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.

MCST-MVS		94.10 5	94.77 5	93.31 6	98.31 1	98.34 3	95.43 4	92.54 3	94.41 13	83.05 27	91.38 15	90.97 4	92.24 9	95.05 5	94.02 5	98.31 1	99.20 7

CNVR-MVS		94.53 3	94.85 4	94.15 3	98.03 2	98.59 2	95.56 3	92.91 1	94.86 8	88.46 11	91.32 17	90.83 5	94.03 2	95.20 3	94.16 4	95.89 24	99.01 12

ESAPD		95.11 1	95.65 1	94.48 1	97.96 3	98.62 1	96.45 1	92.82 2	96.24 3	90.25 5	96.16 2	93.09 1	93.32 3	93.93 13	92.02 19	96.07 19	99.50 3

HPM-MVS++		94.04 6	94.96 3	92.96 8	97.93 4	97.71 13	94.65 9	91.01 8	95.91 4	87.43 13	93.52 8	92.63 2	92.29 8	94.22 12	92.34 16	94.47 47	98.37 22

NCCC		93.59 7	94.00 9	93.10 7	97.90 5	97.93 9	95.40 5	92.39 4	94.47 12	84.94 18	91.21 18	89.32 10	92.53 6	93.90 14	92.98 12	95.44 30	98.22 24

SMA-MVS		93.14 10	93.96 10	92.17 11	97.64 6	97.82 12	94.28 14	90.32 11	94.72 10	85.70 17	87.64 25	90.68 6	91.15 13	94.28 11	93.86 7	93.97 56	98.72 16

APDe-MVS		94.31 4	94.30 7	94.33 2	97.57 7	98.06 7	95.79 2	91.98 5	95.50 6	92.19 1	95.25 3	87.97 14	92.93 4	93.01 20	91.02 35	95.52 28	99.29 5

DeepC-MVS_fast	86.59 2	91.69 16	91.39 22	92.05 14	97.43 8	96.92 27	94.05 15	90.23 12	93.31 20	83.19 25	77.91 39	84.23 28	92.42 7	94.62 8	94.83 2	95.00 38	97.88 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		92.02 14	92.13 19	91.89 15	97.16 9	96.46 35	93.57 18	87.60 21	93.79 15	88.17 12	93.15 10	83.94 32	91.19 12	90.81 40	89.83 43	93.66 70	96.94 54

APD-MVS		93.47 8	93.44 13	93.50 5	97.06 10	97.09 22	95.27 6	91.47 6	95.71 5	89.57 7	93.66 6	86.28 19	92.81 5	92.06 27	90.70 37	94.83 43	98.60 18
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		92.16 13	92.91 17	91.28 17	96.95 11	97.36 18	93.66 17	89.23 17	93.33 17	83.71 22	90.53 19	86.84 16	90.39 14	93.30 18	91.56 28	93.74 65	97.43 37

SteuartSystems-ACMMP		92.31 12	93.31 14	91.15 18	96.88 12	97.36 18	93.95 16	89.44 15	92.62 22	83.20 24	94.34 5	85.55 21	88.95 24	93.07 19	91.90 23	94.51 45	98.30 23
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	80.58 8	88.20 37	86.53 46	90.15 21	96.86 13	96.46 35	91.97 32	83.06 46	85.16 57	83.66 23	62.28 91	82.15 37	88.98 23	90.99 38	92.65 14	96.38 18	96.03 72

HSP-MVS		94.69 2	95.39 2	93.88 4	96.78 14	98.11 5	94.75 7	90.91 9	96.89 2	89.12 10	96.98 1	89.47 9	94.76 1	95.24 2	93.29 10	96.98 7	97.73 30

zzz-MVS		91.59 17	91.12 23	92.13 12	96.76 15	96.68 30	93.39 19	88.00 20	93.63 16	90.76 4	83.97 32	85.33 23	89.89 16	91.60 33	89.65 48	94.00 54	96.97 52

QAPM		87.06 43	86.46 47	87.75 37	96.63 16	97.09 22	91.71 35	82.62 49	80.58 69	71.28 69	66.04 70	84.24 27	87.01 37	89.93 48	89.91 42	97.26 5	97.44 35

ACMMPR		91.15 19	91.44 21	90.81 19	96.61 17	96.25 39	93.09 20	87.08 23	93.32 19	84.78 19	92.08 13	82.10 38	89.71 18	90.24 44	89.82 44	93.61 75	96.30 68

MP-MVS		90.81 22	91.45 20	90.06 22	96.59 18	96.33 38	92.46 29	87.19 22	90.27 34	82.54 31	91.38 15	84.88 25	88.27 31	90.58 42	89.30 53	93.30 99	97.44 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CSCG		89.81 28	89.69 30	89.96 24	96.55 19	97.90 10	92.89 23	87.06 24	88.74 45	86.17 14	78.24 38	86.53 18	84.75 53	87.82 76	90.59 38	92.32 144	98.01 26

PGM-MVS		89.97 26	90.64 27	89.18 29	96.53 20	95.90 46	93.06 21	82.48 51	90.04 36	80.37 36	92.75 11	80.96 43	88.93 25	89.88 49	89.08 54	93.69 69	95.86 74

MSLP-MVS++		90.33 24	88.82 34	92.10 13	96.52 21	95.93 42	94.35 12	86.26 28	88.37 47	89.24 8	75.94 44	82.60 35	89.71 18	89.45 54	92.17 17	96.51 14	97.24 42

X-MVS		89.73 29	90.65 26	88.66 32	96.44 22	95.93 42	92.26 31	86.98 25	90.73 32	76.32 47	89.56 22	82.05 39	86.51 41	89.98 47	89.60 50	93.43 92	96.72 62

train_agg		91.99 15	93.71 11	89.98 23	96.42 23	97.03 24	94.31 13	89.05 18	93.33 17	77.75 41	95.06 4	88.27 12	88.38 30	92.02 28	91.41 30	94.00 54	98.84 15

AdaColmap		88.46 35	85.75 53	91.62 16	96.25 24	95.35 53	90.71 39	91.08 7	90.22 35	86.17 14	74.33 48	73.67 70	92.00 11	86.31 94	85.82 85	93.52 80	94.53 91

CP-MVS		90.57 23	90.68 25	90.44 20	96.13 25	95.90 46	92.77 25	86.86 27	92.12 25	84.19 20	89.18 23	82.37 36	89.43 22	89.65 52	88.43 57	93.27 101	97.13 46

OpenMVS	77.91 11	85.09 52	83.42 62	87.03 42	96.12 26	96.55 33	89.36 47	81.59 56	79.19 72	75.20 53	55.84 119	79.04 49	84.45 55	88.47 66	89.35 52	95.48 29	95.48 80

CDPH-MVS		88.76 32	90.43 28	86.81 46	96.04 27	96.53 34	92.95 22	85.95 30	90.36 33	67.93 80	85.80 29	80.69 44	83.82 58	90.81 40	91.85 26	94.18 50	96.99 51

mPP-MVS					95.90 28							80.22 47

3Dnovator+	81.14 5	88.59 33	87.49 40	89.88 25	95.83 29	96.45 37	91.94 33	82.41 52	87.09 51	85.94 16	62.80 88	85.37 22	89.46 20	91.51 34	91.89 25	93.72 67	97.30 40

TSAR-MVS + ACMM		90.98 21	93.18 15	88.42 34	95.69 30	96.73 29	94.52 11	86.97 26	92.99 21	76.32 47	92.31 12	86.64 17	84.40 57	92.97 21	92.02 19	92.62 138	98.59 19

EPNet		89.30 30	90.89 24	87.44 39	95.67 31	96.81 28	91.13 37	83.12 45	91.14 28	76.31 51	87.60 26	80.40 46	84.45 55	92.13 26	91.12 34	93.96 58	97.01 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	86.71 1	91.00 20	94.05 8	87.43 40	95.58 32	98.17 4	86.22 67	88.59 19	97.01 1	76.77 46	85.11 30	88.90 11	87.29 35	95.02 6	94.69 3	90.15 186	99.48 4

abl_6				89.54 27	95.55 33	97.59 15	89.01 50	85.00 34	94.67 11	83.04 28	84.70 31	91.47 3	89.46 20			95.20 35	98.63 17

MAR-MVS		85.65 49	86.30 48	84.88 56	95.51 34	95.89 48	86.50 65	76.71 81	89.23 43	68.59 77	70.93 58	74.49 64	88.55 26	89.40 55	90.30 40	93.42 93	93.88 110
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
PHI-MVS		89.88 27	92.75 18	86.52 50	94.97 35	97.57 16	89.99 45	84.56 36	92.52 23	69.72 75	90.35 20	87.11 15	84.89 50	91.82 30	92.37 15	95.02 37	97.51 33

DeepC-MVS	84.14 3	88.80 31	88.03 38	89.71 26	94.83 36	96.56 31	92.57 27	89.38 16	89.25 42	79.59 38	70.02 60	77.05 56	88.24 32	92.44 24	92.79 13	93.65 73	98.10 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		87.75 40	86.92 44	88.71 31	94.69 37	97.34 21	92.78 24	84.50 37	77.87 77	81.94 33	67.17 65	75.49 62	82.84 63	95.38 1	95.93 1	95.55 27	99.27 6
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
SD-MVS		93.36 9	94.33 6	92.22 10	94.68 38	97.89 11	94.56 10	90.89 10	94.80 9	90.04 6	93.53 7	90.14 7	89.78 17	92.74 22	92.17 17	93.35 97	99.07 10

ACMMP		88.48 34	88.71 35	88.22 36	94.61 39	95.53 50	90.64 41	85.60 32	90.97 29	78.62 40	89.88 21	74.20 67	86.29 42	88.16 74	86.37 77	93.57 77	95.86 74
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
MVS_111021_HR		87.82 39	88.84 33	86.62 48	94.42 40	97.36 18	88.21 54	83.26 44	83.42 60	72.52 64	82.63 34	76.93 57	84.95 49	91.93 29	91.15 33	96.39 17	98.49 21

TSAR-MVS + MP.		93.07 11	93.53 12	92.53 9	94.23 41	97.54 17	94.75 7	89.87 13	95.26 7	89.20 9	93.16 9	88.19 13	92.15 10	91.79 31	89.65 48	94.99 39	99.16 8

CANet		89.98 25	90.42 29	89.47 28	94.13 42	98.05 8	91.76 34	83.27 43	90.87 31	81.90 34	72.32 51	84.82 26	88.42 28	94.52 9	93.78 8	97.34 4	98.58 20

PLC	81.02 6	84.81 55	81.81 76	88.31 35	93.77 43	90.35 101	88.80 51	84.47 38	86.76 52	82.17 32	66.56 67	71.01 80	88.41 29	85.48 102	84.28 102	92.26 146	88.21 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CPTT-MVS		88.17 38	87.84 39	88.55 33	93.33 44	93.75 64	92.33 30	84.75 35	89.87 38	81.72 35	83.93 33	81.12 42	88.45 27	85.42 104	84.07 104	90.72 178	96.72 62

PVSNet_BlendedMVS		86.98 44	87.05 42	86.90 43	93.03 45	96.98 25	86.57 63	81.82 54	89.78 39	82.78 29	71.54 54	66.07 94	80.73 78	93.46 16	91.97 21	96.45 15	99.53 1

PVSNet_Blended		86.98 44	87.05 42	86.90 43	93.03 45	96.98 25	86.57 63	81.82 54	89.78 39	82.78 29	71.54 54	66.07 94	80.73 78	93.46 16	91.97 21	96.45 15	99.53 1

CNLPA		84.72 56	82.14 72	87.73 38	92.85 47	93.83 63	84.70 85	85.07 33	90.90 30	83.16 26	56.28 115	71.53 76	88.14 33	84.19 112	84.00 107	92.48 141	94.26 96

MVS_111021_LR		87.58 42	88.67 36	86.31 51	92.58 48	95.89 48	86.20 68	82.49 50	89.08 44	77.47 43	86.20 28	74.22 66	85.49 46	90.03 46	88.52 55	93.66 70	96.74 61

EPNet_dtu		78.49 94	81.96 74	74.45 121	92.57 49	88.74 118	82.98 92	78.83 61	83.28 61	44.64 194	77.40 41	67.73 90	53.98 198	85.44 103	84.91 91	93.71 68	86.22 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		86.38 46	86.21 50	86.57 49	92.30 50	94.35 59	87.60 57	83.51 42	92.32 24	77.37 44	72.27 52	77.83 51	86.59 40	87.62 79	85.95 82	92.08 148	93.11 120

CHOSEN 1792x2688		80.23 78	79.16 88	81.48 70	91.97 51	96.56 31	86.18 69	75.40 95	76.17 87	61.32 93	37.43 207	61.08 108	76.52 99	92.35 25	91.64 27	97.46 3	98.86 13

LS3D		78.72 90	75.79 116	82.15 66	91.91 52	89.39 115	83.66 90	85.88 31	76.81 85	59.22 107	57.67 105	58.53 119	83.72 59	82.07 130	81.63 139	88.50 198	84.39 185

TAPA-MVS	80.99 7	84.83 54	84.42 57	85.31 54	91.89 53	93.73 65	88.53 53	82.80 47	89.99 37	69.78 74	71.53 56	75.03 63	85.47 47	86.26 95	84.54 99	93.39 95	89.90 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		88.43 36	89.46 31	87.21 41	91.85 54	97.60 14	92.62 26	81.10 58	87.16 50	73.80 56	72.19 53	83.36 34	87.03 36	94.64 7	93.67 9	96.88 8	97.64 32

MSDG		78.11 99	73.17 134	83.86 61	91.78 55	86.83 134	85.25 76	86.02 29	72.84 102	69.69 76	51.43 135	54.00 133	77.61 91	81.95 134	82.27 128	92.83 134	82.91 195

PCF-MVS	82.38 4	85.52 50	84.41 58	86.81 46	91.51 56	96.23 40	90.27 42	89.81 14	77.87 77	70.67 70	69.20 62	77.86 50	85.55 45	85.92 99	86.38 76	93.03 119	97.43 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		86.17 47	87.35 41	84.80 57	91.41 57	92.37 85	91.05 38	84.35 39	88.52 46	64.21 84	87.05 27	68.91 87	84.80 52	89.12 57	88.16 61	92.96 122	97.31 39

OPM-MVS		81.34 68	78.18 96	85.02 55	91.27 58	91.78 91	90.66 40	83.62 41	62.39 141	65.91 81	63.35 85	64.33 101	85.03 48	87.77 77	85.88 84	93.66 70	91.75 134

TSAR-MVS + COLMAP		84.93 53	85.79 52	83.92 60	90.90 59	93.57 67	89.25 49	82.00 53	91.29 27	61.66 89	88.25 24	59.46 115	86.71 39	89.79 50	87.09 67	93.01 120	91.09 137

HyFIR lowres test		78.08 100	76.81 104	79.56 86	90.77 60	94.64 58	82.97 93	69.85 137	69.81 116	59.53 105	33.52 212	64.66 98	78.97 88	88.77 62	88.38 58	95.27 31	97.86 28

IB-MVS	74.10 12	78.52 93	78.51 92	78.52 94	90.15 61	95.39 52	71.95 186	77.53 75	74.95 91	77.25 45	58.93 101	55.92 128	58.37 188	79.01 169	87.89 62	95.88 25	97.47 34
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
MS-PatchMatch		77.47 104	76.48 109	78.63 93	89.89 62	90.42 100	85.42 74	69.53 139	70.79 109	60.43 102	50.05 140	70.62 83	70.66 141	86.71 87	82.54 123	95.86 26	84.23 186

PVSNet_Blended_VisFu		82.55 62	83.70 61	81.21 74	89.66 63	95.15 56	82.41 98	77.36 77	72.53 104	73.64 57	61.15 96	77.19 55	70.35 148	91.31 37	89.72 47	93.84 61	98.85 14

XVS					89.65 64	95.93 42	85.97 71			76.32 47		82.05 39				93.51 83

X-MVStestdata					89.65 64	95.93 42	85.97 71			76.32 47		82.05 39				93.51 83

ACMM	78.09 10	80.91 70	78.39 94	83.86 61	89.61 66	87.71 123	85.16 78	80.67 59	79.04 73	74.18 54	63.82 83	60.84 109	82.59 64	84.33 111	83.59 110	90.96 173	89.39 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LGP-MVS_train		82.12 66	82.57 69	81.59 69	89.26 67	90.23 103	88.76 52	78.05 67	81.26 67	61.64 90	79.52 36	62.11 105	79.59 85	85.20 105	84.68 98	92.27 145	95.02 86

canonicalmvs		85.93 48	86.26 49	85.54 53	88.94 68	95.44 51	89.56 46	76.01 87	87.83 48	77.70 42	76.43 43	68.66 89	87.80 34	87.02 83	91.51 29	93.25 105	96.95 53

CANet_DTU		83.33 59	86.59 45	79.53 87	88.88 69	94.87 57	86.63 62	68.85 145	85.45 56	50.54 155	77.86 40	69.94 84	85.62 44	92.63 23	90.88 36	96.63 11	94.46 92

DWT-MVSNet_training		82.66 61	83.34 65	81.87 68	88.71 70	92.63 77	82.07 100	72.21 116	86.37 53	72.64 59	64.51 79	71.44 78	80.35 81	84.43 110	87.73 63	95.27 31	96.25 69

conf0.002		80.80 72	80.30 82	81.38 72	88.59 71	93.19 71	85.12 79	78.10 65	70.15 110	61.55 91	63.30 86	62.66 104	81.11 69	88.74 63	86.94 71	93.79 63	97.15 44

conf0.01		80.10 79	79.04 90	81.34 73	88.56 72	93.09 73	85.12 79	78.08 66	70.15 110	61.43 92	60.90 97	58.54 118	81.11 69	88.66 64	84.80 93	93.74 65	97.14 45

PatchMatch-RL		78.75 89	76.47 110	81.41 71	88.53 73	91.10 96	78.09 147	77.51 76	77.33 81	71.98 66	64.38 81	48.10 152	82.55 65	84.06 113	82.35 126	89.78 189	87.97 171

tfpn111		80.42 77	79.77 87	81.18 75	88.42 74	92.55 81	85.12 79	77.94 69	70.15 110	61.00 98	74.56 45	51.22 136	81.11 69	88.23 68	84.80 93	93.50 85	96.90 57

conf200view11		79.04 87	77.21 102	81.18 75	88.42 74	92.55 81	85.12 79	77.94 69	70.15 110	61.00 98	56.65 108	51.22 136	81.11 69	88.23 68	84.80 93	93.50 85	96.90 57

thres100view900		79.83 80	77.79 100	82.21 65	88.42 74	93.54 68	87.07 58	81.11 57	70.15 110	61.01 96	56.65 108	51.22 136	81.78 67	89.77 51	85.95 82	93.84 61	97.26 41

tfpn200view9		79.05 86	77.21 102	81.18 75	88.42 74	92.55 81	85.12 79	77.94 69	70.15 110	61.01 96	56.65 108	51.22 136	81.11 69	88.23 68	84.80 93	93.50 85	96.90 57

thres200		78.69 91	76.71 106	80.99 81	88.35 78	92.56 79	86.03 70	77.94 69	66.27 122	60.66 100	56.08 116	51.11 140	79.45 86	88.23 68	85.54 88	93.52 80	97.20 43

tfpn_ndepth		78.22 98	78.84 91	77.49 101	88.32 79	90.95 98	80.79 105	76.31 85	74.24 93	59.50 106	69.52 61	60.02 114	67.11 161	85.06 106	82.95 121	92.94 127	89.18 157

MVS_Test		84.60 57	85.13 55	83.99 59	88.17 80	95.27 54	88.21 54	73.15 107	84.31 59	70.55 72	68.67 63	68.78 88	86.99 38	91.71 32	91.90 23	96.84 9	95.27 84

ACMP	79.58 9	82.23 64	81.82 75	82.71 63	88.15 81	90.95 98	85.23 77	78.52 63	81.70 66	72.52 64	78.41 37	60.63 110	80.48 80	82.88 122	83.44 112	91.37 166	94.70 88
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSTER		87.68 41	89.12 32	86.01 52	88.11 82	90.05 106	89.28 48	77.05 80	91.37 26	79.97 37	76.70 42	85.25 24	84.89 50	93.53 15	91.41 30	96.73 10	95.55 79

thres400		78.39 95	76.39 111	80.73 82	88.02 83	92.94 74	84.77 84	78.88 60	65.20 130	59.70 104	55.20 121	50.85 141	79.45 86	88.81 60	84.81 92	93.57 77	96.91 56

thresconf0.02		78.87 88	80.50 79	76.96 105	87.88 84	91.71 92	82.90 97	78.51 64	67.91 119	50.85 148	74.56 45	69.93 85	67.32 160	86.86 86	85.65 86	94.32 49	86.89 177

TSAR-MVS + GP.		91.29 18	93.11 16	89.18 29	87.81 85	96.21 41	92.51 28	83.83 40	94.24 14	83.77 21	91.87 14	89.62 8	90.07 15	90.40 43	90.31 39	97.09 6	99.10 9

view600		77.68 102	75.68 117	80.01 84	87.72 86	92.57 78	83.79 88	77.95 68	64.41 133	58.72 109	54.32 126	50.54 142	78.25 89	88.23 68	83.13 117	93.64 74	96.59 66

thres600view7		77.66 103	75.67 118	79.98 85	87.71 87	92.56 79	83.79 88	77.94 69	64.41 133	58.69 110	54.32 126	50.54 142	78.23 90	88.23 68	83.06 119	93.52 80	96.55 67

CLD-MVS		85.43 51	84.24 59	86.83 45	87.69 88	93.16 72	90.01 44	82.72 48	87.17 49	79.28 39	71.43 57	65.81 96	86.02 43	87.33 81	86.96 70	95.25 34	97.83 29
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		80.92 69	84.14 60	77.16 104	87.43 89	93.90 62	80.44 106	74.64 99	75.05 90	61.10 95	65.59 72	76.89 58	67.39 159	90.88 39	90.05 41	91.95 152	96.62 65

view800		77.22 108	75.35 119	79.41 90	87.42 90	92.21 87	82.94 95	77.19 78	63.67 137	57.78 111	53.68 129	50.19 144	77.32 92	87.70 78	83.84 108	93.79 63	96.19 71

UA-Net		78.30 96	80.92 78	75.25 113	87.42 90	92.48 84	79.54 126	75.49 94	60.47 147	60.52 101	68.44 64	84.08 30	57.54 189	88.54 65	88.45 56	90.96 173	83.97 190

tfpn		77.45 105	76.23 113	78.87 91	87.15 92	91.90 90	82.17 99	76.59 82	62.98 139	56.93 113	53.08 132	57.31 124	76.41 101	87.26 82	85.20 89	93.95 59	95.89 73

UGNet		80.71 76	83.09 66	77.93 98	87.02 93	92.71 75	80.28 110	76.53 83	73.83 98	71.35 68	70.07 59	73.71 69	58.93 186	87.39 80	86.97 69	93.48 89	96.94 54
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
EPMVS		77.16 110	79.08 89	74.92 115	86.73 94	91.98 88	78.62 139	55.44 210	79.43 70	56.59 115	61.24 95	70.73 82	76.97 96	80.59 145	81.43 151	95.15 36	88.17 170

tfpn1000		75.39 117	76.18 115	74.47 120	86.71 95	90.10 105	77.57 153	74.78 97	68.76 118	53.33 125	63.57 84	58.37 120	60.84 182	83.80 116	81.24 156	93.58 76	87.42 173

Vis-MVSNet (Re-imp)		78.28 97	82.68 68	73.16 144	86.64 96	92.68 76	78.07 148	74.48 101	74.05 95	53.47 124	64.22 82	76.52 59	54.28 194	88.96 59	88.29 59	92.03 150	94.00 101

tfpnview11		74.85 118	75.06 121	74.61 118	86.58 97	89.54 113	79.98 111	75.81 89	64.95 132	47.47 172	64.85 76	54.72 129	63.86 170	84.54 109	82.20 130	93.97 56	84.64 182

FC-MVSNet-train		79.54 82	78.20 95	81.09 78	86.55 98	88.63 119	79.96 112	78.53 62	70.90 108	68.24 78	65.87 71	56.45 127	80.29 82	86.20 97	84.08 103	92.97 121	95.31 83

CHOSEN 280x420		82.15 65	85.87 51	77.80 99	86.54 99	93.42 69	81.74 101	59.96 199	78.99 74	63.99 85	74.50 47	83.95 31	80.99 74	89.53 53	85.01 90	93.56 79	95.71 78

CostFormer		80.72 73	81.81 76	79.44 89	86.50 100	91.65 93	84.31 87	59.84 200	80.86 68	72.69 58	62.46 90	73.74 68	79.93 83	82.58 125	84.50 100	93.37 96	96.90 57

COLMAP_ROB	66.31 15	69.91 167	66.61 182	73.76 131	86.44 101	82.76 176	76.59 163	76.46 84	63.82 136	50.92 147	45.60 153	49.13 147	65.87 166	74.96 193	74.45 204	86.30 210	75.57 210
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpn_n400		74.36 121	74.39 128	74.32 122	86.37 102	89.86 108	79.71 116	75.69 91	60.00 149	47.47 172	64.85 76	54.72 129	63.70 173	83.80 116	83.35 113	92.96 122	84.16 187

tfpnconf		74.36 121	74.39 128	74.32 122	86.37 102	89.86 108	79.71 116	75.69 91	60.00 149	47.47 172	64.85 76	54.72 129	63.70 173	83.80 116	83.35 113	92.96 122	84.16 187

EPP-MVSNet		80.82 71	82.79 67	78.52 94	86.31 104	92.37 85	79.83 114	74.51 100	73.79 99	64.46 83	67.01 66	80.63 45	74.33 109	85.63 100	84.35 101	91.68 158	95.79 77

DI_MVS_plusplus_trai		83.32 60	82.53 70	84.25 58	86.26 105	93.66 66	90.23 43	77.16 79	77.05 84	74.06 55	53.74 128	74.33 65	83.61 60	91.40 36	89.82 44	94.17 51	97.73 30

ACMH	71.22 14	72.65 135	70.13 150	75.59 110	86.19 106	86.14 150	75.76 172	77.63 74	54.79 180	46.16 182	53.28 131	47.28 154	77.24 94	78.91 171	81.18 160	90.57 180	89.33 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		76.27 115	77.65 101	74.66 117	86.13 107	89.53 114	79.31 131	54.91 211	77.19 83	56.27 116	55.87 118	64.58 99	77.25 93	80.85 143	80.21 171	94.07 52	95.32 82

conf0.05thres1000		74.20 125	71.44 142	77.43 102	86.09 108	89.85 110	80.82 104	75.79 90	53.51 188	54.71 119	44.37 165	49.78 145	74.67 106	85.02 107	83.47 111	92.49 140	94.10 99

diffmvs		83.81 58	84.78 56	82.69 64	86.06 109	94.03 60	86.46 66	72.43 114	85.71 55	75.29 52	65.48 75	79.49 48	81.39 68	85.55 101	86.98 68	94.48 46	96.20 70

PMMVS		82.26 63	85.48 54	78.51 96	85.92 110	91.92 89	78.30 143	70.77 130	86.30 54	61.11 94	82.46 35	70.88 81	84.70 54	88.05 75	84.78 97	90.24 185	93.98 102

Effi-MVS+		79.80 81	80.04 83	79.52 88	85.53 111	93.31 70	85.28 75	70.68 132	74.15 94	58.79 108	62.03 93	60.51 111	83.37 61	88.41 67	86.09 81	93.49 88	95.80 76

CMPMVS	50.59 17	66.74 185	62.72 204	71.42 169	85.40 112	89.72 112	72.69 183	70.72 131	51.24 194	51.75 135	38.91 203	44.40 175	63.74 172	70.84 208	71.52 208	84.19 215	72.45 217
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmp4_e23		78.57 92	78.48 93	78.68 92	85.38 113	89.14 117	84.69 86	60.32 198	78.81 75	70.65 71	57.89 103	65.54 97	79.63 84	80.09 149	83.24 115	91.41 165	94.63 90

ACMH+	72.14 13	72.38 137	69.34 161	75.93 109	85.21 114	84.89 163	76.96 161	76.04 86	59.76 151	51.63 136	50.37 139	48.69 149	76.90 97	76.06 188	78.69 180	88.85 196	86.90 176

tpm cat1		76.93 111	76.19 114	77.79 100	85.08 115	88.58 120	82.96 94	59.33 201	75.72 89	72.64 59	51.25 136	64.41 100	75.74 104	77.90 178	80.10 174	90.97 172	95.35 81

Vis-MVSNet		77.24 107	79.99 86	74.02 128	84.62 116	93.92 61	80.33 109	72.55 113	62.58 140	55.25 118	64.45 80	69.49 86	57.00 190	88.78 61	88.21 60	94.36 48	92.54 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		75.76 116	75.02 122	76.63 107	84.51 117	88.12 121	77.51 154	58.33 203	75.91 88	71.98 66	57.37 106	57.85 121	76.81 98	77.89 179	78.40 184	90.63 179	89.63 147

PatchmatchNet		76.85 112	80.03 85	73.15 145	84.08 118	91.04 97	77.76 152	55.85 209	79.43 70	52.74 129	62.08 92	76.02 60	74.56 107	79.92 150	81.41 152	93.92 60	90.29 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 113	76.33 112	77.35 103	84.07 119	84.11 169	81.54 102	68.52 147	66.17 123	61.74 88	57.84 104	64.31 102	74.88 105	83.48 120	86.21 79	93.34 98	92.16 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
gg-mvs-nofinetune		72.10 141	74.79 124	68.97 178	83.31 120	95.22 55	85.66 73	48.77 223	35.68 223	22.17 232	30.49 216	77.73 52	76.37 102	94.30 10	93.03 11	97.55 2	97.05 47

tpm		73.50 128	74.85 123	71.93 161	83.19 121	86.84 133	78.61 140	55.91 208	65.64 125	48.90 164	56.30 114	61.09 107	72.31 119	79.10 168	80.61 170	92.68 136	94.35 95

Fast-Effi-MVS+		77.37 106	76.68 107	78.17 97	82.84 122	89.94 107	81.47 103	68.01 153	72.99 100	60.26 103	55.07 122	53.20 134	82.99 62	86.47 93	86.12 80	93.46 90	92.98 123

MDTV_nov1_ep13		77.20 109	80.04 83	73.90 130	82.22 123	90.14 104	79.25 132	61.52 192	78.63 76	56.98 112	65.52 74	72.80 74	73.05 117	80.93 142	83.20 116	90.36 182	89.05 159

TDRefinement		67.82 179	64.91 190	71.22 172	82.08 124	81.45 184	77.42 156	73.79 105	59.62 152	48.35 169	42.35 190	42.40 192	60.87 181	74.69 194	74.64 203	84.83 214	79.20 204

test-LLR		79.52 83	83.42 62	74.97 114	81.79 125	91.26 94	76.17 167	70.57 133	77.71 79	52.14 133	66.26 68	77.47 53	73.10 115	87.02 83	87.16 65	96.05 22	97.02 48

test0.0.03 1		71.70 147	74.68 125	68.23 180	81.79 125	83.81 172	68.64 192	70.57 133	68.81 117	43.47 195	62.77 89	60.09 113	51.77 204	82.48 126	81.67 138	93.16 111	83.13 193

CR-MVSNet		74.84 119	77.91 98	71.26 171	81.77 127	85.52 156	78.32 141	54.14 213	74.05 95	51.09 141	50.00 141	71.38 79	70.77 138	86.48 91	84.03 105	91.46 164	93.92 106

RPMNet		73.46 129	77.85 99	68.34 179	81.71 128	85.52 156	73.83 180	50.54 221	74.05 95	46.10 183	53.03 133	71.91 75	66.31 165	83.55 119	82.18 131	91.55 162	94.71 87

Effi-MVS+-dtu		74.57 120	74.60 126	74.53 119	81.38 129	86.74 136	80.39 108	67.70 157	67.36 121	53.06 126	59.86 99	57.50 122	75.84 103	80.19 147	78.62 182	88.79 197	91.95 133

ADS-MVSNet		72.11 140	73.72 132	70.24 176	81.24 130	86.59 139	74.75 176	50.56 220	72.58 103	49.17 162	55.40 120	61.46 106	73.80 112	76.01 189	78.14 185	91.93 153	85.86 180

RPSCF		74.27 123	73.24 133	75.48 112	81.01 131	80.18 193	76.24 166	72.37 115	74.84 92	68.24 78	72.47 50	67.39 91	73.89 110	71.05 207	69.38 216	81.14 223	77.37 206

CDS-MVSNet		76.57 114	76.78 105	76.32 108	80.94 132	89.75 111	82.94 95	72.64 109	59.01 158	62.95 87	58.60 102	62.67 103	66.91 163	86.26 95	87.20 64	91.57 160	93.97 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
USDC		73.43 130	72.31 137	74.73 116	80.86 133	86.21 145	80.42 107	71.83 122	71.69 106	46.94 176	59.60 100	42.58 190	76.47 100	82.66 124	81.22 158	91.88 154	82.24 200

Fast-Effi-MVS+-dtu		73.56 127	75.32 120	71.50 167	80.35 134	86.83 134	79.72 115	58.07 204	67.64 120	44.83 191	60.28 98	54.07 132	73.59 114	81.90 136	82.30 127	92.46 142	94.18 97

IterMVS		72.43 136	74.05 130	70.55 175	80.34 135	81.17 188	77.44 155	61.00 194	63.57 138	46.82 178	55.88 117	59.09 117	65.03 167	83.15 121	83.83 109	92.67 137	91.65 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		73.62 126	74.52 127	72.58 150	79.93 136	89.29 116	78.02 149	71.67 126	60.79 146	42.68 198	54.41 125	49.07 148	70.07 151	89.39 56	86.55 75	93.13 116	92.12 130

tfpnnormal		69.29 175	65.58 184	73.62 137	79.87 137	84.82 164	76.97 160	75.12 96	45.29 213	49.03 163	35.57 210	37.20 211	68.02 155	82.70 123	81.24 156	92.69 135	92.20 128

TESTMET0.1,1		79.15 85	83.42 62	74.18 124	79.81 138	91.26 94	76.17 167	67.83 156	77.71 79	52.14 133	66.26 68	77.47 53	73.10 115	87.02 83	87.16 65	96.05 22	97.02 48

CVMVSNet		68.95 177	70.79 145	66.79 187	79.69 139	83.75 173	72.05 185	70.90 129	56.20 172	36.30 209	54.94 124	59.22 116	54.03 197	78.33 174	78.65 181	87.77 204	84.44 184

FMVSNet3		81.93 67	81.98 73	81.88 67	79.49 140	87.02 129	88.15 56	72.57 110	83.02 62	72.63 61	56.55 111	73.48 71	82.34 66	91.49 35	91.20 32	96.07 19	91.13 136

PatchT		72.66 134	76.58 108	68.09 181	79.02 141	86.09 151	59.81 211	51.78 219	72.00 105	51.09 141	46.84 151	66.70 92	70.77 138	86.48 91	84.03 105	96.07 19	93.92 106

test-mter		77.90 101	82.44 71	72.60 149	78.52 142	90.24 102	73.85 179	65.31 174	76.37 86	51.29 137	65.58 73	75.94 61	71.36 128	85.98 98	86.26 78	95.26 33	96.71 64

TransMVSNet (Re)		66.87 184	64.30 195	69.88 177	78.32 143	81.35 187	73.88 178	74.34 104	43.19 217	45.20 189	40.12 197	42.37 193	55.97 192	80.85 143	79.15 177	91.56 161	83.06 194

GBi-Net		80.72 73	80.49 80	81.00 79	78.18 144	86.19 147	86.73 59	72.57 110	83.02 62	72.63 61	56.55 111	73.48 71	80.99 74	86.57 88	86.83 72	94.89 40	90.77 138

test1		80.72 73	80.49 80	81.00 79	78.18 144	86.19 147	86.73 59	72.57 110	83.02 62	72.63 61	56.55 111	73.48 71	80.99 74	86.57 88	86.83 72	94.89 40	90.77 138

FMVSNet2		79.24 84	78.14 97	80.53 83	78.18 144	86.19 147	86.73 59	71.91 120	72.97 101	70.48 73	50.63 138	66.56 93	80.99 74	90.10 45	89.77 46	94.89 40	90.77 138

TinyColmap		67.16 181	63.51 200	71.42 169	77.94 147	79.54 199	72.80 182	69.78 138	56.58 169	45.52 185	44.53 162	33.53 220	74.45 108	76.91 187	77.06 192	88.03 203	76.41 207

EG-PatchMatch MVS		66.23 187	65.20 187	67.43 184	77.74 148	86.20 146	72.51 184	63.68 185	43.95 215	43.44 196	36.22 209	45.43 166	54.04 196	81.00 141	80.95 168	93.15 115	82.67 198

NR-MVSNet		71.47 153	71.11 144	71.90 163	77.73 149	86.02 152	76.88 162	74.42 102	65.39 128	46.09 184	49.10 144	39.87 202	64.27 169	81.40 138	82.24 129	91.99 151	93.75 117

LTVRE_ROB	63.07 16	64.49 196	63.16 203	66.04 191	77.47 150	82.64 178	70.98 188	65.02 178	34.01 226	29.61 219	49.12 143	35.58 216	70.57 144	75.10 192	78.45 183	82.60 218	87.24 174
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
pm-mvs1		69.62 173	68.07 174	71.44 168	77.21 151	85.32 159	76.11 169	71.05 128	46.55 211	51.17 140	41.83 193	48.20 151	61.81 179	84.00 114	81.14 163	91.28 167	89.42 150

FC-MVSNet-test		67.04 182	72.47 136	60.70 209	76.92 152	81.41 185	61.52 207	69.45 140	65.58 127	26.74 227	61.79 94	60.40 112	41.17 218	77.60 181	77.78 187	88.41 199	82.70 197

UniMVSNet_NR-MVSNet		73.11 132	72.59 135	73.71 132	76.90 153	86.58 140	77.01 158	75.82 88	65.59 126	48.82 165	50.97 137	48.42 150	71.61 125	79.19 166	83.03 120	92.11 147	94.37 93

UniMVSNet (Re)		72.12 139	72.28 138	71.93 161	76.77 154	87.38 125	75.73 173	73.51 106	65.76 124	50.24 157	48.65 147	46.49 155	63.85 171	80.10 148	82.47 124	91.49 163	95.13 85

testpf		59.38 210	64.51 194	53.40 217	76.71 155	66.40 221	50.18 222	38.98 234	64.13 135	35.10 213	47.91 149	51.41 135	43.16 212	66.37 217	71.23 209	76.25 226	84.14 189

TAMVS		72.06 142	71.76 140	72.41 154	76.68 156	88.12 121	74.82 175	68.09 152	53.52 187	56.91 114	52.94 134	56.93 126	66.91 163	81.37 139	82.44 125	91.07 170	86.99 175

v18		71.13 155	68.98 163	73.63 136	76.66 157	79.78 195	79.95 113	65.98 168	61.34 143	54.71 119	44.75 156	46.06 156	71.27 129	79.59 155	81.51 145	93.21 107	89.81 145

v16		70.93 158	68.76 167	73.47 138	76.60 158	79.66 197	79.57 125	65.81 171	60.85 144	54.44 122	44.50 164	45.90 158	71.15 130	79.50 160	81.39 153	93.27 101	89.51 149

v17		70.82 160	68.69 168	73.31 140	76.53 159	79.67 196	79.45 128	65.80 172	60.32 148	53.75 123	44.51 163	45.92 157	71.09 132	79.49 161	81.38 154	93.26 104	89.54 148

v6		72.04 143	70.26 146	74.11 125	76.46 160	87.06 126	79.60 118	71.75 123	59.48 153	52.69 130	44.61 158	45.79 161	71.01 136	79.57 156	81.45 149	93.16 111	93.85 113

v1neww		72.02 144	70.23 148	74.10 126	76.45 161	87.06 126	79.59 121	71.75 123	59.35 154	52.60 131	44.59 160	45.74 162	71.06 133	79.57 156	81.46 147	93.16 111	93.84 114

v7new		72.02 144	70.23 148	74.10 126	76.45 161	87.06 126	79.59 121	71.75 123	59.35 154	52.60 131	44.59 160	45.74 162	71.06 133	79.57 156	81.46 147	93.16 111	93.84 114

v8		71.42 154	69.69 155	73.43 139	76.45 161	85.12 162	79.53 127	67.47 160	59.34 156	52.90 127	44.60 159	45.82 159	71.05 135	79.56 159	81.45 149	93.17 109	91.96 132

gm-plane-assit		64.86 192	68.15 173	61.02 208	76.44 164	68.29 219	41.60 228	53.37 216	34.68 225	26.19 229	33.22 213	57.09 125	71.97 120	95.12 4	93.97 6	96.54 13	94.66 89

divwei89l23v2f112		71.53 150	69.69 155	73.68 133	76.09 165	86.86 131	79.60 118	72.08 117	56.96 166	50.78 150	44.24 168	44.70 170	70.65 142	79.62 152	81.53 140	92.89 128	93.93 104

v1141		71.53 150	69.69 155	73.68 133	76.08 166	86.86 131	79.59 121	72.07 118	57.01 164	50.78 150	44.23 169	44.70 170	70.68 140	79.61 154	81.52 142	92.89 128	93.92 106

v1		71.54 149	69.71 154	73.66 135	76.08 166	86.88 130	79.60 118	72.06 119	57.00 165	50.75 152	44.23 169	44.79 167	70.61 143	79.62 152	81.52 142	92.88 131	93.93 104

v15		70.00 166	67.82 176	72.55 151	76.06 168	79.37 200	79.10 135	65.30 175	56.89 167	51.18 139	43.96 175	44.76 168	70.52 145	79.40 163	81.22 158	93.13 116	89.14 158

v148		70.34 162	68.46 170	72.54 152	76.04 169	86.38 142	74.83 174	72.73 108	55.88 176	55.26 117	43.32 185	43.49 181	64.52 168	76.93 186	80.11 173	91.85 155	93.11 120

V14		69.91 167	67.71 178	72.47 153	76.01 170	79.30 201	78.92 136	65.17 176	56.74 168	51.08 144	43.82 178	44.73 169	70.44 147	79.31 164	81.14 163	93.20 108	88.91 162

TranMVSNet+NR-MVSNet		71.12 156	70.24 147	72.15 158	76.01 170	84.80 165	76.55 164	75.65 93	61.99 142	45.29 187	48.42 148	43.07 187	67.55 157	78.28 175	82.83 122	91.85 155	92.29 126

V9		69.79 171	67.57 179	72.38 155	75.95 172	79.21 202	78.72 138	65.06 177	56.51 170	51.06 145	43.66 179	44.70 170	70.28 149	79.22 165	81.06 166	93.24 106	88.67 166

pmmvs4		73.38 131	71.53 141	75.55 111	75.95 172	85.24 160	77.25 157	71.59 127	71.03 107	63.10 86	49.09 146	44.22 178	73.73 113	82.04 131	80.18 172	91.68 158	88.89 163

DU-MVS		72.19 138	71.35 143	73.17 143	75.95 172	86.02 152	77.01 158	74.42 102	65.39 128	48.82 165	49.10 144	42.81 188	71.61 125	78.67 172	83.10 118	91.22 168	94.37 93

Baseline_NR-MVSNet		70.61 161	68.87 165	72.65 148	75.95 172	80.49 191	75.92 170	74.75 98	65.10 131	48.78 167	41.28 196	44.28 177	68.45 154	78.67 172	79.64 176	92.04 149	92.62 124

v10		70.97 157	69.44 158	72.75 146	75.90 176	84.58 167	79.43 130	66.45 165	58.07 160	49.93 159	43.87 176	43.68 179	71.91 122	82.04 131	81.70 135	92.89 128	92.11 131

v12		69.66 172	67.45 180	72.23 156	75.89 177	79.13 204	78.29 144	64.96 180	56.40 171	50.75 152	43.53 181	44.60 173	70.21 150	79.11 167	80.99 167	93.27 101	88.41 167

v7		71.49 152	69.98 152	73.25 142	75.89 177	86.45 141	79.44 129	69.29 142	58.07 160	50.08 158	43.87 176	43.67 180	71.94 121	82.03 133	81.70 135	92.88 131	94.04 100

v2v482		71.73 146	69.80 153	73.99 129	75.88 179	86.66 138	79.58 124	71.90 121	57.58 162	50.41 156	45.35 154	43.24 186	73.05 117	79.69 151	82.18 131	93.08 118	93.87 111

v13		69.55 174	67.33 181	72.14 159	75.83 180	79.04 205	78.22 145	64.85 181	56.16 173	50.60 154	43.43 183	44.56 174	70.05 152	79.01 169	80.92 169	93.28 100	88.22 168

testgi		63.11 205	64.88 191	61.05 207	75.83 180	78.51 207	60.42 210	66.20 167	48.77 206	34.56 214	56.96 107	40.35 199	40.95 219	77.46 183	77.22 191	88.37 201	74.86 214

pmmvs5		70.01 165	69.31 162	70.82 174	75.80 182	86.26 143	72.94 181	67.91 154	53.84 186	47.22 175	47.31 150	41.47 196	67.61 156	83.93 115	81.93 133	93.42 93	90.42 142

v11		69.84 170	67.85 175	72.17 157	75.78 183	79.15 203	78.20 146	64.76 182	56.10 174	49.50 160	43.54 180	43.36 184	71.62 124	82.21 128	81.52 142	93.17 109	89.05 159

LP		59.72 209	58.23 213	61.44 206	75.67 184	74.97 213	61.05 209	48.34 224	54.02 185	40.82 201	31.61 214	36.92 214	54.69 193	67.52 214	71.18 210	88.08 202	71.42 220

V42		71.58 148	70.11 151	73.30 141	75.66 185	86.68 137	79.17 134	69.92 136	59.29 157	52.80 128	44.36 166	45.66 164	68.83 153	79.48 162	81.49 146	93.44 91	93.82 116

v1144		70.93 158	69.42 160	72.70 147	75.48 186	86.26 143	79.22 133	69.39 141	55.61 177	48.05 170	43.47 182	42.55 191	71.51 127	82.11 129	81.74 134	92.56 139	94.17 98

MVS-HIRNet		64.63 195	64.03 198	65.33 193	75.01 187	82.84 175	58.54 215	52.10 218	55.42 178	49.29 161	29.83 219	43.48 182	66.97 162	78.28 175	78.81 179	90.07 187	79.52 203

v1192		70.32 163	68.77 166	72.12 160	74.76 188	85.62 155	78.73 137	68.53 146	55.08 179	46.34 180	42.39 188	40.67 198	71.90 123	82.27 127	81.53 140	92.43 143	93.86 112

v144192		70.10 164	68.55 169	71.90 163	74.55 189	85.67 154	77.81 150	68.22 151	54.65 181	46.91 177	42.76 186	41.27 197	70.95 137	80.48 146	81.11 165	92.96 122	93.90 109

v1921920		69.85 169	68.38 171	71.58 166	74.35 190	85.39 158	77.78 151	67.88 155	54.64 182	45.39 186	42.11 191	39.97 201	71.10 131	81.68 137	81.17 162	92.96 122	93.69 119

WR-MVS		64.98 191	66.59 183	63.09 201	74.34 191	82.68 177	64.98 203	69.17 143	54.42 183	36.18 210	44.32 167	44.35 176	44.65 208	73.60 195	77.83 186	89.21 195	88.96 161

FMVSNet1		74.26 124	71.95 139	76.95 106	74.28 192	83.94 171	83.61 91	69.99 135	57.08 163	65.08 82	42.39 188	57.41 123	76.98 95	86.57 88	86.83 72	91.77 157	89.42 150

v1240		69.28 176	67.82 176	71.00 173	74.09 193	85.13 161	76.54 165	67.28 162	53.17 189	44.70 192	41.55 195	39.38 203	70.51 146	81.29 140	81.18 160	92.88 131	93.02 122

our_test_3					73.80 194	79.57 198	64.47 205

SixPastTwentyTwo		63.75 202	63.42 201	64.13 200	72.91 195	80.34 192	61.29 208	63.90 183	49.58 204	40.42 202	54.99 123	37.13 212	60.90 180	68.46 212	70.80 211	85.37 213	82.65 199

Anonymous20240521		66.89 183	68.33 172	65.21 194	72.90 196	84.28 168	64.67 204	68.90 144	54.08 184	46.25 181	44.71 157	45.82 159	48.51 206	77.18 185	79.96 175	90.03 188	92.25 127

PEN-MVS		64.35 197	64.29 196	64.42 198	72.67 197	79.83 194	66.97 194	68.24 150	51.21 195	35.29 212	44.09 171	38.51 206	52.36 202	71.06 206	77.65 188	90.99 171	87.68 172

DTE-MVSNet		63.26 204	63.41 202	63.08 202	72.59 198	78.56 206	65.03 202	68.28 149	50.53 199	32.38 216	44.03 172	37.79 209	49.48 205	70.83 209	76.73 196	90.73 177	85.42 181

WR-MVS_H		64.14 201	65.36 186	62.71 203	72.47 199	82.33 181	65.13 200	66.99 163	51.81 193	36.47 208	43.33 184	42.77 189	43.99 210	72.41 201	75.99 199	91.20 169	88.86 164

pmmvs6		64.24 198	61.77 208	67.12 185	72.39 200	81.39 186	71.33 187	65.95 170	36.05 222	48.48 168	30.55 215	43.45 183	58.75 187	77.88 180	76.36 198	85.83 211	86.70 178

Anonymous20231206		62.05 207	61.83 207	62.30 205	72.09 201	77.84 208	63.10 206	67.62 158	50.20 200	36.68 206	29.59 220	37.05 213	43.90 211	77.33 184	77.31 190	90.41 181	83.49 191

CP-MVSNet		64.84 193	64.97 188	64.69 196	72.09 201	81.04 189	66.66 196	67.53 159	52.45 191	37.40 205	44.00 174	38.37 207	53.54 199	72.26 202	76.93 195	90.94 175	89.75 146

PS-CasMVS		64.22 200	64.19 197	64.25 199	71.86 203	80.67 190	66.42 198	67.43 161	50.64 197	36.48 207	42.60 187	37.46 210	52.56 201	71.98 203	76.69 197	90.76 176	89.29 156

v748		65.00 190	63.86 199	66.33 188	71.85 204	82.15 182	66.80 195	65.64 173	48.50 207	47.98 171	39.62 198	39.20 204	56.44 191	71.25 205	77.53 189	89.29 193	88.74 165

test20.03		57.93 213	59.22 211	56.44 212	71.84 205	73.78 215	53.55 219	65.96 169	43.02 218	28.46 223	37.50 206	38.17 208	30.41 227	75.25 191	74.42 205	88.41 199	72.37 218

v7n		66.43 186	65.51 185	67.51 183	71.63 206	83.10 174	70.89 189	65.02 178	50.13 201	44.68 193	39.59 199	38.77 205	62.57 177	77.59 182	78.91 178	90.29 184	90.44 141

anonymousdsp		67.61 180	68.94 164	66.04 191	71.44 207	83.97 170	66.45 197	63.53 186	50.54 198	42.42 199	49.39 142	45.63 165	62.84 176	77.99 177	81.34 155	89.59 192	93.75 117

MDTV_nov1_ep13_2view		64.72 194	64.94 189	64.46 197	71.14 208	81.94 183	67.53 193	54.54 212	55.92 175	43.29 197	44.02 173	43.27 185	59.87 185	71.85 204	74.77 202	90.36 182	82.82 196

FPMVS		50.25 220	45.67 225	55.58 214	70.48 209	60.12 226	59.78 212	59.33 201	46.66 210	37.94 203	30.22 217	27.51 226	35.94 223	50.98 228	47.90 228	70.02 229	56.31 226

V4		65.34 188	64.59 192	66.21 189	69.64 210	82.42 179	69.22 190	62.80 188	49.60 203	45.21 188	39.33 201	41.82 195	60.66 184	72.61 198	77.03 193	89.76 190	89.32 155

v52		65.34 188	64.59 192	66.21 189	69.63 211	82.41 180	69.22 190	62.80 188	49.63 202	45.15 190	39.31 202	41.85 194	60.68 183	72.61 198	77.02 194	89.75 191	89.33 153

N_pmnet		60.52 208	58.83 212	62.50 204	68.97 212	75.61 212	59.72 213	66.47 164	51.90 192	41.26 200	35.42 211	35.63 215	52.25 203	67.07 216	70.08 214	86.35 209	76.10 208

FMVSNet5		72.83 133	73.89 131	71.59 165	67.42 213	76.28 209	75.88 171	63.74 184	77.27 82	54.59 121	53.32 130	71.48 77	73.85 111	81.95 134	81.69 137	94.06 53	75.20 212

test2356		58.43 212	59.52 210	57.16 211	66.71 214	68.00 220	54.69 217	60.91 196	49.22 205	28.63 222	41.86 192	33.68 219	44.36 209	72.98 196	75.47 201	87.69 205	75.40 211

pmmvs-eth3d		64.24 198	61.96 206	66.90 186	66.35 215	76.04 211	66.09 199	66.31 166	52.59 190	50.94 146	37.61 205	32.79 222	62.43 178	75.78 190	75.48 200	89.27 194	83.39 192

EU-MVSNet		58.73 211	60.92 209	56.17 213	66.17 216	72.39 216	58.85 214	61.24 193	48.47 208	27.91 224	46.70 152	40.06 200	39.07 220	68.27 213	70.34 213	83.77 216	80.23 202

testus		55.91 214	56.38 214	55.37 215	65.15 217	65.88 223	50.07 223	60.92 195	45.62 212	26.99 226	41.74 194	24.43 229	42.08 215	69.50 211	73.60 206	86.97 207	73.91 215

MIMVSNet		68.66 178	69.43 159	67.76 182	64.92 218	84.68 166	74.16 177	54.10 215	60.85 144	51.27 138	39.47 200	49.48 146	67.48 158	84.86 108	85.57 87	94.63 44	81.10 201

new-patchmatchnet		53.91 216	52.69 216	55.33 216	64.83 219	70.90 217	52.24 221	61.75 191	41.09 219	30.82 217	29.90 218	28.22 225	36.69 222	61.52 222	65.08 221	85.64 212	72.14 219

PM-MVS		63.52 203	62.51 205	64.70 195	64.79 220	76.08 210	65.07 201	62.08 190	58.13 159	46.56 179	44.98 155	31.31 223	62.89 175	72.58 200	69.93 215	86.81 208	84.55 183

testmv		46.89 223	46.37 223	47.48 223	60.96 221	58.36 230	36.71 231	56.94 205	27.16 230	17.93 234	23.94 224	18.84 233	31.06 225	61.55 220	66.72 219	81.28 221	68.05 222

test1235678		46.88 224	46.36 224	47.48 223	60.96 221	58.35 231	36.71 231	56.94 205	27.15 231	17.93 234	23.93 225	18.82 234	31.06 225	61.55 220	66.71 220	81.27 222	68.04 223

Anonymous20231211		49.72 221	47.45 222	52.38 219	60.54 223	66.16 222	52.47 220	60.87 197	25.32 232	25.16 230	15.98 231	23.66 230	37.00 221	61.01 224	64.41 223	78.25 224	75.60 209

PMVS	36.83 18	40.62 226	36.39 227	45.56 225	58.40 224	33.20 236	32.62 235	56.02 207	28.25 229	37.92 204	22.29 230	26.15 228	25.29 229	48.49 230	43.82 231	63.13 232	52.53 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		48.34 222	47.93 221	48.83 222	58.14 225	59.33 228	37.54 229	43.85 228	31.76 227	29.36 220	23.26 226	34.58 217	42.20 213	65.15 218	68.72 217	81.86 220	52.66 229

.test1245		33.05 228	31.21 230	35.20 229	58.14 225	59.33 228	37.54 229	43.85 228	31.76 227	29.36 220	23.26 226	34.58 217	42.20 213	65.15 218	0.77 235	0.11 239	3.62 237

tmp_tt				39.78 227	56.31 227	31.71 238	35.84 233	15.08 236	82.57 65	50.83 149	63.07 87	47.51 153	15.28 233	52.23 227	44.24 230	65.35 231

ambc			50.35 220		55.61 228	59.93 227	48.73 225		44.08 214	35.81 211	24.01 223	10.64 238	41.57 217	72.83 197	63.35 224	74.99 227	77.61 205

pmmvs3		52.59 218	52.43 218	52.78 218	54.53 229	64.49 225	50.07 223	46.89 227	35.31 224	30.19 218	27.27 222	26.96 227	53.02 200	67.28 215	70.54 212	81.96 219	75.20 212

test12356		41.15 225	41.46 226	40.78 226	53.10 230	49.87 232	33.37 234	52.25 217	25.12 233	15.64 236	22.76 228	15.01 235	15.81 232	52.97 226	64.54 222	74.50 228	59.96 225

MDA-MVSNet-bldmvs		54.99 215	52.66 217	57.71 210	52.74 231	74.87 214	55.61 216	68.41 148	43.65 216	32.54 215	37.93 204	22.11 231	54.11 195	48.85 229	67.34 218	82.85 217	73.88 216

new_pmnet		50.32 219	51.36 219	49.11 221	49.19 232	64.89 224	48.66 226	47.99 226	47.55 209	26.27 228	29.51 221	28.66 224	44.89 207	61.12 223	62.74 225	77.66 225	65.03 224

no-one		32.08 230	31.09 231	33.23 230	46.10 233	46.90 234	20.80 238	49.13 222	16.27 235	7.85 238	10.62 233	10.68 237	13.65 235	31.50 233	51.31 227	61.83 233	50.38 231

Gipuma		35.20 227	33.96 228	36.65 228	43.30 234	32.51 237	26.96 237	48.31 225	38.87 221	20.08 233	8.08 234	7.41 239	26.44 228	53.60 225	58.43 226	54.81 234	38.79 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		52.76 217	53.95 215	51.38 220	41.96 235	70.79 218	53.56 218	63.03 187	39.36 220	27.83 225	22.73 229	33.07 221	34.47 224	70.49 210	72.69 207	87.41 206	68.51 221

EMVS		20.61 233	16.32 234	25.62 234	36.41 236	18.93 241	11.51 240	43.75 230	15.65 236	6.53 240	7.56 237	4.68 240	22.03 230	14.56 236	23.10 234	33.51 237	29.77 235

E-PMN		21.42 231	17.56 233	25.94 233	36.25 237	19.02 240	11.56 239	43.72 231	15.25 237	6.99 239	8.04 235	4.53 241	21.77 231	16.13 235	26.16 233	35.34 236	33.77 234

PMMVS2		32.52 229	33.92 229	30.88 232	34.15 238	44.70 235	27.79 236	39.69 233	22.21 234	4.31 241	15.73 232	14.13 236	12.45 236	40.11 231	47.00 229	66.88 230	53.54 227

MVE	25.07 19	21.25 232	23.51 232	18.62 235	15.07 239	29.77 239	10.67 241	34.60 235	12.51 238	9.46 237	7.84 236	3.82 242	14.38 234	27.45 234	42.42 232	27.56 238	40.74 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		62.08 206	88.31 37	31.46 231	0.16 240	98.10 6	91.57 36	0.09 237	85.07 58	0.21 242	73.90 49	83.74 33	0.19 239	88.98 58	89.39 51	96.58 12	99.02 11

testmvs		0.76 234	1.23 235	0.21 236	0.05 241	0.21 242	0.38 243	0.09 237	0.94 239	0.05 243	2.13 239	0.08 243	0.60 238	0.82 237	0.77 235	0.11 239	3.62 237

test123		0.67 235	1.11 236	0.16 237	0.01 242	0.14 243	0.20 244	0.04 239	0.77 240	0.02 244	2.15 238	0.02 244	0.61 237	0.23 238	0.72 237	0.07 241	3.76 236

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										91.14 2		85.84 20

MTMP										90.95 3		84.13 29

Patchmatch-RL test							8.17 242

NP-MVS									89.55 41

Patchmtry						87.41 124	78.32 141	54.14 213		51.09 141

DeepMVS_CX						48.96 233	43.77 227	40.58 232	50.93 196	24.67 231	36.95 208	20.18 232	41.60 216	38.92 232		52.37 235	53.31 228

High-resolution multi-view benchmark