183	49.88 178	76.56 160	83.19 177	84.76 173	96.59 142	93.60 163

V14		74.48 179	73.00 187	76.20 165	78.65 181	82.09 206	82.79 158	67.88 185	68.04 178	54.75 163	49.68 187	49.92 177	76.51 161	83.12 180	84.67 175	96.63 137	93.44 165

V9		74.37 180	72.87 188	76.11 168	78.58 182	82.02 207	82.68 159	67.75 187	67.80 180	54.63 165	49.50 189	49.86 179	76.40 162	83.05 181	84.59 176	96.63 137	93.30 168

v11		74.62 175	73.41 180	76.03 170	78.54 185	81.97 208	82.34 163	67.33 191	68.08 176	53.39 178	49.73 184	48.87 188	78.01 142	86.66 135	84.97 166	96.56 147	93.58 164

v12		74.29 181	72.82 189	76.02 171	78.52 186	81.96 209	82.27 165	67.65 188	67.88 179	54.63 165	49.40 190	49.74 183	76.40 162	82.99 182	84.52 177	96.64 135	93.23 169

v13		74.20 182	72.72 191	75.92 174	78.49 188	81.90 210	82.28 164	67.55 189	67.64 182	54.29 170	49.25 192	49.75 182	76.30 164	82.92 184	84.47 178	96.63 137	93.08 172

N_pmnet		68.54 207	67.83 211	69.38 206	75.77 205	81.90 210	66.21 214	72.53 147	65.91 193	46.09 208	44.67 208	45.48 206	63.82 203	74.66 215	77.39 213	91.87 212	84.77 206

FMVSNet5		80.56 128	82.53 125	78.26 139	73.80 215	81.52 212	82.26 168	68.36 181	88.85 75	64.21 118	69.09 108	84.38 70	83.49 109	87.13 132	86.76 136	97.44 98	79.95 217

EU-MVSNet		68.07 210	70.25 203	65.52 210	74.68 212	81.30 213	68.53 211	70.31 169	62.40 208	37.43 221	54.62 156	48.36 193	51.34 219	78.32 207	79.27 207	90.84 214	87.47 199

PM-MVS		70.17 205	69.42 206	71.04 202	70.82 217	81.26 214	71.25 206	67.80 186	69.16 170	51.04 188	53.15 161	34.93 224	72.19 177	80.30 199	76.95 214	93.16 205	90.21 189

pmmvs-eth3d		69.59 206	67.57 212	71.95 200	70.04 219	80.05 215	71.48 204	70.00 172	62.57 205	55.99 154	44.92 207	35.73 223	70.64 182	81.56 192	79.69 206	93.55 201	88.43 195

test20.03		65.17 213	67.41 213	62.55 214	75.35 207	79.31 216	62.22 216	68.83 175	56.50 217	35.35 226	51.97 163	44.70 211	40.01 225	80.69 196	79.25 208	93.55 201	79.47 219

testpf		71.11 202	76.92 148	64.33 211	81.95 145	78.78 217	61.99 217	43.97 234	84.31 107	46.81 205	61.76 135	63.32 134	62.03 207	77.13 213	80.68 203	89.25 217	92.50 178

MIMVSNet1		60.51 219	61.43 216	59.44 220	48.75 235	77.21 218	60.98 219	66.84 192	52.09 221	38.74 217	29.29 229	39.40 222	48.08 222	77.60 211	78.87 209	93.22 204	75.56 221

new-patchmatchnet		60.74 217	59.78 219	61.87 216	69.52 221	76.67 219	57.99 226	65.78 196	52.63 220	38.47 218	38.08 219	32.92 227	48.88 221	68.50 221	69.87 224	90.56 215	79.75 218

gm-plane-assit		71.33 200	75.18 155	66.83 209	79.06 170	75.57 220	48.05 229	60.33 206	48.28 223	34.67 227	44.34 210	67.70 117	79.78 128	97.25 8	96.21 13	99.10 9	96.92 104

new_pmnet		61.60 216	62.68 215	60.35 218	63.02 225	74.93 221	60.97 220	58.86 215	64.21 199	35.38 225	39.51 215	39.89 221	57.37 212	72.78 218	72.56 219	86.49 225	74.85 222

test2356		66.34 211	69.50 205	62.65 213	70.77 218	74.02 222	61.29 218	64.23 201	67.61 183	33.88 230	56.51 148	44.92 208	53.09 214	80.01 200	82.24 196	92.66 208	81.22 214

testus		64.41 214	66.39 214	62.10 215	70.01 220	72.88 223	59.74 223	64.99 198	65.18 196	33.49 231	57.35 146	30.48 230	51.71 218	78.09 209	80.75 202	92.69 207	79.97 216

Anonymous20231211		56.40 221	54.23 222	58.92 221	64.68 224	71.87 224	59.09 225	64.63 199	34.66 233	35.73 224	21.99 231	29.42 231	45.81 223	67.46 224	70.30 223	83.57 226	83.94 210

pmmvs3		60.52 218	60.87 218	60.12 219	61.38 226	71.62 225	57.42 227	53.94 225	48.09 224	35.95 223	38.62 217	32.19 229	64.12 201	75.33 214	77.99 212	87.89 220	82.28 212

DeepMVS_CX						70.68 226	59.61 224	67.36 190	72.12 148	38.41 220	53.88 160	32.44 228	55.15 213	50.88 231		74.35 232	68.42 226

ambc			57.08 220		58.68 232	67.71 227	60.07 222		57.13 216	42.79 213	30.00 226	11.64 238	50.18 220	78.89 202	69.14 225	82.64 228	85.02 204

FPMVS		56.54 220	52.82 225	60.87 217	74.90 211	67.58 228	67.69 213	65.38 197	57.86 213	41.51 215	37.83 221	34.19 225	41.21 224	55.88 228	53.09 230	74.55 231	63.31 228

1111		54.82 222	55.44 221	54.10 223	61.33 228	64.37 229	42.52 230	46.65 232	42.29 227	34.21 228	29.57 227	45.65 203	51.95 216	71.47 219	74.60 217	87.95 219	60.10 229

.test1245		40.04 229	40.41 230	39.60 230	61.33 228	64.37 229	42.52 230	46.65 232	42.29 227	34.21 228	29.57 227	45.65 203	51.95 216	71.47 219	5.65 235	0.92 239	23.86 237

testmv		53.23 223	53.37 223	53.06 224	64.78 222	63.76 231	42.27 232	60.18 207	38.40 229	24.60 234	33.04 223	23.85 233	39.28 226	68.05 222	72.53 220	87.23 221	73.98 223

test1235678		53.22 224	53.36 224	53.05 225	64.78 222	63.75 232	42.27 232	60.17 208	38.36 230	24.60 234	33.03 224	23.84 234	39.28 226	68.04 223	72.52 221	87.23 221	73.96 224

tmp_tt				57.89 222	79.94 159	59.29 233	52.84 228	36.65 236	94.77 43	68.22 103	72.96 90	65.62 126	33.65 229	66.20 225	58.02 227	76.06 230

test12356		48.96 225	49.54 226	48.28 227	59.74 230	57.59 234	42.10 234	58.32 218	36.65 232	23.11 236	31.44 225	19.22 235	23.46 233	61.17 227	71.98 222	82.97 227	68.75 225

PMMVS2		41.25 228	42.55 229	39.74 229	43.25 236	55.05 235	38.15 235	47.11 231	31.78 234	11.83 239	21.16 232	19.12 236	20.98 235	49.95 232	56.09 228	77.09 229	64.68 227

no-one		36.24 230	35.28 231	37.36 231	49.42 234	52.08 236	23.67 238	54.16 224	20.93 237	12.98 238	13.94 233	12.99 237	16.68 236	34.98 234	55.52 229	67.24 233	56.51 231

Gipuma		43.95 227	42.62 227	45.50 228	50.79 233	41.20 237	35.55 237	52.51 228	52.95 219	29.09 232	12.92 234	11.48 239	38.15 228	62.01 226	66.62 226	66.89 235	51.17 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	42.57 18	45.71 226	42.61 228	49.32 226	61.35 227	37.82 238	36.96 236	60.10 210	37.20 231	41.50 216	28.53 230	33.11 226	28.82 232	53.45 230	48.70 232	67.22 234	59.42 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	32.98 19	27.61 232	29.89 232	24.94 235	21.97 239	37.22 239	15.56 242	38.83 235	17.49 238	14.72 237	11.64 238	5.62 242	21.26 234	35.20 233	50.95 231	37.29 238	51.13 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		26.96 233	22.96 234	31.63 234	41.91 237	25.73 240	16.30 241	49.10 230	22.38 235	9.03 241	11.22 239	8.12 240	29.93 230	20.16 236	31.04 234	43.49 237	42.04 235

E-PMN		27.87 231	24.36 233	31.97 233	41.27 238	25.56 241	16.62 240	49.16 229	22.00 236	9.90 240	11.75 236	7.86 241	29.57 231	22.22 235	34.70 233	45.27 236	46.41 234

testmvs		5.16 234	8.14 235	1.69 236	0.36 241	1.65 242	3.02 243	0.66 237	7.17 239	0.50 243	12.58 235	0.69 243	4.67 237	5.42 237	5.65 235	0.92 239	23.86 237

test123		4.39 235	7.11 236	1.21 237	0.11 242	1.16 243	1.67 244	0.35 238	5.91 240	0.16 244	11.65 237	0.16 244	4.45 238	1.72 238	4.92 237	0.51 241	24.28 236

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

MTAPA										93.37 3		95.71 20

MTMP										93.84 2		94.86 24

Patchmatch-RL test							19.65 239

mPP-MVS					97.95 24							92.24 38

NP-MVS									94.12 46

High-resolution multi-view benchmark